Mayor and City Council
September 2, 2026

Via Email

June 24, 2026

Michael Rosales, Chair
Jerry Arellano, Committee Member
Ryan Castillo, Committee Member
Saul Guerrero, Committee Member
Victoria Rodriguez, Committee Member
James Lane, Committee Member
Jairo Barreras, Committee Member
Zared Ibarra, Committee Member
Zeena Abdulla, Secretary
Development and Environmental Review Committee
City of San Bernardino
201 North E Street, 3rd Floor
San Bernardino, CA 92401
[email protected]

Chantel Choice, Senior Planner
Community Development &
Housing Department
City of San Bernardino
201 North E Street, 3rd Floor
San Bernardino, CA 92401
[email protected]

Re: Comment on the Initial Study (IS)/Mitigated Negative Declaration (MND) for Verdemont Heights Residential Development (Tentative Parcel Map No. 20707)

Dear Members of the Development and Environmental Review Committee and Planner Choice,

I am writing on behalf of Supporters Alliance for Environmental Responsibility (“SAFER”) regarding the Verdemont Heights Residential Development Project, including all actions related or referring to the proposed development of a residential subdivision encompassing approximately 107.7 acres (“Project”).

SAFER remains concerned that the IS/MND does not adequately analyze or mitigate the Project’s potentially significant impacts on biological resources. SAFER’s comments are supported by expert biologist, Dr. Shawn Smallwood, Ph.D., whose comments and CV are attached hereto as “Exhibit A.”

As discussed below, there is a fair argument that the Project may have significant adverse environmental impacts, and an environmental impact report (“EIR”) is therefore required.

SAFER requests that the City of San Bernardino (“City”) prepare an EIR for the Project pursuant to the California Environmental Quality Act (“CEQA”), Public Resources Code section 21000, et seq.

June 24, 2026
Comment on IS/MND for Verdemont Heights Project

PROJECT DESCRIPTION

The Project is located north of Verdemont Drive, east of Antique Street, and west of Escena Street in Verdemont Heights in the City of San Bernardino. The Project site is approximately 107.7 acres and is currently vacant. The Applicant proposes to subdivide the Project site into 105 single-family residential lots and five lettered lots.

LEGAL STANDARD

An EIR is required rather than a mitigated negative declaration if there is a “fair argument” that a proposed project may have an adverse environmental impact. Communities for a Better Environment v. South Coast Air Quality Management Dist. (ConocoPhillips) (2010) 48 Cal. 4th 310, 319-320 (“CBE v. SCAQMD”). A mitigated negative declaration is proper only if the project revisions would avoid or mitigate the potentially significant effects identified in the initial study “to a point where clearly no significant effect on the environment would occur, and…there is no substantial evidence in light of the whole record before the public agency that the project, as revised, may have a significant effect on the environment.” PRC §§ 21064.5 and 21080(c)(2); Mejia v. City of Los Angeles (2005) 130 Cal.App.4th 322, 331. In that context, “may” means a reasonable possibility of a significant effect on the environment. PRC §§ 21082.2(a), 21100, 21151(a); Pocket Protectors v. City of Sacramento (2004) 124 Cal.App.4th 903, 927; League for Protection of Oakland's etc. Historic Res. v. City of Oakland (1997) 52 Cal.App.4th 896, 904–05.

Under the “fair argument” standard, an EIR is required if any substantial evidence in the record indicates that a project may have an adverse environmental effect—even if contrary evidence exists to support the agency’s decision. 14 CCR § 15064(f)(1); Pocket Protectors, 124 Cal.App.4th at 931; Stanislaus Audubon Society v. County of Stanislaus (1995) 33 Cal.App.4th 144, 150-51; Quail Botanical Gardens Found., Inc. v. City of Encinitas (1994) 29 Cal.App.4th 1597, 1602. The “fair argument” standard creates a “low threshold” favoring environmental review through an EIR rather than through issuance of negative declarations or notices of exemption from CEQA. Pocket Protectors, 124 Cal.App.4th at 928. The “fair argument” standard is virtually the opposite of the typical deferential standard accorded to agencies. As a leading CEQA treatise explains:

This ‘fair argument’ standard is very different from the standard normally followed by public agencies in making administrative determinations. Ordinarily, public agencies weigh the evidence in the record before them and reach a decision based on a preponderance of the evidence. [Citations]. The fair argument standard, by contrast, prevents the lead agency from weighing competing evidence to determine who has a better argument concerning the likelihood or extent of a potential environmental impact.

The lead agency’s decision is thus largely legal rather than factual; it does not resolve conflicts in the evidence but determines only whether substantial evidence exists in the record to support the prescribed fair argument.

June 24, 2026
Comment on IS/MND for Verdemont Heights Project

Kostka & Zishcke, Practice Under CEQA, §6.29, pp. 273–74.

The Courts have explained that “it is a question of law, not fact, whether a fair argument exists, and the courts owe no deference to the lead agency’s determination. Review is de novo, with a preference for resolving doubts in favor of environmental review.” Pocket Protectors, 124 Cal.App.4th at 928 (emphasis in original).

DISCUSSION

I. The IS/MND’s Analysis of the Project’s Impacts on Biological Resources is Inadequate and Not Supported by Substantial Evidence.

Dr. Smallwood reviewed the IS/MND the supporting Biological Resource Assessment (“BRA”) prepared by ELMT Consulting (“ELMT”) and found that ELMT inadequately characterized the existing environmental setting and the site’s ability to provide habitat for special status species. Every CEQA document must start from a “baseline” assumption. The CEQA “baseline” is the set of environmental conditions against which to compare a project’s anticipated impacts. (Communities for a Better Envt. v. So. Coast Air Qual. Mgmt. Dist. (2010) 48 Cal. 4th 310, 321.) Section 15125(a) of the CEQA Guidelines (14 CCR, § 15125(a)) states in pertinent part that a lead agency’s environmental review under CEQA:

“…must include a description of the physical environmental conditions in the vicinity of the project, as they exist at the time [environmental analysis] is commenced, from both a local and regional perspective. This environmental setting will normally constitute the baseline physical conditions by which a Lead Agency determines whether an impact is significant.” (Emphasis added.)

(See Save Our Peninsula Committee v. County of Monterey (2001) 87 Cal.App.4th 99, 124- 125.) Thus, an accurate description of the existing environmental conditions of a site is critical to determining whether a significant impact exists. Conversely, if the environmental conditions of a project site are not adequately characterized, any subsequent impact analysis relying on such a mischaracterization would also be inadequate, as is the case here.

After conducting a site survey on July 22, 2024 for an unspecified amount of time, ELMT concluded that “implementation of the [P]roject will have no significant impacts on additional federally or State listed species.” (IS/MND – Appendix C, p. 12.) However, Dr.

Smallwood found this claim to be unfounded. Dr. Smallwood’s associate, Noriko Smallwood conducted two daytime surveys of the Project Site on April 11 and April 14, 2026 for approximately 4.5 hours total. Noriko conducted subsequent nocturnal surveys on April 14 and 18, 2026, for approximately 4.5 hours total. (Exhibit A, p. 2.) In total, Noriko detected approximately 56 species of vertebrate wildlife. (Id., p. 16.) During her surveys Noriko also observed a Monarch butterfly and western spadefoot, both candidates for listing under the federal Endangered Species Act and a special status species. (Id., p. 15.) Other special status species Noriko detected on the Project Site include the American barn owl, Costa’s hummingbird, Allen’s hummingbird, red-tailed hawk, wrentit, California thrasher, Bullock’s June 24, 2026
Comment on IS/MND for Verdemont Heights Project

oriole, Yuma myotis, California myotis, big brown bat, canyon bat, silver-haired bat, and Mexican free-tailed bat. (Id.)

Based on Noriko’s surveys Dr. Smallwood concluded that the MND mischaracterized the Project’s environmental setting. (Id., p. 21.) First Dr. Smallwood explains that “ELMT’s characterization of the existing environmental setting appears biased.” (Id., p. 22.) This is evident in ELMT’s site photos which are “representative of the most disturbed portions of the [P]roject [S]ite.” (Id.) By comparison, Noriko’s photos “reveal that much of the [P]roject [S]ite is ecologically intact despite past soil disturbance.” (Id.)

Second, comparing the ELMT’s survey results to Noriko’s, “reveals that the wildlife species community depicted by ELMT [] is nowhere close to accurate.” (Id., p. 23.) ELMT detected 12 species of vertebrate wildlife, while Noriko detected 56, and a Monarch. (Id.) Noriko’s detection of Monarch butterflies is important given she observed two “roosting together on a pine tree. Roosting sites along migration routes are vital to Monarch conservation, because they are important for stop-over resting periods that can last from as brief as a day to as long as several weeks. The [P]roject [S]ite must be vitally important to Monarchs.” (Id., p. 4.)

Furthermore, ELMT failed to conduct appropriate surveys for certain special status species such as the burrowing owl and Crotch’s bumblebee and various other types of wildlife with potential to occur on the site including amphibians. (Id., p. 27.) For example, ELMT speculates that amphibians are not expected to occur on the site due to the ephemeral nature of the drainage area on site, but fails to cite “evidence that amphibians require predictable stream water sources, that they even require streams, aka drainages.” (Id.) Indeed, Noriko detected the special status western spadefoot which “breed[s] principally in vernal pools, but also in temporary ponds, flood control basins, and other bodies of water.” (Id., p.

28.)

ELMT further speculates that “no local bat species are expected to occur onsite due to a lack of suitable roosting opportunities.” (MND – Appendix C, p. 6.) However, as Dr.

Smallwood explains:

“[B]ats are known to roost on a variety of substrates [], and not just the narrowly defined conditions that appear in consultant reports. It is highly likely, for example, that bats roost on the sycamores that occur on the [P]roject [S]ite. In two very bri ef surveys, Noriko detected six species of bats on the [P]roject [S]ite, again demonstrating the hazard of speculating why certain species should not occur on a site. In the case of bats. ELMT’s speculation is wrong six times over.”

(Id., p. 28.) Given ELMT’s inadequate survey effort and speculative conclusions, “[m]ore work is needed to accurately characterize the wildlife community as part of the existing environmental setting. (Id., p. 35.)

June 24, 2026
Comment on IS/MND for Verdemont Heights Project

Based on ELMT’s erroneous characterization of the Project Site’s environmental setting, the MND concludes that the Project would have a less than significant impact on special status species with the inclusion of preconstruction surveys for nesting birds and burrowing owls incorporated. (MND, p. 24.) However, this conclusion is not supported by substantial evidence because the characterization of the existing environmental conditions of the Project site on which the IS/MND relies for its impact analysis is inadequate. Furthermore, findings by Dr. Smallwood indicate otherwise, as his analysis concludes that even with the suggested mitigation measures proposed in the MND, the Project may still result in significant unmitigated impacts on special status species, as described below.

II. There is Substantial Evidence of a Fair Argument That the Project May Have Significant Unmitigated Impacts on Special Status Species.

Based on Noriko’s surveys, Dr. Smallwood concluded that the Project may result in significant impacts on special status species. (Ex. A, pp. 35-47.) These impacts include habitat loss, increased wildlife mortality due to project-generated traffic, and increased avian mortality due to window collisions. (Id.) These impacts were not identified or considered by ELMT.
a. Habitat loss

Dr. Smallwood concludes that the Project would result in the loss of habitat for at least the 59 vertebrate species of wildlife Noriko and ELMT collectively observed on the Project Site, including special status species such as the Monarch and western spadefoot. (Id., p. 36.) While the number of avian nests sites is unknown, Dr. Smallwood estimates that the Project Site supports up to 269 bird nests per year, meaning that the Project would result in the loss of 269 nest sites, including those for special status birds Noriko observed on the Project Site such as the American barn owl, Costa’s hummingbird, Allen’s hummingbird, red-tailed hawk, wrentit, California thrasher and Bullock’s oriole. (Id., p. 37.) Dr. Smallwood’s expert opinion, drawing from the actual conditions of the Project site, is substantial evidence of a fair argument that the Project will result in significant unmitigated habitat loss.

b. Traffic impacts on wildlife

Based on the vehicle miles traveled (“VMT”) estimated for Project construction and operation, Dr. Smallwood estimates that the Project would result in “528 vertebrate wildlife fatalities during construction, and 1,018 vertebrate wildlife fatalities per year in the area around the project that is traveled by project-generated traffic.” (Id., p. 46.) Dr. Smallwood concludes that “the project-generated traffic would cause significant impacts on wildlife.” (Id., p. 47.)

c. Bird-window collisions

Dr. Smallwood notes that the Project’s inclusion of several residences would “introduce glass windows into an essential portion of avian habitat,” the aerosphere. “Many special status species of birds have been recorded at or near the aerosphere of the [P]roject [S]ite.” (Id., p. 40.) Based on the birds Noriko observed flying through the Project Site and Dr. Smallwood’s bird- June 24, 2026
Comment on IS/MND for Verdemont Heights Project

window collision research, he estimates that the Project would result in 187 bird collision fatalities per year. (Id., 42.)

The significant impacts identified by Dr. Smallwood here are supported by the site observations and expert experience and therefore constitute substantial evidence warranting the preparation of an EIR.

CONCLUSION

For the foregoing reasons, SAFER requests that the City prepare an EIR to adequately analyze and mitigate the Project’s significant adverse environmental impacts.

Sincerely,

Kylah Staley

LOZEAU DRURY LLP

EXHIBIT A
1 Shawn Smallwood, PhD
3108 Finch Street
Davis, CA 95616

Attn: Chantel Choice, Senior Planner
City of San Bernardino
Community Development and Housing Department, Planning Division 201 North “E” Street
San Bernardino, CA 92401 20 April 2026

RE: Verdemont Heights Residential Development

Dear Ms. Choice,

I write to comment on potential impacts to biological resources that would result from development of the proposed Verdemont Heights Residential Development. The project would add 105 single-family residential units on 107.7 acres northeast of Verdemont Dr and west of Palm Ave in San Bernardino, California (APN: 0348-121-14, -27, -28; and 0261-011-02, -03, -10). My comments that follow address my concerns that the Draft Initial Study/Mitigated Negative Declaration (IS/MND), supported by a biological Resources Assessment prepared by ELMT Consulting (ELMT 2024), mischaracterizes the existing environmental setting, and that its impacts analysis is flawed and its mitigation measures are inadequate.

My qualifications for preparing expert comments are the following. I hold a Ph.D. degree in Ecology from University of California at Davis, where I also worked as a post- graduate researcher in the Department of Agronomy and Range Sciences. My research has been on animal density and distribution, habitat selection, wildlife interactions with the anthrosphere, and conservation of rare and endangered species. I authored many papers on these and other topics. I served as Chair of the Conservation Affairs Committee for The Wildlife Society – Western Section. I am a member of The Wildlife Society, and I’ve lectured part-time at California State University, Sacramento. I was Associate Editor of wildlife biology’s premier scientific journal, The Journal of Wildlife Management, as well as of Biological Conservation, and I was on the Editorial Board of Environmental Management. I have performed wildlife surveys in California for forty- one years. My CV is attached.

THE WILDLIFE COMMUNITY AS A BIOLOGICAL RESOURCE

Most environmental reviews pursuant to the California Environmental Quality Act (CEQA) focus on special-status species because CEQA’s Checklist Evaluation of Environmental Impacts Question IV(a) asks specifically whether the project would adversely affect special-status species. However, this narrowly focused question is part of a larger policy goal of CEQA, which is “to prevent the elimination of fish or wildlife species due to man’s activities, insure that fish and wildlife populations do not drop below self-perpetuating levels, and preserve for future generations representations of all plant and animal communities and examples of the major periods of California history.” 2 Pub. Res. Code § 21001(c). This policy is not restricted to special-status species; it also applies to wildlife populations and plant and animal communities, as it must, because special-status species are inter-dependent members of ecological communities. The CEQA Guidelines Section 21155.1 defines wildlife habitat as “the ecological communities upon which wild animals, birds, plants, fish, amphibians, and invertebrates depend for their conservation and protection.” This definition is consistent with the scientific definition of habitat, which is that portion of the environment that is used by members of a species for survival and reproduction (Hall et al. 1997). An essential portion of the environment used by any special-status species is composed of the collection of other species of plants and wildlife, because these species provide for special-status species their forage, refugia and nest substrates, and some serve as ecological mutualists. The CEQA Checklist Evaluation assigns priority to special-status species to balance information and cost, but it does not exclude the need to evaluate environmental impacts to other species, which, after all, are members of the very communities within which special-status species inter-depend for survival and reproduction.

The species I consider to be special-status species are those listed in California’s Special Animals List inclusive of threatened and endangered species under the California and federal Endangered Species Acts, candidates for listing under CESA and FESA, California’s Fully Protected Species, California species of special concern, and California’s Taxa to Watch List (https://nrm.dfg.ca.gov/FileHandler.ashx? DocumentID=109406), continental and region-specific US Fish and Wildlife Service Birds of Conservation Concern (https://www.fws.gov/sites/default/files/documents/ birds-of-conservation-concern-2021.pdf), and naturally rare species such as raptors protected by California’s Birds of Prey laws, Fish and Game Code Sections 3503, 3503.5, 3505 and 3513 (see https://wildlife.ca.gov/Conservation/ Birds/Raptors).

What follows is a summary of site visits to detect as many of the species of wildlife as possible within the short times available. The visits were also intended to detect as many of the special-status species as possible, but with the understanding that most special- status species are less readily detectable due to rarity and crypticity. Nonetheless, the species detected can indicate the ecological integrity of the site and thus the likelihood of occurrence of special-status species not yet detected.

SITE VISITS

On my behalf, Noriko Smallwood, a wildlife biologist with a Master of Science Degree from California State University Los Angeles, visited the site of the proposed project for

2.67 hours of diurnal survey starting at 06:25 on 11 April 2026, and for 1.82 hours of diurnal survey starting at 17:37 and for 2.5 hours of nocturnal survey starting at 19:26 on 14 April 2026. She visited the site again for 2 hours of nocturnal survey starting at 19:32 on 18 April 2026. During daylight, Noriko walked the site’s perimeter where accessible, stopping to scan for wildlife with use of binoculars. At night, Noriko affixed a Pettersson M500 acoustic bat detector to a 25-foot pole, and cabled the detector to her computer, which ran SonoBat Live. SonoBat Live identifies bat species based on the bats’ sonograms that are detected by the M500. She also scanned for nocturnal wildlife using a Pulsar Telos LRF XG50 5.5-14x 50 mm thermal-imaging monocular. Noriko 3 recorded all species of vertebrate wildlife she detected, including those whose members flew over the site or were seen just off site. Conditions were cloudy with 7 MPH south wind and temperatures 10.5–12.7° C on 11 April 2026, clear with 2 MPH south wind and temperatures 23–15° C on 14 April, and clear with temperatures 19.4–16.7° C on 18 April. The site was covered by annual grassland, sage scrub, groves of western sycamore and eucalyptus, and a retention pond at the southwest corner (Photos 1–3).

Photos 1, 2, and 3. Views of the project site, 11 April 2026. Photos by Noriko Smallwood.

4 Noriko saw American barn owl (Photo 4), red-tailed hawk and Bullock’s oriole (Photo 5), American kestrel and monarch (Photos 6 and 7), Anna’s hummingbird (Photo 8), Costa’s hummingbird and California quail (Photos 9 and 10), California thrasher and Say’s phoebe (Photos 11 and 12), cliff swallow and barn swallow (Photos 13 and 14), California scrub-jay and mourning dove (Photos 15 and 16), California towhee and spotted towhee (Photos 17 and 18), lesser goldfinch and northern house wren (Photos 19 and 20), lark sparrow and Southern California rufous-crowned sparrow (Photos 21 and 22), white-crowned sparrow and desert cottontail (Photos 23 and 24), Great Basin fence lizard and western side-blotched lizard (Photos 25 and 26), coyotes and Allen’s hummingbird (Photos 27 and 28), canyon bat and silver-haired bat (Photos 29 and 30), and California myotis and Yuma myotis (Photos 31 and 32). Noriko detected 57 species of wildlife at or adjacent to the project site, including 6 species of bats and 15 species with special status (Table 1). Two of the species Noriko detected – Western spadefoot and Monarch – are candidates for listing under the federal Endangered Species Act.

There were two Monarchs roosting together on a pine tree. Roosting sites along migration routes are vital to Monarch conservation, because they are important for stop-over resting periods that can last from as brief as a day to as long as several weeks.

The project site must be vitally important to Monarchs.

Noriko Smallwood certifies that the foregoing and following survey results are true and accurately reported.

Photo 4. Two American barn owls foraging on the project site, detected using a thermal imaging camera on 14 April 2026. Photo by Noriko Smallwood.

5 Photo 5. Bullock’s oriole harassing a red-tailed hawk on the project site, 11 April 2026. Photo by Noriko Smallwood.

Photos 6 and 7. American kestrel (left), and monarch roosting in a pine tree (right) on the project site, 14 April 2026. Photos by Noriko Smallwood.

6 Photo 8. Anna’s hummingbirds on the project site, 11 April 2026. Photo by Noriko Smallwood.

Photos 9 and 10. Costa’s hummingbird (left), and California quail (right) on the project site, 11 April 2026. Photos by Noriko Smallwood.

7 Photos 11 and 12. California thrasher (top), and Say’s phoebe eating a ladybug (bottom) on the project site, 11 April 2026. Photos by Noriko Smallwood.

8 Photos 13 and 14. Cliff swallow (left), and barn swallow (right) on the project site, 11 and 14 April 2026. Photos by Noriko Smallwood.

Photos 15 and 16. California scrub-jay (left), and mourning dove (right) on the project site, 11 April 2026. Photos by Noriko Smallwood.

9 Photos 17 and 18. California towhee (left), and spotted towhee (right) on the project site, 11 and 14 April 2026. Photos by Noriko Smallwood.

Photos 19 and 20. Lesser goldfinch (left), and northern house wren (right) on the project site, 11 April 2026. Photos by Noriko Smallwood.

10 Photos 21 and 22. Lark sparrow (left), and Southern California rufous-crowned sparrow (right) on the project site, 11 April 2026. Photos by Noriko Smallwood.

Photos 23 and 24. White-crowned sparrow (left), and desert cottontail (right) on the project site, 11 April 2026. Photos by Noriko Smallwood.

11 Photos 25 and 26. Great Basin fence lizard (top), and western side-blotched lizard (bottom) on the project site, 11 and 14 April 2026. Photos by Noriko Smallwood.

12 Photo 27.

Coyote
viewed at
night using
a thermal
imaging
camera on
18 April
2026. Photo
by Noriko
Smallwood.

Photo 28.

Allen’s
hummingbird
on the project
site, 18 April
2026. Photo by
Noriko
Smallwood.

13 Photos 29 and 30. Sonograms of canyon bat (top), and silver-haired bat (bottom) on the project site detected using SonoBat Live and a Pettersson M500, 14 April 2026.

14 Photos 31 and 32. Sonograms of California myotis (top), and Yuma myotis (bottom) on the project site detected using SonoBat Live and a Pettersson M500, 18 April 2026.

15 Table 1. Species of wildlife Noriko observed during 2.67 hours of diurnal survey on 11 April 2026, during 1.82 hours of diurnal survey and 2.5 hours of nocturnal survey on 14 April 2026, and during 2 hours of nocturnal survey on 18 April 2026.

Common name Species name Status1 Notes
Monarch Danaus plexippus FC 2 roosted in pine tree
Baja California treefrog
Pseudacris h.
hypochondriaca Called from retention pond
Western spadefoot Spea hammondii FC, SSC Called from retention pond Great Basin fence lizard
Sceloporus occidentalis
longipes
Western side-blotched
lizard Uta stansburiana elegans
California quail Callipepla californica Foraged
Rock pigeon Columba livia Non-native Just off site
Mourning dove Zenaida macroura
Greater roadrunner Geococcyx californianus Tracks
Lesser nighthawk Chordeiles acutipennis Foraged
White-throated swift Aeronautes saxatalis
Anna’s hummingbird Calypte anna
Costa’s hummingbird Calypte costae BCC
Allen’s hummingbird Selasphorus sasin BCC
Red-tailed hawk Buteo jamaicensis BOP Harassed by Bullock’s oriole American barn owl Tyto furcata BOP Foraged on site in evening Nuttall’s woodpecker Picoides nuttallii BCC Called just off site American kestrel Falco sparverius BOP
Cassin’s kingbird Tyrannus vociferans
Say’s phoebe Sayornis saya Foraged
California scrub-jay Aphelocoma californica Territorial American crow Corvus brachyrhynchos
Common raven Corvus corax
Tree swallow Tachycineta bicolor Foraged
Northern rough-winged
swallow Stelgidopteryx serripennis Foraged
Barn swallow Hirundo rustica Foraged
Cliff swallow Petrochelidon pyrrhonota Hundreds foraged Bushtit Psaltriparus minimus Foraged
Wrentit Chamaea fasciata BCC
Bewick’s wren Thryomanes bewickii Foraged
Northern house wren Troglodytes aedon
California thrasher Toxostoma redivivum BCC
Northern mockingbird Mimus polyglottos Sang just off site Western bluebird Sialia mexicana
House sparrow Passer domesticus Non-native
House finch Haemorphous mexicanus Foraged
Lesser goldfinch Spinus psaltria
16 Common name Species name Status1 Notes
Lark sparrow Chondestes grammacus Copulated
White-crowned sparrow Zonotrichia leucophrys
Song sparrow Melospiza melodia
California towhee Melozone crissalis Foraged
Southern California
rufous-crowned sparrow
Aimophila ruficeps
canescens
WL
Foraged
Spotted towhee Pipilo maculatus Foraged
Bullock’s oriole Icterus bullockii BCC Harassed red-tailed hawk Yellow-rumped warbler Setophaga coronata
Lazuli bunting Passerina amoena Pair
Yuma myotis Myotis yumanensis WBWG:LM
California myotis Myotis californicus WBWG:L
Big brown bat Episticus fuscus WBWG:L
Canyon bat Parastrellus hesperus WBWG:M
Silver-haired bat Lasionycteris noctivagans WBWG:M
Mexican free-tailed bat Tadarida brasiliensis WBWG:L
Desert cottontail Sylvilagus audubonii
California ground squirrel Otospermophilus beecheyi Burrows Coyote Canis latrans Tracks
Kangaroo rat Dipodomys sp. Burrows
Botta’s pocket gopher Thomomys bottae Burrows
1 Listed on CDFW’s Special Animals List (https://nrm.dfg.ca.gov/FileHandler.ashx? DocumentID=109406) as FT or FE = federal threatened or endangered; FC = federal candidate for listing; BCC = U.S. Fish and Wildlife Service’s Bird of Conservation Concern (https://www.fws.gov/ sites/default/files/documents/birds-of-conservation-concern-2021.pdf); SSCi = California Species of Special Concern with i = priorities 1, 2 and 3; WL = CDFW’s Taxa to Watch List; WBWG = Western Bat Working Group with priority rankings, of low (L), moderate (M), and high (H); and BOP = protected by Birds of Prey (California Fish and Game Code 3503.5, see https://wildlife.ca.gov/Conservation/Birds/Raptors).

ANALYSIS OF RECONNAISSANCE SURVEY DATA

In addition to monarch, Noriko detected 56 species of vertebrate wildlife, which was a relatively large number for the brevity of her survey effort. All the species in Table 1 would lose habitat due to the project and its replacement of natural ground covers with impervious surfaces. Assuming the habitat that would remain within the region is at carrying capacity for each species at issue, habitat loss translates directly to a numerical decline in the species. Smallwood and Smallwood (2023) confirmed this habitat loss by measuring the impacts of similar development projects on species richness and the abundances of wildlife. Smallwood and Smallwood (2023) directly compared the species and the numbers of animals observed prior to development to the those observed after development, while they did the same at control sites. The measured losses of these species resulting from development is indicative of habitat loss, because habitat is defined as that portion of the environment that is used for survival and reproduction by members of a species (Hall et al. 1997).
17 However, the species of wildlife Noriko detected at the project site were not the only species that were present during her survey, as there are always species that are not detected. To demonstrate this, I fit nonlinear regression models to Noriko’s accumulation of first detections of vertebrate wildlife species with time into her daytime surveys to predict the number of species that she would have detected with longer surveys or perhaps with additional biologists available to assist her. The type of model is a logistic growth model, which reaches an asymptote that corresponds with the theoretical maximum number of vertebrate wildlife species that could have been detected during the survey. The model I fit to Noriko’s morning survey data predicts 51 species of vertebrate wildlife were available to be detected by five hours of survey, or 13 more species than she detected that morning (Figure 1). Noriko’s rate of species detections exceeded the upper bound of the 95% confidence interval estimated from many other morning surveys we have completed in the Inland Empire region, thus indicating the project site is relatively species-rich for the region. In fact, the model predicts that by five hours of survey, Noriko would have detected about twice the number of vertebrate wildlife species we detect on average in the region.

Figure 1. Actual
and predicted
relationships
between the
numbers of
vertebrate wildlife
species detected
and the elapsed
survey time based
on Noriko’s visual-
scan surveys at the
project site on 11
and 14 April 2026.

Note the confidence
interval (gray)
applies only to the
morning survey
(red line).

Unknown are the identities of the species Noriko missed, but the species that Noriko did and did not detect on composed only a fraction of the species that would occur at the project site over the period of a year or longer. This is because many species are seasonal in their occurrence, some require more survey effort because they are highly cryptic, and 0 50 100 150 200 250 300
Minutes into survey
0 10
20 30
40 50Cumulative number of wildlife species detected

0.98YAM

0.98YPM
r2
95% CI 2019-2025 in region
18 the members of other species would visit the site only periodically while patrolling large home ranges. Surveys on only two days cannot possibly detect all of the species of the local wildlife community.

At least a year’s worth of surveys would be needed to more accurately report the number of vertebrate species that occur at the project site, but I only have Noriko’s one survey.

However, by use of an analytical bridge, a modeling effort applied to a large, robust data set from a research site can predict the number of vertebrate wildlife species that likely make use of the site over the longer term. This analytical bridge draws inference from the pattern of species detections more than it does from the research site, and I note that the pattern, i.e., rate, of species detections is consistent from site to site.

As part of my research, I completed a much larger survey effort across 167 km2 of annual grasslands of the Altamont Pass Wind Resource Area, where from 2015 through 2019 I performed 721 1-hour visual-scan surveys, or 721 hours of surveys, at 46 stations. I used binoculars and otherwise the methods were the same as the methods I and other consulting biologists use for surveys at proposed project sites. At each of the 46 survey stations, I tallied new species detected with each sequential survey at that station, and then related the cumulative species detected to the hours (number of surveys, as each survey lasted 1 hour) used to accumulate my counts of species detected. I used combined quadratic and simplex methods of estimation in Statistica to estimate least-squares, best-fit nonlinear models of the number of cumulative species detected regressed on hours of survey (number of surveys) at the station: 𝑅̂ = 1 1 𝑎⁄ +𝑏×(𝐻𝑜𝑢𝑟𝑠)𝑐 , where 𝑅̂
represented cumulative species richness detected. The coefficients of determination, r2, of the models ranged 0.88 to 1.00, with a mean of 0.97 (95% CI: 0.96, 0.98); or in other words, the models were excellent fits to the data.

I projected the predictions of each model to thousands of hours to find predicted asymptotes of wildlife species richness. The mean model-predicted asymptote of species richness was 57 after 11,857 hours of visual-scan surveys among the 46 stations of my research site. I also averaged model predictions of species richness at each incremental increase of number of surveys, i.e., number of hours (Figure 2). On average I would have detected 16.25 species over my first 4.49 hours of diurnal surveys at my research site in the Altamont Pass (4.49 hours to match the 4.49 hours Noriko surveyed during daylight hours at the project site), which composed 28.5% of the predicted total number of species I would detect with a much larger survey effort at the research site. Given the example illustrated in Figure 2, the 43 vertebrate wildlife species Noriko detected in her

4.49 hours of daylight survey at the project site likely represented 28.5% of the species to be detected after many more visual-scan surveys over another year or longer. With many more repeat surveys through the year, Noriko would likely detect 43 0.285⁄ = 151 species of vertebrate wildlife in daylight surveys at the site. Assuming Noriko’s ratio of special-status to non-special-status species was to hold through the detections of all 151 predicted species, then continued daylight surveys would eventually detect 32 special- status species of vertebrate wildlife.

19 Because my prediction of 151 species of vertebrate wildlife, including 32 special-status species, is derived from daytime visual-scan surveys, and would detect few nocturnal mammals such as bats, the true number of species composing the wildlife community of the site must be larger. Proof of this in Noriko’s brief nocturnal survey in which she added 6 species of vertebrate wildlife to her tally, including 4 special-status species of bats. Noriko’s reconnaissance survey should serve only as a starting point toward characterization of the site’s wildlife community, but it certainly cannot alone inform of the inventory of species that use the site. More surveys are needed than her one survey to produce an inventory the project site’s wildlife community. Nevertheless, the large number of species I predict at the project site is indicative of a relatively species-rich wildlife community that warrants a serious survey effort.

Considering the number of species – and the number of special-status species – yet to be detected, there is at least a fair argument that can be made for the need to prepare and EIR to accurately characterize the wildlife community as part of the existing environmental setting, which is needed to predict potential project impacts and to formulate an effective mitigation strategy.

Figure 2. Mean (95% CI)
predicted wildlife species
richness, 𝑅̂, as a nonlinear
function of hour-long
survey increments across
46 visual-scan survey
stations across the
Altamont Pass Wind
Resource Area, Alameda
and Contra Costa
Counties, 2015‒2019. Note
that the location of the
study is largely irrelevant
to the utility of the graph
to the interpretation of
survey outcomes at the
project site. It is the
pattern in the data that is
relevant, because the
pattern is typical of the
pattern seen elsewhere.

MISCHARACTERIZATION OF THE EXISTING ENVIRONMENTAL SETTING

CEQA’s primary objectives should guide the design and conduct of CEQA review. Four of CEQA’s primary objectives are to (1) accurately characterize the existing environmental setting, (2) disclose potential project impacts to the public and decision- makers, (3) foster public participation with decision-making over proposed projects, 0 20 40 60 80 100
0 10
20 30
40 50
Cumulative number of surveys (hours)
(95% CI)
20 and (4) identify issues and feasible alternative mitigation measures to minimize environmental impacts. The first objective is essential to the other three, because an accurate understanding of the existing environmental setting is needed to predict impacts, provide the public with the means to meaningfully participate with issues of importance, and effectively strategize mitigation and project alternatives (Figure 3). For biological resources, essential information includes the species composition of the plant and wildlife communities, key ecological interactions, identification of ecological keystone species, determining the presence of special-status species or their habitats, and identifying risk factors that could result in significant direct, indirect and cumulative impacts should the project go forward.

The public and decision-makers need to know which species are susceptible to harm potentially caused by the project and its associated activities, and which are known to be vulnerable to harm once exposed to the environmental changes brought by the project.

For these reasons, field studies are needed to identify as many of the species that occur on the project site as reasonably feasible, and to otherwise examine the environmental conditions for their likelihood to support species that are not detected, otherwise known as “habitat assessment.” A review of species occurrence records – also known as “desktop review” – is needed to identify the other species that potentially occur on the site – a process that is also referred to as “habitat assessment.” Because habitat assessments that are based on either or both field study and desktop review are prone to uncertainty, emphasis needs to be directed to adequate field study to reduce the pool of species subjected to habitat assessment, thereby weighing the review more towards certainty. Appropriately weighted reviews are less susceptible to speculation over species occurrence likelihoods. For this reason, protocol-level detection surveys have been formulated for numerous special-status species of plants and wildlife, and otherwise best scientific practices are needed to provide a reasonable likelihood of detection of species that truly occur on the project site. The negative findings of a properly conducted detection survey are the only findings that can support a determination of species absence.

Even among reviews based on the best field studies, there is always uncertainty over some of the determinations of species occurrences. The uncertainties of habitat assessments need to be managed to achieve CEQA’s primary objectives. This means that the methodological details of both the field studies and desktop review need to be carefully and thoroughly reported, and it means that results need to be reported in context of their uncertainties. For example, the reporting of having detected 10 species in a reconnaissance survey is nearly meaningless without also reporting the numbers of species found in surveys of similar methods and effort levels, and the reporting of the failure to detect a given species is nearly entirely meaningless without also reporting the likelihood that species would be detected given the survey methods and effort level. The same is true of desktop review; that is, there should be some means of validating habitat assessments based on the thousands of habitat assessments that have already been conducted in support of CEQA reviews. If industry standards in environmental review are to be credible, then they must include the use of the comparative method.

21 Figure 3. General flow of information from its gathering through characterizing the existing environment to predicting impacts and their significance.

In the case of the proposed project, the IS/MND fails to explain how or why the steps taken to characterize the ecological community relate to CEQA’s primary objectives.

Additionally, the ecological community is inaccurately characterized as composed of only those species detected in a reconnaissance survey or deemed likely to occur based on desktop review of occurrence records. Furthermore, the IS/MND reports nothing about the uncertainties of ELMT’s (2024) findings, which vastly diminishes the accuracy of the characterization of the existing environmental setting.

Moreover, the area of the project footprint is misreported in both the biological resources technical report (ELMT 2024) and the IS/MND. Both documents report the project area is 107.7 acres, but the project boundary in the IS/MND measures 49 acres, and the project area in ELMT (2024) measures 54.9 acres. Combining the map boundaries in both documents, I estimate the area is 60.6 acres, but this acreage is still 44% smaller than reported. The IS/MND’s characterization of the existing environmental setting is inaccurate even in its characterization of the project’s extent.

Although ELMT (2024) reports having used GIS to calculate the areas of vegetation communities that would be affected by the project, no such acreages are reported. This failure to disclose the acreages is a fundamental shortfall of the environmental review, because it bears directly on the characterization of the existing environmental setting.

ELMT reports a map of vegetation communities, but the polygon that represents Sycamore Woodland appears too small, and the map of Disturbed appears overly inclusive of Riversidean Sage Scrub. Moreover, there is no reporting of any effort to validate the map with field observations, and the map boundaries are presented as unrealistically hard and static.

Assess species occurrence likelihoods

1. Desktop review
a. Species geographic range overlap or database occurrence records b. Crosswalk habitat associations with mapped ground cover

2. Reconnaissance survey/Habitat assessment

3. Detection surveys for special-status species
Outcomes

5. Predict impacts

6. Formulate mitigation strategy

7. Determine significance of impacts
Characterize wildlife community

4. Lists of species detected and of those expected but
not yet detected, and any known trends
Note: Impact predictions and
significance determinations have
been of unknown accuracy in the
absence of experimental
measurement
22 The IS/MND and ELMT (2024) include multiple additional errors that further impinge on the credibility of its conclusions. The IS/MND (p. 3) states, “The Project Site currently supports creosote bush scrub…,” but creosote bush scrub is a desert vegetation community that does not occur on the project site. ELMT (2024) analyzes the occurrence likelihoods of species – e.g., great egret, great blue heron, Dulzura kangaroo rat – that have no special status other than their coverage by the Western Riverside County MSHCP, which has no jurisdiction over the project site.

ELMT’s characterization of the existing environmental setting appears biased. ELMT reports site photos that are representative of the most disturbed portions of the project site, including photos of dirt roads (2), debris along a dirt road (1), disturbed areas mostly cleared of vegetation (3), the rocked inflow of a retainer pond along with cyclone fence and industrial equipment (1), an obscure collection of shrubs and the lowe3r portion of a tree (1), and a sycamore tree (1). The photo of the sycamore is the only photo of the project site in a relatively undisturbed condition. In comparison, Noriko’s photos 1–3 reveal that much of the project site is ecologically intact despite past soil disturbance (also see Photo 33), and the species she detected bears out what is evident in her photos.

Photo 33. Northeastward view of the Sycamore Woodland along the east side of the project site, 14 April 2026. Note that a few Eucalyptus are also visible. Photo by Noriko Smallwood.

Environmental Setting informed by Field Surveys

On 22 July 2024, ELMT (2024) deployed two biologists to conduct a reconnaissance survey to obtain a species inventory and evaluate the habitat condition, verify plant communities mapped from aerial imagery, and locate potential linkages and corridors for wildlife movement. As noted above, ELMT fails to explain why or how these objectives were pursued; for example, there is no explanation of how potential linkages and corridors would have been located. Further, there is no reporting of when the survey started nor how long it lasted – two survey attributes that are essential for contextualizing survey findings.

23 ELMT (2024) reports 12 species of vertebrate wildlife were detected. (I did not count the domestic dog and domestic horse that ELMT reports because these animals are not wildlife, and the lesser goldfinch is reported twice.) Two of these species are special- status species, including red-shouldered hawk and mountain lion, the latter of which is now listed as threatened under the California Endangered Species Act (ELMT 2024 attributes no special status to mountain lion). In comparison, Noriko detected Monarch and 56 species of vertebrate wildlife, including 15 special-status species. ELMT detected 3 vertebrate wildlife species that Noriko did not, but Noriko detected 49 vertebrate wildlife species that ELMT did not, and ultimately Noriko’s survey effort brings the total number of vertebrate wildlife species detected on the site to 59, which is five times the number ELMT (2024) reports. A comparison between ELMT’s and Noriko’s surveys reveals that the wildlife species community depicted by ELMT (2024) is nowhere close to accurate, and it indicates that additional surveys would further increase the number of species known to the site.

ELMT (2024) documents little more than half the number of vertebrate wildlife species as the average among surveys reported by consulting biologists at California project sites, whereas Noriko detected 1.85 times the number we average in our surveys of the same sites (Figure 4). Applying Noriko’s 1.85-fold difference to the consultants’ average predicts ELMT should have detected 42 species of vertebrate wildlife, but ELMT detected only 29% of this expected number.

Figure 4. Frequency distribution of the number of vertebrate wildlife species detected by consulting biologists (left graph) and by me and Noriko (right graph) among project sites surveyed in support of environmental reviews in California.

0 20 40 60 80 100 120 140 160 180 200
Vertebrate wildlife species detected
0 20
40 60
80 100
120
140 Consultants
Number of project sites
0 10 20 30 40 50 60 70 80 90 100
0 20
40 60
80 100
Smallwoods
Number of project sites
= 22.8
= 30.3
ELMT (12)
Noriko (56)
Vertebrate wildlife species detected
24 ELMT (2024) documented half the number of special-status species of wildlife as the average among surveys reported by consulting biologists among California project sites, whereas Noriko detected 2.5 times the number we average in our surveys of the same sites (Figure 5). Applying Noriko’s 2.5-fold difference to the consultants’ average predicts that ELMT should have detected 10 special-status species of wildlife, but ELMT detected only 20% of this expected number. Comparative analysis reveals that the project site supports an unusually high wildlife species richness, that ELMT came up short of its expected number of species detections, and the true number of species remains to be known.

Figure 5. Frequency distribution of the number of special-status species of wildlife detected by consulting biologists (left graph) and by me and Noriko (right graph) among project sites surveyed in support of environmental reviews.

Two comparative contexts are needed to fully disclose the meanings of survey findings in the IS/MND. The first is to contextualize the survey findings relative to the survey effort, because otherwise it is misleading to insinuate that the species detected were definitive of the wildlife community. As Figures 1 and 2 demonstrate, additional surveys contribute to an increasingly more accurate characterization of the wildlife community, but eventually to a point at which the species inventory would be obtained. Quicker progress toward an accurate characterization of the wildlife community can be achieved by adding nocturnal surveys as well as specialized surveys for detecting bats, small mammals, reptiles and amphibians. However the surveys are done, it is imperative to compare survey findings to the findings from other surveys and their associated survey attributes to provide interpretive context such as to quantify the uncertainty of the 0 5 10 15 20 25 30 35 40
0 20
40 60
80 100
Special-status species of
vertebrate wildlife detected
Consultants
Number of project sites
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
0 10
20 30
40 50
60 70
80 Special-status species of
vertebrate wildlife detected
Smallwoods
Number of project sites
= 4
= 6
ELMT (2)
Noriko (15)
25 findings, or alternatively to understand whether the findings fall within the expected range of findings given the survey methods and level of effort. If no such comparison is possible, then the lack of comparability should be discussed as a survey limitation.

Another example of how one can contextualize survey findings can be found in Figure 6, which compares Noriko’s findings to our findings from hundreds of other surveys we have accumulated from other proposed project sites, developed sites, and at relatively intact “references sites,” but it does so relative to our survey efforts. Figure 6 reveals that Noriko’s survey tally at the project site exceeds the upper bound of the 95% confidence interval I estimated from hundreds of other morning surveys at project sites, and it even falls within the 95% CI estimated for ecologically intact reference sites. This approach is more informative than the other comparative contexts because it factors in survey effort.

Because ELMT (2024) fails to report key survey attributes, I cannot compare its reconnaissance survey findings to past consultant findings relative to their survey efforts.

Figure 6. Noriko Smallwood’s survey findings at the Project site relative to our survey findings at other project sites (red), at developed sites (black), and at more pristine sites that we survey as reference sites (green). Noriko’s findings exceed the upper bound of the 95% confidence interval based on the results of hundreds of surveys 0 50 100 150 200 250 300
Minutes into survey
0 10
20 30
40 50
60 Early morning surveys for vertebrate wildlife
Reference sites
Proposed project sites
Developed sites
Mean (95% CI) cumulative species detected
Noriko’s morning tally of 38 vertebrate
wildlife species
26 at project sites, and they approach the lower bound of the 95% CI from many surveys at reference sites.

The second comparative context is to assess the probabilities of detection considering the survey effort of each special-status species, whether detected or not. ELMT (2024) reports having failed to detect most special-status species of wildlife that could conceivably occur on the project site, but failures to detect special-status species can mean that the survey was inadequate. Each species presents survey personnel with a unique range of detection probabilities based on the species’ natural history, its behavior patterns, survey effort, the study area’s distance from known activity areas of the species, and the levels of skill of survey personnel. Many animals maintain home ranges that are larger than most project sites, and they periodically shift their foraging activities to various parts of their home range, hence occurring on a particular site only periodically. To detect these species, survey personnel must be at the site on the same date and time as the one or more members of the species. The probability of detection of any given species during any given survey is less than 100%, and for many species that truly occur on a given site, it is closer to 0%. The IS/MND fails to discuss this.

For example, white-tailed kite was detected by neither ELMT nor Noriko, but among our hundreds of surveys at project sites, Noriko and I have found white-tailed kites in environments similar to the project site. Based on hundreds of surveys within the geographic range of white-tailed kite, Noriko’s 4.49 hours of daylight survey combined with the knowledge that white-tailed kite has been documented between 1.5 and 4 miles from the project site afforded Noriko a 2% likelihood of detection of white-tailed kite (Figure 7). Noriko would need to conduct many more surveys to accumulate a reasonable likelihood of detecting white-tailed kite, but the patterns of our findings in our many other surveys depicted in Figure 7 reveal that it is unlikely that the white- tailed kite is absent from the project site. There is a fundamental difference between detection likelihood and occurrence likelihood, but this difference is not mentioned or discussed in IS/MND.

This second comparative context is very important because it is the basis for the formulation of detection survey protocols. Experienced biologists are aware of survey limitations due to ranges of variation in the activity periods, levels of crypticity, and periodic movement patterns of wildlife species, and due to variation in the skill and commitment of survey personnel. This comparative context acknowledges that the occurrence likelihood of a species on a particular site is rarely if ever zero, so long as the site is within the species’ geographic range and it provides habitat. This comparative context reveals that a determination of zero likelihood of occurrence is typically indicative of insufficient survey effort. A common goal of detection survey protocols is to support absence determinations with a survey effort that should have resulted in a detection had the species been present at the time of the surveys, but it does not prove that the species is always absent from the site. This comparative context and its implications should not be neglected in CEQA review, but it is neglected in the case of the IS/MND prepared for this project.

27 Figure 7. Probability
of detection of white-
tailed kite as a
function of survey
effort and proximity
of occurrence records
to a project site, based
on hundreds of
reconnaissance
surveys we completed
2019–2024.

ELMT (2024) failed to conduct a detection survey for burrowing owls (see CDFW 2012), Crotch’s bumble bee (see CDFW 2023), rare plants (see CDFW 2018), or for any other special-status species that could occur on the project site. Without these surveys, ELMT’s absence determinations could not be supported.

Appropriate surveys were not implemented for various other types of wildlife, hence leaving conclusions in ELMT (2024) unfounded. For example, ELMT (2024:6) reports “No amphibian species were observed during the field investigation,” but ELMT neglects to caveat this finding by explaining that the survey was likely conducted at the wrong time of day for detecting amphibians, especially in late July. Noriko surveyed at night, so she detected two species of amphibian, including western spadefoot – a special-status species. Similarly, ELMT conducted no coverboard surveys for reptiles, no live-trapping for small mammals, and no acoustic survey for bats.

Having failed to complete appropriate surveys for particular taxa, ELMT (2024) resorts to speculation over why these taxa are expected to not occur on the project site. For example, ELMT (2024:6) speculates, “Due to the ephemeral nature of Drainage 1 on site, no suitable foraging and cover habitat is available for local amphibian species.” ELMT cites no evidence that amphibians require predictable stream water sources, or Log10 Survey hours
-0.6 -0.2 0.2 0.6 1.0 1.4 1.8 2.2

0.2 0.4

0.6 0.8

1.0 0
Probability of detection

1.5 and 40 miles
Within 1.5 miles
On site or adjacent
Nearest eBird records
White-tailed kite Elanus leucurus
California Fully Protected

0.4 1584 10 Hours1
P = 0.02
28 that they even require streams, aka drainages. Baja California treefrogs, which Noriko found on the project site, favor temporary pools for egg-laying. Western spadefoots, which Noriko found on the project site, breed principally in vernal pools, but also in temporary ponds, flood control basins, and other bodies of water. Speculation is a poor basis for determining the absence of any species.

In another example, ELMT (2024:6) speculates, “No local bat species are expected to occur onsite due to a lack of suitable roosting opportunities.” However, bats are known to roost on a variety of substrates (Kunz and Lumsden 2003), and not just the narrowly defined conditions that appear in consultant reports. It is highly likely, for example, that bats roost on the sycamores that occur on the project site. In two very brief surveys, Noriko detected six species of bats on the project site, again demonstrating the hazard of speculating why certain species should not occur on a site. In the case of bats, ELMT’s speculation is wrong six times over.

Environmental Setting informed by Desktop Review

ELMT (2024) relies on occurrence records in the California Natural Diversity Data Base (CNDDB) to establish its pool of special-status species for assessing occurrence likelihoods, otherwise referred to as habitat assessments. ELMT (2024) queried the CNDDB for occurrence records of special-status species within two USGS 7.5’ Quadrangles inclusive of the project site. It is noteworthy, however, that the scope of ELMT’s query was only 22% of that typical of other consulting firms, which themselves are inadequate. (The typical scope of 9 Quads for CNDDB occurrence record queries is also too small, in my opinion.) Further, ELMT (2024) fails to mention eBird and iNaturalist, so I assume ELMT made no use of these more publicly accessible data bases of occurrence records.

Special-status species represented by occurrence records in the two Quads queried by ELMT were moved forward in the habitat assessment, but species lacking records were screened out of further analysis. This approach, however, was inconsistent with the design and intent of the CNDDB, which is a volunteer, positive-sightings database, and as such it is useful for confirming presence but not for confirming absence of species. As noted by the CNDDB, “The CNDDB is a positive sighting database. It does not predict where something may be found. We map occurrences only where we have documentation that the species was found at the site. There are many areas of the state where no surveys have been conducted and therefore there is nothing on the map. That does not mean that there are no special status species present.” ELMT (2024) and hence the IS/MND misuse the CNDDB.

The CNDDB relies entirely on volunteer reporting from biologists who were allowed access to whatever properties they report from. Many properties have never been surveyed by biologists. Many properties have been surveyed, but the survey outcomes never reported to the CNDDB. Many properties have been surveyed multiple times, but not all survey outcomes reported to the CNDDB. Furthermore, the CNDDB is interested only in the findings of special-status species, which means that species more recently assigned special status will have been reported many fewer times to the CNDDB than 29 were species assigned special status since the inception of the CNDDB. Therefore, occurrence records in the CNDDB are most abundant for species assigned special status decades ago, but fewest for species only recently assigned special status. And because negative findings are not reported to the CNDDB, the CNDDB is also inappropriate as a basis for weighting occurrence likelihoods such as absent, not expected, unlikely, low, moderate or high. Whereas the CNDDB can be confirmatory of species presence, it cannot support absence determinations or assignments of low likelihood of occurrence.

And again, the screening out of a species due to lack of occurrence records in the CNDDB is the same as an absence determination, but without the support of the detection surveys that have been formulated for this purpose.

In my assessment based on a database review and site visits, 141 special-status species of wildlife are known to occur near enough to the site to warrant analysis of occurrence potential (Table 2). Not all these species should be expected to occur at the project site, but each of them should be given a closer look to determine occurrence likelihoods and whether additional surveys are needed, or implementation of detection surveys, or whether it would be reasonable to assume presence. Of these 141 species, 18 were recorded on or just off the project site (on the fringe), and another 20 (14%) have been documented within 1.5 miles of the site (Very close), another 39 (28%) between 1.5 and 4 miles (Nearby), and another 54 (38%) between 4 and 30 miles (In region). More than half (55%) of the special-status species in Table 2 have been reportedly seen within 4 miles of the project site. The site therefore supports at least 18 special-status species of wildlife, and it carries the potential for supporting many more special-status species of wildlife based on the proximities of recorded occurrences. Evidence certainly suggests that habitat assessments are needed for these species.

ELMT (2024) considers the occurrence likelihoods of only 45 (32%) of the special-status species listed in Table 2, which means that ELMT screened out of the analysis 96 (68%) species in the first step of the desktop review. Of the 45 special-status species ELMT considers, 32 were “Presumed absent,” which effectively means “absent” from the project site. However, of these 32 species determined to be absent, Noriko detected western spadefoot, and nine of the others have been documented within 1.5 miles of the project site, and another 11 of them have been documented within 4 miles of the project site. Of the nine special-status species ELMT determines have only a low likelihood of occurrence, Noriko detected Southern California rufous-crowned sparrow, and one has been documented within 1.5 miles from the site, and three have been documented between 1.5 and 4 miles from the site. ELMT’s process for determining occurrence likelihoods is flawed and inaccurate.

30 Table 2. Occurrence likelihoods of special-status species of wildlife at or near the project site, according to ELMT (2024), eBird and iNaturalist records (https://eBird.org, https://www.inaturalist.org) and on-site surveys, where Very close indicates within

1.5 miles, Nearby indicates between 1.5 and 4 miles, In region indicates between 4 and 30 miles, and In range mmeans the species’ geographic range overlaps the site. Entries in bold font identify species detected by Noriko Smallwood during her site visits.

Common name Species name Status1 ELMT Records, Surveys
Vernal pool fairy shrimp Branchinecta lynchi FT In region San Diego fairy shrimp Branchinecta sandiegonensis FE In range Riverside fairy shrimp Streptocephalus woottoni FE In region Delhi sands flower-loving fly Rhaphiomidas terminatus
abdominalis
FE In region
Monarch Danaus plexippus FC Nearby/On site
Crotch’s bumble bee Bombus crotchii CCE Low Nearby
Western spadefoot Spea hammondii FC, SSC Absent In region/On site Southern mountain yellow-
legged frog
Rana muscosa FE, CE, WL Absent In region
Southwestern pond turtle Actinemys pallida FC, SSC In region Coast horned lizard Phrynosoma blainvillii SSC Low Nearby Orange-throated whiptail Aspidoscelis hyperythra WL Low In region Coastal whiptail Aspidoscelis tigris stejnegeri SSC Absent In region San Diegan legless lizard Anniella stebbinsi SSC Low Very close California glossy snake Arizona elegans occidentalis SSC Low In region Coast patch-nosed snake Salvadora hexalepis virgultea SSC Absent In region Two-striped gartersnake Thamnophis hammondii SSC Absent In region South coast gartersnake Thamnophis sirtalis pop. 1 SSC In region Red-diamond rattlesnake Crotalus ruber SSC Nearby
Brant Branta bernicla SSC2 In region
Cackling goose (Aleutian) Branta hutchinsii leucopareia WL Nearby Redhead Aythya americana SSC2 Nearby
Western grebe Aechmophorus occidentalis BCC Nearby
Clark’s grebe Aechmophorus clarkii BCC In region
Western yellow-billed cuckoo Coccyzus americanus occidentalis FT, CE In region Black swift Cypseloides niger SSC3, BCC In region
Vaux’s swift Chaetura vauxi SSC2 Nearby
31 Common name Species name Status1 ELMT Records, Surveys Costa’s hummingbird Calypte costae BCC High Very close/On site Calliope hummingbird Selasphorus calliope BCC Very close Rufous hummingbird Selasphorus rufus BCC Very close
Allen’s hummingbird Selasphorus sasin BCC Very close/On site Mountain plover Charadrius montanus SSC2, BCC In region Snowy plover Charadrius nivosus BCC In region
Western snowy plover Charadrius nivosus nivosus FT, SSC In region Long-billed curlew Numenius americanus WL In region
Marbled godwit Limosa fedoa BCC In region
Red knot Calidris canutus BCC In region
Pectoral sandpiper Calidris melanotos BCC In region
Short-billed dowitcher Limnodromus griseus BCC In region Lesser yellowlegs Tringa flavipes BCC In region
Willet Tringa semipalmata BCC In region
Laughing gull Leucophaeus atricilla WL In region
Franklin’s gull Leucophaeus pipixcan BCC Nearby
Heermann’s gull Larus heermanni BCC In region
Western gull Larus occidentalis BCC In region
California gull Larus californicus BCC, WL Very close
California least tern Sternula antillarum browni FE, CE, CFP In region Black tern Chlidonias niger SSC2, BCC In region
Elegant tern Thalasseus elegans BCC, WL In region
Black skimmer Rynchops niger BCC, SSC3 In region
Common loon Gavia immer SSC Nearby
Double-crested cormorant Phalacrocorax auritus WL Nearby American white pelican Pelacanus erythrorhynchos SSC1 Nearby Least bittern Ixobrychus exilis SSC2 In region
White-faced ibis Plegadis chihi WL Nearby
Turkey vulture Cathartes aura BOP Very close
Osprey Pandion haliaetus WL, BOP Absent Nearby
White-tailed kite Elanus luecurus CFP, BOP Absent Nearby 32 Common name Species name Status1 ELMT Records, Surveys Golden eagle Aquila chrysaetos BGEPA, CFP, BOP,
WL
Absent Nearby
Northern harrier Circus cyaneus BCC, SSC3, BOP Absent Very close Sharp-shinned hawk Accipiter striatus WL, BOP Nearby
Cooper’s hawk Accipiter cooperii WL, BOP Absent Very close Bald eagle Haliaeetus leucocephalus CE, BGEPA, BOP Nearby Red-shouldered hawk Buteo lineatus BOP Very close
Swainson’s hawk Buteo swainsoni CT, BOP Nearby
Red-tailed hawk Buteo jamaicensis BOP Very close/On site Ferruginous hawk Buteo regalis WL, BOP Absent Very close Zone-tailed hawk Buteo albonotatus BOP In region
Harris’ hawk Parabuteo unicinctus WL, BOP In region
Rough-legged hawk Buteo lagopus BOP In region
American barn owl Tyto furcata BOP Very close/On site
Western screech-owl Megascops kennicotti BOP Very close Great horned owl Bubo virginianus BOP Very close
Burrowing owl Athene cunicularia BCC, CCE, SSC2, BOP Absent Nearby Long-eared owl Asio otus BCC, SSC3, BOP Absent In region Short-eared owl Asia flammeus BCC, SSC3, BOP Nearby
Lewis’s woodpecker Melanerpes lewis BCC Nearby
Nuttall’s woodpecker Picoides nuttallii BCC Very close/Fringe American kestrel Falco sparverius BOP Very close/On site Merlin Falco columbarius WL, BOP Absent Nearby
Peregrine falcon Falco peregrinus BOP Absent Very close Prairie falcon Falco mexicanus WL, BOP Moderate Nearby
Olive-sided flycatcher Contopus cooperi BCC, SSC2 Absent Nearby Willow flycatcher Empidonax trailii CE Absent Nearby
Southwestern willow flycatcher Empidonax traillii extimus FE, CE Absent In region Vermilion flycatcher Pyrocephalus rubinus SSC2 Absent Very close Least Bell’s vireo Vireo bellii pusillus FE, CE Absent Very close Loggerhead shrike Lanius ludovicianus SSC2 Absent Nearby Oak titmouse Baeolophus inornatus BCC Very close
33 Common name Species name Status1 ELMT Records, Surveys California horned lark Eremophila alpestris actia WL Moderate Very close Bank swallow Riparia riparia CT Nearby
Purple martin Progne subis SSC2 Absent Nearby
Wrentit Chamaea fasciata BCC Very close/On site
California gnatcatcher Polioptila c. californica FT, SSC2 Absent Nearby California thrasher Toxostoma redivivum BCC Very close/On site Cassin’s finch Haemorhous cassinii BCC Nearby
Lawrence’s goldfinch Spinus lawrencei BCC Absent Very close Grasshopper sparrow Ammodramus savannarum SSC2 Very close Black-chinned sparrow Spizella atrogularis BCC Nearby
Gray-headed junco Junco hyemalis caniceps WL Nearby
Bell’s sparrow Amphispiza b. belli WL Low Nearby
Oregon vesper sparrow Pooecetes gramineus affinis SSC2 In range Southern California rufous-
crowned sparrow
Aimophila ruficeps canescens WL Low Very close/On site
Yellow-breasted chat Icteria virens SSC3 Absent Nearby
Yellow-headed blackbird Xanthocephalus xanthocephalus SSC3 Nearby Bullock’s oriole Icterus bullockii BCC Very close/On site Tricolored blackbird Agelaius tricolor CT, BCC, SSC1 Nearby Prothonotary warbler Protonotaria citrea BCC In region Lucy’s warbler Leiothlypis luciae SSC3 Nearby
Virginia’s warbler Leiothlypis virginiae WL, BCC Nearby Northern yellow warbler Setophaga aestiva SSC2 Absent Very close Prairie warbler Setophaga discolor BCC In region
Summer tanager Piranga rubra SSC1 Nearby
Little brown bat Myotis lucifugus WBWG:M In range
Yuma myotis Myotis yumanensis WBWG:LM Nearby/On site
Long-eared myotis Myotis evotis WBWG:M In region
Fringed myotis Myotis thysanodes WBWG:H In region
Long-legged myotis Myotis volans WBWG:H In range
California myotis Myotis californicus WBWG:L In region/On site Small-footed myotis Myotis ciliolabrum WBWG:M In region 34 Common name Species name Status1 ELMT Records, Surveys Canyon bat Parastrellus hesperus WBWG:M In region/On site Big brown bat Episticus fuscus WBWG:L In region/Possible on site
Silver-haired bat Lasionycteris noctivagans WBWG:M In range/On site Hoary bat Lasiurus cinereus WBWG:M In region
Western red bat Lasiurus blossevillii SSC, WBWG:H In region Western yellow bat Lasiurus xanthinus SSC, WBWG:H Absent In region Spotted bat Euderma maculatum SSC, WBWG:H In range
Townsend’s big-eared bat Corynorhinus townsendii SSC, WBWG:H In region Pallid bat Antrozous pallidus SSC, WBWG:H In range
Mexican free-tailed bat Tadarida brasiliensis WBWG:L Nearby/On site Pocketed free-tailed bat Nyctinomops femorosaccus SSC, WBWG:M Absent In range Western mastiff bat Eumops perotis SSC, WBWG:H In range San Diego black-tailed jackrabbit Lepus californicus bennettii SSC High In region Northwestern San Diego pocket
mouse
Chaetodipus fallax fallax SSC Low In region
Pallid San Diego pocket mouse Chaetodipus fallax pallidus SSC Low In range San Bernardino kangaroo rat Dipodomys merriami parvus FE, CE, SSC Absent In region Stephens’ kangaroo rat Dipodomys stephensi FT, CT In region Los Angeles pocket mouse Perognathus longimembris
brevinasus
SSC Absent Very close
San Diego Bryant’s woodrat Neotoma bryanti intermedia SSC Absent In region Southern grasshopper mouse Onychomys torridus ramona SSC In range American badger Taxidea taxus SSC Absent In region
Mountain lion Puma concolor CT In region
1 Listed on CDFW’s Special Animals List (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=109406) as FT or FE of FC = federal threatened or endangered or candidate for listing; BGEPA = Bald and Golden Eagle Protection Act; BCC = U.S. Fish and Wildlife Service’s Bird of Conservation Concern (https://www.fws.gov/sites/default/files/documents/birds-of-conservation-concern- 2021.pdf); CT or CE or CCE = California threatened or endangered or candidate for listing; CFP = California Fully Protected (California Fish and Game Code 3511); SSCi = California Species of Special Concern with i = priorities 1, 2 and 3; WL = CDFW’s Taxa to Watch List; WBWG = Western Bat Working Group with priority rankings, of low (L), moderate (M), and high (H); and BOP = protected by Birds of Prey (California Fish and Game Code 3503.5, see https://wildlife.ca.gov/Conservation/Birds/Raptors). 35 Mischaracterization of the Wildlife Community

ELMT (2024) reports on a field survey and a desktop review having been completed, but the field survey was much too cursory, and its results are interpreted without context to survey results from elsewhere, and its desktop review is flawed and incomplete. Despite these gross deficiencies, ELMT (2024:12) concludes, “Based on the literature review and field survey, and existing site conditions discussed in this report, implementation of the project will have no significant impacts on additional federally or State listed species known to occur in the general vicinity of the project site. Additionally, the project will have no effect on designated Critical Habitat, or regional wildlife corridors/linkage due to relevant long-term impacts being consolidated to already disturbed or developed land and the presence of ample open space surrounding the site. No further surveys are recommended.” To the contrary, additional surveys are needed, and so is a comparative analysis of the survey results and a more thorough, careful desktop review.

ELMT’s conclusions are only speculative, which are inappropriate for precious or rare resources such as special-status species (National Research Council 1986). The true wildlife community remains to be described with sufficient accuracy. Without a more accurate characterization of the wildlife community, the basis is inadequate for predicting impacts and formulating appropriate mitigation strategies. The combined findings of Noriko’s and ELMT’s surveys have documented the presence of 17 special- status species on the project site, inclusive of one species listed as threatened under the California ESA and two species that are candidates for listing under the federal ESA.

This many species in one place, and found despite a relatively cursory survey effort, is atypical of project sites in California. More work is needed to accurately characterize the wildlife community as part of the existing environmental setting.

Considering the deficiencies in the survey effort directed to the project site, the deficiencies in reporting of survey methods and the reporting of results, and the inadequacy of the desktop review to analyze the occurrence likelihoods of special-status species, there is at least a fair argument that can be made for the need to prepare an EIR to accurately characterize the wildlife community on the project site as a suitable baseline from which to predict project impacts and to formulate an appropriate mitigation strategy.

BIOLOGICAL IMPACTS ASSESSMENT

The impacts analysis in CEQA review involves prediction. Impact predictions are necessary in environmental review because measuring a project’s impacts directly could not happen until after the impacts occur, and the timing of this type of measurement would come too late for formulating a mitigation strategy or deciding on a project alternative. As noted earlier, the accuracy of the predictions of impacts and their significance ultimately relies on the degree of accuracy in the characterization of the existing environmental setting (Figure 3). Prediction accuracy also relies on experience, of which development projects subjected to CEQA review have been made amply available (Figure 8). CEQA prohibits the use of speculation to predict that potential project impacts would be less than significant (see CEQA Guidelines section 15145. 36 SPECULATION), so it is experience that must be used. The type of experience proven most effective, and therefore most amenable to achieving CEQA’s primary objectives, is scientific inference, as generally depicted in Figure 8. Scientific inference can be drawn from scientific studies or from the many other projects that have undergone CEQA review. The comparative method enables the use of scientific inference to predict impacts and to predict the efficacies of mitigation strategies, the latter of which should be also amply available in the record.

Figure 8. A framework for arriving at predicted project impacts based on experience with other project sites. Ideally, there is a pool of similar projects in similar circumstances where predicted impacts were compared to realized impacts, and into which the proposed project can also contribute to experience.

In the following, I analyze several types of impacts likely to result from the project, none of which are analyzed adequately by ELMT (2024) or the IS/MND, and some of which are not analyzed at all.

REDUCED PRODUCTIVE CAPACITY FROM HABITAT LOSS

Habitat loss results in a reduced productive capacity of affected wildlife species. The site is proven to serve as habitat to at least 59 species of vertebrate wildlife which Noriko and ELMT observed on the project site, but the number of avian nest sites remains unknown. The surveys that have been conducted on the project site were unsuitable for detecting all bird nests or estimating total nest density. The alternative method for estimating avian productive capacity is to infer it from estimates of total nest density Inference
Project site
Preconstruction
study
Predict
project impacts
Preconstruction
studies
Post-construction
studies
Pool of experience
Post-construction
study
Test predictions
37 elsewhere. Noriko and I completed studies to estimate total avian nest density in similar environments (Table 3).

Table 3. My estimates of the acreages of ground cover types identified by ELMT (2024), and total numbers of bird nest sites based on studies of total nest density completed by me an Noriko in 2023 and 2024.

Cover Type Acres Nests/acre Source Total nests
Chaparral 7.1 3.26 A 23.1
Riversidean Sage Scrub 19.7 1.63 B 32.1
Sycamore Woodland 1.8 19.89 C 35.8
Disturbed 32 5.56 D 177.9
Total 60.6 268.9
A Noriko estimated 1.63 bird nests/acres on 1.23 acres of sage scrub in Murrieta.

B I assumed twice the nest density that Noriko estimated in sage scrub.

C Noriko estimated 12.7 bird nests/acre on 0.63 acres of riparian woodland in San Jacinto National Wildlife Refuge, and 18.18 bird nests/acres on 7 acres of riparian woodland in Murrieta, and I estimated 28.79 nests/acre on 1.32 acres of riparian woodland in Rancho Cordova, for a mean of 19.89 nests/acre.

D Noriko Estimated 5.56 bird nests/acre on 3.6 acres of ruderal grassland in Murrieta.

Based on estimates of total nest density at various study sites, I estimate the project site supports 269 bird nests per year (Table 3). Assuming 1.39 broods per nest site based on a review of 322 North American bird species, which averaged 1.39 broods per year, then I estimate 374 nest attempts per year on the project site. Assuming Young’s (1948) study site typifies bird productivity of 2.9 fledged birds per nest attempt, then I predict 1,114 fledglings/year at the project site.

The loss of 269 nest sites and 374 nest attempts per year would qualify as significant impacts that have not been analyzed in the IS/MND. But the impacts would not end with the immediate loss of nest sites. The reproductive capacity of the site would be lost.

The project would prevent the production of 1,114 fledglings per year. Assuming an average bird generation time of 4 years, the lost capacity of both breeders and annual fledgling production can be estimated from an equation in Smallwood (2022):
{(nests/year × chicks/nest × number of years) + (2 adults/nest × nests/year) × (number of years ÷ years/generation)} ÷ (number of years) = 1,249 birds per year lost to California.

The loss of 1,249 birds per year would be a loss of significant habitat value that is currently provided by the project site. Most if not all these birds are protected by the federal Migratory Bird Treaty Act and by California’s Migratory Bird Protection Act, both of which are intended to most strongly protect breeding migratory birds. The loss of 1,249 birds would easily qualify as an unmitigated significant impact.

This same type of analysis is needed to predict losses in productive capacities of lizards, snakes, frogs, toads, salamanders, bats, small mammals, and large mammals.

38 RISK TO WILDLIFE FROM INCREASED WILDFIRE FREQUENCY

The IS/MND fails to analyze the potential impacts on wildlife due to the increased wildfire hazard of building more homes on the lower aspect of a chaparral-covered slope. The average annual extent of wildfires increased 3.25-fold between the years 1995-1998 and 2015-2018 (Figure 9a). This increase in the extent of wildfires correlated with California’s increase in population size (Figure 9b), which between 1998 and 2018 increased 22%, or >7 million people. That the annual extent of wildfires mirrors population growth in California indicates that the increasing interface between human activities and wildlands also increases the annual area burned. Indeed, CalFire attributes 95% of wildfires to human causes.

Figure 9. Annual hectares of wild areas burned by wildfires in California (a) over 76 years (data source: https://www.fire.ca.gov/stats-events/), and (b) by population size (Data source: http://worldpopulationreview.com/states/california-population/). I estimated annual population size intervening decades prior to 2010 by regressing census data by decade, 1940 through 2010 (r2 = 0.996, P < 0.0001).

Wildlife have had to respond to wildfires for as long as wildlife have existed on Earth.

However, Figure 9 shows that modern human presence in California increases the areas that are burned each year. Positively correlated with the extent of areas burned is the frequency of wildfire. The increased frequency of wildfire can alter the species assemblage of the wildlife community, favoring certain species over others (Fredriksson et al. 2025).Whereas wildlife are generally adapted to having to respond to wildfires, an increased frequency of wildfires can degrade wildlife habitat, resulting in the declines of many species (Smith 2000). An analysis of this potential impact on wildlife is missing from the IS/MND, but it needs to be included in the environmental review. There is at least a fair argument that can be made for the need to prepare and EIR to appropriately analyze the potential impact of increased fire frequency on wildlife caused by residential encroachment.

1940 1950 1960 1970 1980 1990 2000 2010 2020
Year
0 100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
Hectares burned
5 10 15 20 25 30 35 40
California population (millions)
a b
39 INTERFERENCE WITH WILDLIFE MOVEMENT

One of CEQA’s principal concerns regarding potential project impacts is whether a proposed project would interfere with wildlife movement in the region. Unfortunately, this concern has not motivated any serious analysis of whether or how the project would interfere with wildlife movement in the region. The IS/MND begins by uniquely defining habitat linkages and corridors, and it abandons consideration of birds and bats when it specifies that adequate cover is essential to corridor function. The analysis in the IS/MND is clearly focused solely on non-volant species of wildlife. Nevertheless, after the analysis concedes that the project site is located within a Major Open Space as mapped by San Bernardino County, and that this Major Open Space is recognized as important to wildlife movement in the region, it claims the project has been designed to avoid impacts on wildlife movement by not encroaching into Myers Canyon. However, encroachment into Myers Canyon has little if anything to do with the fact that the project would eliminate the terrestrial portion of Cajon Pass as a Major Open Space for wildlife movement. The avoidance of Myers Canyon fails to avoid impacts to wildlife movement through Cajon Pass.

WILDLIFE DEPREDATION BY HOUSE CATS

Considering national trends, it is safe to assume that house cats would be introduced to the project area by residents of the proposed residential units. This is significant because house cats serve as one of the largest sources of avian mortality in North America (Dauphiné and Cooper 2009, Blancher 2013, Loss et al. 2013, Loyd et al. 2017).

Loss et al. (2013) estimated 139 million cats in the USA in 2013 (range 114 to 164 million), which killed an estimated 16.95 billion vertebrate wildlife annually (range 7.6 to 26.3 billion). In 2012 there were 0.44 house cats per human in the USA, and 122 vertebrate animals were killed per cat, free-ranging members of which killed disproportionately larger numbers of vertebrate wildlife. The IS/MND predicts 380 new residents in 105 single-family homes. The above rates of cat ownership applied to this number of new residents would predict 167 new cats, which based on the findings of Loss et al. (2013) would kill 20,374 vertebrate wildlife per year.

House cats also contribute to downstream loading of Toxoplasma gondii. According to a UC Davis wildlife health research program, “Toxoplasma gondii is a parasite that can infect virtually all warm-blooded animals, but the only known definitive hosts are cats – domesticated and feral house cats included. Cats catch the parasite through hunting rodents and birds and they offload it into the environment through their feces… and …rain that falls on cement creates more runoff than rain that falls on natural earth, which contributes to increased runoff that can carry fecal pathogens to the sea” (The original link is no longer active, but the quote came from the program described at:
https://whc.vetmed.ucdavis.edu/programs-projects/ca-conservation/sea-otter).

Impacts to wildlife from the introduction of house cats into the environment would be highly significant, and yet these impacts are not considered in the IS/MND. An obvious mitigation measure would be to constrain house cat ownership such as requiring cats to remain indoors.
40 BIRD-WINDOW COLLISION MORTALITY

The project would introduce glass windows into an essential portion of avian habitat – that portion of the gaseous atmosphere that is referred to as the aerosphere (Davy et al. 2017, Diehl et al. 2017). The aerosphere is where birds and bats and other volant animals with wings migrate, disperse, forage, perform courtship and where some of them mate. Birds are some of the many types of animals that evolved wings as a morphological adaptation to thrive by moving through the medium of the aerosphere.

The aerosphere is habitat, to which an entire discipline of ecology has emerged to study this essential aspect of habitat – the discipline of aeroecology (Kunz et al. 2008).

Many special-status species of birds have been recorded at or near the aerosphere of the project site. My database review and Noriko’s site visit indicate there are 101 special- status species of birds with potential to use the site’s aerosphere (Table 1). All the birds represented in Table 1 can quickly fly from wherever they have been documented to the project site, so they would all be within brief flights to the proposed project’s windows.

Noriko confirmed 48 vertebrate wildlife species on the project site, some of them flying across the site.

Window collisions are often characterized as either the second or third largest source or human-caused bird mortality. The numbers behind these characterizations are often attributed to Klem’s (1990) and Dunn’s (1993) estimates of about 100 million to 1 billion bird fatalities in the USA, or more recently by Loss et al.’s (2014) estimate of 365-988 million bird fatalities in the USA or Calvert et al.’s (2013) and Machtans et al.’s (2013) estimates of 22.4 million and 25 million bird fatalities in Canada, respectively. The proposed project would impose windows in the airspace normally used by birds.

Glass-façades of buildings intercept and kill many birds, but they are differentially hazardous to birds based on spatial extent, contiguity, orientation, and other factors. At Washington State University, Johnson and Hudson (1976) found 266 bird fatalities of 41 species within 73 months of monitoring of a three-story glass walkway (no fatality adjustments attempted). Prior to marking the windows to warn birds of the collision hazard, the collision rate was 84.7 per year. At that rate, and by not attempting to adjust the fatality estimate for the proportion of fatalities not found, 4,574 birds were likely killed over the 54 years since the start of their study, and that’s at a relatively small building façade. Accounting for the proportion of fatalities not found in searches, the number of birds killed by this walkway over the last 54 years would have been about 14,270. And this is just for one 3-story, glass-sided walkway between two college campus buildings.

Klem’s (1990) estimate was based on speculation that 1 to 10 birds are killed per building per year, and this speculated range was extended to the number of buildings estimated by the US Census Bureau in 1986. Klem’s speculation was supported by fatality monitoring at only two houses, one in Illinois and the other in New York. Also, the basis of his fatality rate extension has changed greatly since 1986. Whereas his estimate served the need to alert the public of the possible magnitude of the bird- 41 window collision issue, it was highly uncertain at the time and undoubtedly outdated more than three decades hence. Indeed, by 2010 Klem (2010) characterized the upper end of his estimated range – 1 billion bird fatalities – as conservative. Furthermore, the estimate lumped species together as if all birds are the same and the loss of all birds to windows has the same level of impact.

By the time Loss et al. (2014) performed their effort to estimate annual USA bird- window fatalities, many more fatality monitoring studies had been reported or were underway. Loss et al. (2014) incorporated many more fatality rates based on scientific monitoring, and they were more careful about which fatality rates to include. However, they included estimates based on fatality monitoring by homeowners, which in one study were found to detect only 38% of the available window fatalities (Bracey et al. 2016). Loss et al. (2014) excluded all fatality records lacking a dead bird in hand, such as injured birds or feather or blood spots on windows. Loss et al.’s (2014) fatality metric was the number of fatalities per building (where in this context a building can include a house, low-rise, or high-rise structure), but they assumed that this metric was based on window collisions. Because most of the bird-window collision studies were limited to migration seasons, Loss et al. (2014) developed an admittedly assumption-laden correction factor for making annual estimates. Also, only two of the studies included adjustments for carcass persistence and searcher detection error, and it was unclear how and to what degree fatality rates were adjusted for these factors. Although Loss et al. (2014) attempted to account for some biases as well as for large sources of uncertainty mostly resulting from an opportunistic rather than systematic sampling data source, their estimated annual fatality rate across the USA was highly uncertain and vulnerable to multiple biases, most of which would have resulted in fatality estimates biased low.

In my review of bird-window collision monitoring, I found that the search radius around homes and buildings was very narrow, usually 2 meters. Based on my experience with bird collisions in other contexts, I would expect that a large portion of bird-window collision victims would end up farther than 2 m from the windows, especially when the windows are higher up on tall buildings. In my experience, searcher detection rates tend to be low for small birds deposited on ground with vegetation cover or woodchips or other types of organic matter. Also, vertebrate scavengers entrain on anthropogenic sources of mortality and quickly remove many of the carcasses, thereby preventing the fatality searcher from detecting these fatalities. Adjusting fatality rates for these factors – search radius bias, searcher detection error, and carcass persistence rates – would greatly increase nationwide estimates of bird-window collision fatalities.

Buildings can intercept many nocturnal migrants as well as birds flying in daylight. As mentioned above, Johnson and Hudson (1976) found 266 bird fatalities of 41 species within 73 months of monitoring of a four-story glass walkway at Washington State University (no adjustments attempted for undetected fatalities). Somerlot (2003) found 21 bird fatalities among 13 buildings on a university campus within only 61 days.

Monitoring twice per week, Hager at al. (2008) found 215 bird fatalities of 48 species, or 55 birds/building/year, and at another site they found 142 bird fatalities of 37 species for 24 birds/building/year. Gelb and Delacretaz (2009) recorded 5,400 bird fatalities under buildings in New York City, based on a decade of monitoring only during 42 migration periods, and some of the high-rises were associated with hundreds of fatalities each. Klem et al. (2009) monitored 73 building façades in New York City during 114 days of two migratory periods, tallying 549 collision victims, nearly 5 birds per day. Borden et al. (2010) surveyed a 1.8 km route 3 times per week during 12-month period and found 271 bird fatalities of 50 species. Parkins et al. (2015) found 35 bird fatalities of 16 species within only 45 days of monitoring under 4 building façades. From 24 days of survey over a 48-day span, Porter and Huang (2015) found 47 fatalities under 8 buildings on a university campus. Sabo et al. (2016) found 27 bird fatalities over 61 days of searches under 31 windows. In San Francisco, Kahle et al. (2016) found 355 collision victims within 1,762 days under a 5-story building. Ocampo-Peñuela et al. (2016) searched the perimeters of 6 buildings on a university campus, finding 86 fatalities after 63 days of surveys. One of these buildings produced 61 of the 86 fatalities, and another building with collision-deterrent glass caused only 2 of the fatalities, thereby indicating a wide range in impacts likely influenced by various factors. There is ample evidence available to support my prediction that the proposed project would result in many collision fatalities of birds.

Project Impact Prediction

By the time of these comments, I had reviewed and processed results of bird collision monitoring at 213 buildings and façades for which bird collisions per m2 of glass per year could be calculated and averaged (Johnson and Hudson 1976, O’Connell 2001, Somerlot 2003, Hager et al. 2008, Borden et al. 2010, Hager et al. 2013, Porter and Huang 2015, Parkins et al. 2015, Kahle et al. 2016, Ocampo-Peñuela et al. 2016, Sabo et al. 2016, Barton et al. 2017, Gomez-Moreno et al. 2018, Schneider et al. 2018, Loss et al. 2019, Brown et al. 2020, City of Portland Bureau of Environmental Services and Portland Audubon 2020, Riding et al. 2020). These study results averaged 0.073 bird deaths per m2 of glass per year (95% CI: 0.042-0.102). This average and its 95% confidence interval provide a robust basis for predicting fatality rates at a proposed new project.

With the estimated average bird collision mortality above, all I need are estimates of window extents in the project. The IS/MND does not predict the extents of windows in the project, nor does it provide any renderings of the homes in the project. However, it predicts 1,600 sf per 105 single-family homes, which would total 168,000 sf. I can apply this square footage to the average m2 of glass windows relative to the sf of floor space that I have maintained in a database of other single-family residential development projects for which I have been able to measure window extents in renderings of the homes. Assuming 0.015193 m2 of glass window extent per sf, then I predict 2,552 m2 of windows among the single-family homes. This extent of windows multiplied against the above-reported average bird collision deaths per m2 of glass per year predicts 187 (95% CI: 111–262) bird collision fatalities per year.

The vast majority of these predicted deaths would be of birds protected under the Migratory Bird Treaty Act and under the California Migratory Bird Protection Act, thus causing significant unmitigated impacts that were not addressed in IS/MND. Given the predicted level of bird-window collision mortality, and the lack of any proposed 43 mitigation, it is my opinion that the proposed project would result in potentially significant adverse biological impacts, including the unmitigated take of both terrestrial and aerial habitat of birds and other sensitive species. Not only would the project take habitat of rare and sensitive species of birds, but it would transform the project’s airspace into a lethal collision trap to birds.

There is at least a fair argument that can be made for the need to prepare an EIR to appropriately analyze the project for its potential impacts on birds caused by collisions with windows.

TRAFFIC IMPACTS ON WILDLIFE

The IS/MND neglects to address one of the project’s most obvious, substantial impacts on wildlife, and that is wildlife mortality and injuries caused by project-generated traffic. Project-generated traffic would endanger wildlife that must, for various reasons, cross roads used by the project’s traffic (Photos 34―37), including along roads far from the project footprint but which would nevertheless by traversed by automobiles head to or from the project’s building. Vehicle collisions have accounted for the deaths of many thousands of amphibian, reptile, mammal, bird, and arthropod fauna, and the impacts have often been found to be significant at the population level (Forman et al. 2003).

Across North America traffic impacts have taken devastating tolls on wildlife (Forman et al. 2003). In Canada, 3,562 birds were estimated killed per 100 km of road per year (Bishop and Brogan 2013), and the US estimate of avian mortality on roads is 2,200 to 8,405 deaths per 100 km per year, or 89 million to 340 million total per year (Loss et al. 2014). Local impacts can be more intense than nationally.

The nearest study of traffic-caused wildlife mortality was performed along a 2.5-mile stretch of Vasco Road in Contra Costa County, California. Fatality searches in this study found 1,275 carcasses of 49 species of mammals, birds, amphibians and reptiles over 15 months of searches (Mendelsohn et al. 2009). This fatality number needs to be adjusted for the proportion of fatalities that were not found due to scavenger removal and searcher error. This adjustment is typically made by placing carcasses for searchers to find (or not find) during their routine periodic fatality searches. This step was not taken at Vasco Road (Mendelsohn et al. 2009), but it was taken as part of another study next to Vasco Road (Brown et al. 2016). Brown et al.’s (2016) adjustment factors for carcass persistence resembled those of Santos et al. (2011). Also applying searcher detection rates from Brown et al. (2016), the adjusted total number of fatalities was estimated at 9,462 animals killed by traffic on the road. This fatality number projected over 1.25 years and 2.5 miles of road translates to 3,028 wild animals per mile per year. In terms comparable to the national estimates, the estimates from the Mendelsohn et al. (2009) study would translate to 188,191 animals killed per 100 km of road per year, or 22 times that of Loss et al.’s (2014) upper bound estimate and 53 times the Canadian estimate.

An analysis is needed of whether increased traffic generated by the project site would similarly result in local impacts on wildlife.

44 Photos 34 and 35. A
desert cottontail runs
across the road in
Murietta, California (top),
and a great-tailed grackle
crosses a road in the
Imperial Valley (bottom).

Photos 36 and 37.

Raccoon killed on Road 31
just east of Highway 505
in Solano County (left;
photo taken on 10
November 2018), and
California kingsnake
killed by a vehicle on a
rural road in El Dorado
County in 2024.

For wildlife vulnerable to front-end collisions and crushing under tires, road mortality can be predicted from the study of Mendelsohn et al. (2009) as a basis, although it would be helpful to have the availability of more studies like that of Mendelsohn et al.

45 (2009) at additional locations. My analysis of the Mendelsohn et al. (2009) data resulted in an estimated 3,028 animals killed per mile along a county road in Contra Costa County. The estimated numbers of fatalities were 1.75% birds, 26.4% mammals (many mice and pocket mice, but also ground squirrels, desert cottontails, striped skunks, American badgers, raccoons, and others), 67.4% amphibians (large numbers of California tiger salamanders and California red-legged frogs, but also Sierran treefrogs, western toads, arboreal salamanders, slender salamanders and others), and 4.4% reptiles (many western fence lizards, but also skinks, alligator lizards, and snakes of various species). VMT is useful for predicting wildlife mortality because I was able to quantify miles traveled along the studied reach of Vasco Road during the period of the Mendelsohn et al. (2009), hence enabling a rate of fatalities per VMT that can be projected to other sites, assuming similar collision fatality rates.

Predicting project-generated traffic impacts on wildlife

The IS/MND predicts the project would generate 3,037,146 construction VMT, and 5,850,624 annual VMT after construction. During the Mendelsohn et al. (2009) study, 19,500 cars traveled Vasco Road in Contra Costa County daily, so the vehicle miles that contributed to my estimate of non-volant fatalities was 19,500 cars and trucks × 2.5 miles × 365 days/year × 1.25 years = 22,242,187.5 vehicle miles per 9,462 wildlife fatalities, or 2,351 vehicle miles per fatality. This rate divided into the predicted construction VMT would predict 1,292 wildlife fatalities, and this rate divided into annual VMT would predict 2,489 vertebrate wildlife fatalities per year due to project- generated traffic.

However, some might argue that this prediction relies too much on a study along a road through less-disturbed rangeland that likely supports more non-volant wildlife that is more vulnerable to collision mortality. I would argue that the project-generated traffic would pass through similar rural environments with ample wildlife available to be crushed under automobile tires. Nevertheless, to better represent those reaches of road that are more urban than rural, or that would become more urban with the development of the project, I recently completed my own study of wildlife mortality caused by automobiles along roads that were urban and interfaced between urban and agricultural land uses.

I completed one year of daily pedestrian surveys along 2.7 km (1.713 miles) of local, collector and minor arterial roads in north Davis, California, where I tallied 314 fatalities of 40 species of vertebrate wildlife. Most of the fatalities were small-bodied animals such as Sierran treefrogs, western toads, western fence lizards, 10”- to 16”-long juvenile Pacific gophersnakes, valley gartersnakes and California kingsnakes, as well as bushtits, yellow-rumped warblers, deer mice and California ground squirrels. Most of the animals I found would never have been detected from a moving vehicle, which is why people often underestimate how many wild animals are killed by vehicle traffic.

The animals I found in my study did not include all the animals killed by vehicles on the roads I searched. Many had been removed by scavengers before I could find and count them. (American crows patrol the roads every morning, and so does a large flock of wild 46 turkeys.) Some of the animals are knocked off the road in places where I could never find them, and some were undoubtedly caught and carried away in the grills of vehicles or in tire treads. Most of the Sierran treefrogs disappeared from where I found them within several hours, so daily searches missed many of the fatalities. To adjust for these undetected fatalities, I fit a logit regression model to my fatality finds, all of which transitioned to carcass detection trials after I initially found them. Starting with the first day after each detection, I monitored the carcass trials for 30 days, and I assigned each a body mass estimate based on typical body mass reported in the literature. Predictor variables were days since detection and log10 body mass. The relationship between carcass detection probability and the predictor variables is depicted in Figure 10. My fatality finds adjusted by the model results in an estimate of 2,126 vertebrate wildlife fatalities over 1.713 miles of roads in one year, or 1,241 vertebrate wildlife fatalities/mile/year. This rate is 40.9% of the rate measured at Vasco Road, or an estimated 5,748 vehicle miles per fatality. Applying this urban fatality rate to the VMT that is predicted for the project would predict 528 vertebrate wildlife fatalities during construction, and 1,018 vertebrate wildlife fatalities per year in the area around the project that is traveled by project-generated traffic.

Figure 10.

Wildlife
carcass
detection
probability
is a
function of
the number
of days
since
discovery
and log10
body mass.

A reasonable range of predicted mortality is therefore 528 to 1,292 vertebrate wildlife fatalities during construction, and 1,018 to 2,489 vertebrate wildlife fatalities/year due to project-generated traffic.

47 Based on my analysis, the project-generated traffic would cause significant impacts on wildlife. The IS/MND does not address this potential impact, let alone propose to mitigate it. Mitigation measures to improve wildlife safety along roads are available and are feasible, and they need exploration for their suitability with the proposed project.

Given the predicted level of project-generated traffic-caused mortality, and the lack of any proposed mitigation, it is my opinion that the proposed project would result in potentially significant adverse biological impacts, and that these impacts would be unmitigated.

FLAWED CUMULATIVE IMPACTS ANALYSIS

One of CEQA’s principal concerns regarding potential project impacts is whether a proposed project would contribute significantly to cumulative impacts of past, present and future projects, or to multiple types of impacts that are cumulatively considerable.

Unfortunately, the IS/MND fails to seriously analyze the project’s contributions to cumulative impacts on biological resources. According to the IS/MND, “Although cumulative impacts are always possible, by incorporating all mitigation measures outlined herein, as part of approving the Proposed Project, would reduce the Project’s contribution to any such cumulative impacts to levels that are not cumulatively considerable.” This notion that mitigation of project-specific impacts would avoid cumulative impacts is inconsistent with CEQA’s definition of a cumulative impact, which is “created as a result of the combination of the project evaluated in the EIR together with other projects causing related impacts” (CEQA Guidelines §15065 (a)(3)).

Even though a project’s direct or indirect effects might be mitigated to a less than significant impact at the project level, it can still incrementally contribute to the effects of other projects for a significant cumulative impact.

The IS/MND adds a second speculative reason for concluding that the project would not contribute significantly to cumulative impacts: “Additionally, mitigation measures have been adopted by the City of San Bernardino for buildout of the General Plan, Therefore, with the incorporation of mitigation identified in this document, the Project would result in individually limited, but not cumulatively considerable, impacts. Therefore, no significant adverse impacts are identified or are anticipated, and no mitigation measures are required.” However, the CEQA Guidelines (§15064(h)(3)) states, “When relying on a plan, regulation or program, the lead agency should explain how implementing the particular requirements in the plan, regulation or program ensure that the project’s incremental contribution to the cumulative effect is not cumulatively considerable.” The IS/MND fails to explain how the General Plan would minimize, avoid or offset the project’s contributions to cumulative impacts. CEQA requires a cumulative impacts analysis.

The IS/MND’s conclusion that the City’s General Plan and the project’s required mitigation measures for direct and indirect impacts would avoid significant cumulative impacts lacks support in evidence, and it is readily refutable. Noriko Smallwood and I performed an experiment to measure the efficacy of applicable laws and regulations, permitting requirements, and required mitigation measures at avoiding or minimizing 48 project impacts including cumulative impacts. We revisited the project sites we had surveyed as experts to repeat the survey methods at the same time of year, the same start time in the day, and the same methods and survey duration to control these sources of variation to cleanly measure the effects of mitigated development on wildlife.

We structured the experiment in a before-after, control-impact experimental design, as some of the sites had been developed since our initial survey and some had remained undeveloped. All the developed sites had included mitigation measures to avoid, minimize or compensate for impacts to wildlife. Nevertheless, we found that mitigated development resulted in a 66% loss of species on site, and 48% loss of species in the project area. Counts of vertebrate animals declined 90%. We found that “Development impacts measured by the mean number of species detected per survey were greatest for amphibians (-100%), followed by mammals (-86%), grassland birds (-75%), raptors (-53%), special-status species (-49%), all birds as a group (-48%), non-native birds (-44%), and synanthropic birds (-28%). Our results indicated that urban development substantially reduced vertebrate species richness and numerical abundance, even after richness and abundance had likely already been depleted by the cumulative effects of loss, fragmentation, and degradation of habitat in the urbanizing environment,” and despite all the mitigation measures and existing policies, regulations, and habitat plans.

Experimental evidence demonstrates the need to analyze a project’s potentially significant contributions to cumulative impacts on wildlife despite existing laws, policies and General Plans. The environmental review for this project needs to include an appropriate cumulative impacts analysis.

FLAWED MITIGATION STRATEGY

The mitigation strategy needs to be based on a sound understanding of the existing wildlife community. It needs to be known which species occur or are likely to occur on the project site as well as the nature of their occurrences. It needs to be known which species are resident and which are migratory. For special-status species, detection surveys should have been completed to either detect the species or to obtain evidence of its absence. Surveys should have been completed to understand how wildlife use the site in their movement patterns. And what was found of wildlife species needs to be carefully interpreted by comparing the findings to the findings from other survey efforts at other sites. Failures to detect species should be interpreted relative to the probabilities of their detections given the survey effort. The mitigation strategy needs to follow the steps under Assess species occurrence likelihoods and Characterize wildlife community, followed by Step 7: Predict impacts (Figure 3). However, the IS/MND’s mitigation strategy does not follow from these steps. This pointed out, my comments follow in regular font the summary of each required mitigation measure in italics.

Mitigation Measure BR-1: Preconstruction Survey for Nesting Birds …a nesting bird clearance survey should be conducted prior to any ground disturbance or vegetation removal activities that may disrupt the birds during the nesting season. … should be conducted within three (3) days of the start of any vegetation removal or ground disturbing activities to ensure that no nesting birds will be disturbed during construction. ... construction activities should stay outside of a no-disturbance buffer [around the nest]. The size of the no-disturbance buffer will be determined by the 49 wildlife biologist and will depend on the level of noise and/or surrounding anthropogenic disturbances, line of sight between the nest and the construction activity, type and duration of construction activity, ambient noise, species habituation, and topographical barriers. ... Once the young have fledged and left the nest, or the nest otherwise becomes inactive under natural conditions, construction activities within the buffer area can occur.

If the project goes forward, preconstruction surveys for nesting birds should be implemented. However, it needs to be understood that Mitigation Measure BR-1 would not avoid the permanent loss of habitat, nor the collision mortality with automobiles and windows, nor the predation mortality of free-ranging house cats. Even assuming all the nests could be found (not likely), the mitigation measure would apply only to the breeding season of the survey. After the breeding season of the preconstruction survey, there would be no further production of birds from the project site, except for synanthropic species able to nest on homes or in ornamental trees. Moreover, it is a long-standing principle of ecology that animal populations are usually at carrying capacity. (An exception would be the repopulating of an area devastated by a natural disaster.) Ultimately, the same number of birds that are allowed to finish breeding via Mitigation Measure BR-1 would be the number of birds that would subsequently perish through competition for what remains of habitat in the region, and the productive capacity of the region would diminish accordingly. Mitigation Measure BR-1 is a feel- good measure that fails to avoid the project-caused impact of the region’s diminished productive capacity of birds. The project’s impact on birds would be permanent and of large magnitude. The conservation benefits of this measure would be de minimis in comparison to the project’s potential impacts on breeding birds.

The IS/MND does not report whether Mitigation Measure BR-1 has proven effective at any other project site, ever. It provides no evidence that the measure has proven effective at any project where it has been implemented. The known efficacy of this measure needs to be disclosed.

Furthermore, the mitigation language allows a single individual to make a subjective decision, outside the public’s view, to determine the buffer area and buffer timing for any given species. This measure lacks objective criteria, and it is therefore unenforceable.

Lastly, the mitigation language includes the requirement that the preconstruction nest survey shall be conducted by a wildlife biologist, and this requirement carries over to determinations of the buffer areas and buffer timing to avoid take caused by construction activities. Undefined, however, is what qualifies the wildlife biologist in these contexts. As mentioned earlier in this letter, Noriko Smallwood and I have been conducting surveys over the past several years to estimate total nest density, which is relevant to a preconstruction, take-avoidance survey because our surveys were likewise directed to all bird species that could be concurrently nesting on a site. Searching for the nests of one species is difficult, but searching for nests of all bird species at once is vastly more difficult. For this reason, there are very few published estimates of total nest density, and there are very few wildlife biologists who have experience with surveys for 50 the nest attempts of all bird species on a given site. A technical way to qualify a wildlife biologist for the tasks outlined in this measure would be to obtain an agency Memorandum of Understanding in which the biologist is named and said to be qualified for conducting the preconstruction survey and for determining the take-avoidance buffer area and timing. However, a more compelling means of qualifying the biologist would be to require that the biologist has achieved demonstrable experience with nest surveys and avian nesting behavior. Without a stronger definition of what is a qualified wildlife biologist in the contexts of these tasks, Mitigation Measure BR-1 lacks objective criteria, and it is therefore unenforceable.

Mitigation Measure BR-2: Pre-Construction Burrowing Owl Clearance Survey … a Designated Biologist shall conduct a preconstruction presence/absence survey for burrowing owls in accordance with CDFW’s Staff Report on Burrowing Owl Mitigation. Two surveys shall be conducted, the first 14 days prior to ground disturbing activities and the second within 24 hours immediately before ground disturbing activities. If burrowing owls are documented on-site, the Applicant shall prepare and implement a plan for avoidance or passive exclusion, in coordination with the California Department of Fish and Wildlife (CDFW). If no burrowing owls are observed onsite, no further review will be required.

Due to a rapid decline throughout California (Miller 2024), the California Fish and Game Commission voted unanimously in 2024 to designate the burrowing owl as a candidate for listing under the California Endangered Species Act. It is therefore imperative to first consult with the CDFW about whether and how to obtain an incidental take permit (ITP), and otherwise to adhere to the CDFW (2012) survey and mitigation guidelines. The IS/MND does not indicate that there has been any consultation with CDFW, and it misrepresents CDFW’s (2012) survey and mitigation guidelines by skipping the breeding-season detection surveys before performing preconstruction take-avoidance surveys.

For the burrowing owl, there are three types of surveys recommended and described in the CDFW’s (2012) survey and mitigation guidelines: (1) Habitat assessment, (2) Detection surveys, and (3) Preconstruction survey. The habitat assessment is intended to evaluate the likelihood that the site supports burrowing owls, and to decide whether detection surveys should be performed. The detection survey, otherwise described as either or both breeding-season or non-breeding-season survey, is intended to detect whether the site truly supports burrowing owls, and if so, where and how many. The preconstruction survey, otherwise known as a take-avoidance survey, is intended to determine whether burrowing owls immigrated to the site since completion of the detection survey, or whether they returned to the site since passive or active relocations were performed as mitigation. The three types of survey carry distinct but inter-related purposes, and CDFW (2012) intends them to be completed in chronological order as numbered above.

The first two types of survey support impacts analysis, whereas the third type of survey is a mitigation measure. Burrowing owls can be determined absent at the time of the survey based on evidence derived from the habitat assessment or detection survey, but 51 only if the surveys achieved the minimum standards of CDFW (2012). Whereas an absence determination naturally follows from the negative findings of properly performed detection surveys, the following three questions must be answered negatively to determine absence based on the habitat assessment:

A) Are there occurrence records nearby the project site? B) Is the site’s vegetation cover and height typical of where burrowing owls are found? C) Are there fossorial mammals present which typically construct burrows useable by burrowing owls, or are there surrogate cavities that can serve as nest sites?

If the answers to these questions are compellingly negative, then detection surveys are not necessary, but they could be implemented to make certain that burrowing owls are absent on the project site. If the answers to these questions are affirmative or not compellingly negative, then it should be assumed that burrowing owl habitat exists on the site until detection surveys prove otherwise.

To question A, the nearest burrowing owl occurrence records are 3.3 and 5.7 miles from the project site. The occurrence records justify an affirmative answer to question A, and so this part of the habitat assessment warrants a detection survey effort.

To question B, the vegetation cover is typical of conditions where burrowing owls often occur. The answer to question B is affirmative, and so this part of the habitat assessment warrants a detection survey effort.

To question C, ground squirrels are known to occur on the project site. Ground squirrels construct burrows used by burrowing owls, and these two species mutually alarm-call for predators and survive better together (K. S. Smallwood, unpublished data). ELMT (2024) claims that no suitable burrows were available to support burrowing owls on site because all the ground squirrel burrows were smaller than 4 inches in diameter. In support of this claim, ELMT cites CDFW (2012). However, CDFW (2012) recommended a tunnel diameter of >11 cm (4.3 inches) in the context of installing artificial burrows, and this recommendation was a misread of the source that CDFW cited, which was Johnson et al. (2010), who recommended a 10 cm (3.93 inch) tunnel diameter in artificial burrows (the recommendation was for exactly 10 cm rather than >11 cm). And in my experience, having measured the entrances of many ground squirrel burrows, it is the rare ground squirrel burrow that has a diameter <10 cm or <11 cm. Moreover, the ground squirrel burrows on the project site typify ground squirrel burrows throughout the species range, and the tunnel diameters of these burrows are plenty wide enough for burrowing owls (Photo 38). ELMT’s standard is wrong, and its reporting of ground squirrel burrow diameters on the project site is unrealistic and not credible. The answer to question C is affirmative, and so this part of the habitat assessment warrants a detection survey effort.

The answers to all three questions are affirmative. Therefore, breeding-season detection surveys are needed, and these surveys are not to be confused with preconstruction take- avoidance surveys. Breeding-season detection surveys are needed to accurately 52 characterize the existing environmental setting and to publicly disclose potential impacts on the burrowing owl.

Regardless of the survey outcome, the project would permanently eliminate potential burrowing owl habitat from the project site, and in doing so it would reduce the region’s carrying capacity for supporting burrowing owls.

Photo 38. An example California ground squirrel burrow on the project site, photographed on 18 April 2026 by Noriko Smallwood. Width is about 4 inches, and height is about 4.5 inches. I have recorded hundreds of burrowing owls in burrows of about this same size.

53 Mitigation Measure BR-3: Prior to the issuance of a grading permit, the project applicant shall obtain a Streambed Alteration Agreement under Section 1602 of the California Fish and Game Code from CDFW and a Report of Waste Discharge from the RWQCB.

The requirements that would come with the Agreement, and their conservation benefits to the streambed and its ecological relationships with plants and wildlife, would not be disclosed to the public in a timely manner. Without timely disclosure, this measure fails to meet the spirit and intent of CEQA as expressed in its primary objectives for disclosure and public participation.

NEEDED MITIGATION MEASURES

Compensatory Habitat Protection: The project would destroy the productive capacity of the project site for birds and other wildlife. The loss of this capacity would need to be offset by compensatory mitigation as near to the site as possible. I recommend a 5:1 mitigation ratio to achieve a no net loss standard, as a 1:1 ratio would simply ensure a 50% loss of habitat between the project site and mitigation site.

The loss of burrowing owl foraging opportunities would need to be mitigated. The applicant needs to consult with the CDFW to learn what mitigation ratio would be acceptable for loss of burrowing owl habitat on the project site, and whether and where habitat is available to be protected nearby.

Road Mortality: Compensatory mitigation is needed for the increased wildlife mortality that would be caused by collisions with automobiles due to project-generated road traffic in the region. I suggest that this mitigation can be directed toward funding research to identify fatality patterns and effective impact reduction measures such as reduced speed limits and wildlife under-crossings or overcrossings of particularly dangerous road segments. Compensatory mitigation can also be provided in the form of donations to wildlife rehabilitation facilities (see below).

Bird-Window Collision Mortality: If the project goes forward, it should at a minimum adhere to available Bird-Safe Guidelines, such as those prepared by American Bird Conservancy and New York and San Francisco. The American Bird Conservancy (ABC) produced an excellent set of guidelines recommending actions to: (1) Minimize use of glass; (2) Placing glass behind some type of screening (grilles, shutters, exterior shades); (3) Using glass with inherent properties to reduce collisions, such as patterns, window films, decals or tape; and (4) Turning off lights during migration seasons (Sheppard and Phillips 2015). The City of San Francisco (San Francisco Planning Department 2011) also has a set of building design guidelines, based on the excellent guidelines produced by the New York City Audubon Society (Orff et al. 2007). The ABC document and both the New York and San Francisco documents provide excellent alerting of potential bird-collision hazards as well as many visual examples. The San Francisco Planning Department’s (2011) building design guidelines are more comprehensive than those of New York City, but they could have gone further. For example, the San Francisco guidelines probably should have also covered scientific 54 monitoring of impacts as well as compensatory mitigation for impacts that could not be avoided, minimized or reduced.

New research results inform of the efficacy of marking windows. Whereas Klem (1990) found no deterrent effect from decals on windows, Johnson and Hudson (1976) reported a fatality reduction of about 69% after placing decals on windows. In an experiment of opportunity, Ocampo-Peñuela et al. (2016) found only 2 of 86 fatalities at one of 6 buildings – the only building with windows treated with a bird deterrent film. At the building with fritted glass, bird collisions were 82% lower than at other buildings with untreated windows. Kahle et al. (2016) added external window shades to some windowed façades to reduce fatalities 82% and 95%. Brown et al. (2020) reported an 84% lower collision probability among fritted glass windows and windows treated with ORNILUX R UV. City of Portland Bureau of Environmental Services and Portland Audubon (2020) reduced bird collision fatalities 94% by affixing marked Solyx window film to existing glass panels of Portland’s Columbia Building. Many external and internal glass markers have been tested experimentally, some showing no effect and some showing strong deterrent effects (Klem 1989, 1990, 2009, 2011; Klem and Saenger 2013; Rössler et al. 2015). For example, Feather Friendly® circular adhesive markers applied in a grid pattern across all windows reduced bird-window collision mortality by 95% in one study (Riggs et al. 2023) and by 95% in another (de Groot et al. 2021).

Another study tested the efficacy of two filmshades to be applied exteriorly to windows prior to installations: BirdShades increased bird-window avoidance by 47% and Haverkamp increased avoidance by 39% (Swaddle et al. 2023).

Fund Wildlife Rehabilitation Facilities: Compensatory mitigation is needed, and it ought to also include funding contributions to wildlife rehabilitation facilities to cover the costs of injured animals that will be delivered to these facilities for care. Many animals would likely be injured during construction and by collisions with automobiles traveling to and from the project site, as well as by collisions with the windows of homes and encounters with the free-ranging house cats brought to the project site.

Landscaping: If the project goes forward, California native plant landscaping (i.e., grassland and locally appropriate scrub plants) should be considered to be used as opposed to landscaping with lawn and exotic shrubs and trees. Native plants offer more structure, cover, food resources, and nesting substrate for wildlife than landscaping with lawn and ornamental trees. Native plant landscaping has been shown to increase the abundance of arthropods which act as important sources of food for wildlife and are crucial for pollination and plant reproduction (Narango et al. 2017, Adams et al. 2020, Smallwood and Wood 2022.). Further, many endangered and threatened insects require native host plants for reproduction and migration, e.g., monarch butterfly. Around the world, landscaping with native plants over exotic plants increases the abundance and diversity of birds, and it is particularly valuable to native birds (Lerman and Warren 2011, Burghardt et al. 2008, Berthon et al. 2021, Smallwood and Wood 2022).

Landscaping with native plants is a way to maintain or to bring back some of the natural habitat and lessen the footprint of urbanization by acting as interconnected patches of habitat for wildlife (Goddard et al. 2009, Tallamy 2020). Lastly, not only does native 55 plant landscaping benefit wildlife, it requires less water and maintenance than traditional landscaping with lawn and hedges.

Thank you for your consideration,

______________________
Shawn Smallwood, Ph.D.

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