Department of Ecology and Evolutionary Biology

Fucus distichus, a rockweed common to the mid-intertidal shoreline within the San Francisco Estuary (previously known as F. gardneri), was injured during the Cosco Busan oil spill in November 2007 and subsequent clean-up actions. Restoration planning activities are underway to help recover F. distichus at sites within central San Francisco Bay where damage occurred. As a first step, we conducted shoreline surveys during the summers of 2012–2013 to map the occurrence of this rockweed. Of the 151.73 km of rocky shoreline within the central bay, F. distichus covered 32.16 km of shoreline. The alga generally occurred in narrow bands but formed expansive beds at locations with natural, flat bedrock benches. We also observed F. distichus on artificial substrata such as seawalls and riprap, but not on pilings. Samples of F. distichus from 11 sites throughout the central / east San Francisco Bay were genetically analyzed (microsatellite genotyping). The populations analyzed (1) had low genetic diversity, (2) the frequency of homozygotes was higher than expected (suggesting high inbreeding), and (3) also displayed geographic population structure, in part driven by very small differences in the midst of extremely low within-population genetic diversity. However, these genetic data do not raise concerns for restoration methods in terms of choosing donor populations and mixing F. distichus from different sites within the central bay. The choice of donor populations should be based on practical criteria for effective restoration; individuals will nonetheless be taken from locations as nearby to donor sites as possible. Various locations throughout the central San Francisco Bay are composed of cobble or small riprap that are populated with F. distichus, which could provide efficient means of translocating rockweed for future restoration activities.

Despite substantial reductions in the cost of sequencing over the last decade, genetic panels remain relevant due to their cost-effectiveness and flexibility across a variety of sample types. In particular, single nucleotide polymorphism (SNP) panels are increasingly favoured for conservation applications. SNP panels are often used because of their adaptability, effectiveness with low-quality samples, and cost-efficiency for population monitoring and forensics. However, the selection of diagnostic SNPs for population assignment and individual identification can be challenging. The consequences of poor SNP selection are under-powered panels, inaccurate results, and monetary loss. Here, we develop a novel and user-friendly SNP selection pipeline (mPCRselect) that can be used to select SNPs for population assignment and/or individual identification. mPCRselect allows any researcher, who has sufficient SNP-level data, to design a successful and cost-effective SNP panel for a diploid species of conservation concern.

Large-scale marine protected areas (LSMPAs; > 1000 km2) provide important refuge for large mobile species, but most do not encompass species ranges. To better understand current and future LSMPA value, we concurrently tracked nine species (seabirds, cetaceans, pelagic fishes, manta rays, reef sharks) at Palmyra Atoll and Kingman Reef (PKMPA) in the U.S. Pacific Islands Heritage Marine National Monument. PKMPA and the U.S. Exclusive Economic Zone encompassed 39% and 54% of species movements (n = 83; tracking duration range: 0.5-350 days), respectively. Species distribution models indicated 73% of PKMPA contained highly suitable habitat. Under two projected future scenarios (SSP 1-2.6, Sustainability; SSP 3-7.0, Rocky Road), strong sea surface temperature gradients initially could cause abrupt oceanic change resulting in predicted habitat loss in 2040-2050, followed by an equilibrium response and regained habitat by 2090-2100. Current and future suitable habitats were available adjacent to PKMPA, suggesting that increased MPA size could enhance protection. Our three-tiered approach combining animal tracking with publicly available remote sensing data and future projected environmental scenarios could be used to design, study, and monitor protected areas throughout the world. Holistic approaches that encompass diverse species and habitat use can enhance assessments of protected area designs. Animal telemetry and remote sensing may be helpful for ascertaining the extent to which other MPAs protect large mobile species in the future.

The use of quantitative real-time PCR (qPCR) to monitor pathogens is common; however, quantitative frameworks that consider the observation process, dynamics in pathogen presence, and pathogen load are lacking. This can be problematic in the early stages of disease progression, where low level detections may be treated as inconclusive and excluded from analyses. Alternatively, a framework that accounts for imperfect detection would provide more robust inferences. To better estimate pathogen dynamics, we developed a hierarchical multi-scale dynamic occupancy hurdle model (MS-DOHM). The model used data gathered during sampling for Pseudogymnoascus destructans (Pd), the causative agent of white-nose syndrome, a fungal disease that has cause severe declines in several species of hibernating bats in North America. The model allowed us to estimate initial occupancy, colonization, persistence and prevalence of Pd at bat hibernacula. Additionally, utilizing the relationship between cycle threshold and pathogen load, we estimated pathogen detectability and modeled expected colony and bat pathogen loads. To assess the ability of MS-DOHM to estimate pathogen dynamics, we compared MS-DOHMs results to those of a dynamic occupancy model and naïve detection/non-detection. MS-DOHMs estimates of site-level pathogen presence were up to 11.9% higher than estimates from the dynamic occupancy model and 35.7% higher than naïve occupancy. Including prevalence and load in our modeling framework resulted in estimates of pathogen arrival that were two to three years earlier compared to the dynamic occupancy and naïve detection/non-detection, respectively. Compared to naïve values, MS-DOHM predicted greater pathogen loads on colonies; however, we found no difference between model estimates and naïve values of prevalence. While the model predicted no declines in site-level prevalence, there were instances where pathogen load decreased in colonies that had been Pd positive for longer periods of time. Our findings demonstrate that accounting for pathogen load and prevalence at multiple scales changes our understanding of Pd dynamics, potentially allowing earlier conservation intervention. Additionally, we found that accounting for pathogen load and prevalence within hibernacula and among individuals resulted in a better fitting model with greater predictive ability.

PREMISE: Floral pigments such as anthocyanins are well known to influence pollinator attraction, yet they also confer tolerance to abiotic stressors such as harsh soils, extreme temperatures, low precipitation, and UV radiation. In such cases, environmental variation in abiotic stressors over space or time could lead to the maintenance of flower color variation within species. Under this scenario, flower color in natural populations should covary with environmental stressors. METHODS: Using a comparative approach, we tested whether abiotic variables predict flower color in Leptosiphon parviflorus, a species with pink and white flower color morphs. We conducted in-depth field studies to assess morph frequency, soil chemistry, and climate. We then employed community scientist-powered iNaturalist observations to examine patterns across even larger spatial scales. RESULTS: Across 21 field sites, L. parviflorus had a higher frequency of pink morphs in sites with serpentine soil, higher average annual temperatures, and higher average climatic water deficit (a proxy for drought stress). iNaturalist observations supported this finding-the probability of flowers being pink is greater in locations with serpentine-derived soil, especially when the local average UV radiation and climatic water deficit are higher. CONCLUSIONS: Spatial variation in abiotic stressors may contribute to the maintenance of flower color variation across the geographic range of L. parviflorus. Future studies will examine mechanisms by which flower color affects stress tolerance and will assess whether fitness trade-offs in contrasting habitats across the range are associated with flower color.

Abstract: Fisheries science agencies are responsible for informing fisheries management and ocean planning worldwide, often requiring scientific analysis and management actions across multiple spatial scales. For example, catch limits are typically defined annually over regional scales, fishery bycatch rules are defined at fine spatial scales on daily to annual time scales, and aquaculture and energy lease areas are defined over decades for subregional permitting at intermediate scales. Similarly, these activities require synthesizing monitoring data and mechanistic knowledge operating across different spatial resolutions and domains. These needs drive a growing role for models that predict animal presence or densities at fine spatial scales, including daily, seasonal, and interannual variation, often called species distribution/density models (SDMs). SDMs can inform many ocean management needs; however, their development and usage are often haphazard. In this paper we discuss various ways SDMs can and have been used in stock, habitat, protected species, and ecosystem management activities as well as marine spatial planning, survey optimization, and as an interface with ecosystem and climate models. We conclude with a discussion of future directions, focusing on information needs and current development, and highlight avenues for furthering the community of practice around SDM development and use.

Understanding the habitat of highly migratory species is aided by using species distribution models to identify species-habitat relationships and to inform conservation and management plans. While Generalized Additive Models (GAMs) are commonly used in ecology, and particularly the habitat modeling of marine mammals, there remains a debate between modeling habitat (presence/absence) versus density (# individuals). Our study assesses the performance and predictive capabilities of GAMs compared to boosted regression trees (BRTs) for modeling both fin whale density and habitat suitability alongside Hurdle Models treating presence/absence and density as a two-stage process to address the challenge of zero-inflated data. Fin whale data were collected from 2008 to 2022 along fixed transects crossing the NW Mediterranean Sea during the summer period. Data were analyzed using traditional line transect methodology, obtaining the Effective Area monitored. Based on existing literature, we select various covariates, either static in nature, such as bathymetry and slope, or variable in time, for example, SST, MLD, Chl concentration, EKE, and FSLE. We compared both the explanatory power and predictive skill of the different modeling techniques. Our results show that all models performed well in distinguishing presences and absences but, while density and presence patterns for the fin whale were similar, their dependencies on environmental factors can vary depending on the chosen model. Bathymetry was the most important variable in all models, followed by SST and the chlorophyll recorded 2 months before the sighting. This study underscores the role SDMs can play in marine mammal conservation efforts and emphasizes the importance of selecting appropriate modeling techniques. It also quantifies the relationship between environmental variables and fin whale distribution in an understudied area, providing a solid foundation for informed decision making and spatial management.

The federally endangered sister species, Eucyclogobius newberryi (northern tidewater goby, NTG) and E. kristinae (southern tidewater goby) comprise the California endemic genus Eucyclogobius, which historically occurred in all coastal California counties. Isolated lagoons that only intermittently connect to the sea are their primary habitat. Reproduction occurs during lagoon closure, minimizing marine dispersal and generating the most genetically subdivided vertebrate genus on the California coast. We present a new genome assembly for E. newberryi using HiFi long reads and Hi-C chromatin-proximity sequencing. The 980 Mb E. newberryi reference genome has an N50 of 34 Mb with 22 well-described scaffolds comprising 88% of the genome and a complete BUSCO (Benchmarking Universal Single-Copy Orthologs) score of 96.7%. This genome will facilitate studies addressing selection, drift, and metapopulation genetics in subdivided populations, as well as the persistence of the critically endangered E. kristinae, where reintroduction will be an essential element of conservation actions for recovery. It also provides tools critical to the recovery of the genetically distinct management units in the NTG, as well as broader ecological and evolutionary studies of gobies, the most speciose family of fishes in the world.

As global climate change and anthropogenic activities amplify widespread environmental variability, there is a strong need for management strategies that incorporate relationships between ecosystem components. This need is especially apparent when changes in environmental drivers cause threshold responses (abrupt, nonlinear changes) in ecosystems. Such ecological thresholds can provide useful reference points for management decisions. However, methods for detecting thresholds in empirical datasets may fail to find an existing threshold, find one that does not exist, or be biased in their estimates of threshold locations. These types of threshold misspecifications can result in high conservation and socioeconomic costs. Simulation studies can mitigate these risks by providing information about method performance across different scenarios. Here, we constructed a series of simulations to evaluate the robustness of threshold detection with generalized additive models (GAMs) when exposed to a variety of common, real-world data characteristics. GAMs generally performed best when time series were long, observation error was low, thresholds were crossed fairly frequently, and covariates were accounted for. Over realistic ranges of values, observation error and frequency of threshold crossing had stronger effects on threshold detectability than time series length. Importantly, detectability was found to depend on both the shape of the threshold relationship and the statistical definition of the threshold location. As a case study, we applied this threshold detection method to an empirical dataset relating ocean temperature and the spatial distribution of Pacific hake (Merluccius productus), the largest volume fishery on the US West Coast. While the data suggest no statistical evidence for a threshold relationship, our simulations indicated approximately equal chances of true and false threshold detection given currently available data. Our results provide general guidelines for where threshold detection with GAMs is likely to be robust and are useful in the context of indicator development for ecosystem-based management in a variable world.