Modeling the spatiotemporal patterns and drivers of Dungeness crab fishing effort to inform whale entanglement risk mitigation on the U.S. West Coast.

Understanding and characterizing the spatiotemporal dynamics of fishing fleets is crucial for ecosystem-based fisheries management (EBFM). EBFM must not only account for the sustainability of target species catches, but also for the collateral impacts of fishing operations on habitats and non-target species. Increased rates of large whale entanglements in commercial Dungeness crab fishing gear have made reducing whale-fishery interactions a current and pressing challenge on the U.S. West Coast. While several habitat models exist for different large whale species along the West Coast, less is known about the crab fishery and the degree to which different factors influence the intensity and distribution of aggregate fishing effort. Here, we modeled the spatiotemporal patterns of Dungeness crab fishing effort in Oregon and Washington as a function of environmental, economic, temporal, social, and management related predictor variables using generalized linear mixed effects models. We then assessed the predictive performance of such models and discussed their usefulness in informing fishery management. Our models revealed low between-year variability and consistent spatial and temporal patterns in commercial Dungeness crab fishing effort. However, fishing effort was also responsive to multiple environmental, economic and management cues, which influenced the baseline effort distribution pattern. The best predictive model, chosen through out-of-sample cross-validation, showed moderate predictive performance and relied upon environmental, economic, and social covariates. Our results help fill the current knowledge gap around Dungeness crab fleet dynamics, and support growing calls to integrate fisheries behavioral data into fisheries management and marine spatial planning.

Modeling ocean distributions and abundances of natural- and hatchery-origin Chinook salmon stocks with integrated genetic and tagging data.

Background: Considerable resources are spent to track fish movement in marine environments, often with the intent of estimating behavior, distribution, and abundance. Resulting data from these monitoring efforts, including tagging studies and genetic sampling, often can be siloed. For Pacific salmon in the Northeast Pacific Ocean, predominant data sources for fish monitoring are coded wire tags (CWTs) and genetic stock identification (GSI). Despite their complementary strengths and weaknesses in coverage and information content, the two data streams rarely have been integrated to inform Pacific salmon biology and management. Joint, or integrated, models can combine and contextualize multiple data sources in a single statistical framework to produce more robust estimates of fish populations. Methods: We introduce and fit a comprehensive joint model that integrates data from CWT recoveries and GSI sampling to inform the marine life history of Chinook salmon stocks at spatial and temporal scales relevant to ongoing fisheries management efforts. In a departure from similar models based primarily on CWT recoveries, modeled stocks in the new framework encompass both hatchery- and natural-origin fish. We specifically model the spatial distribution and marine abundance of four distinct stocks with spawning locations in California and southern Oregon, one of which is listed under the U.S. Endangered Species Act. Results: Using the joint model, we generated the most comprehensive estimates of marine distribution to date for all modeled Chinook salmon stocks, including historically data poor and low abundance stocks. Estimated marine distributions from the joint model were broadly similar to estimates from a simpler, CWT-only model but did suggest some differences in distribution in select seasons. Model output also included novel stock-, year-, and season-specific estimates of marine abundance. We observed and partially addressed several challenges in model convergence with the use of supplemental data sources and model constraints; similar difficulties are not unexpected with integrated modeling. We identify several options for improved data collection that could address issues in convergence and increase confidence in model estimates of abundance. We expect these model advances and results provide management-relevant biological insights, with the potential to inform future mixed-stock fisheries management efforts, as well as a foundation for more expansive and comprehensive analyses to follow.

Boreal conifers maintain carbon uptake with warming despite failure to track optimal temperatures.

Warming shifts the thermal optimum of net photosynthesis (ToptA) to higher temperatures. However, our knowledge of this shift is mainly derived from seedlings grown in greenhouses under ambient atmospheric carbon dioxide (CO2) conditions. It is unclear whether shifts in ToptA of field-grown trees will keep pace with the temperatures predicted for the 21st century under elevated atmospheric CO2 concentrations. Here, using a whole-ecosystem warming controlled experiment under either ambient or elevated CO2 levels, we show that ToptA of mature boreal conifers increased with warming. However, shifts in ToptA did not keep pace with warming as ToptA only increased by 0.26-0.35 °C per 1 °C of warming. Net photosynthetic rates estimated at the mean growth temperature increased with warming in elevated CO2 spruce, while remaining constant in ambient CO2 spruce and in both ambient CO2 and elevated CO2 tamarack with warming. Although shifts in ToptA of these two species are insufficient to keep pace with warming, these boreal conifers can thermally acclimate photosynthesis to maintain carbon uptake in future air temperatures.

Species redistribution creates unequal outcomes for multispecies fisheries under projected climate change.

Climate change drives species distribution shifts, affecting the availability of resources people rely upon for food and livelihoods. These impacts are complex, manifest at local scales, and have diverse effects across multiple species. However, for wild capture fisheries, current understanding is dominated by predictions for individual species at coarse spatial scales. We show that species-specific responses to localized environmental changes will alter the collection of co-occurring species within established fishing footprints along the U.S. West Coast. We demonstrate that availability of the most economically valuable, primary target species is highly likely to decline coastwide in response to warming and reduced oxygen concentrations, while availability of the most abundant, secondary target species will potentially increase. A spatial reshuffling of primary and secondary target species suggests regionally heterogeneous opportunities for fishers to adapt by changing where or what they fish. Developing foresight into the collective responses of species at local scales will enable more effective and tangible adaptation pathways for fishing communities.

Estimating phenology and phenological shifts with hierarchical modeling.

Climate-driven changes to phenology are some of the most prevalent climate change impacts, yet there is no commonly accepted approach to modeling phenological shifts. Here, we present a hierarchical modeling framework for estimating intra-annual patterns in phenology (e.g., peak phenological expression) and analyzing interannual rates of change in peak phenology. Our approach allows for the estimation of multiple sources of uncertainty, including observation error (e.g., imperfect observations of intra-annual patterns in phenology like peak flowering date) and variation in phenological processes (e.g., uncertainty in the rate of change in annual peak phenological expression). Covariates may be included as predictors of annual peaks or interannual variability in phenological responses. We demonstrate the use of our hierarchical modeling framework in two migratory species-juvenile chum salmon and Swainson's thrush. We acknowledge that the complexity of hierarchical models can be difficult to implement from scratch and present an R package that can be used to model peak dates and range (number of days between 25th- and 75th-quartile dates), as well as a rate of change in peak phenology. Increasing precision, calculating uncertainty, and allowing for imperfect data sets when estimating phenological shifts should help ecologists understand how organisms respond to climate change.

Epidemiology of skin changes in endangered Southern Resident killer whales (Orcinus orca).

Photographic identification catalogs of individual killer whales (Orcinus orca) over time provide a tool for remote health assessment. We retrospectively examined digital photographs of Southern Resident killer whales in the Salish Sea to characterize skin changes and to determine if they could be an indicator of individual, pod, or population health. Using photographs collected from 2004 through 2016 from 18,697 individual whale sightings, we identified six lesions (cephalopod, erosions, gray patches, gray targets, orange on gray, and pinpoint black discoloration). Of 141 whales that were alive at some point during the study, 99% had photographic evidence of skin lesions. Using a multivariate model including age, sex, pod, and matriline across time, the point prevalence of the two most prevalent lesions, gray patches and gray targets, varied between pods and between years and showed small differences between stage classes. Despite minor differences, we document a strong increase in point prevalence of both lesion types in all three pods from 2004 through 2016. The health significance of this is not clear, but the possible relationship between these lesions and decreasing body condition and immunocompetence in an endangered, non-recovering population is a concern. Understanding the etiology and pathogenesis of these lesions is important to better understand the health significance of these skin changes that are increasing in prevalence.

Phenological shifts and mismatch with marine productivity vary among Pacific salmon species and populations.

Global climate change is shifting the timing of life-cycle events, sometimes resulting in phenological mismatches between predators and prey. Phenological shifts and subsequent mismatches may be consistent across populations, or they could vary unpredictably across populations within the same species. For anadromous Pacific salmon (Oncorhynchus spp.), juveniles from thousands of locally adapted populations migrate from diverse freshwater habitats to the Pacific Ocean every year. Both the timing of freshwater migration and ocean arrival, relative to nearshore prey (phenological match/mismatch), can control marine survival and population dynamics. Here we examined phenological change of 66 populations across six anadromous Pacific salmon species throughout their range in western North America with the longest time series spanning 1951-2019. We show that different salmon species have different rates of phenological change but that there was substantial within-species variation that was not correlated with changing environmental conditions or geographic patterns. Moreover, outmigration phenologies have not tracked shifts in the timing of marine primary productivity, potentially increasing the frequency of future phenological mismatches. Understanding population responses to mismatches with prey are an important part of characterizing overall population-specific climate vulnerability.

Modeling impacts of saltwater intrusion on methane and nitrous oxide emissions in tidal forested wetlands.

Emissions of methane (CH4 ) and nitrous oxide (N2 O) from soils to the atmosphere can offset the benefits of carbon sequestration for climate change mitigation. While past study has suggested that both CH4 and N2 O emissions from tidal freshwater forested wetlands (TFFW) are generally low, the impacts of coastal droughts and drought-induced saltwater intrusion on CH4 and N2 O emissions remain unclear. In this study, a process-driven biogeochemistry model, Tidal Freshwater Wetland DeNitrification-DeComposition (TFW-DNDC), was applied to examine the responses of CH4 and N2 O emissions to episodic drought-induced saltwater intrusion in TFFW along the Waccamaw River and Savannah River, USA. These sites encompass landscape gradients of both surface and porewater salinity as influenced by Atlantic Ocean tides superimposed on periodic droughts. Surprisingly, CH4 and N2 O emission responsiveness to coastal droughts and drought-induced saltwater intrusion varied greatly between river systems and among local geomorphologic settings. This reflected the complexity of wetland CH4 and N2 O emissions and suggests that simple linkages to salinity may not always be relevant, as non-linear relationships dominated our simulations. Along the Savannah River, N2 O emissions in the moderate-oligohaline tidal forest site tended to increase dramatically under the drought condition, while CH4 emission decreased. For the Waccamaw River, emissions of both CH4 and N2 O in the moderate-oligohaline tidal forest site tended to decrease under the drought condition, but the capacity of the moderate-oligohaline tidal forest to serve as a carbon sink was substantially reduced due to significant declines in net primary productivity and soil organic carbon sequestration rates as salinity killed the dominant freshwater vegetation. These changes in fluxes of CH4 and N2 O reflect crucial synergistic effects of soil salinity and water level on C and N dynamics in TFFW due to drought-induced seawater intrusion.

Inbreeding depression explains killer whale population dynamics.

Understanding the factors that cause endangered populations to either grow or decline is crucial for preserving biodiversity. Conservation efforts often address extrinsic threats, such as environmental degradation and overexploitation, that can limit the recovery of endangered populations. Genetic factors such as inbreeding depression can also affect population dynamics but these effects are rarely measured in the wild and thus often neglected in conservation efforts. Here we show that inbreeding depression strongly influences the population dynamics of an endangered killer whale population, despite genomic signatures of purging of deleterious alleles via natural selection. We find that the 'Southern Residents', which are currently endangered despite nearly 50 years of conservation efforts, exhibit strong inbreeding depression for survival. Our population models suggest that this inbreeding depression limits population growth and predict further decline if the population remains genetically isolated and typical environmental conditions continue. The Southern Residents also had more inferred homozygous deleterious alleles than three other, growing, populations, further suggesting that inbreeding depression affects population fitness. These results demonstrate that inbreeding depression can substantially limit the recovery of endangered populations. Conservation actions focused only on extrinsic threats may therefore fail to account for key intrinsic genetic factors that also limit population growth.

Assortative mating for reproductive timing affects population recruitment and resilience in a quantitative genetic model.

Quantitative models that simulate the inheritance and evolution of fitness-linked traits offer a method for predicting how environmental or anthropogenic perturbations can affect the dynamics of wild populations. Random mating between individuals within populations is a key assumption of many such models used in conservation and management to predict the impacts of proposed management or conservation actions. However, recent evidence suggests that non-random mating may be underestimated in wild populations and play an important role in diversity-stability relationships. Here we introduce a novel individual-based quantitative genetic model that incorporates assortative mating for reproductive timing, a defining attribute of many aggregate breeding species. We demonstrate the utility of this framework by simulating a generalized salmonid lifecycle, varying input parameters, and comparing model outputs to theoretical expectations for several eco-evolutionary, population dynamic scenarios. Simulations with assortative mating systems resulted in more resilient and productive populations than those that were randomly mating. In accordance with established ecological and evolutionary theory, we also found that decreasing the magnitude of trait correlations, environmental variability, and strength of selection each had a positive effect on population growth. Our model is constructed in a modular framework so that future components can be easily added to address pressing issues such as the effects of supportive breeding, variable age structure, differential selection by sex or age, and fishery interactions on population growth and resilience. With code published in a public Github repository, model outputs may easily be tailored to specific study systems by parameterizing with empirically generated values from long-term ecological monitoring programs.

Delayed trophic response of a marine predator to ocean condition and prey availability during the past century.

Understanding the response of predators to ecological change at multiple temporal scales can elucidate critical predator-prey dynamics that would otherwise go unrecognized. We performed compound-specific nitrogen stable isotope analysis of amino acids on 153 harbor seal museum skull specimens to determine how trophic position of this marine predator has responded to ecosystem change over the past century. The relationships between harbor seal trophic position, ocean condition, and prey abundance, were analyzed using hierarchical modeling of a multi-amino-acid framework and applying 1, 2, and 3 years temporal lags. We identified delayed responses of harbor seal trophic position to both physical ocean conditions (upwelling, sea surface temperature, freshwater discharge) and prey availability (Pacific hake, Pacific herring, and Chinook salmon). However, the magnitude and direction of the trophic position response to ecological changes depended on the temporal delay. For example, harbor seal trophic position was negatively associated with summer upwelling but had a 1-year delayed response to summer sea surface temperature, indicating that some predator responses to ecosystem change are not immediately observable. These results highlight the importance of considering dynamic responses of predators to their environment as multiple ecological factors are often changing simultaneously and can take years to propagate up the food web.

Regularizing priors for Bayesian VAR applications to large ecological datasets.

Using multi-species time series data has long been of interest for estimating inter-specific interactions with vector autoregressive models (VAR) and state space VAR models (VARSS); these methods are also described in the ecological literature as multivariate autoregressive models (MAR, MARSS). To date, most studies have used these approaches on relatively small food webs where the total number of interactions to be estimated is relatively small. However, as the number of species or functional groups increases, the length of the time series must also increase to provide enough degrees of freedom with which to estimate the pairwise interactions. To address this issue, we use Bayesian methods to explore the potential benefits of using regularized priors, such as Laplace and regularized horseshoe, on estimating interspecific interactions with VAR and VARSS models. We first perform a large-scale simulation study, examining the performance of alternative priors across various levels of observation error. Results from these simulations show that for sparse matrices, the regularized horseshoe prior minimizes the bias and variance across all inter-specific interactions. We then apply the Bayesian VAR model with regularized priors to a output from a large marine food web model (37 species) from the west coast of the USA. Results from this analysis indicate that regularization improves predictive performance of the VAR model, while still identifying important inter-specific interactions.

Mangroves provide blue carbon ecological value at a low freshwater cost.

"Blue carbon" wetland vegetation has a limited freshwater requirement. One type, mangroves, utilizes less freshwater during transpiration than adjacent terrestrial ecoregions, equating to only 43% (average) to 57% (potential) of evapotranspiration ([Formula: see text]). Here, we demonstrate that comparative consumptive water use by mangrove vegetation is as much as 2905 kL H2O ha-1 year-1 less than adjacent ecoregions with [Formula: see text]-to-[Formula: see text] ratios of 47-70%. Lower porewater salinity would, however, increase mangrove [Formula: see text]-to-[Formula: see text] ratios by affecting leaf-, tree-, and stand-level eco-physiological controls on transpiration. Restricted water use is also additive to other ecosystem services provided by mangroves, such as high carbon sequestration, coastal protection and support of biodiversity within estuarine and marine environments. Low freshwater demand enables mangroves to sustain ecological values of connected estuarine ecosystems with future reductions in freshwater while not competing with the freshwater needs of humans. Conservative water use may also be a characteristic of other emergent blue carbon wetlands.

Recommendations for quantifying and reducing uncertainty in climate projections of species distributions.

Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change-rather than accurately predict specific outcomes-it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling. The simulations encompass marine species with different functional traits and ecological preferences to more broadly address resource manager and fishery stakeholder needs, and provide a simulated true state with which to evaluate projections. We present our results relative to the degree of environmental extrapolation from historical conditions, which helps facilitate interpretation by ecological modelers working in diverse systems. We found uncertainty associated with species distribution models can exceed uncertainty generated from diverging earth system models (up to 70% of total uncertainty by 2100), and that this result was consistent across species traits. Species distribution model uncertainty increased through time and was primarily related to the degree to which models extrapolated into novel environmental conditions but moderated by how well models captured the underlying dynamics driving species distributions. The predictive power of simulated species distribution models remained relatively high in the first 30 years of projections, in alignment with the time period in which stakeholders make strategic decisions based on climate information. By understanding sources of uncertainty, and how they change at different forecast horizons, we provide recommendations for projecting species distribution models under global climate change.

Introducing zoid: A mixture model and R package for modeling proportional data with zeros and ones in ecology.

Many ecological data sets are proportional, representing mixtures of constituent elements such as species, populations, or strains. Analyses of proportional data are challenged by categories with zero observations (zeros), all observations (ones), and overdispersion. In lieu of ad hoc data adjustments, we describe and evaluate a zero-and-one inflated Dirichlet regression model, with its corresponding R package (zoid), capable of handling observed data x $$ x $$ consisting of three possible categories: zeros, proportions, or ones. Instead of fitting the model to observations of single biological units (e.g., individual organisms) within a sample, we sum proportional contributions across units and estimate mixture proportions using one aggregated observation per sample. Optional estimation of overdispersion and covariate influences expand model applications. We evaluate model performance, as implemented in Stan, using simulations and two ecological case studies. We show that zoid successfully estimates mixture proportions using simulated data with varying sample sizes and is robust to overdispersion and covariate structure. In empirical case studies, we estimate the composition of a mixed-stock Chinook salmon (Oncorhynchus tshawytscha) fishery and analyze the stomach contents of Atlantic cod (Gadus morhua). Our implementation of the model as an R package facilitates its application to varied ecological data sets composed of proportional observations.