Sean C. Anderson

Understanding the impact of changing temperature on population densities is necessary to predict the likely impact of climate anomalies (e.g., marine heatwaves) or forecast distribution shifts under future climate scenarios. Population densities are often analyzed using spatio‐temporal models (STMs), which typically predict densities based on local habitat conditions while also estimating latent spatial and spatio‐temporal variation. Recent research extends STMs by also estimating density responses to habitat conditions at nearby locations using a “spatially distributed lag” (SDL) that averages habitat conditions in the vicinity of samples. Here, we extend the SDL by incorporating insights from diffusion‐enhanced STMs to simultaneously estimate spatially distributed and time‐lagged responses to nearby and past habitat conditions (a “spatio‐temporal distributed lag” STDL). We then use summer bottom trawl survey data from the eastern Bering Sea (1982–2024) to measure whether spatial and/or temporal lags are parsimonious when predicting population density from temperature anomalies for six ecologically important fishes. Results show that time‐lagged responses are parsimonious, positive, and substantial (correlation of 0.20–0.83 per year) for five species, and that density responses to temperature anomalies also diffuse outward over time for four species at 30–53 km per year. A self‐ and cross‐test simulation experiment shows that model selection can identify the appropriate model and that parameter estimates are approximately unbiased. We therefore conclude that temperature carryover effects arise in marine fishes and implement the method in the R package sdmTMB so that future studies can estimate nonlocal and time‐lagged responses when measuring density responses to habitat.

Marine fishes often suffer from parasitism, which can affect life-history traits like maturity and mortality. Rockfishes (Sebastes spp.) in the northeast Pacific are commonly parasitized by Sarcotaces copepods that form large cysts in muscle or body cavities. Baseline data on infection prevalence and effects are limited. We analyzed Sarcotaces infection in 23 rockfish species (including two thornyheads Sebastolobus), using over 37,000 individuals sampled in Fisheries and Oceans Canada groundfish surveys in British Columbia. Infection occurred throughout surveyed areas and depths, with rates up to 10%, highest in Pacific Ocean Perch, Silvergray, Rougheye/Blackspotted Complex, Yellowmouth, and Yelloweye rockfishes. Infection was linked to delayed maturation, particularly in males, which matured at lengths 22.3 mm larger (95% CrI: 5.5, 39.8). Immature fish were more likely to be infected than mature ones. Although speculative, Sarcotaces infection may act as a co-morbidity reducing survival. Body condition showed little association with infection, aside from a slight increase (~5%) in some species. To our knowledge, this study provides the first large-scale assessment of Sarcotaces infection and subsequent effect on rockfish life-history.

Aim: The temperature‐size rule is often described as a reduction in ectothermic asymptotic or maximum body size with warming. Although this coincides with the expectation that faster growth under warming would lead to larger sizes early in life but smaller sizes later in life, there remain few tests of changes in size across ontogeny. Here, we use > 99,000 observations of weight‐at‐age of commercially important fishes over 25 years to ask whether changes in size across ontogeny can be explained by temperature. We also examine whether oxygen, a key part of a proposed mechanism behind the temperature‐size rule, explains patterns of weight‐at‐age better than temperature. Understanding how temperature and oxygen affect size across age offers a test of macroecological theory and can inform the future productivity of fisheries. Location: Bering Sea, largest subarctic system (Latitude: 52° N–66° N, Longitude: 160° E–170° W). Time Period: 1987–2023. Major Taxa Studied: Commercially important fishes. Methods: We coupled fisheries‐independent survey data and climate model output with spatiotemporal generalised linear mixed‐effects models, a novel framework for testing the temperature‐size rule. These models account for uneven sampling across space and time to assess long‐term trends, as well as the effects of temperature and oxygen on size across age. We additionally explore the effect of model structure on our results by comparing functional forms and how spatial and/or spatiotemporal random effects are included. Results: Weight‐at‐age was variable over time for all species with no long‐term trend. Temperature better explained this variability than oxygen, but effects were small and not age‐specific, counter to the temperature‐size rule. Our results were sensitive to model structure as models with shared spatial effects across ages appeared to support temperature‐size rule predictions, but this support reflected spatial autocorrelation in size rather than age‐specific temperature responses. Collectively, this work shows that support for the temperature‐size rule in these fishes was an artifact of spatial patterns in size and growth. Main Conclusions: Our work tests macroecological theory using nearly complete body size trajectories to understand not only changes in adult stages but how size changes across age. We highlight that relationships among size, growth, temperature, and oxygen are not as straightforward as theory suggests and illustrate that modelling decisions can have a large effect on tests of ecological theory, and more broadly, our ability to understand biological responses to climate change.

Climate change is impacting marine ecosystems in increasingly rapid and unpredictable ways. The spatial extent of these impacts on marine fisheries and endangered and threatened species often spans regional and international boundaries. The transboundary nature of changing ocean conditions can challenge detection and anticipation of changes, and delay coordinated actions at the scales required to mitigate and respond. Here, we identify challenges and opportunities for transboundary science in the Northeast Pacific Ocean. Specifically, we aim to support the management of fisheries and protected species in response to a changing climate, while highlighting successful efforts. Challenges include (1) limited coordination of monitoring surveys; (2) institutional, cultural, and technological barriers to the sharing of data and analytical tools; (3) incomplete understanding of relationships among oceanographic conditions, trophic interactions, population dynamics, and species’ distributions; (4) limited availability of high-resolution Earth system model projections that can be linked to ecosystem and fishery responses; and (5) differing prioritization of ecosystem information and limited communication among nations. Three opportunities to overcome these challenges include (1) coordinated monitoring and sharing of data at a transboundary scale to detect and understand marine ecosystem responses to climate change; (2) common assessment frameworks and modeling approaches to improve understanding and projections of ecological responses to climate change; and (3) increased communication of ecosystem information to support management needs across jurisdictions, enhance the use of existing science products, and strengthen pathways for science to inform management of marine resources. We focus on examples of these opportunities drawn from our collective experience as government scientists working on Northeast Pacific Ocean ecosystems. We suggest that by strategically focusing on these opportunities, transboundary science worldwide can improve predictions of ecosystem responses to climate change and better support regional and international management of shared resources.

Marine species are widely expected to shift poleward or into deeper waters in response to rising ocean temperatures. However, our knowledge is primarily based on studies examining range shifts along single dimensions at a time (e.g., latitude or depth). Failing to address how movements along multiple dimensions interact, including associated changes in thermal exposure, may result in misleading conclusions and predictions of species distribution and community composition under global warming. To address this knowledge gap, we here develop and apply a multidimensional framework that jointly evaluates climate-driven redistribution of marine fish across latitude, longitude, depth and realized thermal niches, based on long-term scientific bottom-trawl surveys throughout the North Atlantic and Northeast Pacific. Our results show that net redistributions are generally small and highly region-specific, while the realized thermal niches of species have warmed substantially over the past three decades. These findings demonstrate that spatial redistribution is generally failing to keep pace with rising temperatures and challenge the prevailing assumption that marine species will move to escape warming. This has direct implications for biodiversity indicators that rely on distributional shifts as evidence of climate impacts, as well as climate-informed management and conservation of marine ecosystems, fisheries, and biodiversity at large.

Fishery-independent indices of abundance are important inputs for stock assessments and empirical control rules to inform fisheries management. However, changes in survey protocols may hinder the calculation of such indices if population abundance is conflated with changes in catchability. Typically, when a survey vessel or sampling gear is replaced, comparative fishing (CF) experiments are used to estimate the relative catchability between vessels or gear configurations to calibrate expected catch rates. However, direct estimates are not always available. In these cases, we propose the use of spatiotemporal models to indirectly estimate the calibration factor as part of index standardization. Indirect estimates are possible without CF data if the population process in the spatiotemporal model is not independent across time steps. Across six case studies for species with CF data, we show that indices from indirect calibration are similar in trend as those from direct calibration across the vessel change. While the precision of the calibration factor is lower with indirect calibration than with direct calibration, indirect calibration performed better than no calibration and provided a statistically rigorous approach to develop an integrated synthetic index. We show how statistical diagnostics can support or reject the suitability of indirect calibration when comparison to direct calibration is not possible in real world cases. Simulation experiments additionally supported the use of indirect calibration. While direct calibration remains the recommended approach, we propose indirect calibration as a potential tool when comparative fishing experiments are not possible.

Marine harvest refugia help conserve biodiversity and ecosystems; however, evaluating their effectiveness with adequate statistical power is challenging. Quantifying adequately powered sampling needs can improve data collection methods and study design, precision, and minimize costs. Conventional power equations are not designed for complex ecosystem models and, together with sparse pre-intervention data, have resulted in limited evidence of refugia effectiveness. We conditioned a spatially explicit Gaussian Markov random field model to fish density data from remotely operated vehicles, and compared two sampling schemes using a simulation-based power analysis. We quantified power to detect abundance recovery by comparing Early-Later-Control-Impact and Control-Impact designs with different sampling strategies and effort levels. Results indicate that (1) early samples were highly valuable, especially given initial inside vs. outside refugia fish abundance differed substantially from expectations; (2) small sample sizes inflated effect estimates, suggesting that type M (magnitude) errors should be considered; and (3) repeatedly sampling fixed locations—rather than selecting new random sites within or across an expanded sampling domain—improved power up to 35%, reducing the necessary sample size. These findings provide guidance for designing cost-effective monitoring programs that can reliably detect recovery in marine harvest refugia.

Detecting positive responses to marine spatial protections, both in situ and as spillover effects, is critical for fisheries management yet difficult in practice. We leveraged 18 years of data on 28 groundfish species collected by four Indigenous Nations in Pacific Canada to assess ecological responses to spatially protected Rockfish Conservation Areas (RCAs). Body sizes and abundances were greatest inside RCAs and increased with proximity to RCAs at unprotected sites up to 150 km away. Abundance responses emerged faster (at younger RCA ages) for species with high r _max , as predicted, but body size responses emerged faster for species with low r _max , counter to predictions. Our study illustrates a general framework for jointly inferring in situ recovery and spillover benefits using a novel hierarchical geostatistical model that integrated long time series; observations made inside, near, and far from seven RCAs; and multiple species and survey methods spanning a 500-m depth range.

Quantifying broad‐scale population trends and distribution change is critical for effective management and conservation of marine species, particularly under climate change. However, fragmented regional survey data often hinder such efforts for transboundary populations. A prime example is Pacific Spiny Dogfish ( Squalus suckleyi , Squalidae), a small shark with a remarkably slow life history and wide‐ranging distribution. Dogfish are now caught incidentally but were heavily fished along the Pacific US–Canada coast ≈80 years ago. Reports on local population trends have conflicted along the coast, suggesting that movement between regions may be responsible. We fit spatiotemporal models integrating data from 10 surveys to synthesise trends in biomass, abundance, distribution and thermal niche for dogfish across their entire eastern Pacific Ocean range. Prior to 2003, Alaskan biomass increased through the 1990s whereas California to British Columbia indices were variable and imprecise. However, during 2003–2023, we found a coastwide 51% (95% CI: 38%–61%) decline in dogfish biomass with mature females and immature dogfish showing the largest proportional declines. Regionally, declines were steepest for the US West Coast (71%–85%) and Canada (58%–82%), while Alaska showed less severe declines (13%–54%). Off the US West Coast, dogfish shifted into deeper waters as temperatures in their habitat increased, but these patterns do not explain the coastwide declines. Our results suggest population declines are primarily driven by reduced abundance rather than between‐region movement, indicating elevated coastwide conservation concern and helping focus investigations of causal mechanisms.

Species distribution models (SDMs) are widely used to standardize spatially unbalanced data, project climate impacts and identify habitat for conservation. SDMs typically estimate the impact of local environmental conditions by estimating a dome‐shaped or non‐parametric ‘environmental response function’. However, ecological responses often integrate across local habitat conditions, such that species density depends on habitat at the location of sampling but also at nearby locations. To address this, we extend methods from the stochastic partial differential equation (SPDE) method that is widely used in INLA, which approximates spatial correlations based on local diffusion over a finite‐element mesh (FEM). We specifically introduce the sparse inverse‐diffusion operator on a FEM and apply this operator to covariates to efficiently calculate a spatially weighted average of local habitat that is then passed through pointwise basis expansion to predict species densities. We show that this operator has several useful properties, that is conservation of mass, efficient scaling of computational time with spatial resolution, and invariance to linear (scale and offset) transformations of covariates. We test this covariate‐diffusion method using a simulation experiment and show that it can correctly recover a non‐local environmental response while collapsing to a local (pointwise) response when warranted. We apply it to monitoring data for 25 bottom‐associated fishes in the eastern Bering Sea and 20 bird species in the western United States. This application confirms that non‐local responses in the eastern Bering Sea case study are parsimonious for 26 species–maturity combinations, while 18 collapse to the pointwise method. Estimates suggest that some species–maturity combinations avoid proximity to the continental slope, beyond what is predicted by local bathymetry in isolation. By contrast, in four of the 20 bird species the diffused human population density covariate is more parsimonious than the original covariate. The covariate‐diffusion method introduced here constitutes a fast and efficient approach to modelling non‐local covariate effects. This flexible method may be useful in cases when covariates influence nearby population densities, for instance due to movement of the sampled species or its important biological or physical drivers.