Visual surveys are a common method of obtaining data for ecological studies, including studies that develop models (e.g. species distribution models). In the case of transect or non-transect line surveys, data must often be partitioned into spatiotemporal units (i.e. samples) before being modelled. This processing typically follows one of three approaches -the grid, segment and point approaches- each with its own variations. Currently, processing of visual survey data is done in custom scripts that can be time-consuming and technically demanding to develop, which hinders the development of ecological models. This issue is exacerbated if multiple approaches and/or variations are to be applied. Here, we present sampley, a user-friendly Python package for processing visual survey data into samples. It operates on various common formats and filetypes, ensuring its applicability to diverse datasets and compatibility with other software. It can process data by multiple variations of the grid, segment and point approaches. As this processing follows a straightforward sequence of stages and involves a limited number of functions, it is easy to learn and use. The combination of these traits will increase efficiency and reproducibility of survey data processing and allow researchers to readily trial different methods. In this paper, we provide a description of the package - the types of data that can be processed, the approaches and variations available and the stages involved in processing. An example application, based on a mock dataset consisting of survey tracks, sightings and in situ environmental data, is also provided with descriptions of each stage.

Climate change is necessitating renewable energy development while contributing to shifting wildlife phenology and distribution. Renewable energy may require anthropogenic activity and wildlife to co-occur. Here we develop a framework to identify trade-offs between financial costs to developers and conservation, considering the critically endangered North Atlantic right whale (Eubalaena glacialis) and offshore wind energy development. By coupling an offshore wind energy construction simulation with a density surface model to quantify the number of right whales exposed to pile-driving noise under ten mitigation scenarios, we highlight mitigation measures, notably seasonal restrictions and noise attenuating devices, that can yield conservation value with minimal financial burden. As societies continue to refine and determine suitable financial and ecological trade-offs for development, frameworks like ours can be useful for exploring conservation challenges to ensure co-benefits for climate and biodiversity. The authors consider the potential conflict between renewable energy development (offshore wind) and wildlife (North Atlantic right whales, Eubalaena glacialis). They demonstrate a framework to identify trade-offs under different scenarios, maximizing conservation with minimal financial burden.

Understanding a population's distribution depends on observing the presence and movement of individuals throughout their range. For highly mobile marine species, these observations typically rely on high effort monitoring programs. Tracking enough individuals to understand trends in movement behavior is not always logistically feasible, and animals are less likely to be observed in migratory or transitional habitats. To optimize observation data we built a Brownian bridge movement model to generate spatially and temporally explicit space use estimations of individuals along tracks created from intermittent sightings of North Atlantic right whales from 1980 to 2022. Right whales can be identified by unique callosities and markings, providing a noninvasive opportunity to link sightings to individual movement. This model generated location probability estimates of medium- and large-scale movements in biologically plausible habitats. A total of 351,214 d of occurrence distributions were calculated from 67,840 sightings attributed to 806 individuals, representing more than a five-fold increase in individual spatial information. From 1980 to 2022, the model generated space use estimates for at least one whale on 75.7% of days, compared to the underlying visual sightings data, which only provided observations on 38.7% of days. Model outputs compared to tracks of tagged whales demonstrated proficiency estimating space use across regions. The occurrence distributions produced depict known changes in seasonal and decadal space use and estimate transitional space use where observations are sparse. These methods improve spatial distribution predictions in intermittently observed animals and expand the utility of stationary sightings without constraining predictions to historical relationships with the environment.

Abstract Knowledge of when species remain in specified areas is essential for survey design, conservation, and management. Using species occurrence data to predict persistence in space and time (i.e., presence of one or more individuals of the species of interest within a defined spatial area over a duration of a specified number of days) may be possible with extensive survey effort and complex modeling, but such requirements pose challenges. Here, we present a method for estimating wildlife spatiotemporal persistence by (1) filtering data to contain detections and all surveys occurring 7 days following each detection within a spatial buffer around each detection (i.e., spatiotemporal detection buffer), (2) identifying redetections in each spatiotemporal detection buffer, and (3) grouping detection buffers temporally and spatially for bootstrap resampling. Our method avoids the need for mechanistic models of animal behavior while accommodating survey effort that may, at times, be sparse. We illustrate the approach using spatiotemporal data from 2010 to 2020 vessel‐based and aerial surveys of the North Atlantic right whale (Eubalaena glacialis), an endangered species that experiences various anthropogenic threats that are the focus of significant management actions. Our analyses suggested that persistence probabilities of North Atlantic right whales varied across time and space, which could guide management measures associated with forecasting or nowcasting spatiotemporal persistence for new detections. Our method can be applied to any species with repeated survey data and could facilitate dynamic management practices that effectively target conservation efforts. The presented method is especially helpful for rapid decision‐making.

Predictions of North Atlantic right whale Eubalaena glacialis distributions are an increasingly important tool used in conservation efforts for this Critically Endangered species. Right whales feed upon calanoid copepods, primarily Calanus finmarchicus. Incorporating prey distributions and characteristics into right whale density surface models (DSMs) has the potential to improve predicted whale distribution and provide a more mechanistic basis for interpretation. To explore this possibility, we tested different prey and prey proxy covariates to represent prey within a right whale DSM. We then assessed the relationship fitted to each prey or prey proxy covariate and determined which covariates added the most predictive power. The top-performing model included a combination of covariates representing high-density C. finmarchicus, Centropages typicus, and Pseudocalanus spp. aggregations and resulted in density predictions consistent with the observed distribution patterns of right whales. Predicted density was most prominent in the deep basins of the Gulf of Maine and the Great South Channel. Density generally increased in the summer and decreased in the winter, consistent with the current understanding of right whale foraging phenology. Continued monitoring of prey resources and development of prey fields for use in models are imperative to successful conservation of endangered marine predators.

The Critically Endangered North Atlantic right whale Eubalaena glacialis entered a population decline around 2011. To save this species without closing the ocean to human activities requires detailed information about its intra-annual density patterns that can be used to assess and mitigate human-caused risks. Using 2.9 million km of visual line-transect survey effort from the US Atlantic and Canadian Maritimes conducted in 2003-2020 by 11 institutions, we modeled the absolute density (ind. km-2) of the species using spatial, temporal, and environmental covariates at a monthly time step. We accounted for detectability differences between survey platforms, teams, and conditions, and corrected all data for perception and availability biases, accounting for platform differences, whale dive behavior, group composition, and group size. We produced maps of predicted density and evaluated our results using independently collected passive acoustic monitoring (PAM) data. Densities correlated positively (r = 0.46, ρ = 0.58, τ = 0.46) with acoustic detection rates obtained at 492 stationary PAM recorders deployed across the study area (mean recorder duration = 138 d). This is the first study to quantify the concurrence of visual and acoustic observations of the species in US waters. We summarized predictions into mean monthly density and uncertainty maps for the 2003-2009 and 2010-2020 eras, based on the significant changes in the species’ spatial distribution that began around 2010. The results quantify the striking distribution shifts and provide effort- and bias-corrected density surfaces to inform risk assessments, estimations of take, and marine spatial planning.

Methods to evaluate strategies to reduce the risk of vessels striking whales are needed to balance species protections with economic consequences. Previously used simplistic methods do not include important elements of vessel-strike risk. More complex methods often include parameters that have not been estimated for whales. Additionally, the whale and vessel metrics used in all methods are important because they may lead to biases in estimated risk reductions. We build a simple metric, Total PLETHd, from three components: (1) the relationship between vessel speed and the probability that a strike is lethal (PLETH), (2) vessel transit distance, and (3) whale distributions. Total PLETHd is calculated by multiplying estimates of whale distribution by the sum of transit distance multiplied by transit PLETH. We use this metric to assess risk reductions for North Atlantic right, humpback, fin, and sei whales on the United States East Coast. We found that a 10 kt speed restriction was necessary for reducing risk and that speed restrictions applied in broad areas defined by whale habitat were almost as effective as restrictions applied throughout all East Coast waters. While our areas were primarily defined to protect right whales, our results suggest they also protect humpback, fin, and sei whales. Total PLETHd represents an improvement over previous methods for estimating risk reductions because it addresses limitations in these methods. It can be used to estimate risk reductions for multiple species associated with management strategies, including changing vessel routes and implementing speed restrictions in different areas and time periods.

Predicting the impact of marine ecosystem warming on the timing and magnitude of phytoplankton production is challenging. For example, warming can advance the progression of stratification thereby changing the availability of nutrients to surface phytoplankton, or influence the surface mixed layer depth, thus affecting light availability. Here, we use a time series of sea surface temperature (SST) and chlorophyll remote sensing products to characterize the response of the phytoplankton community to increased temperature in the Northeast US Shelf Ecosystem. The rate of change in SST was higher in the summer than in winter in all ecoregions resulting in little change in the timing and magnitude of the spring thermal transition compared to a significant change in the autumn transition. Along with little phenological shift in spring thermal conditions, there was also no evidence of a change in spring bloom timing and duration. However, we observed a change in autumn bloom timing in the Georges Bank ecoregion, where bloom initiation has shifted from late September to late October between 1998 and 2020-on average 33 d later. Bloom duration in this ecoregion also shortened from similar to 7.5 to 5 weeks. The shortened autumn bloom may be caused by later overturn in stratification known to initiate autumn blooms in the region, whereas the timing of light limitation at the end of the bloom remains unchanged. These changes in bloom timing and duration appear to be related to the change in autumn thermal conditions and the significant shift in autumn thermal transition. These results suggest that the spring bloom phenology in this temperate continental shelf ecosystem may be more resilient to thermal climate change effects than blooms occurring in other times of the year.

Climate change can affect the habitat of marine species and hence their persistence and adaptation. Trends in area of occurrence and population biomass were examined for 177 fish and macroinvertebrates resident to the Northeast U.S. Continental Shelf ecosystem. Samples of these organisms were taken during a time series of research bottom trawl surveys conducted in the spring and autumn 1976-2019. The occurrence area of each taxon was modeled as the distribution of occurrence probability based on a random forest presence/absence classification model. Following, a population biomass of each taxon was modeled as a minimum swept area estimate, where the ecosystem was stratified biannually based on each taxon's spatial distribution. In both seasons, the sum of occurrence area and biomass across all modeled species increased over the study period. The summation of biomass is problematic since catchability is not known for most species; more importantly, most time series of individual species biomass trended higher. We found that the ratio of biomass to occurrence area, intended as a measure of productivity, showed no change in the autumn and had a weak increasing trend in spring. For the majority of taxa, the rate of change in biomass tracked changes in occurrence area (either positive or negative), but there were cases where the direction of change in biomass was opposite to the direction of change in occurrence area. Thermal conditions in surface waters appear to be a more important driver of occurrence area and biomass change than the change in thermal conditions near the bottom. These findings provide critical insights into the expected changes in ecosystem productivity transpiring with climate change.

Climate change and climate variability are affecting marine mammal species and these impacts are projected to continue in the coming decades. Vulnerability assessments provide a framework for evaluating climate impacts over a broad range of species using currently available information. We conducted a trait-based climate vulnerability assessment using expert elicitation for 108 marine mammal stocks and stock groups in the western North Atlantic, Gulf of Mexico, and Caribbean Sea. Our approach combined the exposure (projected change in environmental conditions) and sensitivity (ability to tolerate and adapt to changing conditions) of marine mammal stocks to estimate vulnerability to climate change, and categorize stocks with a vulnerability index. The climate vulnerability score was very high for 44% (n = 47) of these stocks, high for 29% (n = 31), moderate for 20% (n = 22), and low for 7% (n = 8). The majority of stocks (n = 78; 72%) scored very high exposure, whereas 24% (n = 26) scored high, and 4% (n = 4) scored moderate. The sensitivity score was very high for 33% (n = 36) of these stocks, high for 18% (n = 19), moderate for 34% (n = 37), and low for 15% (n = 16). Vulnerability results were summarized for stocks in five taxonomic groups: pinnipeds (n = 4; 25% high, 75% moderate), mysticetes (n = 7; 29% very high, 57% high, 14% moderate), ziphiids (n = 8; 13% very high, 50% high, 38% moderate), delphinids (n = 84; 52% very high, 23% high, 15% moderate, 10% low), and other odontocetes (n = 5; 60% high, 40% moderate). Factors including temperature, ocean pH, and dissolved oxygen were the primary drivers of high climate exposure, with effects mediated through prey and habitat parameters. We quantified sources of uncertainty by bootstrapping vulnerability scores, conducting leave-one-out analyses of individual attributes and individual scorers, and through scoring data quality for each attribute. These results provide information for researchers, managers, and the public on marine mammal responses to climate change to enhance the development of more effective marine mammal management, restoration, and conservation activities that address current and future environmental variation and biological responses due to climate change.

Incorporating the effects of climate change in species management strategies is one of today's greatest conservation challenges. Mechanistic models can be used to address these challenges because they explain how climate change effects cascade through ecosystems and influence species distributions. We used structural equation models to test hypotheses about the cascading effects of climate change and basin-scale variables on the local abundance of North Atlantic right whales, a critically endangered species, in a historically important feeding habitat. We found that effects of the North Atlantic Oscillation, a basin-scale variable, on local right whale abundance occurred through a cascade of effects on other ecosystem variables, including chlorophyll a concentration, Calanus finmarchicus abundance, and zooplankton patchiness. These effects varied by month. We also found that the western Gulf of Maine spring thermal transition date (a proxy for climate change) is a major direct and indirect driver of variations in local right whale abundance. The indirect effect of earlier spring transition dates, through a pathway of prey abundance, suggested a decrease in local right whale abundance. However, right whale abundance increased because of the direct effect of regional spring transition date. The direct effect suggests that right whales may be using regional temperatures as a movement cue. The counteracting direct and indirect effects of spring transition date suggest that right whales could face a mismatch with their prey, which could ultimately result in another large-scale distribution shift. Our causal modeling approach demonstrates that the influence of climate change on local right whale abundance in the Gulf of Maine cascades through a network of variables. These cascading effects make predicting local right whale abundance challenging and suggest that successful endangered species conservation requires identifying the mechanisms underlying species distributions.

Marine Protected Areas (MPAs) are a widely‐used tool for conserving biodiversity. Features that support marine mammal foraging have been suggested as important components to include in MPAs, but research is needed to understand the relationship between these features and diversity. For example, the Northeast Canyons and Seamounts Marine National Monument represents an area known to support marine mammal foraging and was designated to protect an area of high marine mammal diversity. However, no comparisons have been made between marine mammal diversity in the Monument and other areas. We used 3,174,167 km of survey effort and 189,175 sightings to assess alpha and beta diversity in the Monument and 500 randomly selected sites along the east coast of the United States. We used linear models to relate diversity to variables that represent marine mammal foraging areas. Our analyses showed a gradient of higher to lower diversity from north to south and that the shelf‐edge, canyons, and areas of likely upwelling support high diversity. We also found that the Monument protects a diverse and unique marine mammal community. Our analyses contribute to efforts to designate MPAs to conserve habitat that is important for protecting species by identifying drivers of biodiversity and potential sites for protecting 30% of the planet by 2030.

Climate change is affecting species distributions in space and time. In the Gulf of Maine, one of the fastest-warming marine regions on Earth, rapid warming has caused prey-related changes in the distribution of the critically endangered North Atlantic right whale ( Eubalaena glacialis ). Concurrently, right whales have returned to historically important areas such as southern New England shelf waters, an area known to have been a whaling ground. We compared aerial survey data from two time periods (2013–2015; 2017–2019) to assess trends in right whale abundance in the region during winter and spring. Using distance sampling techniques, we chose a hazard rate key function to model right whale detections and used seasonal encounter rates to estimate abundance. The mean log of abundance increased by 1.40 annually between 2013 and 2019 (p = 0.004), and the mean number of individuals detected per year increased by 2.23 annually between 2013 and 2019 (R 2 = 0.69, p = 0.001). These results demonstrate the current importance of this habitat and suggest that management options must continually evolve as right whales repatriate historical habitats and potentially expand to new habitats as they adapt to climate change.

Species' response to rapid climate change can be measured through shifts in timing of recurring biological events, known as phenology. The Gulf of Maine is one of the most rapidly warming regions of the ocean, and thus an ideal system to study phenological and biological responses to climate change. A better understanding of climate-induced changes in phenology is needed to effectively and adaptively manage human-wildlife conflicts. Using data from a 20+ year marine mammal observation program, we tested the hypothesis that the phenology of large whale habitat use in Cape Cod Bay has changed and is related to regional-scale shifts in the thermal onset of spring. We used a multi-season occupancy model to measure phenological shifts and evaluate trends in the date of peak habitat use for North Atlantic right (Eubalaena glacialis), humpback (Megaptera novaeangliae), and fin (Balaenoptera physalus) whales. The date of peak habitat use shifted by +18.1 days (0.90 days/year) for right whales and +19.1 days (0.96 days/year) for humpback whales. We then evaluated interannual variability in peak habitat use relative to thermal spring transition dates (STD), and hypothesized that right whales, as planktivorous specialist feeders, would exhibit a stronger response to thermal phenology than fin and humpback whales, which are more generalist piscivorous feeders. There was a significant negative effect of western region STD on right whale habitat use, and a significant positive effect of eastern region STD on fin whale habitat use indicating differential responses to spatial seasonal conditions. Protections for threatened and endangered whales have been designed to align with expected phenology of habitat use. Our results show that whales are becoming mismatched with static seasonal management measures through shifts in their timing of habitat use, and they suggest that effective management strategies may need to alter protections as species adapt to climate change.

The Gulf of Maine has recently experienced its warmest 5-year period (2015–2020) in the instrumental record. This warming was associated with a decline in the signature subarctic zooplankton species, Calanus finmarchicus. The temperature changes have also led to impacts on commercial species such as Atlantic cod (Gadus morhua) and American lobster (Homarus americanus) and protected species including Atlantic puffins (Fratercula arctica) and northern right whales (Eubalaena glacialis). The recent period also saw a decline in Atlantic herring (Clupea harengus) recruitment and an increase in novel harmful algal species, although these have not been attributed to the recent warming. Here, we use an ensemble of numerical ocean models to characterize expected ocean conditions in the middle of this century. Under the high CO2 emissions scenario (RCP8.5), the average temperature in the Gulf of Maine is expected to increase 1.1°C to 2.4°C relative to the 1976–2005 average. Surface salinity is expected to decrease, leading to enhanced water column stratification. These physical changes are likely to lead to additional declines in subarctic species including C. finmarchicus, American lobster, and Atlantic cod and an increase in temperate species. The ecosystem changes have already impacted human communities through altered delivery of ecosystem services derived from the marine environment. Continued warming is expected to lead to a loss of heritage, changes in culture, and the necessity for adaptation.

The article by Meyer-Gutbrod and colleagues in this issue demonstrates that the endangered North Atlantic right whale's preferred prey has declined as the Northwest Atlantic has warmed. Right whales are now spending more time foraging in historically colder habitats, but they are producing fewer calves. The low calf production could reflect a delay between the decline in the potential productivity of their traditional habitats and its increase in their new habitats. This delay would result in a "climate deficit" in their fitness. Right whales must also learn to forage successfully in their new habitats, creating an additional loss of fitness termed an "adaptation deficit." Humans will also face unavoidable climate deficits, but we have more options for minimizing adaptation deficits.

North Atlantic right whales (Eubalaena glacialis) are critically endangered, and recent changes in distribution patterns have been a major management challenge. Understanding the role that environmental conditions play in habitat suitability helps to determine the regions in need of monitoring or protection for conservation of the species, particularly as climate change shifts suitable habitat. This study used three species distribution modeling algorithms, together with historical whale abundance data (1993–2009) and environmental covariate data, to build monthly ensemble models of past E. glacialis habitat suitability in the Gulf of Maine. The model was projected onto the year 2050 for a range of climate scenarios. Specifically, the distribution of the species was modeled using generalized additive models, boosted regression trees, and artificial neural networks, with environmental covariates that included sea surface temperature, bottom water temperature, bathymetry, a modeled Calanus finmarchicus habitat index, and chlorophyll. Year-2050 projections used downscaled climate anomaly fields from Representative Concentration Pathway 4.5 and 8.5. The relative contribution of each covariate changed seasonally, with an increase in the importance of bottom temperature and C. finmarchicus in the summer, when model performance was highest. A negative correlation was observed between model performance and sea surface temperature contribution. The 2050 projections indicated decreased habitat suitability across the Gulf of Maine in the period from July through October, with the exception of narrow bands along the Scotian Shelf. The results suggest that regions outside of the current areas of conservation focus may become increasingly important habitats for E. glacialis under future climate scenarios.

Aim Species distribution models (SDMs) are a widely used tool to estimate and map habitat suitability for wildlife populations. Most studies that model marine mammal density or distributions use oceanographic proxies for marine mammal prey. However, proxies could be a problem for forecasting because the relationships between the proxies and prey may change in a changing climate. We examined the use of model-derived prey estimates in SDMs using an iconic species, the western Arctic bowhead whale (Balaena mysticetus). Location Western Beaufort Sea, Alaska, USA. Methods We used Biology Ice Ocean Modeling and Assimilation System (BIOMAS) to simulate ocean conditions important to western Arctic bowhead whales, including important prey species. Using both static and dynamic predictors, we applied Maxent and boosted regression tree (BRT) SDMs to predict bowhead whale habitat suitability on an 8-day timescale. We compared results from models that used bathymetry with those that used only BIOMAS simulated variables. Results The best model included bathymetry and BIOMAS variables. Inclusion of dynamic variables in SDMs produced predictions that reflected temporal dynamics evident from the survey data. Bathymetry was the most influential variable in models that included that variable. Zooplankton was the most important variable for models that did not include bathymetry. Models with bathymetry performed slightly better than models with only BIOMAS derived variables. Main conclusions Bathymetry and modelled zooplankton were the most important predictor variables in bowhead whale distribution models. Our predictions reflected within-year variability in bowhead whale habitat suitability. Using modelled prey availability, rather than oceanographic proxies, could be important for forecasting species distributions. Predictor variables used in our study were derived from a biophysical ocean model with demonstrated ability to project future ocean conditions. A natural next step is to use output from our biophysical ocean model to understand the effects of Arctic climate change.