Building use-inspired species distribution models: Using multiple data types to examine and improve model performance

Multi-Species Telemetry Quantifies Current and Future Efficacy of a Remote Marine Protected Area

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.

Salinity drives the distribution of a top-order predator, the tiger shark (Galeocerdo cuvier), in an inverse estuary

Understanding how dynamic environmental processes influence the distributions of top-order predators is fundamental to assess top-down effects on ecosystems. Tiger sharks (Galeocerdo cuvier) are a large top-predator that can trigger trophic cascades and structure communities. However, the dynamic physical processes that influence the distributions of these animals in coastal systems are largely unknown. Here, we assess the environmental processes influencing tiger shark movements in the inverse estuary of Shark Bay, Western Australia, a shallow coastal embayment with salinities consistently above that of the adjacent ocean. We applied Bayesian generalized linear mixed-effects models to generate dynamic predictions of suitable habitat for tiger sharks in this region. These habitats were associated with dense and shallow seagrass beds and largely reflected the spatial variability of hypersaline water (< 40). Under future climate scenarios, coastal areas worldwide are predicted to experience inverse estuarine conditions. We anticipate that the physical processes that influence tiger shark distributions in this study will become applicable to numerous other species of gill-breathing fauna in coastal ecosystems across the globe.

Evaluating Three Modelling Frameworks for Assessing Changes in Fin Whale Distribution in the Mediterranean Sea

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.

Improving Ocean Management Using Insights from Space

Advancements in space-based ocean observation and computational data processing techniques have demonstrated transformative value for managing living resources, biodiversity, and ecosystems of the ocean. We synthesize advancements in leveraging satellite-derived insights to better understand and manage fishing, an emerging revolution of marine industrialization, ocean hazards, sea surface dynamics, benthic ecosystems, wildlife via electronic tracking, and direct observations of ocean megafauna. We consider how diverse space-based data sources can be better coupled to modernize and improve ocean management. We also highlight examples of how data from space can be developed into tools that can aid marine decision-makers managing subjects from whales to algae. Thoughtful and prospective engagement with such technologies from those inside and outside the marine remote sensing community is, however, essential to ensure that these tools meet their full potential to strengthen the effectiveness of ocean management.

Ship collision risk threatens whales across the world's oceans

After the near-complete cessation of commercial whaling, ship collisions have emerged as a primary threat to large whales, but knowledge of collision risk is lacking across most of the world's oceans. We compiled a dataset of 435,000 whale locations to generate global distribution models for four globally ranging species. We then combined >35 billion positions from 176,000 ships to produce a global estimate of whale-ship collision risk. Shipping occurs across 92% of whale ranges, and <7% of risk hotspots contain management strategies to reduce collisions. Full coverage of hotspots could be achieved by expanding management over only 2.6% of the ocean's surface. These inferences support the continued recovery of large whales against the backdrop of a rapidly growing shipping industry.

Climate-driven global redistribution of an ocean giant predicts increased threat from shipping

Climate change is shifting animal distributions. However, the extent to which future global habitats of threatened marine megafauna will overlap existing human threats remains unresolved. Here we use global climate models and habitat suitability estimated from long-term satellite-tracking data of the world's largest fish, the whale shark, to show that redistributions of present-day habitats are projected to increase the species' co-occurrence with global shipping. Our model projects core habitat area losses of >50% within some national waters by 2100, with geographic shifts of over 1,000 km (∼12 km yr-1). Greater habitat suitability is predicted in current range-edge areas, increasing the co-occurrence of sharks with large ships. This future increase was ∼15,000 times greater under high emissions compared with a sustainable development scenario. Results demonstrate that climate-induced global species redistributions that increase exposure to direct sources of mortality are possible, emphasizing the need for quantitative climate-threat predictions in conservation assessments of endangered marine megafauna.

Twenty-five emerging questions when detecting, understanding, and predicting future fish distributions in a changing climate

The 2023 Annual Symposium of the Fisheries Society of the British Isles hosted opportunities for researchers, scientists, and policy makers to reflect on the state of art of predicting fish distributions and consider the implications to the marine and aquatic environments of a changing climate. The outcome of one special interest group at the Symposium was a collection of questions, organized under five themes, which begin to capture the state of the field and identify priorities for research and management over the coming years. The five themes were Physiology, Mechanisms, Detect and Measure, Manage, and Wider Ecosystems. The questions, 25 of them, addressed concepts which remain poorly understood, are data deficient, and/or are likely to be impacted in measurable or profound ways by climate change. Moving from the first to the last theme, the questions expanded in the scope of their considerations, from specific processes within the individual to ecosystem-wide impacts, but no one question is bigger than any other: each is important in detecting, understanding, and predicting fish distributions, and each will be impacted by an aspect of climate change. In this way, our questions, particularly those concerning unknown mechanisms and data deficiencies, aimed to offer a guide to other researchers, managers, and policy makers in the prioritization of future work as a changing climate is expected to have complex and disperse impacts on fish populations and distributions that will require a coordinated effort to address.

A joint model for the estimation of species distributions and environmental characteristics from point-referenced data

BACKGROUND

Predicting and explaining species occurrence using environmental characteristics is essential for nature conservation and management. Species distribution models consider species occurrence as the dependent variable and environmental conditions as the independent variables. Suitable conditions are estimated based on a sample of species observations, where one assumes that the underlying environmental conditions are known. This is not always the case, as environmental variables at broad spatial scales are regularly extrapolated from point-referenced data. However, treating the predicted environmental conditions as accurate surveys of independent variables at a specific point does not take into account their uncertainty.

METHODS

We present a joint hierarchical Bayesian model where models for the environmental variables, rather than a set of predicted values, are input to the species distribution model. All models are fitted together based only on point-referenced observations, which results in a correct propagation of uncertainty. We use 50 plant species representative of the Dutch flora in natural areas with 8 soil condition predictors taken during field visits in the Netherlands as a case study. We compare the proposed model to the standard approach by studying the difference in associations, predicted maps, and cross-validated accuracy.

FINDINGS

We find that there are differences between the two approaches in the estimated association between soil conditions and species occurrence (correlation 0.64-0.84), but the predicted maps are quite similar (correlation 0.82-1.00). The differences are more pronounced in the rarer species. The cross-validated accuracy is substantially better for 5 species out of the 50, and the species can also help to predict the soil characteristics. The estimated associations tend to have a smaller magnitude with more certainty.

CONCLUSION

These findings suggests that the standard model is often sufficient for prediction, but effort should be taken to develop models which take the uncertainty in the independent variables into account for interpretation.

Widespread habitat loss and redistribution of marine top predators in a changing ocean

The Northwest Atlantic Ocean and Gulf of Mexico are among the fastest warming ocean regions, a trend that is expected to continue through this century with far-reaching implications for marine ecosystems. We examine the distribution of 12 highly migratory top predator species using predictive models and project expected habitat changes using downscaled climate models. Our models predict widespread losses of suitable habitat for most species, concurrent with substantial northward displacement of core habitats >500 km. These changes include up to >70% loss of suitable habitat area for some commercially and ecologically important species. We also identify predicted hot spots of multi-species habitat loss focused offshore of the U.S. Southeast and Mid-Atlantic coasts. For several species, the predicted changes are already underway, which are likely to have substantial impacts on the efficacy of static regulatory frameworks used to manage highly migratory species. The ongoing and projected effects of climate change highlight the urgent need to adaptively and proactively manage dynamic marine ecosystems.