Assess the structural importance of threatened and non-threatened species within European food webs by quantifying their roles in maintaining network connectivity.
Europe.
We constructed a dataset of local potential food webs by combining species distribution data with a European metaweb of tetrapod trophic interactions (Tetra-EU 1.0). Next, we added information on species' threat statuses and assessed their ecological importance using node-level network structural metrics within the food web. These metrics included in-degree (number of prey species), out-degree (number of predator species), trophic level (species' vertical position in the food web), betweenness centrality (frequency with which a species lies on the shortest paths between all other species), closeness centrality (proximity of a species to all others in the network), and the integrated value of influence (a composite measure to identify the most influential species, capturing both direct and indirect effects).
While threatened species occasionally hold key ecological roles in specific regions (e.g., the Iberian Peninsula), non-threatened species more frequently exhibit a high integrated value of influence and betweenness centrality, underscoring their substantial contributions to overall potential food web integrity.
We highlight the importance of integrating threatened and non-threatened species into conservation strategies, recognising their complementary roles in maintaining food web stability.
Assess the structural importance of threatened and non-threatened species within European food webs by quantifying their roles in maintaining network connectivity.
Europe.
We constructed a dataset of local potential food webs by combining species distribution data with a European metaweb of tetrapod trophic interactions (Tetra-EU 1.0). Next, we added information on species' threat statuses and assessed their ecological importance using node-level network structural metrics within the food web. These metrics included in-degree (number of prey species), out-degree (number of predator species), trophic level (species' vertical position in the food web), betweenness centrality (frequency with which a species lies on the shortest paths between all other species), closeness centrality (proximity of a species to all others in the network), and the integrated value of influence (a composite measure to identify the most influential species, capturing both direct and indirect effects).
While threatened species occasionally hold key ecological roles in specific regions (e.g., the Iberian Peninsula), non-threatened species more frequently exhibit a high integrated value of influence and betweenness centrality, underscoring their substantial contributions to overall potential food web integrity.
We highlight the importance of integrating threatened and non-threatened species into conservation strategies, recognising their complementary roles in maintaining food web stability.
Species diversity patterns and their drivers are critical pieces of ecological knowledge needed to mitigate the threats faced by freshwater ecosystems globally. We used information on lake glacial history and environmental conditions to identify factors driving fish biodiversity patterns in Ontario and European lakes.
9350 lakes across Ontario; 1824 lakes across northern and western Europe.
Log–log linear mixed-effects models (LMM) and generalised linear mixed-effects models (GLMM) with a Poisson distribution were used to identify associations between fish species richness and climatic, geographic, physical, and historical variables. Local variation in fish diversity patterns was identified by applying beta diversity analysis and nested ‘temperature’ analysis to local richness data defined by sub-basins identified in the global watershed database HydroBASINS.
Lake area was the most important predictor of species richness in both regions. Other drivers evident in both regions were: local and regional measures of lake elevation, lake morphometry, lake age, and longitude. Results from our LMM models were similar to those from our GLMM models. In both Ontario and Europe, the error structure of these models was shaped by the HydroBASINS watershed sub-basin structure defined for each landscape. At all spatial scales, beta diversity was > 0.9, with species turnover being more important than nestedness. Nestedness contributed no more than 22% to total beta diversity. Matrix ‘temperature’ covered a similar range on both continents (~4° to ~40°).
Similar physical, geographic, and historical predictors influenced lake fish species richness in Ontario and northern and western Europe. The spatial structure of the watershed sub-basins in each region played a similar role in shaping the optimal error structure included in our richness models. By identifying variables that influence biodiversity generally and by mapping local variation in biodiversity in both regions, we provide new information that can inform landscape level conservation management strategies for lakes in both Ontario and Europe.
Species diversity patterns and their drivers are critical pieces of ecological knowledge needed to mitigate the threats faced by freshwater ecosystems globally. We used information on lake glacial history and environmental conditions to identify factors driving fish biodiversity patterns in Ontario and European lakes.
9350 lakes across Ontario; 1824 lakes across northern and western Europe.
Log–log linear mixed-effects models (LMM) and generalised linear mixed-effects models (GLMM) with a Poisson distribution were used to identify associations between fish species richness and climatic, geographic, physical, and historical variables. Local variation in fish diversity patterns was identified by applying beta diversity analysis and nested ‘temperature’ analysis to local richness data defined by sub-basins identified in the global watershed database HydroBASINS.
Lake area was the most important predictor of species richness in both regions. Other drivers evident in both regions were: local and regional measures of lake elevation, lake morphometry, lake age, and longitude. Results from our LMM models were similar to those from our GLMM models. In both Ontario and Europe, the error structure of these models was shaped by the HydroBASINS watershed sub-basin structure defined for each landscape. At all spatial scales, beta diversity was > 0.9, with species turnover being more important than nestedness. Nestedness contributed no more than 22% to total beta diversity. Matrix ‘temperature’ covered a similar range on both continents (~4° to ~40°).
Similar physical, geographic, and historical predictors influenced lake fish species richness in Ontario and northern and western Europe. The spatial structure of the watershed sub-basins in each region played a similar role in shaping the optimal error structure included in our richness models. By identifying variables that influence biodiversity generally and by mapping local variation in biodiversity in both regions, we provide new information that can inform landscape level conservation management strategies for lakes in both Ontario and Europe.
Identifying climate refugia is a pressing priority for conservation planning under global change, particularly in oceanic archipelagos with high levels of endemicity and topographic complexity.
Canary Islands.
Here, we developed a spatial framework to identify and recursively map refugia probability in the Canary Islands across a broad set of indicators, including multivariate climate analogues, topographic complexity, poleward aspect, wetness, and forestry cover. We integrated these into continuous refugia probability maps and assessed their spatial patterns across the islands.
Our results reveal clear east–west patterns, with highest refugia potential values concentrated in poleward-oriented, rugged and forested areas in the western part of the archipelago. The additive integration of the different blocks enables the identification of refugia probability. The tree-cover block highlights the role of vegetation and forest patches, promoting refugia. In drier areas, however, topography and poleward-facing aspects become more influential, improving refugia detection on islands with strong environmental contrasts. Critically, many of the most resilient refugia remain outside existing protected areas, especially on biodiversity-rich and heterogeneous islands, highlighting how our approach can help identify areas of high conservation potential.
Our proposed framework is reproducible, data-efficient, and transferable to other oceanic regions, providing a flexible tool for conservation decisions and adaptive design of Protected Areas systems in vulnerable landscapes. By identifying areas likely to host biodiversity under climate change, our approach supports adaptive conservation planning, protected area expansion, and climate-informed prioritisation for managers.
Climate change is causing distributional shifts in many species globally. identifying and anticipating these shifts is critical to understanding ecosystem impacts and implementing successful management strategies. species distribution models (SDMs) are useful tools often employed to describe current and changing habitat use, particularly for marine predators. However, most SDMs assume the statistical relationships between species and their environment are temporally static, which may not be true. We examined how incorporating temporal variability improved SDM performance and estimated range shifts for six Odontocete species. We used a high performing model to quantify changes in Odontocete distribution over a 24-year period.
Waters of the United States, east coast, from Florida to Nova Scotia.
We utilised nearly 1.4 million kilometres of line transect survey data collected from 1997 to 2020 along the East Coast of the United States to evaluate changes in the distribution of six Odontocete species. We assessed six model specifications of generalise additive models that varied in the extent of temporal and spatial variability incorporated.
We found that the best performing model specifications included temporally dynamic species–environment relationships and temporally dynamic spatial terms. These model specifications identified significant poleward range shifts in all species for which we had sufficient data across their range. In contrast, model specifications which only included static terms performed poorly and identified limited or no spatial shifts.
These results advance our predictive capabilities from static species–environment relationships for marine predators and demonstrate the importance of carefully considering assumptions and model specifications when modelling changes to distributions. The odontocete range shifts we identified are likely to have substantial ecosystem impacts, and the framework we present offers a diagnostic approach for modelling and identifying range shifts in other wide-ranging species.
Reliable biodiversity assessments using species distribution models (SDMs) are essential for effective conservation and management. Understanding factors influencing SDM performance is crucial for improving model reliability, yet such links remain underexplored in marine systems. To address this gap, we quantified the effects of geographical and species-level factors on SDM performance in tropical reef fishes, aiming to provide practical guidelines on which species are more likely to yield reliable predictions.
Global tropical reef ecosystems.
We built ensemble SDMs for 1941 tropical reef fish species using occurrence records. We evaluated model predictive performance using the continuous Boyce index, the most suitable performance metric given that we lacked quality absence data, and two other commonly applied metrics (the area under the receiving operating characteristic curve and the true skill statistic). We compiled 10 factors related to species' geographical and ecological characteristics and assessed their influence on model performance using phylogenetic generalised linear models.
Ensemble SDMs for tropical reef fishes exhibited high predictive performance based on the three evaluation metrics. Phylogenetic generalised linear models relating evaluation metrics to species geographical and ecological characteristics showed modest explanatory power, with R2 varying from 0.253 to 0.341. Across evaluation metrics, SDM performance was strongly associated with species' latitude, proximity to shore, and environmental similarity between training and evaluation datasets. For continuous Boyce index, there were additional significant effects for range size, parental care, range coverage, and species description year.
Our study provides a practical framework for identifying tropical reef fish species more likely to yield reliable SDM predictions. The identified factors offer guidance for researchers to anticipate model reliability before undertaking extensive modelling efforts. This large-scale, multi-species comparative approach is broadly applicable to other marine taxa and regions, advancing our ability to model and conserve marine biodiversity.
Site network approaches to waterbird conservation are easily biased towards species that occur in high densities and locations and periods of the annual cycle with dense concentrations of birds, thereby potentially failing to address underlying factors driving certain population declines. Here, for shorebird populations that migrate along the East Asian–Australasian Flyway, we examine the extent to which recent conservation efforts at key sites in the Yellow Sea—that is, sites that are vital for stabilising densely concentrated, rapidly declining shorebird species—might address factors underlying low adult survival and steep population declines in shorebird species that migrate and winter along a broad front (i.e., the East China Sea, Yellow Sea and Japan).
East Asia.
Using geolocator-derived migration tracks, an integrated survival model and a population matrix model to estimate winter-population-specific survival rates and population trends in a quintessential East Asian shorebird population—the arcticola subspecies of the Dunlin (Calidris alpina)—we show that in 2010–2014 differences between arcticola winter populations in the intensity of their declines were most likely linked to conditions on their wintering grounds, with individuals that wintered in the East China Sea or Japan showing the steepest population declines (mean: −12% year−1 [50% credible interval: −3%, −22%] and −17% year−1 [−6%, −30%], respectively) and individuals that wintered in the Yellow Sea surprisingly stable (+4% year−1 [−5%, +14%]).
For shorebird populations that winter in East Asia and migrate along a broad front, additional conservation efforts in the East China Sea and Japan are likely necessary to reverse population declines.
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