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Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.

To understand how two dominant African savanna trees will continue to respond to climate changes, we examined their regeneration niche and adult tree distributions. Specifically, we wanted to (1) determine if distributional patterns were shifting, (2) predict future distributions under different climate change scenarios and (3) evaluate the realism of predicted future distributions. We randomly placed 40 grids into 6 strata across a climate gradient in the kingdom of Eswatini. Within these grids, we sampled adult and seedling marula ( Scelerocarya birrea ) and knobthorn ( Senegalia nigrecens ) trees and used the data to model their abundance. Next, we quantified shifts in distributional patterns (e.g., expansion or contraction) by measuring the current and projected areas of overlap between seedling and adult trees. Finally, we predicted future distributions of abundance based on predicted climate conditions. We found knobthorn seedlings within a small portion of the adult distribution, suggesting it was unlikely to track climate changes. Alternatively, finding marula seedlings on and beyond one edge of the adult distribution, suggested its range would shift toward cooler climates. Predicted future distributions suggest suitable climate for both species would transition out of savannas and into grasslands. Future projections (2041–2070) appeared consistent with observed distributions of marula, but knobthorn predictions were unrealistic given the lack of evidence for regeneration outside of its current range. The idiosyncratic responses of these species to climate change are likely to decouple these keystone structures in the coming decades and are likely to have considerable cascading effects including the potential rearrangement of faunal communities.

Understanding how species will respond to a rapidly changing global climate is requisite to conserving biodiversity. Though habitat losses from human development and land use change remain the most critical threats to biodiversity globally, some regions, such as low-lying islands, are particularly vulnerable to the effects of climate change. Despite this vulnerability, there may be opportunities for imperiled species on islands to adapt to the effects of climate-induced sea level rise. To understand how the response to rising seas may influence the amount of future habitat, we investigated shifts in the elevational range of the endangered silver rice rat (Oryzomys palustris natator; hereafter “rice rat”), a species endemic to tidal environments of the Lower Florida Keys, USA. We quantified fine-scale habitat use using radio telemetry of collared animals, first in 2004, and again in 2021, thus spanning a 17-year period during which the local sea level rose by 0.142 m. We observed a shift in the elevational range limits of rice rats which closely mirrored the rise in sea level, and that this apparent ability to adapt to rising sea level decreased the extent of habitat loss in subsequent decades. However, over longer time scales (~ 100 yrs), the extent of habitat loss from sea level rise outpaced rice rats’ ability to adapt. As such, the conservation of biodiversity on low-lying islands hinges on the ability of the global community to decrease anthropogenic greenhouse gas emissions and mitigate the associated consequences for the global climate. Otherwise, conservation practitioners will be increasingly forced to make difficult decisions about how to conserve imperiled species on low-lying islands.

People are increasingly affected by climate change. In response, some people adapt, but others may not. The aim of our research was to understand what factors influence successful adaptation to climate change in context. We surveyed rural small-scale farmers in Eswatini who had recently experienced a severe drought, and collected data on adaptive capacity, measured as household access to five types of capital: natural (access to and use of natural resources), social (integration within local social networks), financial (income and wealth), physical (access to assets and infrastructure), and human (education and employment). Our goals were to identify which type of capital was most important in driving adaptation and to identify the specific ways in which capital supported adaptation. We found that social capital and natural capital increased successful drought adaptation more than other types of capital. Primarily, social and natural capital increased access to other resources and opportunities that enabled adaptation. Our research highlights the complex pathways along which capital operates to support adaptation, and furthers our understanding of in-situ adaptation experiences.

ContextThe landscape heterogeneity hypothesis states that increased heterogeneity in agricultural landscapes will promote biodiversity. However, this hypothesis does not detail which components of landscape heterogeneity (compositional or configurational) most affect biodiversity and how these compare to the effects of surrounding agricultural land-use.ObjectivesOur objectives were to: (1) assess the influence of the components of structural landscape heterogeneity on taxonomic diversity; and (2) compare the effects of landscape heterogeneity to those of different types of agricultural land-use in the same landscape across different taxonomic groups.MethodsWe identified a priori independent gradients of compositional and configurational landscape heterogeneity within an agricultural mosaic of north-eastern Swaziland. We tested how bird, dung beetle, ant and meso-carnivore richness and diversity responded to compositional and configurational heterogeneity and agricultural land-use across five different spatial scales.ResultsCompositional heterogeneity best explained species richness in each taxonomic group. Bird and ant richness were both positively correlated with compositional heterogeneity, whilst dung beetle richness was negatively correlated. Commercial agriculture positively influenced bird species richness and ant diversity, but had a negative influence on dung beetle richness. There was no effect of either component of heterogeneity on the combined taxonomic diversity or richness at any spatial scale.ConclusionsOur results suggest that increasing landscape compositional heterogeneity and limiting the negative effects of intensive commercial agriculture will foster diversity across a greater number of taxonomic groups in agricultural mosaics. This will require the implementation of different strategies across landscapes to balance the contrasting influences of compositional heterogeneity and land-use. Strategies that couple large patches of core habitat across broader scales with landscape structural heterogeneity at finer scales could best benefit biodiversity.

Mid-sized mammals (i.e., mesomammals) fulfill important ecological roles, serving as essential scavengers, predators, pollinators, and seed dispersers in the ecosystems they inhabit. Consequently, declines in mesomammal populations have the potential to alter ecological processes and fundamentally change ecosystems. However, ecosystems characterized by high functional redundancy, where multiple species can fulfil similar ecological roles, may be less impacted by the loss of mesomammals and other vertebrates. The Greater Everglades Ecosystem in southern Florida is a historically biodiverse region that has recently been impacted by multiple anthropogenic threats, most notably the introduction of the Burmese python ( Python molurus bivittatus ) . Since pythons became established, mesomammal populations have become greatly reduced. To assess whether these declines in mesomammals have affected two critical ecosystem functions—scavenging and frugivory—we conducted experiments in areas where mesomammals were present and absent. We did not observe significant differences in scavenging or frugivory efficiency in areas with and without mesomammals, but we did observe significant differences in the communities responsible for scavenging and frugivory. Despite the observed evidence of redundancy, the changes in community composition could potentially lead to indirect consequences on processes like seed dispersal and disease dynamics within this ecosystem, emphasizing the need for further study.

Camera traps are an important tool used to study rare and cryptic animals, including snakes. Time‐lapse photography can be particularly useful for studying snakes that often fail to trigger a camera's infrared motion sensor due to their ectothermic nature. However, the large datasets produced by time‐lapse photography require labor‐intensive classification, limiting their use in large‐scale studies. While many artificial intelligence‐based object detection models are effective at identifying mammals in images, their ability to detect snakes is unproven. Here, we used camera data to evaluate the efficacy of an object detection model to rapidly and accurately detect snakes. We classified images manually to the species level and compared this with a hybrid review workflow where the model removed blank images followed by a manual review. Using a ≥0.05 model confidence threshold, our hybrid review workflow correctly identified 94.5% of blank images, completed image classification 6× faster, and detected large (>66 cm) snakes as well as manual review. Conversely, the hybrid review method often failed to detect all instances of a snake in a string of images and detected fewer small (<66 cm) snakes than manual review. However, most relevant ecological information requires only a single detection in a sequence of images, and study design changes could likely improve the detection of smaller snakes. Our findings suggest that an object detection‐assisted hybrid workflow can greatly reduce time spent manually classifying data‐heavy time‐lapse snake studies and facilitate ecological monitoring for large snakes. Camera traps are powerful tools used to study animals. However, camera trap studies often produce large datasets requiring labor‐intensive classification. While many object detection models have been developed to facilitate image analysis, their efficacy when presented with novel species and systems is poorly understood. Here, we used the snake community, a notoriously difficult‐to‐study and data‐poor group, to test the limits of a publicly available object detection model. We found that integrating an object detection model with a conservative 0.05 confidence threshold and secondary manual review enabled us to analyze our dataset six times faster than manual image review alone, without reducing the number of large (>66 cm) snakes detected. However, the object detector performed worse at detecting smaller snake species (<66 cm) and often failed to identify all instances of a snake in a sequence of images.

Open terrestrial ecosystems such as savannas have been experiencing marked increases in woody cover driven by shrub encroachment. Despite this widespread pattern, understanding the consequences for faunal communities remains challenging because long-term data are often not available and other structural changes, such as changing tree cover, may confound conclusions on shrub encroachment effects. We used satellite data and surveys of bird communities spanning 22 years to assess vegetation-cover dynamics and its effects on bird communities across the savanna ecosystem of Eswatini. We employed a hierarchical multi-species occupancy model that accounted for imperfect detection to assess changes in species occurrence, richness, and community assemblages. Between 1998 and 2020, shrub cover increased from 16% to 44% and tree cover increased from 17% to 28%. Across 64 species, shrub cover tended to have greater effects on bird occupancy than tree cover, with 34 (53%) species exhibiting positive linear associations with shrub cover and 15 (23.4%) species exhibiting a non-linear response to shrubs, where occupancy peaked at < 50% shrub cover. Shrub cover generated non-linear responses across all four bird diet groups and four of the five nesting groups. Species richness increased over time, with changes in the composition of bird assemblages being driven more by changes in shrub than tree cover. With recent predictions indicating a potential increase in shrub cover that is driven by global factors, community-wide changes in bird communities may intensify. To minimize negative consequences of changes in bird communities, land managers should initiate and expand existing woody cover management regimes in southern African savannas, where shrub cover remains high.

Recent advances in optical remote sensing (RS) technology in combination with lightweight Global Positioning System (GPS) tracking devices now make analyzing the multi-scale habitat selection (HS) of small mammals < 2 kg possible. However, there have been relatively few multi-scale HS studies integrating fine-scale RS data with data-rich, GPS-derived movement data from small mammals. This is critical because small mammals commonly select habitat features across multiple scales. To address this gap, we investigated the HS of a small mammal, fox squirrels (Sciurus niger), which are known to cover relatively large areas and select fine-scale environmental features. We specifically asked the following questions: (1) Do next-generation RS variables improve HS models at single spatial scales? (2) Do multi-scale HS models improve upon those at single spatial scales? Using data from 45 individuals, we constructed HS models at three spatial scales: 4 ha (210 m × 210 m), 0.09 ha (30 m × 30 m), and 0.01 ha (10 m × 10 m) using traditional and next-generation RS data. The 4-ha model, using traditional and next-generation RS data, produced the best single-scale model, explaining 58% of the variations in HS. However, the multi-scale model provided the most informative model, explaining 68% of the variations in HS. Our models provide evidence for the value of next-generation RS data when quantifying HS and additional support for the idea of studying HS at multiple spatial scales.