Interactive spatial scale effects on species distribution modeling: The case of the giant panda

Multi-dimensional landscape ecological risk assessment and its drivers in coastal areas

The eastern coastal areas of Japan are threatened by multiple ecological risks due to frequent natural disasters, climate changes, human activities, etc. Identification spatio-temporal variations and driving mechanisms of landscape ecological risk could be used as significant basis for policymakers. In this study, taking the eastern coastal areas of Japan affected by the 2011 Great East Japan Earthquake and Tsunami Disaster as the study area, the "Nature-Landscape Pattern-Human Society" (NA-LP-HS) multi-dimensional ecological risk assessment framework was established to analyze the spatio-temporal patterns, and identity driving factors using spatial cluster analysis and spatial principal component analysis (SPCA) based on ArcGIS from 2009 to 2021. The findings revealed the distinct geographic patterns in landscape ecological risk, with a noticeable decline from the southwest to the northeast. During the period from 2009 to 2015, the driving factors leading to a sharp risk increase were natural disasters and vegetation coverage. These high-risk areas were concentrated in Sendai Bay and its surroundings. From 2015 to 2021, ecological instability was primarily attributed to a reduction in vegetation coverage, the occurrence of natural disasters, and heightened rainfall erosion. These high-risk areas were mainly clustered within the Tokyo-centered urban agglomeration. Spatial clustering of ecological risks was obvious across all time periods. The key factors contributing to the clustering of high ecological landscape risks focused on the "landscape pattern" criterion, specifically including vegetation coverage, land use land cover. This study demonstrated the ability of multi-dimensional ecological risk assessment to identify high-risk areas and driving factors, and these results could provide a visual analysis and decision-making basis for sustainable development of coastal areas.

Mapping biocrust distribution in China's drylands under changing climate

Biological soil crusts (biocrusts) are widely distributed in global drylands and have multiple significant roles in regulating dryland soil and ecosystem multifunctionality. However, maps of their distribution over large spatial scales are uncommon and sometimes unreliable, because our current remote sensing technology is unable to efficiently discriminate between biocrusts and vascular plants or even bare soil across different ecosystem and soil types. The lack of biocrust spatial data may limit our ability to detect risks to dryland function or key tipping points. Here, we indirectly mapped biocrust distribution in China's drylands using spatial prediction modeling, based on a set of occurrences of biocrusts (379 in total) and high-resolution soil and environmental data. The results showed that biocrusts currently cover 13.9 % of China's drylands (or 5.7 % of China's total area), with moss-, lichen-, and cyanobacterial-dominated biocrusts each occupying 5.7 % to 10.7 % of the region. Biocrust distribution is mainly determined by soil properties (soil type and contents of gravel and nitrogen), aridity stress, and altitude. Their most favorable habitat is arenosols with low contents of gravel and nitrogen, in climate with a drought index of 0.54 and an altitude of about 500 m. By 2050, climate change will lead to a 5.5 %-9.0 % reduction in biocrust cover. Lichen biocrusts exhibit a high vulnerability to climate change, with potential reductions of up to 19.0 % in coverage. Biocrust cover loss is primarily caused by the combined effects of the elevated temperature and increased precipitation. Our study provides the first high-resolution (250 × 250 m) map of biocrust distribution in China's drylands and offers a reliable approach for mapping regional or global biocrust colonization. We suggest incorporating biocrusts into Earth system models to identify their significant impact on global or regional-scale processes under climate change.

Combining environmental niche models, multi-grain analyses, and species traits identifies pervasive effects of land use on butterfly biodiversity across Italy

Understanding how species respond to human activities is paramount to ecology and conservation science, one outstanding question being how large-scale patterns in land use affect biodiversity. To facilitate answering this question, we propose a novel analytical framework that combines environmental niche models, multi-grain analyses, and species traits. We illustrate the framework capitalizing on the most extensive dataset compiled to date for the butterflies of Italy (106,514 observations for 288 species), assessing how agriculture and urbanization have affected biodiversity of these taxa from landscape to regional scales (3-48 km grains) across the country while accounting for its steep climatic gradients. Multiple lines of evidence suggest pervasive and scale-dependent effects of land use on butterflies in Italy. While land use explained patterns in species richness primarily at grains ≤12 km, idiosyncratic responses in species highlighted "winners" and "losers" across human-dominated regions. Detrimental effects of agriculture and urbanization emerged from landscape (3-km grain) to regional (48-km grain) scales, disproportionally affecting small butterflies and butterflies with a short flight curve. Human activities have therefore reorganized the biogeography of Italian butterflies, filtering out species with poor dispersal capacity and narrow niche breadth not only from local assemblages, but also from regional species pools. These results suggest that global conservation efforts neglecting large-scale patterns in land use risk falling short of their goals, even for taxa typically assumed to persist in small natural areas (e.g., invertebrates). Our study also confirms that consideration of spatial scales will be crucial to implementing effective conservation actions in the Post-2020 Global Biodiversity Framework. In this context, applications of the proposed analytical framework have broad potential to identify which mechanisms underlie biodiversity change at different spatial scales.

Regional models do not outperform continental models for invasive species

Aim: Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents.

Location: Continental United States.

Methods: We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions.

Results: We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response.

Main conclusions: Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions.

Scale-dependent environmental effects on phenotypic distributions in Heliconius butterflies

Identifying the relative importance of different mechanisms responsible for the emergence and maintenance of phenotypic diversity can be challenging, as multiple selective pressures and stochastic events are involved in these processes. Therefore, testing how environmental conditions shape the distribution of phenotypes can offer important insights on local adaptation, divergence, and speciation. The red-yellow Müllerian mimicry ring of Heliconius butterflies exhibits a wide diversity of color patterns across the Neotropics and is involved in multiple hybrid zones, making it a powerful system to investigate environmental drivers of phenotypic distributions. Using the distantly related Heliconius erato and Heliconius melpomene co-mimics and a multiscale distribution approach, we investigated whether distinct phenotypes of these species are associated with different environmental conditions. We show that Heliconius red-yellow phenotypic distribution is strongly driven by environmental gradients (especially thermal and precipitation variables), but that phenotype and environment associations vary with spatial scale. While co-mimics are usually predicted to occur in similar environments at large spatial scales, patterns at local scales are not always consistent (i.e., different variables are best predictors of phenotypic occurrence in different locations) or congruent (i.e., co-mimics show distinct associations with environment). We suggest that large-scale analyses are important for identifying how environmental factors shape broad mimetic phenotypic distributions, but that local studies are essential to understand the context-dependent biotic, abiotic, and historical mechanisms driving finer-scale phenotypic transitions.

The current and future distribution of the yellow fever mosquito (Aedes aegypti) on Madeira Island

The Aedes aegypti mosquito is the main vector for several diseases of global importance, such as dengue and yellow fever. This species was first identified on Madeira Island in 2005, and between 2012 and 2013 was responsible for an outbreak of dengue that affected several thousand people. However, the potential distribution of the species on the island remains poorly investigated. Here we assess the suitability of current and future climatic conditions to the species on the island and complement this assessment with estimates of the suitability of land use and human settlement conditions. We used four modelling algorithms (boosted regression trees, generalized additive models, generalized linear models and random forest) and data on the distribution of the species worldwide and across the island. For both climatic and non-climatic factors, suitability estimates predicted the current distribution of the species with good accuracy (mean area under the Receiver Operating Characteristic curve = 0.88 ±0.06, mean true skill statistic = 0.72 ±0.1). Minimum temperature of coldest month was the most influential climatic predictor, while human population density, residential housing density and public spaces were the most influential predictors describing land use and human settlement conditions. Suitable areas under current climates are predicted to occur mainly in the warmer and densely inhabited coastal areas of the southern part of the island, where the species is already established. By mid-century (2041–2060), the extent of climatically suitable areas is expected to increase, mainly towards higher altitudes and in the eastern part of the island. Our work shows that ongoing efforts to monitor and prevent the spread of Ae. aegypti on Madeira Island will have to increasingly consider the effects of climate change.

The Aedes aegypti mosquito is an invasive species on Madeira Island and recently responsible for a dengue outbreak that affected more than 2000 people. To help control the activity of this mosquito, the local health authorities have an entomological surveillance program in place throughout the island. However, the full extent of the areas that can be colonized by this species remains unknown. We estimate the current and future potential distribution of Ae. aegypti on Madeira Island accounting for climatic, land use and human settlement conditions. Our results suggest that suitable conditions are predominantly distributed along the southern coast of the island. However, as climate change progresses, climatically suitable areas are expected to increase, particularly at mid-altitudes and in eastern part of the island. Minimum temperature of the coldest month was the most influential predictor variable in climatic suitability models, while human population density, housing density and public spaces were the most influential in models of land use and human settlement suitability. Our work provides valuable insight on the potential distribution of Ae. aegypti on Madeira Island, which can be used to inform ongoing and future monitoring and prevention initiatives.

The shadow model: how and why small choices in spatially explicit species distribution models affect predictions

The use of species distribution models (SDMs) has rapidly increased over the last decade, driven largely by increasing observational evidence of distributional shifts of terrestrial and aquatic populations. These models permit, for example, the quantification of range shifts, the estimation of species co-occurrence, and the association of habitat to species distribution and abundance. The increasing complexity of contemporary SDMs presents new challenges-as the choices among modeling options increase, it is essential to understand how these choices affect model outcomes. Using a combination of original analysis and literature review, we synthesize the effects of three common model choices in semi-parametric predictive process species distribution modeling: model structure, spatial extent of the data, and spatial scale of predictions. To illustrate the effects of these choices, we develop a case study centered around sablefish (Anoplopoma fimbria) distribution on the west coast of the USA. The three modeling choices represent decisions necessary in virtually all ecological applications of these methods, and are important because the consequences of these choices impact derived quantities of interest (e.g., estimates of population size and their management implications). Truncating the spatial extent of data near the observed range edge, or using a model that is misspecified in terms of covariates and spatial and spatiotemporal fields, led to bias in population biomass trends and mean distribution compared to estimates from models using the full dataset and appropriate model structure. In some cases, these suboptimal modeling decisions may be unavoidable, but understanding the tradeoffs of these choices and impacts on predictions is critical. We illustrate how seemingly small model choices, often made out of necessity or simplicity, can affect scientific advice informing management decisions-potentially leading to erroneous conclusions about changes in abundance or distribution and the precision of such estimates. For example, we show how incorrect decisions could cause overestimation of abundance, which could result in management advice resulting in overfishing. Based on these findings and literature gaps, we outline important frontiers in SDM development.

On the relationship between environmental suitability and habitat use for three neotropical mammals

Recent studies have used occupancy models (OM) and ecological niche models (ENM) to provide a better understanding of species’ distributions at different scales. One of the main ideas underlying the theoretical foundations of both OM and ENM is that they are positively related to abundance: higher occupancy implies higher density and more suitable areas are likely to have more abundant populations. Here, we analyze the relationship between habitat use measured in terms of occupancy probabilities from OM and environmental suitability derived from ENM in three different Neotropical mammal species: Leopardus wiedii, Cuniculus paca, and Dasypus novemcinctus. For ENM, we used climatic and vegetation cover variables and implemented a model calibration and selection protocol to select the most competitive models. For OM, we used a single-species, single-season model with site covariates for camera-trap data from six different sites throughout the Neotropical realm. Covariates included vegetation percentage, normalized difference vegetation index, normalized difference water index, and elevation. For each site, we fit OM using all possible combinations of variables and selected the most competitive (ΔAICc < 2) to build an average OM. We explored relationships between estimated suitability and occupancy values using Spearman correlation analysis. Relationships between ENM and OM tended to be positive for the three Neotropical mammals, but the strength varied among sites, which could be explained by local factors such as site characteristics and conservation status of areas. We conjecture that ENM are suitable to understand spatial patterns at coarser geographic scales because the concept of the niche is about the species as a whole, whereas OM are more relevant to explain the distribution locally, likely reflecting transient dynamics of populations resulting from many local factors such as community composition and biotic processes.

Autumn larval cold tolerance does not predict the northern range limit of a widespread butterfly species

Climate change is driving range shifts, and a lack of cold tolerance is hypothesized to constrain insect range expansion at poleward latitudes. However, few, if any, studies have tested this hypothesis during autumn when organisms are subjected to sporadic low-temperature exposure but may not have become cold-tolerant yet. In this study, we integrated organismal thermal tolerance measures into species distribution models for larvae of the Giant Swallowtail butterfly, Papilio cresphontes (Lepidoptera: Papilionidae), living at the northern edge of its actively expanding range. Cold hardiness of field-collected larvae was determined using three common metrics of cold-induced physiological thresholds: the supercooling point, critical thermal minimum, and survival following cold exposure. P. cresphontes larvae were determined to be tolerant of chilling but generally die at temperatures below their SCP, suggesting they are chill-tolerant or modestly freeze-avoidant. Using this information, we examined the importance of low temperatures at a broad scale, by comparing species distribution models of P. cresphontes based only on environmental data derived from other sources to models that also included the cold tolerance parameters generated experimentally. Our modeling revealed that growing degree-days and precipitation best predicted the distribution of P. cresphontes, while the cold tolerance variables did not explain much variation in habitat suitability. As such, the modeling results were consistent with our experimental results: Low temperatures in autumn are unlikely to limit the distribution of P. cresphontes. Understanding the factors that limit species distributions is key to predicting how climate change will drive species range shifts.

Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach

System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.

Projecting the Impact of Climate Change on the Spatial Distribution of Six Subalpine Tree Species in South Korea Using a Multi-Model Ensemble Approach

Climate change is recognized as a major threat to global biodiversity and has already caused extensive regional extinction. In particular danger are the plant habitats in subalpine zones, which are more vulnerable to climate change. Evergreen coniferous trees in South Korean subalpine zones are currently designated as a species that need special care given their conservation value, but the reason for their decline and its seriousness remains unclear. This research estimates the potential land suitability (LS) of the subalpine zones in South Korea for six coniferous species vulnerable to climate change in the current time (1970–2000) and two future periods, the 2050s (2041–2060) and the 2070s (2061–2080). We analyze the ensemble-averaged loss of currently suitable habitats in the future, using nine species distribution models (SDMs). Korean arborvitae (Thuja koraiensis) and Khingan fir (Abies nephrolepis) are two species expected to experience significant habitat losses in 2050 (−59.5% under Representative Concentration Pathway (RCP) 4.5 to −65.9% under RCP 8.5 and −56.3% under RCP 4.5 to −57.7% under RCP 8.5, respectively). High extinction risks are estimated for these species, due to the difficulty of finding other suitable habitats with high LS. The current habitat of Korean fir (Abies koreana), listed as a threatened species on the International Union for Conservation of Nature (IUCN) Red List, is expected to decrease by −23.9% (RCP 4.5) to −28.4% (RCP 8.5) and −36.5% (RCP 4.5) to −36.7% (RCP 8.5) in the 2050s and 2070s, respectively. Still, its suitable habitats are also estimated to expand geographically toward the northern part of the Baekdudaegan mountain range. In the context of forest management and adaptation planning, the multi-model ensemble approach to mapping future shifts in the range of subalpine tree species under climate change provides robust information about the potential distribution of threatened and endanger

The scale-dependent effectiveness of wildlife management: A case study on British deer

Impacts of herbivory by wild ungulates represent a significant issue world-wide. To be effective, management of populations and impacts needs to be coordinated above the site scale, yet little research has investigated the appropriate spatial scale over which management should be integrated to be fully effective. In consideration of reduction of impacts in deciduous or mixed woodland habitats, we tested scale-specific management effectiveness in a lowland area of UK where moderate- to high-density populations of four deer species were the target of deliberate control programmes, and nonhuman predators were absent. We modelled the annual impact recorded between 2009 and 2015 in 98 woodlands as a function of cumulative culls of deer taken since the commencement of management. Analysis was repeated at different spatial scales by increasing the circular area around each focal woodland, from 2.5 km-radius up to 100 km-radius. Our findings suggest for the first time the geographical scale over which deer management needs to be coordinated for optimum effectiveness in decreasing their impact on woodland across relatively homogenous landscapes. For small bodied and relatively sedentary species (roe deer Capreolus capreolus; Reeves' muntjac Muntiacus reevesi), reductions in impacts within woodlands can be achieved by culling at the immediately local level, but some modest increase in effectiveness (probably relating to reductions in the degree of source-sink movement) may be expected with an increase in spatial scale of culling to around 30-70 km-radius. For larger-bodied, herding species with more extensive home-ranges (fallow deer Dama dama; red deer Cervus elaphus) management for reduction of woodland impacts was only really effective when coordinated above the single woodland-scale, with marked increases shown again up to a scale of 100 km-radius. Whilst future studies for different landscape types are still needed, our work emphasises that the spatial scale at which control plans are conducted can determine the effectiveness of wildlife management, possibly providing an advance on how to manage wildlife populations more effectively.

Small spaces, big impacts: contributions of micro-environmental variation to population persistence under climate change

Individuals within natural populations can experience very different abiotic and biotic conditions across small spatial scales owing to microtopography and other micro-environmental gradients. Ecological and evolutionary studies often ignore the effects of micro-environment on plant population and community dynamics. Here, we explore the extent to which fine-grained variation in abiotic and biotic conditions contributes to within-population variation in trait expression and genetic diversity in natural plant populations. Furthermore, we consider whether benign microhabitats could buffer local populations of some plant species from abiotic stresses imposed by rapid anthropogenic climate change. If microrefugia sustain local populations and communities in the short term, other eco-evolutionary processes, such as gene flow and adaptation, could enhance population stability in the longer term. We caution, however, that local populations may still decline in size as they contract into rare microhabitats and microrefugia. We encourage future research that explicitly examines the role of the micro-environment in maintaining genetic variation within local populations, favouring the evolution of phenotypic plasticity at local scales and enhancing population persistence under global change.