Improved prediction of Canada lynx distribution through regional model transferability and data efficiency

Reliable absence data remain a major limitation in the development and application of species distribution models (SDMs). Both field-sampled absences and algorithmically generated pseudo-absences are fundamental to model parameterization, yet efficient approaches for obtaining robust absence information and how absence data shape SDM outcomes are still lacking. Here, using Biodiversity Modelling (BIOMOD2), we systematically assessed SDM performance across multiple pseudo-absence generation strategies and sample sizes, leveraging a presence/absence dataset ( n = 2261; 1743 presences and 518 absences) for plateau pika ( Ochotona curzoniae ) derived from unmanned aerial vehicle (UAV) surveys on the Qinghai–Tibetan Plateau. UAV-derived absence data consistently produced the highest model accuracy, particularly for Random Forest models (Kappa = 0.566; TSS = 0.611; AUC = 0.863). Among pseudo-absence strategies, the surface range envelope (SRE) strategy performed the best, whereas sample size showed relatively minor influence. Importantly, the spatial configuration of absence samples altered the weighting of environmental predictors, resulting in pronounced differences in model performance. Our findings demonstrate that absence data are pivotal to SDM accuracy and that UAV technology fundamentally advances the acquisition of presence/absence data by enabling efficient, spatially extensive, and reliable sampling. These methodological innovations are essential for improving biodiversity forecasting and informing management strategies under accelerating climate change and intensifying anthropogenic pressures. • Absence data from unmanned aerial vehicles improve model accuracy for plateau pika. • Spatially stratified pseudo-absence outperforms random absence sampling. • Absence sample design affects predictor importance and model performance. • Aerial surveys enable large-scale, field-validated species distribution modelling. • Improved distribution models support biodiversity forecasting and conservation.

Modelling species distributions with remote sensing data: bridging disciplinary perspectives

Current applications of species distribution models (SDM) are typically static, in that they are based on correlations between where a species has been observed (ignoring the date of the observation) and environmental features, such as long‐term climate means, that are assumed to be constant for each site. Because of this SDMs do not account for temporal variation in the distribution of suitable habitat across the range of a species. Here, we demonstrate the temporal variability in the potential geographic distributions of an endangered marsupial, the northern bettong Bettongia tropica as a case study. Models of the species distribution using temporally matched observations of the species with weather data (including extreme weather events) at the time of species observations, were better able to define habitat suitability, identify range edges and uncover competitive interactions than models based on static long‐term climate means. Droughts and variable temperature are implicated in low densities and local extinctions of northern bettong populations close to range edges. Further, we show how variable weather can influence the results of competition with the common rufous bettong Aepyprymnus rufescens. Because traditional SDMs do not account for temporal variability of suitable habitat, static SDMs may underestimate the impacts of climate change particularly as the incidence of extreme weather events is likely to rise.

Progress in practical applications of large, passively collected data sets is often hindered by the lack of appropriate analytical tools or the proprietary nature of applicable software. One of the most widely used data sources in the United States is truck GPS data that are commercially available from a few sources nationwide. Although many large GPS data sets are used in the development of tour-based truck models, the development of a fairly general approach to data analysis and processing that can be readily applied to various GPS data sets without need of proprietary software is still of interest. First, this paper presents a set of tools and techniques used to transform low-frequency truck GPS data available from commercial sources into complete trajectories on the network, that is, sequences of links constituting continuous paths traversed by each truck, with corresponding time stamps on each of the nodes. For this exercise, only open-source software was used, and the algorithm implementation was released as an open-source tool under a business-friendly license. Second, use of the truck GPS data was expanded beyond the standard extraction of trip matrices and estimation of tour models. Additional applications include select link analysis, time-of-day analysis, and trajectory data visualization.

news and update ISSN 1948‐6596 update Recent advances in using species distributional models to un‐ derstand past distributions The first wave of climate‐driven, paleoecological, species distribution models (SDMs) focused pri‐ marily on testing global climate models (GCMs; e.g., Bartlein et al. 1986). Recently, a second wave of research, using increasingly sophisticated SDMs, expanded the use of paleo‐SDMs into bio‐ geographic questions. This new focus is contribut‐ ing substantially to discussions about outstanding questions in biogeography, such as stability of niches through time and fundamental spatiotem‐ poral controls on geographic patterns of biodiver‐ sity. The value of paleo‐data in biogeographically oriented distributional models gained prominence when Nogues‐Bravo (2009) highlighted the use of SDMs for investigating past distributions of spe‐ cies. Nogues‐Bravo (2009) outlined areas where paleo‐data can help understand and test some of the assumptions underlying SDMs, highlighted methodological considerations when creating and projecting SDMs through time, and recommended a set of best practices when extending SDMs to include paleo‐data. Since then, many new studies have utilized paleo‐SDMs, alone or in combination with other approaches, and the number of papers relying on paleo SDMs has grown rapidly (e.g. 27 studies reviewed by Nogues‐Bravo 2009; 82 stud‐ ies reviewed by Svenning et al. 2011). Two recent review papers (Svenning et al. 2011; Varela et al. 2011) deepen the discussion about paleo‐SDMs by synthesizing this new research, discussing ad‐ vances made in paleo‐biogeography, and outlining the challenges going forward. While there are many points of agreement between the two re‐ views, reading them in parallel makes obvious some areas of disagreement that point to unre‐ solved issues with paleo‐SDMs. SDMs based on modern distribution data are influenced by a number of different uncertain‐ ties, including incomplete distribution informa‐ tion, simplified or uncertain mechanistic relation‐ ships between distribution and climate, and ex‐ trapolation into no‐analog climates, to name a few (e.g. Guisan & Zimmermann 2000; Guisan & Thuiller 2005; Elith et al. 2006; Jimenez‐Valverde et al. 2008). Paleo‐SDMs are subject to the same limitations, but often to a greater extent. Many of these technical issues are discussed in detail by Varela et al. (2011), with the goal of establishing a “robust and scientifically‐based theoretical and methodological framework” for the use of SDMs in paleobiology. They focus largely on the particu‐ lars of paleontological data, and highlight issues of spatial, temporal, taphonomic, and collection bias that should be considered when modeling and interpreting paleo‐SDMs. Varela et al. (2011) ad‐ vocate cautious use of paleo‐SDMs, arguing that paleo‐SDMs are promising but that key gaps in knowledge currently limit their widespread appli‐ cation. The use of SDMs in paleobiology has grown rapidly despite these limitations. Svenning et al. (2011) synthesize the many ways that SDMs have been and could be applied to outstanding ques‐ tions in paleoecology. Their review is broader (82 papers vs. 42 reviewed by Varela et al. 2011) and focuses in particular on the integration of SDMs with genetic data. They outline four primary appli‐ cations of SDMs, including the use of paleo‐SDMs to test hypotheses about glacial refugia, the end‐ Pleistocene megafaunal extinctions, Holocene pa‐ leoecology, and deep‐time biogeography. Sven‐ ning et al. (2011) provide a more optimistic view of the use of SDMs in paleobiology, perhaps be‐ cause they focus more on applications of paleo‐ SDMs and less on potential issues with the under‐ lying data. However, some recommendations by Varela et al. (2011) imply that work highlighted by Svenning et al. (2011) should not be attempted at large spatial or temporal scales at this time. As one example, both groups correctly argue for cau‐ tion when using and interpreting statistically‐ downscaled climate simulations. However, Varela et al. (2011) take a highly conservative approach and argue that statistical downscaling should not frontiers of biogeography 3.4, 2012 — © 2012 the authors; journal compilation © 2012 The International Biogeography Society

The long-standing view in ecology is that disparity in overall resource selection is the basis for identifying niche breadth patterns, with species having narrow selection being classified “specialists” and those with broader selection being “generalists”. The standard model of niche breadth characterizes generalists and specialists as having comparable levels of overall total resource exploitation, with specialists exploiting resources at a higher level of performance over a narrower range of conditions. This view has gone largely unchallenged. An alternate model predicts total resource use being lower for the specialized species with both peaking at a comparable level of performance over a particular resource gradient. To reconcile the niche breadth paradigm we contrasted both models by developing range-wide species distribution models for Canada lynx, Lynx canadensis, and bobcat, Lynx rufus. Using a suite of environmental factors to define each species’ niche, we determined that Canada lynx demonstrated higher total performance over a restricted set of variables, specifically those related to snow and altitude, while bobcat had higher total performance across most variables. Unlike predictions generated by the standard model, bobcat level of exploitation was not compromised by the trade-off with peak performance, and Canada lynx were not restricted to exploiting a narrower range of conditions. Instead, the emergent pattern was that specialist species have a higher total resource utilization and peak performance value within a smaller number of resources or environmental axes than generalists. Our results also indicate that relative differences in niche breadth are strongly dependent on the variable under consideration, implying that the appropriate model describing niche breadth dynamics between specialists and generalists may be more complex than either the traditional heuristic or our modified version. Our results demonstrate a need to re-evaluate traditional, but largely untested, assumptions regarding resource utilization in species with broad and narrow niches.

Incorporating biotic relationships improves species distribution models: Modeling the temporal influence of competition in conspecific nesting birds

Application of species distribution models for protected areas threatened by invasive plants

In this paper, the radar target recognition is given by projected features of frequency-diversity RCS (radar cross section). The frequency diversity means signals are collected by sweeping the frequency of the incident illumination. Initially, the frequency- diversity RCS data from targets are collected and projected onto the PCA (principal components analysis) space. The elementary recognition of targets is efficiently performed on the PCA space. To achieve well separate recognition of targets, the features of the PCA space are further projected onto the LDA (linear discriminant algorithm) space. Simulation results show that accurate results of radar target recognition can be obtained by the proposed frequency- diversity scheme. In addition, the proposed frequency-diversity scheme has good ability to tolerate noise effects in radar target recognition.

Peripheral populations are often small and isolated compared to those in the range core, in part due to the patchy distribution of suitable habitats at range margins. It follows that peripheral populations typically occur at lower densities and are more susceptible to extinction, but their persistence may be facilitated through connectivity with core areas. Relationships between connectivity and the distribution of animal populations have not yet been fully evaluated, especially for large carnivores having extensive spatial needs and specialized habitat requirements. Using observations of snow tracks, we modeled occurrence of Canada lynx (Lynx canadensis) in relation to landscape characteristics along their southern range periphery in Ontario, Canada; we sought to assess functional connectivity of lynx habitat along the southern margins of the range. As observed in other studies, young coniferous forests had the highest probability of lynx occurrence, likely due to their association with snowshoe hares (Lepus americanus). We used the occurrence model to parameterize a resistance surface and then circuit theory to predict functional connectivity along the southern periphery of lynx distribution. Lynx typically travelled through landscapes with higher connectivity than random paths, implying that lynx habitat requirements in their southern range likely extend beyond habitat composition, and that conservation efforts should seek to preserve metapopulation dynamics through functional connectivity of suitable habitat across larger spatial scales.

The synergistic effects of climate change, wildfires, fire suppression, and past forest management are challenging efforts to protect and recover Canada lynx (Lynx canadensis) in the North Cascades of Washington, USA. Canada lynx is a threatened species in the United States and a focal species used to gain insights into the structure and function of boreal forest ecosystems. To understand how multiple stressors are influencing lynx populations and the boreal forest in Washington, we developed a spatially explicit carrying capacity model in HexSim using local data on lynx resource selection and life history. We used this model to estimate changes in carrying capacity and population persistence for 3 time steps: year 2000, which represented limited historical wildfire and aggressive fire suppression; year 2013, after nearly 2,000 km2 of wildfires burned about 17% of lynx habitat; and year 2020, after an additional 2,000 km2 of wildfires burned another 15% of lynx habitat in our study area. Fires altered habitat distribution and landscape capacity to support Canada lynx. There was a 66–73% reduction in lynx carrying capacity in our study area because of large, high‐severity fires that have occurred from 2000–2020, despite aggressive fire suppression. This reduction in carrying capacity was concurrent with decreases in the probability of lynx persistence from year 2000 to year 2020 simulations and was most pronounced for simulations that included no immigration and the largest home range size. The negative synergistic influences of long‐term fire suppression, timber harvest, increased drought, longer wildfire seasons, declining mountain snowpack, and increasingly frequent large fires pose considerable challenges to the conservation and recovery of Canada lynx and the boreal forest ecosystem upon which they depend. We discuss an alternative approach to vegetation and fire management to conserve and restore lynx habitat and populations in Washington.

In this work, I evaluate the impact of species distribution models (SDMs) on the current status of environmental and ecological journals by asking the question to which degree development of SDMs in the literature is related to recent changes in the impact factors of ecological journals. The hypothesis evaluated states that research fronts are likely to attract research attention and potentially drive citation patterns, with journals concentrating papers related to the research front receiving more attention and benefiting from faster increases in their impact on the ecological literature. My results indicate a positive relationship between the number of SDM related articles published in a journal and its impact factor (IF) growth during the period 2000–09. However, the percentage of SDM related papers in a journal was strongly and positively associated with the percentage of papers on climate change and statistical issues. The results support the hypothesis that global change science has been critical in the development of SDMs and that interest in climate change research in particular, rather than the usage of SDM per se, appears as an important factor behind journal IF increases in ecology and environmental sciences. Finally, our results on SDM application in global change science support the view that scientific interest rather than methodological fashion appears to be the major driver of research attraction in the scientific literature.

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.

The range of the Canada lynx (Lynx canadensis) has contracted substantially from its historical range. Using harvest records, we found that the southern range of the lynx in Ontario in the late 1940s collapsed and then, in a short period of time, increased to its largest extent in the mid‐1960s when the lynx range spread south of the boreal forest for a decade. After this expansion, the southern range contracted northwards beginning in the 1970s. Most recently, there has been a slight expansion between 2010 and 2017. We have attributed these dynamics on the southern range periphery to the fluctuation of the boreal lynx population in the core of the species' range. In addition, connectivity to boreal lynx populations and snow depth seemed to condition whether the lynx expanded into an area. However, we did not find any evidence to suggest that these changes were due to anthropogenic landscape disturbances or competition. The boreal lynx population does not reach the peak abundance it once did, without which we would not expect to see large expansions of the southern lynx range as in the mid‐1960s. Our results suggest that the southern lynx range in Ontario has been driven by the magnitude of the boreal lynx population cycle, connectivity to the boreal forest, and snow conditions. Future persistence of lynx in the southern range periphery will likely depend on dynamics in the range core.

Highly Pathogenic Avian Influenza (HPAI) has recently (2014–2015) re-emerged in the United States (US) causing the largest outbreak in US history with 232 outbreaks and an estimated economic impact of $950 million. This study proposes to use suitability maps for Low Pathogenic Avian Influenza (LPAI) to identify areas at high risk for HPAI outbreaks. LPAI suitability maps were based on wild bird demographics, LPAI surveillance, and poultry density in combination with environmental, climatic, and socio-economic risk factors. Species distribution modeling was used to produce high-resolution (cell size: 500m x 500m) maps for Avian Influenza (AI) suitability in each of the four North American migratory flyways (NAMF). Results reveal that AI suitability is heterogeneously distributed throughout the US with higher suitability in specific zones of the Midwest and coastal areas. The resultant suitability maps adequately predicted most of the HPAI outbreak areas during the 2014–2015 epidemic in the US (i.e. 89% of HPAI outbreaks were located in areas identified as highly suitable for LPAI). Results are potentially useful for poultry producers and stakeholders in designing risk-based surveillance, outreach and intervention strategies to better prevent and control future HPAI outbreaks in the US.