Figure - available from: Landscape Ecology
This content is subject to copyright. Terms and conditions apply.
Habitat suitability maps provided by the optimized multi-scale model for every season: a spring, b summer, c fall, d winter and e year-round. Seasonal and year-round results correspond to the entire period 2000–2010. See Fig. 1 for the location of the area here shown
Source publication
Context
Multi-scale approaches to habitat modeling have been shown to provide more accurate understanding and predictions of species-habitat associations. It remains however unexplored how spatial and temporal variations in habitat use may affect multi-scale habitat modeling.
Objectives
We aimed at assessing how seasonal and temporal differences in...
Similar publications
Many animals avoid roads due to traffic disturbance, but there are also some species that use roads in their everyday life and even obtain resources from them. Understanding the factors that influence the intensity of road use by these species can help understand temporal patterns of road mortality and thereby maximize the cost-effectiveness of mit...
To clarify the linkage between flowering phenology and pollination success in alpine plant communities, we quantified the seasonality of flower visitors, the temporal transition of floral resources, and the variation in pollination success of alpine plants in northern Japan. Bumble bees, syrphid flies, and non-syrphid flies were the predominant flo...
Some animal species exhibit sex-specific patterns as an adaptation to their habitats, however, adaptability to a human-dominated landscape is commonly explored without considering intraspecific sexual differences. Differences between males and females lead to a sexual segregation in habitat use. In southern Mexico, we explored sex-specific response...
Climate change may affect the foraging success of bowhead whales Balaena mysti -cetusby altering the diversity and abundance of zooplankton species available as food. However,assessing climate-induced impacts first requires documenting feeding conditions under currentenvironmental conditions. We collected seasonal movement and dive-behaviour data f...
As food is recognised as an important factor affecting the intestinal microbiota, seasonal changes in diet can influence the community composition. The hooded crane (Grus monacha) is an endangered migratory waterbird species, with some of the population wintering in the sallow lakes in the middle and lower Yangtze River floodplain. Their food resou...
Citations
... They found that temporal resolution was more important for SDMs than spatial resolution. In addition, Mateo-Sanchez et al. (2016) tested the impact of temporal and spatial scales on SDMs. They focused on modelling the distribution of brown bears in the Cantabrian Range. ...
Species distribution models (SDMs) are invaluable for delineating ecological niches and assessing habitat suitability, facilitating the projection of species distributions across spatial and temporal dimensions. This capability is crucial for conservation planning, habitat management and understanding the impacts of climate change. Remote sensing has emerged as a superior alternative to traditional field surveys in developing SDMs, offering cost-effective, repetitive data collection over comprehensive spatial and temporal scales. Despite the rapid advancements in remote sensing technologies and analytical methods, the specific contributions of remote sensing to SDMs historically, and the potential pathways for its integration with SDMs remain ambiguous. Therefore, our study has set forth two objectives: firstly, to conduct a thorough review of remote sensing's role in SDMs, focusing on environmental pre-dictors, response variables, scalability and validation; secondly, to outline prospective research trajectories for remote sensing within SDMs. Our findings reveal that remote sensing offers a plethora of environmental predictors for SDMs, encompassing climate, topography , land cover and use, spectral metrics and biogeochemical cycles. A variety of remote sensing techniques, including random forest, deep learning and linear unmixing, facilitate the derivation of SDM response variables and the development of species distribution models across diverse scales. Furthermore, remote sensing enables the validation of SDMs through its mapping outputs. ARTICLE HISTORY
... Vol.: (0123456789) that this species tends to avoid humanized elements (roads, settlements) of the landscape, as well as more densely populated areas (Clevenger et al. 1990;Naves et al. 2003;Mateo-Sánchez et al. 2016). Therefore, we hypothesize that the brown bears in the study area tend to occur in areas more distant from these humanized elements of the landscape. ...
... In these cases, different signs were combined in a single presence point. Variables were selected to be representative of three groups of predictors identified as highly influential in shaping the bear distribution, and were related to three main ecological drivers of presence: disturbance (Naves et al. 2003;Mateo-Sánchez et al. 2016); food resources (Clevenger et al. 1990;Mateo-Sánchez et al. 2016); and refuge (Naves et al. 2003;Mateo-Sánchez et al. 2016) (Table 1). Altitude was also considered a likely predictor of brown bear presence in all three groups of predictor variables, given that: (i) human settlements are often at lower altitudes; (ii) food resources are more often abundant at lower (when provided by human activities, such as agriculture) and intermediate altitudes (Posillico et al. 2004); and (iii) refuge (i.e., areas inaccessible to humans and/or natural areas rich in vegetation that provides cover) is often found at intermediate to higher altitudes. ...
... In these cases, different signs were combined in a single presence point. Variables were selected to be representative of three groups of predictors identified as highly influential in shaping the bear distribution, and were related to three main ecological drivers of presence: disturbance (Naves et al. 2003;Mateo-Sánchez et al. 2016); food resources (Clevenger et al. 1990;Mateo-Sánchez et al. 2016); and refuge (Naves et al. 2003;Mateo-Sánchez et al. 2016) (Table 1). Altitude was also considered a likely predictor of brown bear presence in all three groups of predictor variables, given that: (i) human settlements are often at lower altitudes; (ii) food resources are more often abundant at lower (when provided by human activities, such as agriculture) and intermediate altitudes (Posillico et al. 2004); and (iii) refuge (i.e., areas inaccessible to humans and/or natural areas rich in vegetation that provides cover) is often found at intermediate to higher altitudes. ...
Context
Large carnivores have faced severe extinction pressures throughout Europe during the last centuries, where human-induced disturbances reached unprecedented levels. In the late twentieth century, the Cantabrian brown bear population was on the verge of extinction, due to poaching. Yet, the end of the last century was a turning point for this population. Presence data on the western Cantabrian subpopulation was collected since the beginning of the century and insights provided by this long-term monitoring may be useful for brown bear conservation.
Objectives
Here, we aim to: (i) identify the landscape features relevant to bears’ recovery; and (ii) understand if and how the landscape use patterns by bears changed over time.
Methods
We tested the influence of landscape structure (i.e., composition and configuration) on bear occurrence patterns using MAXENT in three periods representative of land cover change.
Results
Despite variation across the 19-year monitoring period, brown bears were more often detected near broad-leaf forests and bare rock areas and at lower to intermediate altitudes, but avoided arable lands, permanent crops, and burnt areas. Human population density or distance to roads—often used for modelling habitat suitability for Cantabrian brown bears—were not identified as relevant variables for this brown bear subpopulation. Artificial areas were identified as relevant landscape features, but not as disturbance.
Conclusions
These findings reinforce the importance of preserving bears’ native habitats and provide new insights, namely on the use of humanized landscapes.
... However, using fine-scale predictors may also be misleading if animals were only travelling through an area. The selection of appropriate spatio-temporal scales in SDMs has been studied across many terrestrial and marine species (Becker et al., 2010;Gottschalk et al., 2011;Graf et al., 2005;Levin, 1992;Mateo-Sánchez et al., 2016;Redfern et al., 2006;Stuber & Fontaine, 2019;Wiens, 1989). For example, Gottschalk et al. (2011) examined the effects of different spatial scales of a land-use map (from 1 to 1000 m) on predicting bird distribution and found that the occurrence of different bird species was better predicted using models with different spatial resolutions. ...
... A study by Graf et al. (2005) on a forest grouse species (Tetrao urogallus) compared single spatial scale habitat models with a multi-scale model (1 to 1100 ha) and found that the latter performed better. Likewise, brown bears (Ursus arctos) in the Cantabrian Range in Spain responded to environmental factors at different spatial scales (0.25-64 km) across seasons and time periods, suggesting the influence of processes underlying spatial and temporal variations in habitat use (Mateo-Sánchez et al., 2016). ...
The importance of scale when investigating ecological patterns and processes is recognised across many species. In marine ecosystems, the processes that drive species distribution have a hierarchical structure over multiple nested spatial and temporal scales. Hence, multi‐scale approaches should be considered when developing accurate distribution models to identify key habitats, particularly for populations of conservation concern. Here, we propose a modelling procedure to identify the best spatial and temporal scale for each modelled and remotely sensed oceanographic variable to model harbour porpoise (Phocoena phocoena) distribution within the Irish Exclusive Economic Zone. Harbour porpoise sightings were recorded during dedicated line‐transect aerial surveys conducted in the summers of 2016, 2021 and 2022. Binary generalised additive models were used to assess the relationships between porpoise presence and oceanographic variables at different spatial (5–40 km) and temporal (daily, monthly and across survey period) scales. Selected variables included sea surface temperature, thermal fronts, chlorophyll‐a, sea surface height, mixed layer depth and salinity. A total of 30,514 km was covered on‐effort with 216 harbour porpoise sightings recorded. Overall, the best spatial scale corresponded to the coarsest resolution considered in this study (40 km), while porpoise presence showed stronger association with oceanographic variables summarised at a longer temporal scale. Habitat models including covariates at coarse spatial and temporal scales may better reflect the processes driving availability and abundance of resources at these large scales. These findings support the hypothesis that a multi‐scale approach should be applied when investigating species distribution. Identifying suitable spatial and temporal scale would improve the functional interpretation of the underlying relationships, particularly when studying how a small marine predator interacts with its environment and responds to climate and ecosystem changes.
... However, using fine-scale predictors may also be misleading if animals were only travelling through an area. The selection of appropriate spatio-temporal scales in SDMs has been studied across many terrestrial and marine species (Becker et al., 2010;Gottschalk et al., 2011;Graf et al., 2005;Levin, 1992;Mateo-Sánchez et al., 2016;Redfern et al., 2006;Stuber & Fontaine, 2019;Wiens, 1989). For example, Gottschalk et al. (2011) examined the effects of different spatial scales of a land-use map (from 1 to 1000 m) on predicting bird distribution and found that the occurrence of different bird species was better predicted using models with different spatial resolutions. ...
... A study by Graf et al. (2005) on a forest grouse species (Tetrao urogallus) compared single spatial scale habitat models with a multi-scale model (1 to 1100 ha) and found that the latter performed better. Likewise, brown bears (Ursus arctos) in the Cantabrian Range in Spain responded to environmental factors at different spatial scales (0.25-64 km) across seasons and time periods, suggesting the influence of processes underlying spatial and temporal variations in habitat use (Mateo-Sánchez et al., 2016). ...
The importance of scale when investigating ecological patterns and processes is recognised across many species. In marine ecosystems, the processes that drive species distribution have a hierarchical structure over multiple nested spatial and temporal scales. Hence, multi-scale approaches should be considered when developing accurate distribution models to identify key habitats, particularly for populations of conservation concern. Here, we propose a modelling procedure to identify the best spatial and temporal scale for each modelled and remotely sensed oceanographic variable to model harbour porpoise (Phocoena phocoena) distribution. Harbour porpoise sightings were recorded during dedicated line-transect aerial surveys conducted in the summer of 2016, 2021 and 2022 in the northeast Atlantic. Binary generalised additive models were used to assess the relationships between porpoise presence and oceanographic variables at different spatial (5, 20 and 40 km) and temporal (daily, monthly and across survey period) scales. Selected variables included sea surface temperature, thermal fronts, chlorophyll-a, sea surface height, mixed layer depth and salinity. A total of 30,514 km was covered on-effort with 216 harbour porpoise sightings recorded. Overall, the best spatial scale corresponded to the coarsest resolution considered in this study (40 km), while porpoise presence showed stronger association with oceanographic variables summarised at a longer temporal scale (monthly and averaged over survey period). Habitat models including covariates at coarse spatial and temporal scales may better reflect the processes driving availability and abundance of prey resources at the large scales covered during the surveys. These findings support the hypothesis that a multi-scale approach should be applied when investigating species distribution. Identifying suitable spatial and temporal scale would improve the functional interpretation of the underlying relationships, particularly when studying how a small marine predator interacts with its environment and responds to climate and ecosystem changes.
... Monitoring biodiversity is essential for evaluating species status (Nielsen et al., 2009), community structure (Favila and Halffter, 1997), responses to global environmental change (Bellard et al., 2014) and identifying areas to be prioritised for conservation (Sutter et al., 2015). Various habitat characteristics influence species' habitat selection at different spatial scales and are often associated with habitat quality (Wiens et al., 1987;Mateo-Sanchez et al., 2016). For biodiversity monitoring efforts to be successful and practical, different spatial scales must be considered in assessing species' habitat selection (Poiani et al., 2000). ...
Monitoring species' habitat selection and microhabitat requirements is vital for conservation and management, though studies on bird species' habitat selection at relatively fine scales are often limited. Camera traps are useful techniques for studying bird communities, particularly elusive species that are challenging to document using traditional survey techniques. Here, we installed 184 camera traps during the non-breeding and breeding seasons to study understorey forest-specialist birds' habitat requirements in 14 selected Southern Mistbelt Forest patches of KwaZulu-Natal, South Africa. We conducted foliage profile and forest structure surveys and an inventory of tree species richness to characterise forest microhabitat. Over 7182 trap days, we had 615 detections of ten understorey forest-specialists, most of which were insectivores. We modelled the occupancy of Lemon Doves (Aplopelia larvata), Chorister Robin-chats (Cossypha dichroa), Crested Guineafowls (Guttera pucherani), and Red-necked Spurfowls (Pternistis afer) to determine microhabitat characteristics that influenced detection probability and occupancy. The main microhabitat characteristics influencing forest-specialist understorey birds were tree species richness, leaf litter, and water cover. Forest structural characteristics that influenced the occupancy of the selected understorey forest-specialists were those within 5 m of the forest floor. Microhabitat requirements for the birds were species-specific, with seasonal variation for Lemon Doves. Conservation strategies should maintain undisturbed forest understorey to allow for the persistence of understorey forest-specialist bird species.
... Moreover, different scenarios of species habitat suitability requirements need revision to account for uncertainties in habitat requirement parameterizations in habitat suitability assessments (Rubio et al., 2012;Qin et al., 2015;Lechner et al., 2015). For example, changes in patches and landscapes patterns caused by variation in assessment scenarios (Mateo-Sánchez et al., 2016;Morelle and Lejeune, 2015;Yabuhara et al., 2019) will alter the location and availability of suitable habitat, thereby influencing landscape structures and the evaluation of priority sites based on graph-network analysis (Szipl et al., 2019;Parks et al., 2020). Consequently, determining source patches from the perspective of species habitat demands and considering the impacts of seasonal variation and uncertainty in habitat suitability parameterization on landscape pattern modelling is critical for reasonably assessing landscape priority areas (Simpkins and Perry, 2017). ...
... Further, purely spatial or temporally static landscape connectivity analyses may often be insufficient for assessing habitat suitability (Zeigler and Fagan, 2014;Littlefield et al., 2019;Préau et al., 2020). Previous studies have suggested that species responses to patch and landscape structures likely vary across seasons (Mateo-Sánchez et al., 2016;Morelle and Lejeune, 2015;Yabuhara et al., 2019). However, few studies have concomitantly evaluated the impacts of seasonal variation and uncertainty in habitat suitability parameterization on landscape connectivity. ...
Improving connectivity is the most commonly used approach employed in biodiversity conservation to mitigate habitat fragmentation. Graph-based networks relying on species-oriented definitions of nodes and edges are powerful tools for evaluating landscape connectivity. However, few studies have examined the effects of seasonal variation and uncertainty in parameterization of habitat suitability on the identification of priority sites. Here, a combination of the habitat suitability index, morphological spatial pattern analysis (MSPA), and graph-network analysis were combined to assess the influence of habitat variability on the identification of priority sites for reintroduction of South China tiger (Panthera tigris amoyensis). The combined approach was applied to the Hupingshan and Houhe National Nature Reserve and neighbourhood that are considered the most suitable places to harbour small populations of South China tigers. Six spatial or temporally static assessment scenarios for habitat suitability were used to account for uncertainties in the habitat requirement parameterizations of wild boars as the main prey for South China tigers. For each assessment scenario, three classification schemes (i.e., the P50, P70, and P90 schemes) were adopted to explain uncertainties in priority sites evaluation. In the static scenario, the minimum area of priority sites in the P50, P70, and P90 schemes was 321, 354, and 2,371 km2, respectively, while the areas encompassed by priority sites in the P50, P70, and P90 schemes considering the seasonal variability and habitat uncertainty were 0.2, 1.6, and 229.5 km2, respectively. These results suggest that priority areas with high conservation value differed among the six assessment scenarios. Conservation planning of landscapes consequently should consider the impacts of changes in landscape structures caused by seasonal variation and uncertainty in the parameterization of habitat suitability for the assessment of landscape priority sites rather than relying on purely spatial or temporally static connectivity analyses.
... The compilation of these data has been very varied: systematic procedures over time as presence and damage records (regional governments field staff) or females with cubs censuses (Palomero et al., 2007), systematic but more specific procedures such as genetic studies (Pérez et al., 2014) and other studies that collected data by mixing direct observations with testimonials Purroy, 1991). Different types of works on demography, reproduction, habitat use, habitat suitability or potential expansion areas have used these data (Mateo-S anchez et al., 2016;Martínez Cano et al., 2016;Zarzo-Arias et al., 2019;Penteriani et al., 2018). ...
... However, in our results, the extinct areas had a higher habitat quality for bears according to the human index than the ones that were colonized. This seems counterintuitive since we would expect bears to avoid areas that have a low habitat quality based on the human predictors (e.g., higher human density, more roads) and use areas with better quality and low impact (Mateo-S anchez et al., 2016;Martin et al., 2012;Oberosler et al., 2017;Naves et al., 2003). The demography itself could be behind the absence of bears in these territories with high human habitat quality because the change in the presence range between these two periods is mostly decreasing (43 colonizations vs. 136 extinctions). ...
Abstract Monitoring changes in the status of threatened and endangered species is critical for conservation, especially when these changes can be more dynamic than management actions. We studied how the range of the endangered Cantabrian brown bear (Ursus arctos) population has recovered after a long period of decrease. We estimated the presence range (using all available data on bear presence) and the breeding range (using data on females with cubs presence) in: 1982–1992, 1993–2002, and 2003–2012; to analyze temporal dynamics in the spatial extension of these ranges between periods. For the presence range, we observed an initial reduction of 25% between the first two periods maintaining two isolated subpopulations, followed by an increase of 70% in the third period that merged the subpopulations into a single population. The breeding range represented about 50% of the presence range and was stable between the two first periods and increased by 30% in the last period. Despite that increase two spatial breeding cores remained separated across all periods. Generalized linear models analyzing the factors affecting extinction and colonization between periods showed that bears expanded to areas closer to the center of the population and with low human disturbance. Our model projection predicted correctly a 77% of the areas newly colonized for the period 2013–2022 (112% increases in relation to 2003–2012). Finally, we identified that the recovery plans defining the guidelines for the management of this population are outdated and its application only covers around 50% of the current presence range and 40% of the predicted range. More dynamic legal and management approaches are needed to avoid conservation success turning into failure, especially for charismatic species whose management is often subject to social conflicts.
... The region is mainly covered by forests, shrublands, and farmland. The forests on southern slopes are mainly composed of semideciduous and evergreen oaks (Quercus spp.), whereas the northern slopes host mostly deciduous forests (Fagus sylvatica; Q. robur, Q. petraea; Betula sp.; Mateo-Sánchez et al. 2016). Nonforested areas are covered with shrubs such as broom (Cytisus spp.) and heather (Erica spp., Calluna spp.); while above the tree line, berry shrubs such as bilberries (Vaccinium myrtillus) appear (Pato andObeso 2012, Mateo-Sánchez et al. 2016). ...
Patches of color may be used to communicate to conspecifics, mainly in species showing uniform coloration, and may (a) help individuals maintain visual contact, such as between mothers and their young; (b) function as signals of subordination or to frighten rivals; (c) warn conspecifics of approaching predators; and/or (d) signal reproductive condition, health, or genetic quality to potential mates. Intraspecific communication represents one of the major evolutionary forces responsible for the coloration of body parts, but the meaning of many of these signals is still unclear. One of the first steps to understanding whether fur marks have a role in social communication is to understand whether such body patches are stable over time (i.e., whether they represent a unique visual signature for every individual). During the period 1999–2021, we recorded yearly pictures of 7 female (mean no. of monitoring years per bear = 13.6, standard deviation [SD] = 4.6; range = 9–22 yr) and 6 male (mean no. of monitoring years per bear = 9.3, SD = 4.3; range = 5–15 yr) brown bears (Ursus arctos) in the Cantabrian Mountains (NW Spain). We show that body mark shapes are stable over time and, because of their uniqueness, might represent a distinctive signature of individuals. Brown bear body marks may act as multicomponent signals, where different features of a given mark may inform about different aspects of the bearer or act as back-ups. For example, a quality-signaling capacity does not preclude the same mark from being used in other functions at the same time, such as individual recognition. Noninvasive techniques helpful for identifying individuals have been developed for estimating population size, reproductive rates, and the survival of several carnivore species. Fur marks that are stable over time can thus be useful in field research (e.g., body marks that are persistent and do not vary over time are an important tool in longitudinal photographic capture–recapture studies).
... Then, we divided the data into (1) the whole time frame (1985-2019), (2) the older , and (3) the recent period (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020). This temporal division corresponds to the main trend change observed in this bear population, which shifted from slow recovery to population increase and colonization of new areas that occurred around 2005 (Mateo-S anchez et al., 2015;Zarzo-Arias et al., 2019). We also grouped the data according to the western and eastern subpopulations, reflecting the highway dividing the area and acting as the main dispersal barrier ( Figure 1). ...
... This is especially supported by conservation and management efforts increasingly focusing on mitigating human impact on populations with poor conservation status (Chapron et al., 2014;Ripple et al., 2014). Indeed, the brown bears' population in the Cantabrian Mountains has also improved from slow recovery to population increase and colonization of new areas (Mateo-S anchez et al., 2015;Zarzo-Arias et al., 2019). Occurrences from the first years of monitoring show only very specific and limited areas to be suitable. ...
... This is especially important if their application is required at a local level and for species that react to the environment at large spatial scales. Additionally, models can help answer different questions using species input data with different filtering in order to, for example, explore habitat selection by individuals of different age (Milanesi et al., 2016) or sex (Kwon et al., 2019), different life cycle stages (Mateo-S anchez et al., 2015), or focus only on damages to get conflict hotspots (Behdarvand et al., 2014). In short, we highlight several widely overlooked issues preceding the construction of SDMs that can affect important predictions on species' future and bring the issues to the attention of ecologists building SDMs. ...
Species distribution models (SDMs) are powerful tools in ecology and conservation. Choosing the right environmental drivers and filtering species' occurrences taking their biases into account are key factors to consider before modeling. In this case study, we address five common problems arising during the selection of input data for presence-only SDMs on an example of a general-ist species: the endangered Cantabrian brown bear. First, we focus on the selection of environmental variables that may drive its distribution, testing if climatic variables should be considered at a 1-km analysis grain. Second, we investigate how filtering the species' data in view of (1) their collection procedures , (2) different time frames, (3) dispersal areas, and (4) subpopulations affects the performance and outputs of the models at three different spatial analysis grains (500 m, 1 km, and 5 km). Our results show that models with different input data yielded only minor differences in performance and behaved properly in terms of model validation, although coarsening the analysis grain deteriorated model performance. Still, the contribution of individual variables and the habitat suitability predictions differed among models. We show that a combination of limited data availability and poor selection of environmental variables can lead to inaccurate predictions. Specifically for the brown bear, we conclude that climatic variables should not be considered for exploring habitat suitability and that the best input data for modeling habitat suitability in the study area originate from (1) observations and traces from the (2) most recent period (2006-2019) in which the population is expanding, (3) not considering cells of dispersing bear occurrences and (4) modeling sub-populations independently (as they show distinct habitat preferences). In conclusion , SDMs can serve as a useful tool for generalist species including all available data; still, expert evaluation from the perspective of data suitability for the purpose of modeling and possible biases is recommended. This is especially important when the results are intended for management and conservation purposes at the local level, and for species that respond to the
environment at coarse analysis grains.
... In general, elevation and rugged landscapes are positively related to bear habitat suitability because they provide optimal climatic and vegetation conditions and limit human presence (Almasieh et al. 2019;Zarzo-Arias et al. 2019;Mohammadi et al. 2021). However, brown bears in Spain generally avoid high-elevation meadows because the food resources are scarce compared to forests at lower elevations (Mateo- Sanchez et al. 2016), and they prefer forests with relatively dense cover further away from settlements at higher elevations (Zarzo-Arias et al. 2019). These differences were likely due to inherent differences in climate, vegetation patterns, and human density and behavior between the two study areas. ...
Background
Humans have altered fire regimes across ecosystems due to climate change, land use change, and increasing ignition. Unprecedented shifts in fire regimes affect animals and contribute to habitat displacement, reduced movement, and increased mortality risk. Mitigating these effects require the identification of habitats that are susceptible to wildfires. We designed an analytical framework that incorporates fire risk mapping with species distribution modeling to identify key habitats of Ursus arctos with high probability of fire in Iran. We applied the random forest algorithm for fire risk mapping. We also modeled brown bear habitats and predicted connectivity between them using species distribution models and connectivity analysis, respectively. Finally, the fire risk map, critical habitats, and corridors were overlaid to spatially identify habitats and corridors that are at high risk of fire.
Results
We identified 17 critical habitats with 5245 km ² of corridors connecting them, 40.06% and 11.34% of which are covered by conservation areas, respectively. Our analysis showed that 35.65% of key habitats and 23.56% of corridors are at high risk of fire.
Conclusions
Since bears habitat in this semi-arid landscape rely on forests at higher altitudes, it is likely that shifting fire regimes due to changing climate and land use modifications reduce the extent of habitats in the future. While it is not well known how fire affects bears, identifying its key habitat where wildfires are likely to occur is the first step to manage potential impacts from increasing wildfires on this species.
