Habitats as predictors in species distribution models: Shall we use continuous or binary data?

The representation of a land cover type (i.e. habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g. wetlands) while for common habitats (e.g. forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e. the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g. using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology and ecological conservation

Density and reproductive characteristics of female brown bears in the Cantabrian Mountains, NW Spain

Here we present annual nearest-neighbour distances (as a proxy of density) between females with cubs-of-the-year (hereafter FCOY) and reproductive characteristics of brown bears Ursus arctos in the Cantabrian Mountains (NW Spain), from 1989 to 2017. FCOY nearest-neighbour distances and reproduction parameters of 19 focal females followed over several consecutive years (from 2004 to 2017) were obtained from bears inhabiting the western sector of the Cantabrian Mountains, where most of the bear population resides. In contrast, general reproductive characteristics were studied in the whole Cantabrian Mountains (western and eastern sectors together) on a sample of 362 litter sizes and 695 cubs. Mean nearest-neighbour distance between FCOY was 2559 ± 1222 m (range = 1305–4757 m). Mean litter size was significantly larger in the west (1.8 ± 0.2 cubs) than in the east (1.3 ± 0.6 cubs). Mean litter size for the whole of the Cantabrian Mountains was 1.6 ± 0.3 cubs. Litter sizes of one, two and three cubs represented 33.4, 56.1 and 10.5% of observed family groups, respectively. Interannual variations in litter size were not significant for both the western and the eastern areas. Mean cub mortality was 0.2 ± 0.5 cubs and did not vary among years. Cub mortality per litter size was 3.9% for one cub, 69.2% for two cubs and 26.9% for three cubs. Mean reproductive rate of the 19 focal females was 1.5 ± 0.6 cubs (n = 58 litters). Litter size of focal FCOY did not differ from the litter size obtained from systematic observations in the whole Cantabrian Mountains. During this period, cub mortality occurred in 24.1% of the 58 litters. Females usually bred every second year (average litter interval = 2.2 years). The estimated reproductive rate for the bear population was 0.7 young born/year/reproductive adult female

Identifying potential areas of expansion for the endangered brown bear (Ursus arctos) population in the cantabrian mountains (NW Spain)

Many large carnivore populations are expanding into human-modified landscapes and the subsequent increase in coexistence between humans and large carnivores may intensify various types of conflicts. A proactive management approach is critical to successful mitigation of such conflicts. The Cantabrian Mountains in Northern Spain are home to the last remaining native brown bear (Ursus arctos) population of the Iberian Peninsula, which is also amongst the most severely threatened European populations, with an important core group residing in the province of Asturias. There are indications that this small population is demographically expanding its range. The identification of the potential areas of brown bear range expansion is crucial to facilitate proactive conservation and management strategies towards promoting a further recovery of this small and isolated population. Here, we used a presence-only based maximum entropy (MaxEnt) approach to model habitat suitability and identify the areas in the Asturian portion of the Cantabrian Mountains that are likely to be occupied in the future by this endangered brown bear population following its range expansion. We used different spatial scales to identify brown bear range suitability according to different environmental, topographic, climatic and human impact variables. Our models mainly show that: (1) 4977 km2 are still available as suitable areas for bear range expansion, which represents nearly half of the territory of Asturias; (2) most of the suitable areas in the western part of the province are already occupied (77% of identified areas, 2820 km2), 41.4% of them occurring inside protected areas, which leaves relatively limited good areas for further expansion in this part of the province, although there might be more suitable areas in surrounding provinces; and (3) in the eastern sector of the Asturian Cantabrian Mountains, 62% (2155 km2) of the land was classified as suitable, and this part of the province hosts 44.3% of the total area identified as suitable areas for range expansion. Our results further highlight the importance of increasing: (a) the connectivity between the currently occupied western part of Asturias and the areas of potential range expansion in the eastern parts of the province; and (b) the protection of the eastern sector of the Cantabrian Mountains, where most of the future population expansion may be expected.S1 Fig. Brown bear occurrence data and location of the study area in Europe. https://doi.org/10.1371/journal.pone.0209972.s001S2 Fig. Evaluation metrics for 130 candidate models containing different levels of complexity defined by a range of five feature type combinations including linear (L), quadratic (Q), product (P), threshold (T) and hinge (H) features, each evaluated over a range of regularization multipliers ranging from 0 to 10, for (a) the coarse and (b) fine scales of the distribution of the Cantabrian brown bear in Asturias. Evaluation metrics include delta AICc, which is the difference in AICc (Akaikes Information Criterion corrected for small sample sizes, calculated as the sum of the log transformed raw output penalized by the number of model parameters), AUC test, which is the AUC (area Under the receiving operator characteristics Curve) score for the testing data set, AUC diff, which is the difference in AUC scores between the training and testing data sets, and OR min, which is a threshold dependent statistic corresponds to the proportion of testing localities that have MaxEnt output values lower than the value associated with the training locality with the lowest value. https://doi.org/10.1371/journal.pone.0209972.s002S3 Fig. Jacknife evaluations of variable contributions to the (a) coarse and (b) fine scale models. The variables with the highest gain when used in isolation are slope for the coarse scale (a) and forest cover foir the fine scale model (b). These variables therefore seem to have provided the most useful information by themselves for each scale. The variables that decreased the gain most when omitted, and thus possessed the greatest amount of information not present in the other variables, were slope for the coarse scale (a) and population density for the fine scale model (b). https://doi.org/10.1371/journal.pone.0209972.s003S4 Fig. Output of the coarse scale model with a 5 x 5 km resolution. The map presents a clog-log transformation of the raw MaxEnt output, which can be interpreted as a probability of brown bear range occurrence. https://doi.org/10.1371/journal.pone.0209972.s004S5 Fig. Schematic examples of incremental range expansion (a) out of an initial core area as well as (b) a patchy range expansion were no area is occupied two consecutive years, their nestedness values as well as the association matrices used to calculate nestedness. https://doi.org/10.1371/journal.pone.0209972.s005S6 Fig. Associations between predicted suitability estimated from the coarse scale model each of the included environmental predictors. https://doi.org/10.1371/journal.pone.0209972.s006S7 Fig. Associations between predicted suitability estimated from the fine scale model each of the included environmental predictors. https://doi.org/10.1371/journal.pone.0209972.s007S1 Table. Description, source and original format of the 25 environmental variables initially developed for the construction of the models. Variables marked with * are the ones not correlated and ultimately used in the modelling. https://doi.org/10.1371/journal.pone.0209972.s008S2 Table. Variable contribution to the construction of the coarse and fine scale models. https://doi.org/10.1371/journal.pone.0209972.s009S3 Table. Centre coordinates of the 5 x 5 km grids classed as bear home range used as bear occurrence data in the coarse scale model. https://doi.org/10.1371/journal.pone.0209972.s010S4 Table. Centre coordinates of the 1 x 1 km grids that contained a bear observation used as bear occurrence data in the fine scale model. https://doi.org/10.1371/journal.pone.0209972.s011The Gobierno del Principado de Asturias (with FEDER co-financing); the Spanish Ministry of Economy Industry and Competitiveness ((MINECO); the Agencia Estatal de Investigación (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER, EU) as well as Ramon & Cajal research contracts from the Spanish Ministry of Economy, Industry and Competitiveness.http://www.plosone.org/pm2020Mammal Research InstituteZoology and Entomolog

Brown bear behaviour in human-modified landscapes: The case of the endangered Cantabrian population, NW Spain

Large carnivores are recolonizing parts of their historical range in Europe, a heavily modified human landscape. This calls for an improvement of our knowledge on how large carnivores manage to coexist with humans, and on the effects that human activity has on large carnivore behaviour, especially in areas where carnivore populations are still endangered. Brown bears Ursus arctos have been shown to be sensitive to the presence of people and their activities. Thus, bear conservation and management should take into account potential behavioural alterations related to living in human-modified landscapes. We studied the behaviour of brown bears in the Cantabrian Mountains, NW Spain, where an endangered population thrives in a human-modified landscape. We analysed bear observations video-recorded over a 10-year period to try to identify human and landscape elements that could influence bear behaviour. Neither the occurrence nor the duration of vigilance behaviour in Cantabrian bears seemed to be influenced by the proximity of human infrastructures and activity. Our findings suggest that the general pattern of human avoidance by bears is adapted to the human-modified landscape they inhabit. Bears generally avoid people, but close presence of human infrastructures or activity did not seem to trigger an increased bear behavioural response. Coexistence between large carnivores and humans in human-modified landscapes is possible, even when human encroachment is high, provided that carnivores are not heavily persecuted and direct interactions are avoided. Further research should also document the potential existence of other responses to human presence and activity, e.g., hunting, traffic noise, and measuring stress levels with physiological indicators.This research was financially supported by the IBA (International Association for Bear Research and Management) grant project IBA-RG_16_2016 ‘Brown bear behaviour in human-dominated landscapes: the effect of human density and ecotourism’. During this research, G.B. was financially supported by a collaboration contract with the MUSE – Museo delle Scienze of Trento (Italy), J.M-P. was supported by the ARAID foundation and V.P., A.O. and R.G.G. were also financially supported by the Excellence Project CGL2017-82782-P financed by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the Agencia Estatal de Investigación (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER, EU)

Bears in Human-Modified Landscapes: The Case Studies of the Cantabrian, Apennine, and Pindos Mountains

Edited by Vincenzo Penteriani and Mario Melletti.-- Part III - Human–Bear Coexistence.-- This material has been published in "Bears of the World. Ecology, Conservation and Management" by / edited by Vincenzo Penteriani and Mario Melletti / Cambridge University Press. This version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works.Brown bears Ursus arctos were historically persecuted and almost eradicated from southern Europe in the twentieth century as a result of hunting and direct persecution. The effects of human-induced mortality were exacerbated by other threats, such as habitat loss and fragmentation, due to the expansion of human populations. As a result, nowadays there are only small fragmented populations of bears in southern Europe. Brown bears in the Cantabrian (north-western Spain), Apennine (central Italy), and Pindos (north-western Greece) mountains represent three examples of small and threatened bear populations in human-modified landscapes. Most of their range is characterized by high human densities, widespread agricultural activities, livestock raising and urban development, connected by dense networks of transport infrastructures. This has resulted in a reduction of continuous habitat suitable for the species. Here, we summarize the past and present histories and fates of these three populations as examples on how the coexistence of bears and people in human-modified landscapes can take different turns depending on human attitudes

Human–Bear Conflicts at the Beginning of the Twenty-First Century: Patterns, Determinants, and Mitigation Measures

Edited by Vincenzo Penteriani and Mario Melletti.-- Part III - Human–Bear Coexistence.-- This material has been published in "Bears of the World. Ecology, Conservation and Management" by / edited by Vincenzo Penteriani and Mario Melletti / Cambridge University Press. This version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works.Conflicts between humans and bears have occurred since prehistory. Through time, the catalogue of human–bear conflicts (HBC) has been changing depending on the values and needs of human societies and their interactions with bears. Even today, conflict situations vary among the eight species of bears and geographically across these species’ ranges. This results in a broad range of interactions between bears and humans that may be considered as conflicts, including: (1) predation of domestic or semiwild animals, including bees, hunting dogs, and pet animals; (2) damage due to foraging on cultivated berries, fruits, agricultural products, and the tree bark in forest plantations; (3) economic loss due to destruction of beehives, fences, silos, houses, and other human property; (4) bear attacks on humans causing mild or fatal trauma; (5) bluff charges, bear intrusions into residential areas; and (6) vehicle collisions with bears and traffic accidents. In this chapter we aim to outline the principal types of HBC and geographical differences in the occurrence of conflicts and the coexistence between people and bears

Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection

There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resolution of all predictor and response variables.Peer reviewe

Evolutionary and ecological traps for brown bears Ursus arctos in human-modified landscapes

Evolutionary traps, and their derivative, ecological traps, occur when animals make maladaptive decisions based on seemingly reliable environmental cues, and are important mechanistic explanations for declines in animal populations. Despite the interest in large carnivore conservation in human%modified landscapes, the emergence of traps and their potential effects on the conservation of large carnivore populations has frequently been overlooked. The brown bear Ursus arctos typifies the challenges facing large carnivore conservation and recent research has reported that this species can show maladaptive behaviours in human%modified landscapes. Here we review, describe and discuss scenarios recognised as evolutionary or ecological traps for brown bears, and propose possible trap scenarios and mechanisms that have the potential to affect the dynamics and viability of brown bear populations. Six potential trap scenarios have been detected for brown bears in human%modified landscapes: 1) food resources close to human settlements2) agricultural landscapes3) roads4) artificial feeding sites5) hunting by humansand 6) other human activities. Because these traps are likely to be of contrasting relevance for different demographic segments of bear populations, we highlight the importance of evaluations of the relative demographic consequences of different trap types for wildlife management. We also suggest that traps may be behind the decreases in brown bear and other large carnivore populations in human-modified landscapes

Endangered populations

The survival of an increasing number of species is threatened by climate change: 20%–30% of plants and animals seem to be at risk of range shift or extinction if global warming reaches levels projected to occur by the end of this century. Plant range shifts may determine whether animal species that rely on plant availability for food and shelter will be affected by new patterns of plant occupancy and availability. Brown bears in temperate forested habitats mostly forage on plants and it may be expected that climate change will affect the viability of the endangered populations of southern Europe. Here, we assess the potential impact of climate change on seven plants that represent the main food resources and shelter for the endangered population of brown bears in the Cantabrian Mountains (Spain). Our simulations suggest that the geographic range of these plants might be altered under future climate warming, with most bear resources reducing their range. As a consequence, this brown bear population is expected to decline drastically in the next 50 years. Range shifts of brown bear are also expected to displace individuals from mountainous areas towards more humanized ones, where we can expect an increase in conflicts and bear mortality rates. Additional negative effects might include: (a) a tendency to a more carnivorous diet, which would increase conflicts with cattle farmers; (b) limited fat storage before hibernation due to the reduction of oak forests; (c) increased intraspecific competition with other acorn consumers, that is, wild ungulates and free-ranging livestock; and (d) larger displacements between seasons to find main trophic resources. The magnitude of the changes projected by our models emphasizes that conservation practices focused only on bears may not be appropriate and thus we need more dynamic conservation planning aimed at reducing the impact of climate change in forested landscapes.Spanish Ministry of of Science, Innovation and Universities, the Agencia Estatal de Investigación (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER, EU). Grant Number: Excellence Project CGL2017-8278

Where to go? Habitat preferences and connectivity at a crossroad of European brown bear metapopulations

Natural habitats become increasingly degraded and fragmented due to rapid human expansion. The decreasing availability of high-quality habitats combined with a lack of connectivity among suitable patches and the low permeability of human-transformed landscapes endangers the survival of many species. Understanding the environmental conditions favoring a species’ distribution and the identification of movement corridors between populations is crucial for sustainable conservation and management. Serbia is the only European country inhabited by three different brown bear metapopulations, highlighting its crucial geographical position for establishing functional connections among these metapopulations. We used species distribution modeling to predict suitable habitats for the three bear metapopulations in Serbia at two spatial scales (5 and 1 km2). We combined the predictions from each metapopulation to define suitable habitats for range expansion. Further, we created landscape resistance maps to identify possible connectivity areas to promote gene flow between these metapopulations. Our results highlight that 1) the underlying processes of bear habitat selection at the coarse scale differ between metapopulations, mainly due to the differences in habitat availability; 2) > 60% of areas predicted as suitable for bears in Serbia are currently still unoccupied; 3) the south-eastern part of Serbia represents a key area for the connectivity between bear metapopulations in the future. However, the presence of several movement barriers, such as highways, highlights the need to implement adequate mitigation measures to increase habitat permeability. Because bears are a useful umbrella species for conservation actions, improvement of habitat quality and permeability will also positively affect many other species in this region.This research was supported by the Serbian Ministry of Education, Science and Technological Development (451–03–68/2022–14/200178). The funder provided support in the form of salaries for NB and D´C. AZA was supported by a Margarita Salas Contract financed by the European Union-NextGenerationEU, Ministerio de Universidades y Plan de Recuperacion, Tranformacion y Resiliencia, Spain. AGH was funded by the German Science Foundation (HE 8857/1–1). AZ was supported by the 2015–2016 BiodivERsA COFUND, with the national funders ANR (ANR-16-EBI3–0003), NCN (2016/22/Z/NZ8/00121), DLR-PT (01LC1614A), UEFISCDI (BiodivERsA3–2015–147-BearConnect (96/2016), and RCN (269863).Peer reviewe