The contributions of topoclimate and land cover to species distributions and abundance: fine‐resolution tests for a mountain butterfly fauna

Aim Models relating species distributions to climate or habitat are widely used to predict the effects of global change on biodiversity. Most such approaches assume that climate governs coarse‐scale species ranges, whereas habitat limits fine‐scale distributions. We tested the influence of topoclimate and land cover on butterfly distributions and abundance in a mountain range, where climate may vary as markedly at a fine scale as land cover. Location Sierra de Guadarrama (Spain, southern Europe) Methods We sampled the butterfly fauna of 180 locations (89 in 2004, 91 in 2005) in a 10,800 km² region, and derived generalized linear models (GLMs) for species occurrence and abundance based on topoclimatic (elevation and insolation) or habitat (land cover, geology and hydrology) variables sampled at 100‐m resolution using GIS. Models for each year were tested against independent data from the alternate year, using the area under the receiver operating characteristic curve (AUC) (distribution) or Spearman's rank correlation coefficient (r_s) (abundance). Results In independent model tests, 74% of occurrence models achieved AUCs of > 0.7, and 85% of abundance models were significantly related to observed abundance. Topoclimatic models outperformed models based purely on land cover in 72% of occurrence models and 66% of abundance models. Including both types of variables often explained most variation in model calibration, but did not significantly improve model cross‐validation relative to topoclimatic models. Hierarchical partitioning analysis confirmed the overriding effect of topoclimatic factors on species distributions, with the exception of several species for which the importance of land cover was confirmed. Main conclusions Topoclimatic factors may dominate fine‐resolution species distributions in mountain ranges where climate conditions vary markedly over short distances and large areas of natural habitat remain. Climate change is likely to be a key driver of species distributions in such systems and could have important effects on biodiversity. However, continued habitat protection may be vital to facilitate range shifts in response to climate change.     

Fine-scale distributions of carnivores in a logging concession in Sarawak,Malaysian Borneo

Coarse-scale patterns of distribution and abundance of species are the outcome of processes occurring at finer spatial scales, hence the conservation of species depends on understanding their fine-scale ecology. For Bornean carnivores, little is known about fine-scale predictors of species occurrence and it is assumed that the two main threats to wildlife on Borneo, habitat disturbance and hunting, also impact their occurrence. To increase our understanding of the Borneo carnivore community, we deployed 60 cameras in a logging concession in northern Sarawak, Malaysian Borneo, where different landscape covariates, both natural and anthropogenic, were present. We built single-species occupancy models to investigate fine-scale carnivore occupancy. Overall, forest disturbance had a negative effect on Hose’s civet (Diplogale hosei), banded civet (Hemigalus derbyanus) and yellow-throated marten (Martes flavigula). Further, banded civet had greater occupancy probabilities in more remote areas. Logging roads had the most diverse effect on carnivore occupancy, with Hose’s civet and masked palm civet (Paguma larvata) negatively affected by roads, whereas Malay civet (Viverra tangalunga), short-tailed mongoose (Herpestes brachyurus) and leopard cat (Prionailurus bengalensis) showed higher occupancy closer to roads. Canopy height, canopy closure, number of trees with holes (cavities) and distance to nearest village also affected occupancy, though the directions of these effects varied among species. Our results highlight the need to collect often overlooked habitat variables: moss cover and ‘kerangas’ (tropical heath forest) were the most important variables predicting occurrence of Hose’s civet. The preservation of such forest conditions may be crucial for the long-term conservation of this little-known species. Our results confirm that logged forests, when left to regenerate, can host diverse carnivore communities on Borneo, provided less disturbed habitat is available nearby, though human access needs to be controlled. We recommend easy-to-implement forest management strategies including maintaining forest next to logging roads; preserving fruiting trees and trees with cavities, both standing and fallen; and blocks of remote, less disturbed, mid- to high-elevation forest with understorey vegetation.     

Spatially explicit conservation issues for threatened bird species in Mediterranean farmland landscapes

Coupling habitat models based on GIS and on ground variables could help identify suitable areas (by means of landscape models obtained by GIS variables) to concentrate management actions for species’ conservation. In this study, the habitat requirements of Lesser Greys (LGS) and Woodchat Shrikes (WS), two threatened farmland bird species declining in Europe, were assessed in Apulia (south-eastern Italy) by means of binary logistic regression at two different levels: landscape (using GIS-measured variables); and, territory (using ground-measured variables) scales. The LGS occurrence at landscape scale was correlated to steppe-like areas and cereal crops. At the territory level, significant effects were detected for deciduous forests and the presence of isolated trees and shrubs. The WS occurrence at landscape scale was promoted by steppe-like areas and cereal crops, whereas, at the territory level significant effects were detected for steppe-like areas positively and suburban areas negatively. The landscape model was extrapolated to the entire region. Within highly suitable areas (occurrence probability higher than 0.66 according to the landscape model), we measured average habitat features and compared them with the optimal mosaic depicted by the territory level models. This allowed us to give spatially explicit and site-specific management recommendations for these two threatened species. LGS will mostly benefit from an increase in isolated shrubs and trees; whereas for WS, the most widespread recommendations are to increase steppe-like habitat and to prevent further urbanisation.Coupling “coarse” landscape models with the species ecology provided by fine-scaled models can integrate relevant information on species potential distribution and territory level requirements, making planning fine-tuned habitat management (within potentially suitable landscapes) in a spatially explicit way possible.     

Using species distribution and occupancy modeling to guide survey efforts and assess species status

Habitat loss and fragmentation continue to be major issues affecting the persistence and conservation of species, but identification of critical habitat remains a challenge. Species distribution modeling and occupancy modeling are both approaches that have been used to predict species distributions and can identify critical habitat characteristics associated with species occurrence. Additionally, occupancy sampling can provide measures of detectability, increasing the confidence that a species is truly absent when not detected. While increasingly popular, these methods are infrequently used in synergy, and rarely at fine spatial scales. We provide a case study of using distribution and occupancy modeling in unison to direct survey efforts, provide estimates of species presence/absence, and to identify local and landscape features important for species occurrence. The focal species for our study was Ambystoma jeffersonianum, a threatened salamander in the state of Illinois, U.S.A. We found that fine-scale distribution models accurately discriminated occupied from unoccupied breeding ponds (78–91% accuracy), and surveys could be effectively guided using a well-fit model. We achieved a high detection rate (0.774) through occupancy sampling, and determined that A. jeffersonianum never used ponds inhabited by fish, and the probability of a pond being used for breeding increased as canopy cover increased. When faced with limited resources, combining fine-scale distribution modeling with a robust occupancy sampling design can expedite survey efforts, confidently designate species occupancy status, prioritise habitat for future surveys and/or restoration, and identify critical habitat features. This approach is broadly applicable to other taxa that have specific habitat requirements.     

Can habitat modelling for the octopus Eledone cirrhosa help identify key areas for Risso’s dolphin in Scottish waters?

Marine protected areas (MPAs) are becoming increasingly important tools in the conservation of cetaceans. This has led to an interest in the use of species distribution models to predict where cetacean species are likely to occur based on the distribution of environmental variables. However, relationships between cetacean distribution and environmental variables are generally assumed to reflect the environmental preferences of their prey. Thus, understanding the distribution of prey may increase our ability to identify important areas for cetaceans. Here, we describe the diet of Risso’s dolphin (Grampus griseus) by analysing stomach contents of individuals stranded in Scotland over the last twenty years. Next, we use habitat modelling to test whether Rissos’s dolphin distribution in Scottish waters, as inferred from sightings, is related to the distribution of its main prey, the octopus Eledone cirrhosa. While good models of the relationship between the distribution of E. cirrhosa and environmental variables were obtained, there was no evidence of a relationship between modelled octopus distribution and the occurrence of Risso’s dolphins. These results suggest that identifying key areas for its main prey species is unlikely to help identify potential MPAs for Risso’s dolphin, at least at the spatial resolution used in this study.     

The effect of habitat fragmentation and abiotic factors on fen plant occurrence

Human landscape modification has led to habitat fragmentation for many species. Habitat fragmentation, leading to isolation, decrease in patch size and increased edge effect, is observed in fen ecosystems that comprise many endangered plant species. However, until now it has remained unclear whether habitat fragmentation per se has a significant additional negative effect on plant species persistence, besides habitat loss and degradation. We investigated the relative effect of isolation, habitat size, and habitat edge compared to the effect of habitat degradation by including both ‘fragmentation variables’ and abiotic variables in best subsets logistic regression analyses for six fen-plant species. For all but one species, besides abiotic variables one or more variables related to fragmentation were included in the regression model. For Carex lasiocarpa, isolation was the most important factor limiting species distribution, while for Juncus subnodulosus and Menyanthes trifoliata, isolation was the second most important factor. The effect of habitat size differed among species and an increasing edge had a negative effect on the occurrence of Carex lasiocarpa and Pedicularis palustris. Our results clearly show that even if abiotic conditions are suitable for certain species, isolation of habitat patches and an increased habitat edge caused by habitat fragmentation affect negatively the viability of characteristic fen plant species. Therefore, it is important not only to improve habitat quality but also to consider spatial characteristics of the habitat of target species when deciding on plant conservation strategies in intensively used landscapes, such as fen areas in Western Europe and North America.     

Alpine bird distributions along elevation gradients: the consistency of climate and habitat effects across geographic regions

Many species have shown recent shifts in their distributions in response to climate change. Patterns in species occurrence or abundance along altitudinal gradients often serve as the basis for detecting such changes and assessing future sensitivity. Quantifying the distribution of species along altitudinal gradients acts as a fundamental basis for future studies on environmental change impacts, but in order for models of altitudinal distribution to have wide applicability, it is necessary to know the extent to which altitudinal trends in occurrence are consistent across geographically separated areas. This was assessed by fitting models of bird species occurrence across altitudinal gradients in relation to habitat and climate variables in two geographically separated alpine regions, Piedmont and Trentino. The ten species studied showed non-random altitudinal distributions which in most cases were consistent across regions in terms of pattern. Trends in relation to altitude and differences between regions could be explained mostly by habitat or a combination of habitat and climate variables. Variation partitioning showed that most variation explained by the models was attributable to habitat, or habitat and climate together, rather than climate alone or geographic region. The shape and position of the altitudinal distribution curve is important as it can be related to vulnerability where the available space is limited, i.e. where mountains are not of sufficient altitude for expansion. This study therefore suggests that incorporating habitat and climate variables should be sufficient to construct models with high transferability for many alpine species.     

Fine-Scale Habitat Selection by Breeding White-Tailed Ptarmigan in Colorado

Species distributions are influenced by climate and topography in alpine ecosystems, yet resource selection studies of alpine species are uncommon. Basic characteristics of habitats used by alpine-endemic white-tailed ptarmigan (Lagopus leucura) have been described to explain foraging behavior, morphology, and survival in many alpine regions; however, there is a lack of information about fine-scale habitat selection for nesting and brood-rearing, particularly in the southern extent of the species’ range. Few studies have tested whether nest and brood-site selection by white-tailed ptarmigan are influenced by fine-scale components such as vegetation and arthropod communities. We assessed these fine-scale habitat characteristics analyzing paired use-available resource selection for nest (n = 61) and brood (n = 54) sites. We used conditional logistic regression for data collected in 2 alpine areas along the Front Range of Colorado, USA, during 2014 and 2015. We evaluated resource selection at larger (patch) and finer (nest site) scales. Nest-site selection at the patch scale was best predicted by cover (%) of forage forbs, rock and gravel, and shrubs. Forage forb cover explained more variation in our top nest model at the patch scale when compared to models with specific vegetation species. Females placed their nests along elevational gradients but more so at lower elevations and selected for less graminoid cover at the nest-site scale. Brood habitat selection at the patch level was influenced by cover (%) of rock and gravel and proximity to shrubs (m). Analysis of a subset of our brood data (n = 34) revealed females selected brood habitat that contained high arthropod abundance (e.g., Cicadellidae) over high vegetation cover, likely as a response to meet dietary requirements of chicks. Our results demonstrate how and where white-tailed ptarmigan are currently selecting these different breeding sites in Colorado's alpine, giving us insight into consequences this alpine-endemic bird may face if their breeding habitat is altered. © 2019 The Wildlife Society.     

Modelling seasonal changes in the distribution of Common Quail Coturnix coturnix in farmland landscapes using remote sensing

Species’ distribution models are widely used in landscape ecology but usually lack explicit information about species’ responses to ecosystem dynamics, leading to uncertainty when applied to the prediction of seasonal change in distributions. In this study, we aimed to build a species’ distribution model for the Common Quail Coturnix coturnix, a farmland species that shows changes in its distribution in response to seasonal changes in habitat suitability. During the course of three breeding seasons we collected temporal replicates of presence–absence data in 13 sampling locations in four countries (Morocco, Portugal, Spain and France). We used generalized linear mixed models to relate the species’ presence or absence to environmental variables and to the normalized difference vegetation index at each sampling location through the seasons, the latter variable being an indicator of within‐ and between‐season habitat changes. The preferred model showed that occurrence was highly dependent on habitat changes associated with crop seasonality, as measured by the normalized difference vegetation index. Common Quail selected areas with dense vegetation and warm climate and tracked spatial changes in these two parameters. The model allows accurate mapping of within‐ and between‐season distribution changes. Such changes are related to habitat variations caused mainly by drought and agricultural practices. Our results demonstrate that seasonal changes in farmland ecosystems can be incorporated into a simple distribution model, and our approach could be applied to other species to predict the effects of agricultural changes on the distribution of birds inhabiting farmland landscapes.     

Using Bobcat Habitat Suitability to Prioritize Habitat Preservation on a Suburbanizing Barrier Island

ABSTRACT Many land-trust organizations attempt to preserve habitat that will benefit specific wildlife species or suites of species. With limited resources available, these organizations need tools to prioritize preservation efforts. One such organization, the Kiawah Island Natural Habitat Conservancy (KINHC), is attempting to preserve wildlife habitat in the face of ever-increasing property values and development pressure on Kiawah Island, South Carolina, USA. We modified an existing bobcat (Lynx rufus) habitat suitability index model, which focuses on suitability of habitats for food, by including components for concealment cover and den habitat. We developed a windows-based computer program that calculates modified habitat suitability index (MHSI) values that can easily be imported into a Geographic Information System for display in map form, allowing for frequent reevaluation of site-specific habitat suitability as land-cover patterns change. We used locations collected from radiocollared bobcats to assess validity of the food and cover components of the MHSI. Bobcats used areas identified as highly suitable for food more than expected during nocturnal time periods (G₅₂ = 640.9, P < 0.001) and areas identified as highly suitable for cover more than expected during diurnal time periods (G₃₇ = 1,194.0, P < 0.001). Our approach for evaluating bobcat habitat suitability will allow KINHC to identify parcels that likely provide the greatest ecological benefit to bobcats and their associated wildlife community. Our approach could be altered to consider habitat requirements of other species, or multiple species, at virtually any location for which fine-scale land-cover data are available.     

Thinking spatially: The importance of geospatial techniques for carnivore conservation

Today, 27% of the known mammalian carnivore species are either extinct or threatened, undermining the health of many ecosystems, which provide resources and services that are crucial for human development. Carnivore research and management have been limited by the predominantly cryptic nature of carnivores, sometimes also by their large-scale habitat requirements and their remote distributions. As a consequence, many carnivore species currently remain under-studied. The increased availability and facilitated interpretation of remote-sensing imagery, combined with recent developments in landscape ecology and geographic information systems, have provided a wealth of analytical tools to overcome many of these traditional setbacks. These can be coupled with advances in multivariate statistics and species distribution models to formalise the link between spatial patterns in environmental variables and species occurrence. Such methods allow a greater understanding of the processes shaping habitat use, the effects of poaching and land-cover change, and assist in the design and monitoring of more targeted actions towards carnivores' long-term conservation.     

Searching for weeds – a GIS tool

Sampling for rare events, such as a new weed incursion, can be surprisingly efficient when adaptive, unequal probability survey designs are used. Spatially explicit habitat models and expert knowledge of weedy species can be used to identify areas of varied survey intensity. We introduce a GIS-based tool that can be used for designing such a survey. The user-friendly tool interfaces (behind the scenes) with the US Environmental Protection Agency’s spatially balanced sampling design functions in R. The functions ensure that the location of the sample points are spatially balanced while at the same time, allowing the user to specify survey intensity in area of special interest (preferred habitats, areas of high conservation value, areas of high public use, etc). We discuss the use of the GIS tool in a case study where we designed a 5-year weed monitoring plan for a local authority in New Zealand. The plan includes ‘over sample’ sites to replace any original sample sites that were impractical or costly to visit. Initial results include estimates of what proportion of the total region has weeds present and an estimate of weed density. More detailed results are produced for specified known weed hot spots, such as areas adjacent to roads and rivers. These estimates are available for all weed species, and for individual species. Because the system is GIS-based, spatial information is stored. Over time, as the weed surveillance and monitoring progresses, regional changes in weed distribution can be tracked, and species and locations that require more targeted weed management can be identified. Further results of such a probability-based design can be used to develop habitat models for predicting future distributions.     

Assessing congruence of opportunistic records and systematic surveys for predicting Hispaniolan mammal species distributions

Comparative assessment of the relative information content of different independent spatial data types is necessary to evaluate whether they provide congruent biogeographic signals for predicting species ranges. Opportunistic occurrence records and systematically collected survey data are available from the Dominican Republic for Hispaniola’s surviving endemic non‐volant mammals, the Hispaniolan solenodon (Solenodon paradoxus) and Hispaniolan hutia (Plagiodontia aedium); opportunistic records (archaeological, historical and recent) exist from across the entire country, and systematic survey data have been collected from seven protected areas. Species distribution models were developed in maxent for solenodons and hutias using both data types, with species habitat suitability and potential country‐level distribution predicted using seven biotic and abiotic environmental variables. Three different models were produced and compared for each species: (a) opportunistic model, with starting model incorporating abiotic‐only predictors; (b) total survey model, with starting model incorporating biotic and abiotic predictors; and (c) reduced survey model, with starting model incorporating abiotic‐only predictors to allow further comparison with the opportunistic model. All models predict suitable environmental conditions for both solenodons and hutias across a broadly congruent, relatively large area of the Dominican Republic, providing a spatial baseline of conservation‐priority landscapes that might support native mammals. Correlation between total and reduced survey models is high for both species, indicating the substantial explanatory power of abiotic variables for predicting Hispaniolan mammal distributions. However, correlation between survey models and opportunistic models is only moderately positive. Species distribution models derived from different data types can provide different predictions about habitat suitability and conservation‐priority landscapes for threatened species, likely reflecting incompleteness and bias in spatial sampling associated with both data types. Models derived using both opportunistic and systematic data must therefore be applied critically and cautiously.     

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.     

Habitat preferences of sympatric sandgrouse during the breeding season in Spain: a multi-scale approach

Predictive species’ distribution models may answer ecological questions about habitat selection, co-occurrence of species and competition between them. We studied the habitat preferences and segregation of two sympatric species of declining sandgrouse, the black-bellied sandgrouse (Pterocles orientalis) and the pin-tailed sandgrouse (Pterocles alchata), during the breeding season. We developed predictive models that related sandgrouse presence to environmental variables at three different spatial levels: large geographical, landscape and microhabitat scales. At the large geographical scale, differences between sandgrouse distributions, in the Iberian Peninsula, seem to be explained mainly in terms of bioclimatology: pin-tailed sandgrouse appear to be a more thermophilous species and occupy warmer sites usually located in flatter areas. At the landscape spatial level, in those areas that exhibit environmental conditions allowing for both species’ co-existence at a large geographical scale, black-bellied sandgrouse appear to be more tolerant to environmental variation than pin-tailed sandgrouse. At the microhabitat level, however, differences between species could be related to different flocking behaviour as a consequence of different sensitivities to vegetation structure and predators. Thus, the observed spatial distribution patterns are the result of different ecological factors that operate at different spatial levels. Conservation guidelines for these species should therefore consider their habitat preferences at large geographical, landscape and microhabitat scales.     

Distribution of medium- to large-sized African mammals based on habitat suitability models

The knowledge of the areas inhabited by a species within its distribution range and the connections among patches are critical pieces of information for successful conservation actions. The internal structure of the extent of occurrence (EO) of a species is almost always unknown, even for “well-known” flagship species. We developed a methodology to infer the area of occupancy (AO) within the EO of a species using the limited available data. We present here the results of a three years project funded by European Union to develop high-resolution models of habitat suitability for 281 medium- to large-sized African mammals across the whole continent. The existing literature was reviewed and all data on the geographic distribution and environmental preferences of the selected species were collected. For each species, these data were then expressed in terms of key variables available as GIS layers at a resolution of 1 km² over the entire African continent. The AO of each species was obtained merging the information on the ecological needs of the species and the values of ecological variables over the region identified as EO. The habitat suitability models were evaluated through direct field work in four countries (Morocco, Cameroon, Uganda, Botswana) chosen as representatives of the environmental and species diversity of Africa. More than 81% of models had positive true skill statistics (TSS) values, indicating models performing better than random. Rigorous modeling procedures supported by ad-hoc field evaluation allowed the production of high-resolution habitat suitability models useful for conservation applications.     

Habitat availability and gene flow influence diverging local population trajectories under scenarios of climate change: a place‐based approach

Ecological niche theory holds that species distributions are shaped by a large and complex suite of interacting factors. Species distribution models (SDMs) are increasingly used to describe species’ niches and predict the effects of future environmental change, including climate change. Currently, SDMs often fail to capture the complexity of species’ niches, resulting in predictions that are generally limited to climate‐occupancy interactions. Here, we explore the potential impact of climate change on the American pika using a replicated place‐based approach that incorporates climate, gene flow, habitat configuration, and microhabitat complexity into SDMs. Using contemporary presence–absence data from occupancy surveys, genetic data to infer connectivity between habitat patches, and 21 environmental niche variables, we built separate SDMs for pika populations inhabiting eight US National Park Service units representing the habitat and climatic breadth of the species across the western United States. We then predicted occurrence probability under current (1981–2010) and three future time periods (out to 2100). Occurrence probabilities and the relative importance of predictor variables varied widely among study areas, revealing important local‐scale differences in the realized niche of the American pika. This variation resulted in diverse and – in some cases – highly divergent future potential occupancy patterns for pikas, ranging from complete extirpation in some study areas to stable occupancy patterns in others. Habitat composition and connectivity, which are rarely incorporated in SDM projections, were influential in predicting pika occupancy in all study areas and frequently outranked climate variables. Our findings illustrate the importance of a place‐based approach to species distribution modeling that includes fine‐scale factors when assessing current and future climate impacts on species’ distributions, especially when predictions are intended to manage and conserve species of concern within individual protected areas.     

Generalizability of neotropical bird abundance and richness models

Predicting the consequences of land-cover change on tropical biotas is a pressing task. However, testing the applicability of models developed with data from one region to another region has rarely been done. Bird faunas were sampled along 3.0-km routes in southern Costa Rica (Coto Brus) to develop statistical models to describe the abundance and richness of groups as a function of land-cover characteristics. The relative value of the land-cover models was assessed by comparing them with null models. The generalizability of the models was tested with data from north-western Costa Rica (Monteverde) to determine whether the models were applicable to another area that has undergone significant land-cover change in the last 60 years. The richness and abundance of understory, open-country and edge non-insectivore groups showed clear relationships with land-cover variables, and the land-cover models had lower prediction errors than the null models for Coto Brus. With one exception, useful models for canopy birds, edge insectivores and hummingbirds could not be developed. The land-cover models of abundance of canopy insectivores, understory insectivores and non-insectivores, and edge non-insectivores were generalizable to Monteverde whereas the land-cover models of abundance of open-country birds and species richness for any of the groups were not better than null models for Monteverde. The results indicate that land-cover models that describe the abundance or richness of various bird groups provide useful predictions in the area where the data were collected and that models of abundance of some canopy, understory and edge birds may perform well in areas that are similar in elevation, life zones and land use to the area from which data were collected. Land-cover models of the abundance of other groups, and of the richness of the majority of groups, may be less generalizable to other areas, or it may be difficult to develop models at all.     

Current and future distribution of a parasite with complex life cycle under global change scenarios: Echinococcus multilocularis in Europe

Global change is expected to have complex effects on the distribution and transmission patterns of zoonotic parasites. Modelling habitat suitability for parasites with complex life cycles is essential to further our understanding of how disease systems respond to environmental changes, and to make spatial predictions of their future distributions. However, the limited availability of high quality occurrence data with high spatial resolution often constrains these investigations. Using 449 reliable occurrence records for Echinococcus multilocularis from across Europe published over the last 35 years, we modelled habitat suitability for this parasite, the aetiological agent of alveolar echinococcosis, in order to describe its environmental niche, predict its current and future distribution under three global change scenarios, and quantify the probability of occurrence for each European country. Using a machine learning approach, we developed large-scale (25 × 25 km) species distribution models based on seven sets of predictors, each set representing a distinct biological hypothesis supported by current knowledge of the autecology of the parasite. The best-supported hypothesis included climatic, orographic and land-use/land-cover variables such as the temperature of the coldest quarter, forest cover, urban cover and the precipitation seasonality. Future projections suggested the appearance of highly suitable areas for E. multilocularis towards northern latitudes and in the whole Alpine region under all scenarios, while decreases in habitat suitability were predicted for central Europe. Our spatially explicit predictions of habitat suitability shed light on the complex responses of parasites to ongoing global changes.     

Spatio-temporal analysis of Egyptian flower mantis Blepharopsis mendica (order: mantodea), with notes of its future status under climate change

Egyptian flower mantis Blepharopsis mendica (Order: Mantodea) is a widespread mantis species throughout the southwest Palearctic region. The ecological and geographical distribution of such interesting species is rarely known. So, through this work, habitat suitability models for its distribution through Egyptian territory were created using MaxEnt software from 90 occurrence records. One topographic (altitude) and eleven bioclimatic variables influencing the species distribution were selected to generate the models. The predicted distribution in Egypt was focused on the Delta, South Sinai, the north-eastern part of the country, and some areas in the west including Siwa Oasis. Temporal analysis between the two periods (1900–1961) and (1961–2017) show current reduction of this species distribution through Delta and its surrounding areas, may be due to urbanization. On the other hand, it increases in newly protected areas of South Sinai. Under the future climate change scenario, the MaxEnt model predicted the habitat gains for B. mendica in RCP 2.6 for 2070 and loss of habitat in RCP 8.5 for the same year. Our results can be used as a basis for conserving this species not only in Egypt, but also throughout the whole of its range, also, it show how the using of geo-information could help in studying animal ecology.