Modelling the distribution of badgers Meles meles: comparing predictions from field-based and remotely derived habitat data

• 1 Environmental heterogeneity is important in determining the distribution and abundance of organisms at various spatial scales. The ability to understand and predict distribution patterns is important for solving many management problems in conservation biology and wildlife epidemiology.
• 2 The badger Meles meles is a highly adaptable, medium‐sized carnivore, distributed throughout temperate Eurasia, which shows a wide diversity of social and spatial organization. Within Britain, badgers are not only legally protected, but they also serve as a wildlife host for bovine tuberculosis Mycobacterium bovis. An evaluation of the role of badgers in the dynamics of this infection depends on understanding the responses of badgers to the environment at different spatial scales.
• 3 The use of digital data to provide information on habitats for distribution models is becoming common. Digital data are increasingly accessible and are generally cheaper than field surveys. There has been little research, however, to compare the accuracy of models based on field‐derived and remotely derived data.
• 4 In this paper, we make quantified comparisons between large‐scale presence/absence models for badgers in Britain, based on field‐surveyed habitat data and remotely derived digital data, comprising elevation, geology and soil.
• 5 We developed four models: 1980s badger survey data using field‐based and digital data, and 1990s badger survey data using field‐based and digital data. We divided each of the four datasets into two subsets and used one subset for training (developing) the model and the other for testing it.
• 6 All four training models had classification accuracies in excess of 69%. The models generated from digital data were slightly more accurate than those generated from field‐derived habitat data.
• 7 The high classificatory ability of the digital‐based models suggests that the use of digital data may overcome many of the problems associated with field data in wildlife‐habitat modelling, such as cost and restricted geographical coverage, without any significant impact on model performance for some species. The more widespread use of digital data in wildlife‐habitat models should enhance their accuracy, repeatability and applicability and make them better‐suited as tools to aid policy‐ and decision‐making processes.

     

Geographical distribution and habitat occurrence of the Water Shrew (Neomys fodiens) in the Weald of South-East England

A survey of 96 sites in a range of riparian habitats in the catchments of five rivers during June–August 1998 used the bait tube method to investigate the geographical distribution and habitat occurrence of Water Shrews (Neomys fodiens) in the Weald of South‐East England. Water Shrews were found at 42% of sites, and were widely distributed in all river catchments except the Mole. They occurred in many riparian habitats, including rivers, streams, canals and ditches, with a range of physical and biotic characteristics. There were no signs of habitat avoidance in response to human disturbance but Water Shrews were absent from the river catchment with lowest water quality. Logistic regression analysis was used to model the effect of habitat variables on the presence of Water Shrews, with current speed, water depth, bank incline and bank‐side vegetation identified as important variables. Fast‐flowing shallow waters had a significant positive effect on their presence, whereas scarce herbaceous vegetation and a bank of low incline had a significant negative effect. These habitat variables appear to be reliable indicators of the probability of finding Water Shrews at a particular site, and have implications for habitat management and conservation.     

Fauna habitat modelling and mapping: A review and case study in the Lower Hunter Central Coast region of NSW

Abstract Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.     

High interindividual variability in habitat selection and functional habitat relationships in European nightjars over a period of habitat change

An animal's choice of foraging habitat reflects its response to environmental cues and is likely to vary among individuals in a population. Analyzing the magnitude of individual habitat selection can indicate how resilient populations may be to anthropogenic habitat change, where individually varying, broadly generalist populations have the potential to adjust their behavior. We collected GPS point data from 39 European nightjars (Caprimulgus europaeus) at a UK breeding site where restoration measures have altered large areas of habitat between breeding seasons. We calculated individual habitat selection over four breeding seasons to observe changes that might align with change in habitat. We also analyzed change in home range size in line with change in habitat availability, to examine functional relationships that can represent trade‐offs made by the birds related to performance of the habitat. Individual explained more of the variation in population habitat selection than year for most habitat types. Individuals differed in the magnitude of their selection for different habitat types, which created a generalist population composed of both generalist and specialist individuals. Selection also changed over time but only significantly for scrub habitat (60% decrease in selection over 4 years). Across the population, individual home range size was 2% smaller where availability of cleared habitat within the home range was greater, but size increased by 2% where the amount of open water was higher, indicating the presence of trade‐offs related to habitat availability. These results highlight that using individual resource selection and specialization measures, in conjunction with functional responses to change, can lead to better understanding of the needs of a population. Pooling specialist and generalist individuals for analysis could hide divergent responses to change and consequently obscure information that could be important in developing effective conservation strategies.     

Predicting the distribution of a suitable habitat for the white stork in Southern Sweden: identifying priority areas for reintroduction and habitat restoration

The loss of wetlands and semi-natural grasslands throughout much of Europe has led to a historic decline of species associated with these habitats. The reinstatement of these habitats, however, requires spatially explicit predictions of the most suitable sites for restoration, to maximize the ecological benefit per unit effort. One species that demonstrates such declines is the white stork Ciconia ciconia , and the restoration of habitat for this flagship species is likely to benefit a suite of other wetland and grassland biota. Storks are also being reintroduced into southern Sweden and elsewhere, and the a priori identification of suitable sites for reintroduction will greatly improve the success of such programmes. Here a simple predictive habitat-use model was developed, where only a small but reliable presence-only dataset was available. The model is based on the extent and relative soil moisture of semi-natural pastures, the extent of wetlands and the extent of hayfields in southern Sweden. Here the model was used to predict the current extent of stork habitat that is suitable for successful breeding, and the extent of habitat that would become suitable with moderate habitat restoration. The habitat model identifies all 10 occupied nesting sites where breeding is currently successful. It also identifies ∼300 km² of habitat that is predicted to be suitable stork habitat, but that is presently unused; these sites were identified as potential areas for stork reintroduction. The model also identifies over 100 areas where moderate habitat restoration is predicted to have a disproportionate effect (relative to the restoration effort) on the area of suitable habitat for storks; these sites were identified as priorities for habitat restoration. By identifying areas for reintroduction and restoration, such habitat suitability models have the potential to maximize the effectiveness of such conservation programmes.     

Habitat-based statistical models for predicting the spatial distribution of butterflies and day-flying moths in a fragmented landscape

1.  Most species' surveys and biodiversity inventories are limited by time and money. Therefore, it would be extremely useful to develop predictive models of animal distributions based on habitat, and to use these models to estimate species' densities and range sizes in poorly sampled regions.
2.  In this study, two sets of data were collected. The first set consisted of over 2000 butterfly transect counts, which were used to determine the relative density of each species in 16 major habitat types in a 35-km² area of fragmented landscape in north-west Wales. For the second set of data, the area was divided into 140 cells using a 500-m grid, and the extent of each habitat and the presence or absence of each butterfly and moth species was determined for each cell.
3.  Logistic regression was used to model the relationship between species' distribution and predicted density, based on habitat extent, in each grid square. The resultant models were used to predict butterfly distributions and occupancy at a range of spatial scales.
4.  Using a jack-knife procedure, our models successfully reclassified the presence or absence of species in a high percentage of grid squares (mean 83% agreement). There were highly significant relationships between the modelled probability of species occurring at regional and local scales and the number of grid squares occupied at those scales.
5.  We conclude that basic habitat data can be used to predict insect distributions and relative densities reasonably well within a fragmented landscape. It remains to be seen how accurate these predictions will be over a wider area.     

A new method for motion capture of the scapula using an optoelectronic tracking device: a feasibility study

Optoelectronic tracking systems are rarely used in 3D studies examining shoulder movements including the scapula. Among the reasons is the important slippage of skin markers with respect to scapula. Methods using electromagnetic tracking devices are validated and frequently applied. Thus, the aim of this study was to develop a new method for in vivo optoelectronic scapular capture dealing with the accepted accuracy issues of validated methods.

Eleven arm positions in three anatomical planes were examined using five subjects in static mode. The method was based on local optimisation, and recalculation procedures were made using a set of five scapular surface markers.

The scapular rotations derived from the recalculation-based method yielded RMS errors comparable with the frequently used electromagnetic scapular methods (RMS up to 12.6° for 150° arm elevation). The results indicate that the present method can be used under careful considerations for 3D kinematical studies examining different shoulder movements.     

Status and habitat preferences of Uganda cheetahs: an attempt to predict carnivore occurrence based on vegetation structure

In this paper we examine whether the occurrence of cheetahs (Acinonyx jubatus) in Uganda can be predicted from habitat characteristics extracted from a vegetation map. We first established the status of the cheetah in Uganda through field-interviews that Gros conducted in 1990. Cheetahs occurred almost exclusively in the Karamoja region where we estimated 53–310 individuals. Based on 216 sightings, the average number of adults in all-adults sightings was 1.65 + SD 0.95 and the average number of cubs in family groups 2.5 + SD 1.65. Compared to Graham and Parker's 1965 East African survey, average adult group size was slightly smaller in 1990 and large family groups were rarer. Comparison with Gros 1990 survey showed considerably lower cub-to-adult ratio and percent of observations with cubs in Uganda than in Kenya. A Geographic Information System (GIS) analysis of vegetation structure in areas where cheetahs were observed and in those where none were reported suggested that cheetahs might favor habitats with 25–50% woody cover and grasses of medium height (50–100 cm). A discriminant analysis correctly classified 72.1% of used habitats and 70.4% of no-report habitats. A logistic regression analysis improved the correct allocation of used habitats by 2.2%. Either the discriminant function or the logistic regression, which require only four easily obtainable vegetation characteristics, may help to pinpoint suitable cheetah habitats for conservation purposes. Our approach could be adapted for analyzing habitat suitability for other species of carnivores.     

Habitat selection of an endangered primate,the samango monkey (Cercopithecus albogularis schwarzi): Integrating scales to prioritize habitat for wildlife management

AimAs habitat loss continues to accelerate with global human population growth, identifying landscape characteristics that influence species occurrence is a key conservation priority in order to prevent global biodiversity loss. In South Africa, the arboreal samango monkey (Cercopithecus albogularis sp.) is threatened due to loss and fragmentation of the indigenous forests it inhabits. The aim of this study was to determine the habitat preferences of the samango monkey at different spatial scales, and to identify key conservation areas to inform management plans for this species.LocationThis study was carried out in the western Soutpansberg Mountains, which represents the northernmost population of samango monkeys within South Africa, and the only endangered subspecies (C. a. schwarzi).MethodsWe used sequentially collected GPS points from two samango monkey groups followed between 2012 and 2017 to quantify the used and available habitat for this species within the western Soutpansberg Mountains. We developed 2nd‐order (selection of ranging area), 3rd‐order (selection within range), and 4th‐order (feeding site selection) resource selection functions (RSFs) to identify important habitat features at each scale. Through scale integration, we identified three key conservation areas for samango monkeys across Limpopo Province, South Africa.ResultsHabitat productivity was the most important landscape variable predicting probability of use at each order of selection, indicating the dependence of these arboreal primates on tall‐canopy indigenous forests. Critical habitat across Limpopo was highly fragmented, meaning complete isolation between subpopulations is likely.Main conclusionsUnderstanding the habitat characteristics that influence samango monkey distribution across South Africa is crucial for prioritizing critical habitat for this species. Our results indicated that large, contiguous patches of tall‐canopy indigenous forest are fundamental to samango monkey persistence. As such, protected area expansion of large forest patches and creation of forest corridors are identified as key conservation interventions for this species.     

Home on the range: spatial ecology of loggerhead turtles in Atlantic waters of the USA

Aim Although satellite tracking has yielded much information regarding the migrations and habitat use of threatened marine species, relatively little has been published about the environmental niche for loggerhead sea turtles Caretta caretta in north‐west Atlantic waters. Location North Carolina, South Carolina and Georgia, USA. Methods We tracked 68 adult female turtles between 1998 and 2008, one of the largest sample sizes to date, for 372.2 ± 210.4 days (mean ± SD). Results We identified two strategies: (1) ‘seasonal’ migrations between summer and winter coastal areas (n = 47), although some turtles made oceanic excursions (n = 4) and (2) occupation of more southerly ‘year‐round’ ranges (n = 18). Seasonal turtles occupied summer home ranges of 645.1 km² (median, n = 42; using α‐hulls) predominantly north of 35 ° latitude and winter home ranges of 339.0 km² (n = 24) in a relatively small area on the narrow shelf off North Carolina. We tracked some of these turtles through successive summer (n = 8) and winter (n = 3) seasons, showing inter‐annual home range repeatability to within 14.5 km of summer areas and 10.3 km of winter areas. For year‐round turtles, home ranges were 1889.9 km². Turtles should be tracked for at least 80 days to reliably estimate the home range size in seasonal habitats. The equivalent minimum duration for ‘year‐round’ turtles is more complex to derive. We define an environmental envelope of the distribution of North American loggerhead turtles: warm waters (between 18.2 and 29.2 °C) on the coastal shelf (in depths of 3.0–89.0 m). Main conclusions Our findings show that adult female loggerhead turtles show predictable, repeatable home range behaviour and do not generally leave waters of the USA, nor the continental shelf (< 200m depth). These data offer insights for future marine management, particularly if they were combined with those from the other management units in the USA.     

Plant species richness of nature reserves: the interplay of area, climate and habitat in a central European landscape

Aim To detect regional patterns of plant species richness in temperate nature reserves and determine the unbiased effects of environmental variables by mutual correlation with operating factors. Location The Czech Republic. Methods Plant species richness in 302 nature reserves was studied by using 14 explanatory variables reflecting the reserve area, altitude, climate, habitat diversity and prevailing vegetation type. Backward elimination of explanatory variables was used to analyse the data, taking into account their interactive nature, until the model contained only significant terms. Results A minimal adequate model with reserve area, mean altitude, prevailing vegetation type and habitat diversity (expressed as the number of major habitat types in the reserve) accounted for 53.9% of the variance in species number. After removing the area effect, habitat diversity explained 15.6% of variance, while prevailing vegetation type explained 29.6%. After removing the effect of both area and vegetation type, the resulting model explained 10.3% of the variance, indicating that species richness further increased with habitat diversity, and most obviously towards warm districts. After removing the effects of area, habitat diversity and climatic district, the model still explained 9.4% of the variance, and showed that species richness (i) significantly decreased with increasing mean altitude and annual precipitation, and with decreasing January temperature in the region of the mountain flora, and (ii) increased with altitudinal range in regions of temperate and thermophilous flora. Main conclusions We described, in quantitative terms, the effects of the main factors that might be considered to be determining plant species richness in temperate nature reserves, and evaluated their relative importance. The direct habitat effect on species richness was roughly equal to the direct area effect, but the total direct and indirect effects of area slightly exceeded that of habitat. It was shown that the overall effect of composite variables such as altitude or climatic district can be separated into particular climatic variables, which influence the richness of flora in a context‐specific manner. The statistical explanation of richness variation at the level of families yielded similar results to that for species, indicating that the system of nature conservation provides similar degrees of protection at different taxonomic levels.     

Predicting patch occupancy in fragmented landscapes at the rangewide scale for an endangered species: an example of an American warbler

AIM: Our objective was to identify the distribution of the endangered golden-cheeked warbler (Setophaga chrysoparia) in fragmented oak-juniper woodlands by applying a geoadditive semiparametric occupancy model to better assist decision-makers in identifying suitable habitat across the species breeding range on which conservation or mitigation activities can be focused and thus prioritize management and conservation planning. LOCATION: Texas, USA. METHODS: We used repeated double-observer detection/non-detection surveys of randomly selected (n = 287) patches of potential habitat to evaluate warbler patch-scale presence across the species breeding range. We used a geoadditive semiparametric occupancy model with remotely sensed habitat metrics (patch size and landscape composition) to predict patch-scale occupancy of golden-cheeked warblers in the fragmented oak-juniper woodlands of central Texas, USA. RESULTS: Our spatially explicit model indicated that golden-cheeked warbler patch occupancy declined from south to north within the breeding range concomitant with reductions in the availability of large habitat patches. We found that 59% of woodland patches, primarily in the northern and central portions of the warbler's range, were predicted to have occupancy probabilities ≤0.10 with only 3% of patches predicted to have occupancy probabilities >0.90. Our model exhibited high prediction accuracy (area under curve = 0.91) when validated using independently collected warbler occurrence data. MAIN CONCLUSIONS: We have identified a distinct spatial occurrence gradient for golden-cheeked warblers as well as a relationship between two measurable landscape characteristics. Because habitat-occupancy relationships were key drivers of our model, our results can be used to identify potential areas where conservation actions supporting habitat mitigation can occur and identify areas where conservation of future potential habitat is possible. Additionally, our results can be used to focus resources on maintenance and creation of patches that are more likely to harbour viable local warbler populations.     

Modelling spatial and environmental effects at the edge of the distribution: the red-backed shrike Lanius collurio in Northern Portugal

This study examined the interplay of spatial and environmental effects shaping the range margin of the red‐backed shrike (Lanius collurio) in northern Portugal. The occurrence of shrikes in 10 × 10 km UTM squares was related to three sets of explanatory variables, reflecting environmental effects (climate and habitat), large‐scale spatial trends, and neighbourhood influences (considering an autologistic term); spatial variables were used as surrogates for historical and demographic factors. Multiple logistic regression models were built for each set, and then variation partitioning based on partial regressions isolated the unique and shared components of explained variation. The environmental model revealed a dominant influence of climate effects, with the occurrence of shrikes increasing with frost and thermal amplitude, declining with insolation, and responding unimodally to rainfall. There was a weaker influence of habitat conditions, though shrikes were more likely with increasing cover by annual crops and pastures, and decreasing forest cover. Only a relatively small proportion of explained variation was due to a ‘pure’ environmental component (10.4%), as most variation explained by environmental factors appeared spatially structured (51.9%). The unique contributions of spatial variables to the overall model were also small, though the neighbourhood effects appeared relatively stronger than large‐scale trends. Taken together, results suggested that the south‐western range margin of the red‐backed shrike was largely determined by spatially structured environmental factors. Nevertheless, there were also ‘pure’ environmental factors determining some isolate occurrences irrespective of any spatial structure, and ‘pure’ spatial factors that appeared to favour the occupation of squares surrounding the core distribution areas irrespective of environmental conditions. These results add to the growing evidence that both environmental and spatial factors need to be considered in predictive modelling of species range margins.