Occupied Grid Cells Research Articles

A Sensitivity Test on the Modifiable Areal Unit Problem in the Spatial Aggregation of Fossil Data

In paleobiology and macroevolution research, the spatial aggregation of fossil data can be influenced by the modifiable areal unit problem (MAUP), wherein the selection of different grid-cell sizes for data aggregation can lead to variations in statistical results. This study presents a case analysis focused on the spatial extent of marine bivalves and brachiopods over time across three Areas of Interest (AOIs) to evaluate the potential impact of the MAUP in grid-based fossil data processing. By employing rectangular grid matrices with cell sizes of 50, 100, 200, and 400 km, this research assesses the MAUP-related sensitivity of two commonly used grid-based proxies for species’ spatial distribution. The results reveal that the proxy based on the number of occupied grid cells (OGCs) is particularly sensitive to changes in cell size, whereas the proxy based on minimum-spanning-tree distance (MST distance) demonstrates greater robustness across varying grid scales. This study underscores that when constructing proxies for species’ spatial distribution ranges using grid matrices, the OGC method is more susceptible to MAUP effects than the MST distance method, warranting increased caution in studies employing the OGC approach.

The last of the maculineans: can we save the emblematic Alcon Blue butterfly Phengaris alcon under climate change when its habitat continues to deteriorate?

In the NW-European region of Flanders (northern Belgium), wet heathlands harbour several threatened species among which the emblematic Alcon Blue butterfly Phengaris alcon is rapidly declining. Since 1990, the number of occupied grid cells in Flanders has decreased from 61 to only six in 2023, a decline of 90%. Due to the loss of many populations, the mean distance between populations tripled from 9 km in the period 1950–1990 to 27 km now. Site-wide egg counts in one of the strongholds of the Alcon Blue in Flanders revealed a strong increase after some initial intensive care conservation measures in the 2000s, but since 2013 a steep decline of 99% has been observed. The standardised egg counts in the remaining six Flemish populations (i.e., monitoring within the framework of the Natura2000 reporting) showed a yearly decrease of 18% since 2016. Despite increased conservation attention and targeted management since the end of the 1990s, the current situation of P. alcon in Belgium is more precarious than ever. Causes of this rapid decline are manifold and they are likely to be amplified by extreme weather conditions caused by climate change. More efficient intensive care measures and more intense cooperation across national borders are now quickly required to regionally preserve this habitat specialist with a unique eco-evolutionary profile.Implications for insect conservation By analysing the causes of the decline of the threatened Alcon Blue butterfly in Flanders, we suggest management and policy measures in an attempt to conserve this and other threatened heathland species in a highly anthropogenic region.

Improving citizen science data for long-term monitoring of plant species in the Netherlands

In 2012, a new volunteer-based recording scheme for vascular plants was launched in the Netherlands. Its purpose is to track the changes in the number of occupied 1-km grid cells for as many native plant species as possible between survey rounds of 8 years. We did not prescribe a strict field protocol to minimize variation in observer effort, but instead chose to statistically correct for this variation with occupancy models. These models require replicated visits to a grid cell per season, which was implemented by having two independent observers survey grid cells and record all plant species observed. Now that a first survey round has ended (2012–2019), we evaluate our approach, i.e. we tested whether the scheme has the potential to produce proper trend estimates. The number of occupied grid cells in the first round was estimated per species, using an occupancy model with day of year, visit duration and observer experience as covariates for detection. The detection probability, which was 0.43 on average, strongly depended on visit duration and day of year. It was possible to estimate the number of occupied grid cells quite precisely for several hundreds of species, such that the statistical power is expected to be high enough to detect changes of 10% between survey rounds. For rare species, however, the power to detect changes is expected to be quite low. We conclude that the approach works well, but further improvements are suggested.

Climate and land‐use change drive habitat loss in a mountain bird species

Mountain areas still feature remnants of traditional land use and consequently constitute a large proportion of the remaining species‐rich habitats in Central Europe. However, their biodiversity is increasingly threatened by changes in climate and land use. The Alpine Ring Ouzel Turdus torquatus alpestris is a typical mountain bird that has recently declined in most of its breeding range. In this study we compared the historical (1986–1987) and recent (2017) distribution of breeding Ring Ouzels in 62 randomly selected 750 × 750 ‐m plots and analysed local colonization and extinction patterns in 558 subunits (= ‘grid cells’) in the southern Black Forest (south‐western Germany). Our study revealed that habitat occupancy has decreased by about one‐third, mainly at lower elevations and in depressed landforms, during the past three decades. Local colonization amounted to 25% and extinction to 66% of the previously vacant or occupied grid cells, respectively. Habitat occupancy and local colonization and extinction were driven by climate and habitat parameters. The Ring Ouzel preferred convex landscape formations such as mountain peaks and ridges with long snow‐cover duration for breeding. Sites with high proportions of deciduous forest and abandoned pasture were avoided. Local colonization was higher at convex landscape formations and by high coniferous forest coverage and forest‐edge length. Local extinction, on the other hand, was lower at convex landscape formations and high‐elevation sites. Our results suggest that shorter persistence of snow fields caused by climate change and degradation of feeding grounds through land‐use abandonment might severely deteriorate food availability for the species, which is likely to have contributed to the observed decline. For conservation of the Alpine Ring Ouzel, we recommend adopting measures to mitigate the negative effects of climate warming and improve habitat quality. The creation of small‐scale mosaics of sparse conifer forests and regularly grazed pastures, especially on north‐ and east‐facing slopes on the lee sides of hills, mountain peaks or ridges, should be supported.

Improving Area of Occupancy Estimates for Parapatric Species Using Distribution Models and Support Vector Machines

As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN's extent of occurrence (EOO) is the general region including the species' range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data-poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data-poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice: Heteromys australis (Least Concern) and Heteromys teleus (Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species' congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low-data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations for H. teleus as its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. For H. teleus, the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species' true occupancy within the post-processed SDM prediction, we recommend that an updated listing for H. teleus include these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post-processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation.

Improving area of occupancy estimates for parapatric species using distribution models and support vector machines.

As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN’s extent of occurrence (EOO) is the general region including the species’ range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data‐poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data‐poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice: Heteromys australis (Least Concern) and Heteromys teleus (Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species’ congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low‐data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations for H. teleus as its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. For H. teleus, the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species’ true occupancy within the post‐processed SDM prediction, we recommend that an updated listing for H. teleus include these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post‐processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation.

Distribution and small‐scale spread of the invasive weed Solanum carolinense in Austria

Solanum carolinense is a perennial herb native to the United States which was accidentally introduced to Austria over 20 years ago. In this study, the current distribution of S. carolinense in Austria and its local spread dynamics in a selected agricultural area have been evaluated based on a literature search and a survey carried out in 2019. The species is an example of an invasive weed that is currently still rare in Austria as its distribution is restricted to southern Austria with only nine occupied grid cells. This corresponds to 0.3 % of all grid cells (n = 2625) in Austria. However, the clustered appearance of the occupied grid cells and the small‐scale spread observed within grid cells indicate an incipient spread. Thus, it can be assumed that the species will likely expand its range in Austria and beyond. It is therefore of importance to further survey S. carolinense and to observe its appearance in new areas in particular by raising awareness among farmers and agricultural advisors.

Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps

Abstract Sampling-based motion planning is a powerful tool in solving the motion planning problem for a variety of different robotic platforms. As its application domains grow, more complicated planning problems arise that challenge the functionality of these planners. One of the main challenges in the implementation of a sampling-based planner is its weak performance when reacting to uncertainty in robot motion, obstacles motion, and sensing noise. In this paper, a multi-query sampling-based planner is presented based on the optimal probabilistic roadmaps algorithm that employs a hybrid sample classification and graph adjustment strategy to handle diverse types of planning uncertainty such as sensing noise, unknown static and dynamic obstacles and an inaccurate environment map in a discrete-time system. The proposed method starts by storing the collision-free generated samples in a matrix-grid structure. Using the resulting grid structure makes it computationally cheap to search and find samples in a specific region. As soon as the robot senses an obstacle during the execution of the initial plan, the occupied grid cells are detected, relevant samples are selected, and in-collision vertices are removed within the vision range of the robot. Furthermore, a second layer of nodes connected to the current direct neighbors are checked against collision, which gives the planner more time to react to uncertainty before getting too close to an obstacle. The simulation results for problems with various sources of uncertainty show a significant improvement compared with similar algorithms in terms of the failure rate, the processing time and the minimum distance from obstacles. The planner is also successfully implemented and tested on a TurtleBot in four different scenarios with uncertainty.

Mind the gap: Can downscaling Area of Occupancy overcome sampling gaps when assessing IUCN Red List status?

AbstractAimThe Area of Occupancy (AOO) of a species is often utilized to assess extinction risk for determining IUCN Red List status. However, the recommended raw‐counts method of summing occupied grid cells likely reflects only sampling effort, as the majority of species have not been sampled across their entire range at the fine grains required by IUCN. More accurate measurements can be generated at coarser grains (so‐called atlas data) as false absences are reduced. If we fit the occupancy‐area relationship to these data, we can extrapolate the relationship down to estimate occupancy at finer grains. Numerous models have been proposed to carry out such occupancy downscaling, but have only been tested on a limited range of species.MethodsWe test the ability of downscaling models to recover fine grain AOO against the raw‐counts method for 28,900 virtual species with a wide range of prevalence and aggregation characteristics, subsampled to reflect common spatial biases in sampling effort. We address several questions for ensuring accurate downscaling: How to generate accurate atlas data? How far can we accurately extrapolate the occupancy‐area relationship given perfect data? Can occupancy downscaling overcome false absences at fine grain sizes? And how does sampling bias and coverage affect accuracy?ResultsDownscaling was more accurate than the raw‐counts method in all scenarios except where sampling coverage was very high and/or the sampling bias was positively related to the species distribution. However, if atlas data contained many false absences, then even downscaling under‐estimated actual occupancy.Main conclusionsOccupancy downscaling has the potential to be a useful tool for estimating AOO for IUCN Red List assessments, especially when sampling coverage is low and the currently recommended method is ineffective. However, its application should be tailored to the species’ characteristics, as well as the sampling coverage and bias of the species’ records.

Widely distributed native and alien plant species differ in arbuscular mycorrhizal associations and related functional trait interactions

It is debated whether alien plants in new environments benefit from being mycorrhizal and whether widely distributed natives and aliens differ in their associations with mycorrhizal fungi. Here, we compared whether species differing in their origin status, i.e. natives, archaeophytes (alien species introduced before the year 1500) and neophytes (introduced after the year 1500), and arbuscular mycorrhizal (AM) status (obligate, facultative, non‐mycorrhizal) differ in their area of occupancy in Germany (i.e. number of occupied grid cells, each ~130 km²). We used generalized linear models, incorporating main effects and up to three‐way interactions combining AM status, origin status and plant functional traits. The latter were chosen to describe the possible trade‐off in carbon allocation either towards the symbiosis or to other plant structures, such as storage organs (significant interactions involving traits were assumed to indicate the existence of such trade‐offs). AM status significantly explained the area of occupancy of natives and neophytes – with facultative mycorrhizal species occupying the largest area in both groups – but was less pronounced among archaeophytes. Archaeophytes may have reduced dependency on AM fungi, as they are generally agricultural weeds and the symbiosis potentially becomes obsolete for plants growing in habitats providing a steady provision of nutrients. Trait interactions between AM status and other functional traits were almost exclusively detected for neophytes. While facultative mycorrhizal neophytes benefit from trade‐offs with other traits related to high C cost in terms of area of occupancy, such trade‐offs were almost absent among natives. This indicates that natives and neophytes benefit differently from the symbiosis and suggests that native AM fungal partners might be less important for neophytic than for native plant species or that more time is required to establish similar relationships between neophytes and native fungal symbionts.

Quantifying the long-term decline of the West European hedgehog in England by subsampling citizen-science datasets

It is increasingly important to be aware of trends in species abundances in order to be able to act ahead of possible irrecoverable declines and extinctions. Long-term monitoring of species is generally used to determine how a species is faring, which is essential knowledge for conservation planning and design. However, monitoring programmes that encompass large areas and long timespans are rare or non-existent for the vast majority of species. Citizen-science-based datasets provide a wealth of data on past and present species’ occurrences but are often biased to a large extent. We evaluate the potential use of such datasets by subsampling datasets collected over different time periods to detect trends in the long-term temporal abundance of West European hedgehogs (Erinaceus europaeus), a species which is thought to be in decline in parts of its geographic range. We used subsampling as a means to account for quantitative differences between two non-systematic datasets of West European hedgehog occurrences in England; one dataset was collected by the public between 1960 and 1975 and one was collected between 2000 and 2015. Here, we confirm and quantify previous anecdotal evidence of a long-term decline of the species throughout England. We find that although the West European hedgehog is still widespread in England, a 5.0 to 7.4 % decline in occupied grid cells was observed when comparing the 2000–2015 dataset to the previous survey in 1960–1975 after adjusting for differences in effort. This suggests that the decline of the relative abundance of West European hedgehogs is moderate in England, 25 % being an amber alert for birds of conservation concern in the UK. Importantly, we show that subsampling disparate citizen-science datasets is a useful tool for monitoring species population trends.

A georeferenced implementation of weighted endemism

Summary Areas of endemism are conservation priorities and indicators of biogeographical processes, representing concentrations of range‐restricted biodiversity. While the term endemism describes geographical range restriction, the technical definition has often been re‐worked. Categorical definitions are increasingly replaced by weighting of species according to range sizes. In various contexts, range sizes have been estimated by the number of occupied map grid cells, the latitudinal or longitudinal range of point locations, or the area of a polygon surrounding point locations. Existing implementations of weighted endemism use the number of occupied grid cells to estimate range. However, this represents area of occupancy (AOO), not extent of occurrence (EOO). We developed endemism calculations that include strictly geographical (georeferenced) interpretations of range (‘span’ and ‘area’), and compared them to cell frequency‐based measures. We compared species weights and endemism scores for 330 004 records of 3083 vascular plant species in 14 328 plots (Biological Survey of South Australia). We provide a self‐contained R function that calculates endemism using alternative range weights and integrates point data with automatically generated rasters. A parallel function tests for deviance from the expected (null) distribution, given observed species richness (nonparametric significance test; outlier metrics). Species range weights based on grid cell frequency (AOO) versus georeferenced ‘span’ and ‘area’ (EOO) are conceptually different and were poorly correlated for the same data set, resulting in differences in the classification of grid cells as outliers from expected endemism. For example, the correlation (Kendall's τ) between endemism based on geographical ‘span’ and cell frequency was just 0·57, when partialling out the influence of species richness, potentially leading to contrasting conservation priorities over 8% or 46 000 km2 of the state of South Australia. Weighting endemism by georeferenced range estimates provides an alternative approach that more explicitly highlights restriction in EOO, generates differences in endemism metrics and requires only georeferenced species records. This approach need not replace existing implementations, but provides an alternative measure of range‐restricted biodiversity. The functions provide efficient calculation of species ranges (with or without geographical outlier exclusion) and weighted endemism with novel implementation of significance tests and outlier detection, in the R environment.

Using ecological and life-history characteristics for projecting species' responses to climate change

Assessing the impact of climate change on range dynamics is difficult in the absence of large-extent distribution data. We developed a novel two-step approach as an instrument for biodiversity risk assessment. First, we established relationships between modelled loss of occupied grid cells (‘range loss’, R2=0.29), or gain of currently unoccupied grid cells (‘range gain’, R2=0.30), for 195 plant species with distributional data under the A1FI climate change scenario up to 2080, and ecological and life history traits (life form, leaf persistence, ecological strategy, pollen vector, Ellenberg indicator values and characteristics derived from species’ ranges). Secondly, we used the resulting coefficients to predict climatic sensitivity for 688 plant species without spatially explicit distributional information. The models predicted range losses of 34±20 % (mean±standard deviation) and range gains of 3±4 %. Specifically, measures of species’ distribution, such as range size, were significantly related to both range loss and range gain. Other traits associated with range loss (e.g. life form, number of floristic zones) were not necessarily related to range gain (instead related to Ellenberg temperature indicator), indicating that two distinct sets of ecological processes govern range expansion and contraction. We found interaction effects between moisture indicator values and life form for range loss, and between moisture and temperature indicator values for range gain. The responses of species to climate change are complex and context dependent. Thus, our results highlight the importance of incorporating trait interactions in models to assess risks of climate change. &nbsp

Using ecological and life-history characteristics for projecting species' responses to climate change

Assessing the impact of climate change on range dynamics is difficult in the absence of large-extent distribution data. We developed a novel two-step approach as an instrument for biodiversity risk assessment. First, we established relationships between modelled loss of occupied grid cells (‘range loss’, R2=0.29), or gain of currently unoccupied grid cells (‘range gain’, R2=0.30), for 195 plant species with distributional data under the A1FI climate change scenario up to 2080, and ecological and life history traits (life form, leaf persistence, ecological strategy, pollen vector, Ellenberg indicator values and characteristics derived from species’ ranges). Secondly, we used the resulting coefficients to predict climatic sensitivity for 688 plant species without spatially explicit distributional information. The models predicted range losses of 34±20 % (mean±standard deviation) and range gains of 3±4 %. Specifically, measures of species’ distribution, such as range size, were significantly related to both range loss and range gain. Other traits associated with range loss (e.g. life form, number of floristic zones) were not necessarily related to range gain (instead related to Ellenberg temperature indicator), indicating that two distinct sets of ecological processes govern range expansion and contraction. We found interaction effects between moisture indicator values and life form for range loss, and between moisture and temperature indicator values for range gain. The responses of species to climate change are complex and context dependent. Thus, our results highlight the importance of incorporating trait interactions in models to assess risks of climate change. &nbsp

Impacts of land cover data selection and trait parameterisation on dynamic modelling of species' range expansion.

Dynamic models for range expansion provide a promising tool for assessing species’ capacity to respond to climate change by shifting their ranges to new areas. However, these models include a number of uncertainties which may affect how successfully they can be applied to climate change oriented conservation planning. We used RangeShifter, a novel dynamic and individual-based modelling platform, to study two potential sources of such uncertainties: the selection of land cover data and the parameterization of key life-history traits. As an example, we modelled the range expansion dynamics of two butterfly species, one habitat specialist (Maniola jurtina) and one generalist (Issoria lathonia). Our results show that projections of total population size, number of occupied grid cells and the mean maximal latitudinal range shift were all clearly dependent on the choice made between using CORINE land cover data vs. using more detailed grassland data from three alternative national databases. Range expansion was also sensitive to the parameterization of the four considered life-history traits (magnitude and probability of long-distance dispersal events, population growth rate and carrying capacity), with carrying capacity and magnitude of long-distance dispersal showing the strongest effect. Our results highlight the sensitivity of dynamic species population models to the selection of existing land cover data and to uncertainty in the model parameters and indicate that these need to be carefully evaluated before the models are applied to conservation planning.

Dynamic occupancy models for analyzing species' range dynamics across large geographic scales

Large-scale biodiversity data are needed to predict species' responses to global change and to address basic questions in macroecology. While such data are increasingly becoming available, their analysis is challenging because of the typically large heterogeneity in spatial sampling intensity and the need to account for observation processes. Two further challenges are accounting for spatial effects that are not explained by covariates, and drawing inference on dynamics at these large spatial scales. We developed dynamic occupancy models to analyze large-scale atlas data. In addition to occupancy, these models estimate local colonization and persistence probabilities. We accounted for spatial autocorrelation using conditional autoregressive models and autologistic models. We fitted the models to detection/nondetection data collected on a quarter-degree grid across southern Africa during two atlas projects, using the hadeda ibis (Bostrychia hagedash) as an example. The model accurately reproduced the range expansion between the first (SABAP1: 1987–1992) and second (SABAP2: 2007–2012) Southern African Bird Atlas Project into the drier parts of interior South Africa. Grid cells occupied during SABAP1 generally remained occupied, but colonization of unoccupied grid cells was strongly dependent on the number of occupied grid cells in the neighborhood. The detection probability strongly varied across space due to variation in effort, observer identity, seasonality, and unexplained spatial effects. We present a flexible hierarchical approach for analyzing grid-based atlas data using dynamical occupancy models. Our model is similar to a species' distribution model obtained using generalized additive models but has a number of advantages. Our model accounts for the heterogeneous sampling process, spatial correlation, and perhaps most importantly, allows us to examine dynamic aspects of species ranges.

Resource‐based determinants of range sizes of forest vascular plants in Germany

AbstractAimTo examining whether niche breadth, niche position and a compound measure of the two variables help to explain the range sizes of forest vascular plants.LocationsDeciduous forests in two regions of Germany, the Weser–Elbe region and Bavarian Alps.MethodsWe compiled range size data for vascular plant species (30 in the Weser–Elbe region, 35 in the Bavarian Alps) on regional, national and continental scales by determining the area of occupancy (number of occupied grid cells) in both regions, Germany and Eurasia. Estimates of realized niche breadth and niche position (ecological optimum) for soil pH and light were based on measurements in 46 sites for all species. Frequency distributions of pH values on regional and national scales served to calculate the ‘available niche breadth’, i.e. niche breadth values corrected for the different availabilities of pH values in the region and country.ResultsRegional range size in the Weser–Elbe region increased with increasing niche breadth for both soil pH and light and with decreasing pH niche position. pH niche breadth was positively correlated to national range size in the Weser–Elbe region and to Eurasian range size in the Bavarian Alps. In the latter region, all other relationships with range size were (partly marginally) non‐significant. Available niche breadth was generally more closely related to the regional and national distribution of species in both regions than either niche breadth or position alone.Main conclusionsNiche breadth and position performed well as predictors of range size only for soil pH and in the Weser–Elbe region, which shows more homogeneous environmental conditions than the Bavarian Alps. If the frequencies of different ranges of pH values can be quantified, the calculation of available niche breadth for soil pH appears to be a promising approach for assessing the possible effects of niche variables on the range sizes of species.

Newt decline in Western Europe: highlights from relative distribution changes within guilds

The recent increase in the number of monitoring schemes has formed the basis for high quality distribution atlases. This provides the opportunity of estimating global and specific decline patterns across regional and national borders. In this framework, this study focused on four sympatric newt species—including the great crested newt (Triturus cristatus), an Annex 2 European Habitats Directive species, over six geographic areas (five countries) in W estern Europe. A relative comparison of distribution maps across time is used here and is based on more than twelve thousands occupied grid cells. It benefits from the definition of a guild, as these species are simultaneously detectable in wetlands. T. cristatus and the alpine newt (Mesotriton alpestris) were the most and the least threatened newt species, respectively, whereas the palmate (Lissotriton helveticus) and smooth newt (Lissotriton vulgaris) had an intermediate decline level at both coarse and fine grain resolutions. However, regional variations across Europe and scale effects were also found. On one hand, these results show that T. cristatus is not only regionally threatened but suffers from a global decline in W estern Europe. On another hand, the results indicate that patterns of decline are not uniform within Europe and that species often considered as common and not threatened are, in fact, declining more than others. Finally, the proposed methodology, i.e. using guilds to assess relative decline, would be useful as a complement to other standardized methods in correctly advising conservation managers and policy makers, particularly for species with more subtle declines.

Assessing the vulnerability of European butterflies to climate change using multiple criteria

Vulnerability of 100 European butterfly species to climate change was assessed using 13 different criteria and data on species distributions, climate, land cover and topography from 1,608 grid squares 30′ × 60′ in size, and species characteristics increasing the susceptibility to climate change. Four bioclimatic model-based criteria were developed for each species by comparing the present-day distribution and climatic suitability of the occupied grid cells with projected distribution and suitability in the future using the HadCM3-A2 climate scenario for 2051–2080. The proportions of disadvantageous land cover types (bare areas, water, snow and ice, artificial surfaces) and cultivated and managed land in the occupied grid squares and their surroundings were measured to indicate the amount of unfavourable land cover and dispersal barriers for butterflies, and topographical heterogeneity to indicate the availability of potential climatic refugia. Vulnerability was also assessed based on species dispersal ability, geographical localization and habitat specialization. Northern European species appeared to be amongst the most vulnerable European butterflies. However, there is much species-to-species variation, and species appear to be threatened due to different combinations of critical characteristics. Inclusion of additional criteria, such as life-history species characteristics, topography and land cover to complement the bioclimatic model-based species vulnerability measures can significantly deepen the assessments of species susceptibility to climate change.

Grid origin affects scaling of species across spatial scales

ABSTRACTAim Distribution maps of species based on a grid are useful for investigating relationships between scale and the number or area of occupied grid cells. A species is scaled up simply by merging occupied grid cells on the observation grid to successively coarser cells. Scale–occupancy relationships (SORs) obtained in this way can be used to extrapolate species down, in other words to compute occupancies at finer scales than the observation scale. In this paper we demonstrate that the SOR is not unique but depends on where one positions the origin of the grid map.Innovation The effect of grid origin on SORs was explored with the aid of the Dutch national data base FLORBASE, which contains the observation records of all 1410 wild vascular plants in the Netherlands on a 1‐km square basis. For each species, we generated 2500 unique SORs by scaling up from 1 km, in steps of 1 km, to squares of 50 km. We computed the sensitivity of the SOR to the grid origin for each species, and subsequently analysed the factors that determined this sensitivity. The effect of grid origin on downscaling was demonstrated by means of a simple power function that we used to extrapolate down from both a 2‐km and a 5‐km grid, to the original 1‐km grid.It appeared that the position of grid origin could have a substantial effect on SORs. The sensitivity of SORs to the position of the grid origin depended on three characteristics of a species’ spatial distribution: rarity, degree of spatial clustering and the position of the distribution relative to the border of the investigated area. Rare species with a clustered distribution near the border were particularly highly sensitive. The dependence of SOR on grid origin caused unpredictable and non‐random errors in downscaled occupancies.Main conclusions In future, the whole bandwidth of scaled occupancies should be considered when testing and interpreting mathematical relationships between scale and occupancy. Moreover, downscaled occupancies should be interpreted cautiously.