Species distribution models as a tool to predict range expansion after reintroduction: A case study on Eurasian beavers (Castor fiber)

Species Distribution Models (SDMs) may provide important information for the follow-up phase of reintroduction operations by identifying the main areas most likely to be colonized by the reintroduced species. We used SDMs to identify the potential distribution of Eurasian beavers (Castor fiber) reintroduced to Serbia and Bosnia and Herzegovina in 2004–2006 after being historically driven to extinction by overhunting. Models were also used to carry out a gap analysis to assess the degree of protection granted by the national reserve networks to the potentially expanding population. Distances from hydrographic network, broadleaved forest, main watercourses and farmland were the main factors influencing model performance. We estimated that suitable habitat covers 14.0% (31,000 km²) of the whole study area. In Serbia, in 2004–2013 beavers expanded their range at a mean colonization speed of 70.9 ± 12.8 km/year (mean ± SD). Only 2.89% of and 9.72% of beaver’s suitable habitat lie within the national network of protected areas of Bosnia and Serbia respectively. We detected new potential areas where beavers will likely settle in the near future, advising on where further monitoring should be carried out. We also identified low suitability areas to be targeted with appropriate management to improve their conditions as well as important regions falling outside reserve boundaries to which protection should be granted.     

Are predators negative or positive predictors of farmland bird species community on a large geographical scale?

Species distribution models (SDMs) are numerical tools that combine species occurrence/density or species richness with environmental data in order to predict particular species’ distribution. In most cases only abiotic environmental parameters are used as predictors, while biotic interactions which control distribution of species and influence the goodness of fit of the SDM, such as predator–prey systems, have been broadly neglected. For this reason, we tested the usefulness of easy to detect predators, such as the Common Buzzard and the Common Raven, as positive and negative predictors, respectively, of farmland bird species richness. We analyzed factors affecting the density of both predators and farmland bird species using data from 958 1 × 1 km² study plots in Poland and a set of 22 environmental variables. Next, we also included these predators’ densities as additional predictors of farmland bird species. Habitat and climatological predictors were aggregated using the Principal Components Analysis and then related to the Common Raven's and the Common Buzzard's densities as well as farmland bird species richness using General Additive Models. Finally, completed models were assessed according to information – theoretic criteria. Our results showed that all the analyzed groups occurred in open areas; the Common Buzzard and passerine bird species preferred traditional farmland, while the Common Raven reached its highest density in modern intensive farmland. Importantly, we documented a significant increase in the goodness of fit of SDMs for farmland bird species, having added the density of predators as negative (Common Raven) and positive (Common Buzzard) predictors. Consequently, our findings suggest that species’ specific models can improve the predictive power of SDMs and can be used as an effective tool for predicting bird diversity with higher accuracy.     

Assessing spatial distribution of Coffea arabica L. in Ethiopia's highlands using species distribution models and geospatial analysis methods

Though there is an increase in popularity of predictive modelling for assessing the geographical distribution of species, there is still a clear gap on explaining geospatial methods to derive the presence/absence of species in terms of geospatial extent besides the ambiguity of robust models. In this paper, we evaluate four major species distribution modelling methods: Artificial Neural Network (ANN), Support Vector Machines (SVM), Maximum Entropy (MaxEnt) and Generalized Linear Model (GLM) with pseudo absence and background absence data. To investigate the efficacy of these models, we present a case study using Coffea arabica L. species in Ethiopia as there was no species distribution modelling that has been done at a local scale especially in the coffee growing areas. We made predictions on 75% subsets and validation on 25% of the 112 presence of the species records that were collected from field observation and 0.5 m spatial resolution of true colour aerial photographs. Twelve biophysical explanatory variables; climatic, remote sensing based and landscape variables were employed in modelling. The results show that MaxEnt with pseudo absence data and SVM with background absence have highest area of understory coffee presence prediction with 12.2% and 23.1% area coverage of indigenous forest, respectively. The result from the model performance test using True Positive Rate (TPR) shows that GLM and SVM with pseudo absence data performed highest (TPR = 0.821). MaxEnt and SVM were the robust modelling methods (TPR = 0.964) using background absence data.     

From topography to hydrology—The modifiable area unit problem impacts freshwater species distribution models

Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors—a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90‐m digital‐elevation model by using the GRASS‐GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km². We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land‐use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1–93.2 and 0.61–0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.     

A habitat quality indicator for common birds in Europe based on species distribution models

The EU 2020 Biodiversity Strategy requires the gathering of information on biodiversity to aid in monitoring progress towards its main targets. Common species are good proxies for the diversity and integrity of ecosystems, since they are key elements of the biomass, structure, functioning of ecosystems, and therefore of the supply of ecosystem services. In this sense, we aimed to develop a spatially-explicit indicator of habitat quality (HQI) at European level based on the species included in the European Common Bird Index, also grouped into their major habitat types (farmland and forest). Using species occurrences from the European Breeding Birds Atlas (at 50 km × 50 km) and the maximum entropy algorithm, we derived species distribution maps using refined occurrence data based on species ecology. This allowed us to cope with the limitations arising from modelling common and widespread species, obtaining habitat suitability maps for each species at finer spatial resolution (10 km × 10 km grid), which provided higher model accuracy. Analysis of the spatial patterns of local and relative species richness (defined as the ratio between species richness in a given location and the average richness in the regional context) for the common birds analysed demonstrated that the development of a HQI based on species richness needs to account for the regional species pool in order to make objective comparisons between regions. In this way, we proved that relative species richness compensated for the bias caused by the inherent heterogeneous patterns of the species distributions that was yielding larger local species richness in areas where most of the target species have the core of their distribution range. The method presented in this study provides a robust and innovative indicator of habitat quality which can be used to make comparisons between regions at the European scale, and therefore potentially applied to measure progress towards the EU Biodiversity Strategy targets. Finally, since species distribution models are based on breeding birds, the HQI can be also interpreted as a measure of the capacity of ecosystems to provide and maintain nursery/reproductive habitats for terrestrial species, a key maintenance and regulation ecosystem service.     

Where the bears roam in Majella National Park,Italy

The Brown bear (Ursus arctos Linnaeus, 1758) occupies contiguous areas in Eastern and Northern Europe. In Western Europe, the largest remnant populations occur in Cantabria, Spain and the Apennines, Italy. Under Italian law the bear and its occupied range are protected. The occupied range of the Apennine brown bear includes Majella National Park. However, information on the distribution within Majella NP is extrapolated and inconsistent, thus precluding evidence-based protected area and species management. To address this lack of information, bear presence records (1996–2010) were collated and corrected for observational bias. Multiple Species Distribution Models (SDMs) were created at 800 m resolution to predict year-round and seasonal bear distribution. A hierarchical, stepwise maxent SDM approach was applied using climatic, terrain, vegetation, and anthropogenic predictors of bear distribution. Occupied ranges were identified by point density analysis of bear presence. Our climate-only SDMs predicted bear presence in areas with relatively low snowfall and temperate temperatures. Year-round bear distribution was also accurately predicted by using temperate-montane elevations and mesic, mesotrophic vegetation substrates, irrespective of vegetation. Ski-resorts were negative predictors of year-round bear occurrence. Bears were predicted in autumn and winter by beech forest, in spring by meadows and in summer by a variety of vegetation categories. The regional and our local models predicted bear throughout the south. However, our predicted and occupied range in the north includes the Orta valley and exclude alpine heights, contrary to the regional models. Only our summer bear range is similar to a regional SDM. We demonstrated that multiple maxent SDMs using a modest number of observations and a comprehensive set of environmental variables may generate essential distributional information for protected area and species management where full wildlife and food source censuses are lacking.     

Three-dimensional visualization of membrane phospholipid distributions in Arabidopsis thaliana seeds: A spatial perspective of molecular heterogeneity

Arabidopsis thaliana has been widely used as a model plant to study acyl lipid metabolism. Seeds of A. thaliana are quite small (approximately 500 × 300 μm and weigh ~ 20 μg), making lipid compositional analyses of single seeds difficult to achieve. Here we have used matrix assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) to map and visualize the three-dimensional spatial distributions of two common membrane phospholipid classes, phosphatidylcholine (PC) and phosphatidylinositol (PI), in single A. thaliana seeds. The 3D images revealed distinct differences in distribution of several molecular species of both phospholipids among different seed tissues. Using data from these 3D reconstructions, the PC and PI mol% lipid profiles were calculated for the embryonic axis, cotyledons, and peripheral endosperm, and these data agreed well with overall quantification of these lipids in bulk seed extracts analyzed by conventional electrospray ionization-mass spectrometry (ESI-MS). In addition, MALDI-MSI was used to profile PC and PI molecular species in seeds of wild type, fad2–1, fad3–2, fad6–1, and fae1–1 acyl lipid mutants. The resulting distributions revealed previously unobserved changes in spatial distribution of several lipid molecular species, and were used to suggest new insights into biochemical heterogeneity of seed lipid metabolism. These studies highlight the value of mass spectrometry imaging to provide unprecedented spatial and chemical resolution of metabolites directly in samples even as small as a single A. thaliana seeds, and allow for expanded imaging of plant metabolites to improve our understanding of plant lipid metabolism from a spatial perspective.     

High-resolution bioclimatic dataset derived from future climate projections for plant species distribution modeling

Species distribution modeling is playing an increasingly prominent role in ecology and global change biology, owing to its potential to predict species range shifts, biodiversity losses, and biological invasion risks for future climates. Such models are now well-established as important tools for biological conservation. However, the lack of high-resolution data for future climate scenarios has seriously limited their application, particularly because of the scale gap between general circulation models (GCMs) and species distribution models (SDMs). A recently introduced change-factor downscaling technique provides a convenient way to build high-resolution datasets from GCM projections. Here, we present a high-resolution (10’ × 10’) global bioclimatic dataset (BioPlant) for plant species distribution. The 15 bioclimatic variables we select are considered those most eco-physiologically relevant. They can be easily calculated from climatic variables common to all GCM projections. In addition to the traditional classes of variables regarding temperature and precipitation, the BioPlant dataset emphasizes the interactions between temperature and precipitation, particularly within plant growing seasons. A preliminary visual analysis shows that variations among GCMs are more significant on a species range scale than on a global scale. Thus, the formerly advocated ensemble modeling method should be applied not only to different SDMs, but also to various GCMs. Statistic analysis suggests that divergent behavior among GCM variations for temperature class variables and classes of precipitation variables requires special attention. Our dataset may provide a common platform for ensemble modeling, and can serve as an example to develop higher-resolution regional datasets.     

Microclimate species distribution models estimate lower levels of climate-related habitat loss for salamanders

Species distribution models (SDMs) largely rely on free-air temperatures at coarse spatial resolutions to predict habitat suitability, potentially overlooking important microhabitat. Integrating microclimate data into SDMs may improve predictions of organismal responses to climate change and support targeting of conservation assets at biologically relevant scales, especially for small, dispersal-limited species vulnerable to climate-change-induced range loss. We integrated microclimate data that account for the buffering effects of forest vegetation into SDMs at a very high spatial resolution (3 m²) for three plethodontid salamander species in Great Smoky Mountains National Park (North Carolina and Tennessee). Microclimate SDMs were used to characterize potential changes to future plethodontid habitat, including habitat suitability and habitat spatial patterns. Additionally, we evaluated spatial discrepancies between predictions of habitat suitability developed with microclimate and coarse-resolution, free-air climate data. Microclimate SDMs indicated substantial losses to plethodontid ranges and highly suitable habitat by mid-century, but at much more conservative levels than coarse-resolution models. Coarse-resolution SDMs generally estimated higher mid-century losses to plethodontid habitat compared to microclimate models and consistently undervalued areas containing highly suitable microhabitat. Furthermore, microclimate SDMs revealed potential areas of future gain in highly suitable habitat within current species’ ranges, which may serve as climatic microrefugia. Taken together, this study highlights the need to develop microclimate SDMs that account for vegetation and its biophysical effects on near-surface temperatures. As microclimate datasets become increasingly available across the world, their integration into correlative and mechanistic SDMs will be imperative for accurately estimating organismal responses to climate change and helping environmental managers tasked with spatially prioritizing conservation assets.     

Landscape heterogeneity metrics as indicators of bird diversity: Determining the optimal spatial scales in different landscapes

Species distribution models are often used to study the biodiversity of ecosystems. The modelling process uses a number of parameters to predict others, such as the occurrence of determinate species, population size, habitat suitability or biodiversity. It is well known that the heterogeneity of landscapes can lead to changes in species’ abundance and biodiversity. However, landscape metrics depend on maps and spatial scales when it comes to undertaking a GIS analysis.We explored the goodness of fit of several models using the metrics of landscape heterogeneity and altitude as predictors of bird diversity in different landscapes and spatial scales. Two variables were used to describe biodiversity: bird richness and trophic level diversity, both of which were obtained from a breeding bird survey by means of point counts. The relationships between biodiversity and landscape metrics were compared using multiple linear regressions. All of the analyses were repeated for 14 different spatial scales and for cultivated, forest and grassland environments to determine the optimal spatial scale for each landscape typology.Our results revealed that the relationships between species’ richness and landscape heterogeneity using 1:10,000 land cover maps were strongest when working on a spatial scale up to a radius of 125–250 m around the sampled point (circa 4.9–19.6 ha). Furthermore, the correlation between measures of landscape heterogeneity and bird diversity was greater in grasslands than in cultivated or forested areas. The multi-spatial scale approach is useful for (a) assessing the accuracy of surrogates of bird diversity in different landscapes and (b) optimizing spatial model procedures for biodiversity mapping, mainly over extensive areas.     

The use of spatial ecological modelling as a tool for improving the assessment of geographic range size of threatened species

We analysed endemic threatened tree and reptile species of Socotra Island (Yemen), characterised by different ecological requirements and spatial distribution, in order to evaluate the usefulness of spatial ecological modelling in the estimation of species extent of occurrence (EOO) and area of occupancy (AOO). Point occurrences for the entire species range were used to model their spatial distribution by Random Forest (RF) and Generalised Linear Model (GLM). For each species the suitability area (SA) was obtained by applying the 0% omission error criterion on the probability map, and compared or integrated with EOO and AOO area obtained by topological methods such as the minimum convex polygon (MCP), α-hull and 2 km × 2 km grid.RF showed a lower prediction error than GLM. Higher accuracy was achieved for species with higher number of occurrences and narrower ecological niche. SA was always greater than AOO measured with the 2 km × 2 km grid method. SA was greater than EOO, measured by both MCP and α-hull methods, for species with localised distribution, while it was smaller for widely distributed species. EOO-α-hull area was equal or smaller than that calculated by MCP depending on the spatial distribution of species. AOO measured considering the SA within the EOO-MCP was greater than that measured using the standard 2 km × 2 km grid. Conversely, AOO calculated considering the suitable area within the EOO-α-hull showed variable results, being smaller or greater than the 2 km × 2 km grid AOO depending on the ecological niche and spatial distribution of species. According to our results, SEM does not provide an effective alternative to topological methods for the estimate of EOO and AOO. However, it may be considered a useful tool to estimate AOO within the boundaries of EOO measured by the α-hull method, because it reduces some potential sources of inconsistency and bias.     

Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index

Accurate estimates of the spatial variability of soil organic matter (SOM) are necessary to properly evaluate soil fertility and soil carbon sequestration potential. In plains and gently undulating terrains, soil spatial variability is not closely related to relief, and thus digital soil mapping (DSM) methods based on soil–landscape relationships often fail in these areas. Therefore, different predictors are needed for DSM in the plains. Time-series remotely sensed data, including thermal imagery and vegetation indices provide possibilities for mapping SOM in such areas. Two low-relief agricultural areas (Peixian County, 28 km × 28 km and Jiangyan County, 38 km × 50 km) in northwest and middle Jiangsu Province, east China, were chosen as case study areas. Land surface diurnal temperature difference (DTD) extracted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and soil-adjusted vegetation index (SAVI) at the peak of growing season calculated from Landsat ETM+ image were used as predictors. Regression kriging (RK) with a mixed linear model fitted by residual maximum likelihood (REML) and residuals interpolated by simple kriging (SK) were used to model and map SOM spatial distribution; ordinary kriging (OK) was used as a baseline comparison. The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy. Results showed that the proposed covariates provided added value to the observations. SAVI aggregated to MODIS resolution was able to identify local highs and lows not apparent from the DTD imagery alone. Despite the apparent similarity of the two areas, the spatial structure of residuals from the linear mixed models were quite different; ranges on the order of 3 km in Jiangyan but 16 km in Peixian, and accuracy of best models differed by a factor of two (3.3 g/kg and 6.3 g/kg SOM, respectively). This suggests that time-series remotely sensed data can provide useful auxiliary variable for mapping SOM in low-relief agricultural areas, with three important cautions: (1) image dates must be carefully chosen; (2) vegetation indices should supplement diurnal temperature differences, (3) model structure must be calibrated for each area.     

A probability distribution model of small -scale species richness in plant communities

We devised a probability distribution model that best expressed species richness per quadrat in grassland communities, and clarified the mechanism by which the mean richness per quadrat was always larger than the variance among quadrats. Our model will aid in the understanding of community structures, and allow comparisons among different communities. The model was constructed based on relatively simple theoretical assumptions about the mechanisms in play in target communities. We assumed in the model that the number of species occurring in an actual quadrat, j, is the sum of “the fundamental number of species”, k (constant), and “a fluctuating number of species”, i (a Poisson variate with the mean of μ); that is, j = k + i, where i, j and k are non-negative integers. The probability that j species occur in a quadrat is given by a Poisson-like distribution (extended Poisson), with two parameters k and μ. The mean species richness in the probability distribution is expressed by λ (= k + μ), and the variance is λ − k. The proposed model afforded a good fit for the observed frequency distribution of species richness per quadrat. If even one species is common among many quadrats, the mean number of species per quadrat is greater than the variance. The greater the number of common species among quadrats is, the larger is the value of k, and then the more pronounced is the difference between the mean and the variance (although the variance does not change). We fitted the model to 55 datasets collected by ourselves from grasslands in various locations (Tibet, Inner Mongolia, Slovakia, or Japan), with varying quadrat size (0.25, 0.0625, or 0.01 m²), and under differing management status (various stocking densities).     

Growth and root distribution of Vallisneria natans in heterogeneous sediment environments

Plant growth, biomass allocation and root distribution were investigated in the submerged macrophyte Vallisneria natans growing in heterogeneous sediments. Experimentally heterogeneous sediment environments were constructed by randomly placing 4 cm of clay or sandy loam into the top (0–4 cm) or bottom (4–8 cm) layer within an experimental tray, providing two homogeneous and two heterogeneous treatments. Biomass accumulation was significantly affected by the experimental treatments: higher in the homogeneous sediment of clay (32 mg per plant) and the two heterogeneous treatments (about 27 mg per plant), but lower in the homogeneous sediment of sandy loam (15 mg per plant). Root: shoot ratio was also different among the four treatments. Compared with the treatments of clay in the top layer, plants allocated more biomass to roots at the treatments of sandy loam in the top layer. Heterogeneous sediments significantly affected root distribution pattern. Compared with the treatments of sandy loam in the bottom layer, root number (7–8 versus 13–14) and total root length (3.6–4.0 cm versus 29.5–40.0 cm) in the bottom layer were significantly higher in the treatments with clay in the bottom layer. These results indicate that both sediment structure and nutrient availability influence growth and root system distribution of V. natans.     

At-sea abundance and spatial distribution of South American sea lion (Otaria byronia) in Chilean Northern Patagonia: How many are there?

The South American sea lion (Otaria byronia) (SSL) is a widespread opportunistic predator that inhabits waters ranging from Southern Ecuador to Southern Chile in the Pacific Ocean and from Southern Brazil to Southern Argentina in the Atlantic. SSL abundance estimates, as for many pinniped species, have relied on shore censuses, with the uncertainty that an indeterminate number of individuals at sea might remain uncounted. The proportion of the population that remains at sea during censuses and their distribution patterns are not clear and has been scarcely assessed. We used line transect sampling to gather information about at-sea abundance, density and spatial distribution of SSL in coastal waters off the Chilean Northern Patagonia. A total of 123 groups were sighted while on-effort, with an estimated density of 0.393 ind./km² (95%CI = 0.262–0.591) and a total abundance, for the surveyed area, of 13,721 individuals (95%CI = 9127–20,627). Even when our survey was rather restricted in spatial range, generalized additive model results showed that depth, distance to SSL rockeries, coastline complexity and geographical coordinates had a significant influence on SSL spatial distribution. The results on abundance and spatial distribution of SSL at sea are discussed in terms of current population estimates. Our substantial at-sea abundance estimate suggests that shore censuses might have been historically biased. Several aspects should be taken into account in further research on SSL abundance, such as including pelagic waters and undertaking simultaneously on-rockery and marine SSL abundance estimates. The results presented here provide valuable insights for revisiting and possibly improve population estimates of SSL and other pinnipeds species worldwide. This is particularly relevant when management and conservation actions need to be taken on those species that have strong interactions with fisheries and aquaculture.     

Development and implementation of a spatial unit non-overlapping water stress index for water scarcity evaluation with a moderate spatial resolution

Water scarcity is a serious global problem, and accurate estimations are urgently needed. The Water stress index (WSI) is one of the most commonly used methods for global or large scale water scarcity evaluation, but this method lacks of consideration of water demand and water supply positions non-overlapped spatial distribution, tends to overestimate water stress when applied to a moderate resolution grid (e.g., 1 km). In this study, we used a non-overlapping water supply-and-demand unit approach to improve the calculation scheme and constructed a spatial unit non-overlapping WSI model (Sun-WSI model). We then applied the new model to the Yarlung Tsangpo-Brahmaputra River (TBR) and estimated monthly water stress from 2006 to 2012 with a 1 km spatial resolution. The results showed that the determination coefficient (R²) between the normalized Drought Index (DI) and water stress was mainly in the range of 0.2–0.7, accounting for 77.4% of the study area. The spatial pattern of water stress estimated by the Sun-WSI model was consistent with the DI. Further analysis showed that both overall and grid water stress estimated by the Sun-WSI model were close to the results from existing studies; however the Sun-WSI model had a higher spatial resolution. With a 1 km resolution, the Sun-WSI model performed better than the conventional WSI with respect to both overall results and spatial details. This suggests that the Sun-WSI model is suitable for evaluating regional or moderate-resolution grid water scarcity.     

The distribution of vascular plant species of conservation concern in Ireland,and their coincidence with designated areas

Protected areas (that are usually designated) play an important role in the effort to halt on-going losses of biodiversity. However, areas outside of designated sites for protection can also hold important elements of biodiversity, and knowledge of their distribution is necessary to ensure effective conservation strategies. We collated and mapped vascular plant distribution data for species of conservation concern on the island of Ireland. For the first time in Ireland, we extracted 6078 distribution records of 176 species of conservation concern and mapped them at the tetrad (2 km × 2 km) scale. We examined the extent to which tetrads with records of species of conservation concern overlapped with designated areas (Natura 2000, Natural Heritage Areas, Areas of Special Scientific Interest). A conservative estimate suggests that many of these tetrads do not overlap with designated areas (in the range of 22–40% for available records). The coincidence of records of individual species with designated areas ranged from 0% to 100% (mean = 79%). The mapped distribution data for all vascular plant species offers guidance to where additional recording may be helpful in supporting conservation activities. The analysis of the distribution of species of conservation concern indicates the importance of both designated areas and the (non-designated) wider countryside for biodiversity conservation. In particular the presence of species of conservation concern in non-designated areas highlights the need for conservation measures outside of designated areas.     

Alexandrium pacificum Litaker sp. nov (Group IV): Resting cyst distribution and toxin profile of vegetative cells in Bizerte Lagoon (Tunisia,Southern Mediterranean Sea)

A high spatial resolution sampling of Alexandrium pacificum cysts, along with sediment characteristics (% H₂O, % organic matter (OM), granulometry), vegetative cell abundance and environmental factors were investigated at 123 study stations in Bizerte Lagoon (Tunisia). Morphological examination and ribotyping of cells obtained from a culture called ABZ1 obtained from a cyst isolated in lagoon sediment confirmed that the species was A. pacificum. The toxin profile from the ABZ1 culture harvested during exponential growth phase was simple and composed of the N-sulfocarbamoyl toxins C1 (9.82 pg toxin cell⁻¹), the GTX6 (3.26 pg toxin cell⁻¹) and the carbamoyl toxin Neo-STX (0.38 pg toxin cell⁻¹). The latter represented only 2.8% of the total toxins in this strain.High abundance of A. pacificum cysts correlated with enhanced percentages of water and organic matter in the sediment. In addition, sediment fractions of less than 63 μm were examined as a favorable potential seedbed for initiation of future blooms and outbreaks of A. pacificum in the lagoon. A significant difference in the cyst distribution pattern was recorded among the lagoon's different zones, with the higher cyst abundance occurring in the inner waters. Also, no correlation due to the specific hydrodynamics of the lagoon was observed in the spatial distribution of A. pacificum cysts and vegetative cells.     

Modelling potential impacts of climate change on the bioclimatic envelope and conservation of the Maned Wolf (Chrysocyon brachyurus)

Forecasting the influence of climatic changes on the distribution of the Maned Wolf (Chrysocyon brachyurus) is important for the conservation of the species. We explored the environmental characteristics than best explain the current distribution of the species, modelled the past and present distribution, projected the niche model into the future, and identified suitable areas for conservation. Niche modelling was performed using Maxent and 21 environmental variables. For past conditions, we considered the Last Glacial Maximum (LGM) and the mid-Holocene (MH) climates. For future conditions, we used the A2a greenhouse gas emission scenario for 2050. Four General Circulation Models (FGOALS 1.0, HADCM3, IPSL-CM4 and MIROC 3.2) were used. The resulting niche model (AUC = 0.89 ± 0.02) predicts maximum probability of presence at precipitation of 106 mm during the coldest quarter, of 396 mm during the warmest quarter, and in totally flat areas. The suitable area for the Maned Wolf currently covers 4,320,364 km². For the LGM, there were inter-model differences in predicted areas (from 819,324 km² to 6,395,886 km²) and in geographic location. The MH models showed drastic changes with respect to the present and considerable inter-model variation. Predictions for 2050 show significant (at least 33%) reductions in distribution. Only a minor fraction (39%) of the current distribution can be considered stable for the period LGM-2050. The FGOALS model was the best option for projecting species occurrence into the future because it included the three localities known for the Maned Wolf from the late Pleistocene and predicts stable areas that coincide with spatial patterns of genetic diversity. The FGOALS projection for 2050 predicts a 33% reduction in suitable habitats, indicating some stable areas (central South America) that will probably be key sites for the conservation of the species.