A time series of 14-year distribution data of Zostera marina in the Ems estuary (The Netherlands) was used to build different data subsets: (1) total presence area; (2) a conservative estimate of the total presence area, defined as the area which had been occupied during at least 4 years; (3) core area, defined as the area which had been occupied during at least 2/3 of the total period; and (4–6) three random selections of monitoring years. On average, colonized and disappeared areas of the species in the Ems estuary showed remarkably similar transition probabilities of 12.7% and 12.9%, respectively. SDMs based upon machine-learning methods (Boosted Regression Trees and Random Forest) outperformed regression-based methods. Current velocity and wave exposure were the most important variables predicting the species presence for widely distributed data. Depth and sea floor slope were relevant to predict conservative presence area and core area.
We generated 380 S‐SDMs of 1224 tree species in Mesoamerica by combining 19 distribution modelling methods with 20 different thresholds using presence‐only data from the Global Biodiversity Information Facility. We compared the predicted richness and composition with inventory data obtained from the BIOTREE‐NET forest plot database. We designed two indicators of predictive performance that were based on the diversity factors used to measure species turnover: a (shared species between the observed and predicted compositions), b and c (the exclusive species of the predicted and observed compositions respectively) and compared them with the Sorensen and Beta‐Simpson turnover measures. Some modelling methods – especially machine learning and ensemble model forecasting methods performed significantly better than others in minimizing the error in predicted richness and composition. Our results also indicate that restrictive thresholds (with high omission errors) lead to more accurate S‐SDMs in terms of species richness and composition. Here, we demonstrate that particular combinations of modelling methods and thresholds provide results with higher predictive performance.
Our assessments showed little association between bird richness patterns and the cover of protected areas (PAs) across EU countries. The congruence between high-value richness areas of all bird species and IBS with PAs cover was moderate, suggesting that different conservation planning targets should be taken into account to safeguard IBS, or the composition of bird species. Our results also showed that 16 (3.9%) threatened species were present in gaps of PAs. The poor relationship between PAs cover and bird richness pattern found herein may provide evidence that the establishment of SPAs across Europe may not be fully accounting for richness patterns to enhance the performance of the current network.
In this paper, we present the development of ModeleR, a repository of models accessible from the web, which enables the user to design, document, manage, and execute environmental models.
The results indicate that greenhouses and construction activities (mainly for tourist purposes) exert a strong impact on the populations of this endangered species. The habitat depletion showed peaks that constitute the destruction of 85% of the initial area in only 20 years for some populations of L. nigricans. According to the forecast established by the model, a rapid extinction could take place and some populations may disappear as early as the year 2030. Fragmentation-cadence analysis can help identify population units of primary concern for its conservation, by means of the adoption of improved management and regulatory measures.
According to the simulations, the suitable habitat for the key species inhabiting the summit area, where most of the endemic and/or rare species are located, may disappear before the middle of the century. The other key species considered show moderate to drastic suitable habitat loss depending on the considered scenario. Climate warming should provoke a strong substitution dynamics between species, increasing spatial competition between both of them. In this study, we introduce the application of differential suitability concept into the analysis of potential impact of climate change, forest management and environmental monitoring, and discuss the limitations and uncertainties of these simulations.
Quaternary palaeopalynological records collected throughout the Iberian Peninsula and species distribution models (SDMs) were integrated to gain a better understanding of the historical biogeography of the Iberian Abies species (i.e. Abies pinsapo and Abies alba). We hypothesize that SDMs and Abies palaeorecords are closely correlated, assuming a certain stasis in climatic and topographic ecological niche dimensions. In addition, the modelling results were used to assign the fossil records to A. alba or A. pinsapo, to identify environmental variables affecting their distribution, and to evaluate the ecological segregation between the two taxa.
In this paper, we propose the application of SDMs to assess the extinction-risk of plant species in relation to the spread of greenhouses in a Mediterranean landscape, where habitat depletion is one of the main causes of biodiversity loss.
We we develop a methodology predicting the expansion of greenhouses by combining a species distribution model (MaxEnt) and a simulator of land use change (Geomod).