Updating known distribution models for forecasting climate change impact on endangered species

To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli’s Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species’ distribution, instead of building new models that are based on climate change variables only.Ministerio de Ciencia e Innovación and FEDER (project CGL2009-11316/BOS

Estimating how inflated or obscured effects of climate affect forecasted species distribution

Climate is one of the main drivers of species distribution. However, as different environmental factors tend to co-vary, the effect of climate cannot be taken at face value, as it may be either inflated or obscured by other correlated factors. We used the favourability models of four species (Alytes dickhilleni, Vipera latasti, Aquila fasciata and Capra pyrenaica) inhabiting Spanish mountains as case studies to evaluate the relative contribution of climate in their forecasted favourability by using variation partitioning and weighting the effect of climate in relation to non-climatic factors. By calculating the pure effect of the climatic factor, the pure effects of non-climatic factors, the shared climatic effect and the proportion of the pure effect of the climatic factor in relation to its apparent effect (r), we assessed the apparent effect and the pure independent effect of climate. We then projected both types of effects when modelling the future favourability for each species and combination of AOGCM-SRES (two Atmosphere-Ocean General Circulation Models: CGCM2 and ECHAM4, and two Special Reports on Emission Scenarios (SRES): A2 and B2). The results show that the apparent effect of climate can be either inflated (overrated) or obscured (underrated) by other correlated factors. These differences were species-specific; the sum of favourable areas forecasted according to the pure climatic effect differed from that forecasted according to the apparent climatic effect by about 61% on average for one of the species analyzed, and by about 20% on average for each of the other species. The pure effect of future climate on species distributions can only be estimated by combining climate with other factors. Transferring the pure climatic effect and the apparent climatic effect to the future delimits the maximum and minimum favourable areas forecasted for each species in each climate change scenario.Ministerio de Ciencia e Innovación and FEDER (project CGL2009-11316/BOS). D. Romero is a PhD student at the University of Malaga with a grant of the Ministerio de Educacio´n y Ciencia (AP 2007-03633

Projected Range Contractions of European Protected Oceanic Montane Plant Communities: Focus on Climate Change Impacts Is Essential for Their Future Conservation

Global climate is rapidly changing and while many studies have investigated the potential impacts of this on the distribution of montane plant species and communities, few have focused on those with oceanic montane affinities. In Europe, highly sensitive bryophyte species reach their optimum occurrence, highest diversity and abundance in the northwest hyperoceanic regions, while a number of montane vascular plant species occur here at the edge of their range. This study evaluates the potential impact of climate change on the distribution of these species and assesses the implications for EU Habitats Directive-protected oceanic montane plant communities. We applied an ensemble of species distribution modelling techniques, using atlas data of 30 vascular plant and bryophyte species, to calculate range changes under projected future climate change. The future effectiveness of the protected area network to conserve these species was evaluated using gap analysis. We found that the majority of these montane species are projected to lose suitable climate space, primarily at lower altitudes, or that areas of suitable climate will principally shift northwards. In particular, rare oceanic montane bryophytes have poor dispersal capacity and are likely to be especially vulnerable to contractions in their current climate space. Significantly different projected range change responses were found between 1) oceanic montane bryophytes and vascular plants; 2) species belonging to different montane plant communities; 3) species categorised according to different biomes and eastern limit classifications. The inclusion of topographical variables in addition to climate, significantly improved the statistical and spatial performance of models. The current protected area network is projected to become less effective, especially for specialised arctic-montane species, posing a challenge to conserving oceanic montane plant communities. Conservation management plans need significantly greater focus on potential climate change impacts, including models with higher-resolution species distribution and environmental data, to aid these communities’ long-term survival

Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns?

The objective of this study was to evaluate the performance of stacked species distribution models in predicting the alpha and gamma species diversity patterns of two important plant clades along elevation in the Andes. We modelled the distribution of the species in the Anthurium genus (53 species) and the Bromeliaceae family (89 species) using six modelling techniques. We combined all of the predictions for the same species in ensemble models based on two different criteria: the average of the rescaled predictions by all techniques and the average of the best techniques. The rescaled predictions were then reclassified into binary predictions (presence/absence). By stacking either the original predictions or binary predictions for both ensemble procedures, we obtained four different species richness models per taxa. The gamma and alpha diversity per elevation band (500 m) was also computed. To evaluate the prediction abilities for the four predictions of species richness and gamma diversity, the models were compared with the real data along an elevation gradient that was independently compiled by specialists. Finally, we also tested whether our richness models performed better than a null model of altitudinal changes of diversity based on the literature. Stacking of the ensemble prediction of the individual species models generated richness models that proved to be well correlated with the observed alpha diversity richness patterns along elevation and with the gamma diversity derived from the literature. Overall, these models tend to overpredict species richness. The use of the ensemble predictions from the species models built with different techniques seems very promising for modelling of species assemblages. Stacking of the binary models reduced the over-prediction, although more research is needed. The randomisation test proved to be a promising method for testing the performance of the stacked models, but other implementations may still be developed

Using habitat models to identify marine Important Bird and Biodiversity Areas for Chinstrap penguins in the South Orkney Islands

Tracking individual marine predators can provide vital information to aid the identification of important activity (foraging, commuting, rafting, resting, etc.) hotspots and therefore also to delineate priority sites for conservation. However, in certain locations (e.g. Antarctica) many marine mammal or seabird colonies remain untracked due to logistical constraints, and the colonies that are studied may not be the most important in terms of conservation priorities. Using data for one of the most abundant seabirds in the Antarctic as a case study (the Chinstrap penguin Pygoscelis antarcticus), we tested the use of correlative habitat models (used to predict distribution around untracked colonies) to overcome this limitation, and to enable the identification of important areas at-sea for colonies where tracking data are not available. First, Important Bird and Biodiversity Areas (IBA) were identified using a standardised, published approach using empirical data from birds tracked from colonies located in the South Orkney Islands. Subsequently, novel approaches using predicted distributions of Chinstrap penguins derived from habitatcorrelative habitat models were applied to identify important marine areas, and the results compared with the IBAs. Data were collected from 4 colonies over 4 years and during different stages of the breeding season. Results showed a high degree of overlap between the areas identified as important by observed data (IBAs) and by predicted distributions, revealing that habitat preference models can be used with a high degree of confidence to identify marine IBAs for these penguins. We provide a new method for designating a network of marine IBAs for penguins in Antarctic waters, based on outputs from habitatcorrelative habitat models when tracking data are not available. This can contribute to an evidence-based and precautionary approach to aid the management framework for Antarctic fisheries and for the protection of birds

Dva zanimljiva terminološka rječnika

Authors thank United States Fleet Forces Command and Naval Facilities Engineering Command Atlantic for funding and support for the development of this gap analysis.Heterogeneous data collection in the marine environment has led to large gaps in our knowledge of marine species distributions. To fill these gaps, models calibrated on existing data may be used to predict species distributions in unsampled areas, given that available data are sufficiently representative. Our objective was to evaluate the feasibility of mapping cetacean densities across the entire Mediterranean Sea using models calibrated on available survey data and various environmental covariates. We aggregated 302,481 km of line transect survey effort conducted in the Mediterranean Sea within the past 20 years by many organisations. Survey coverage was highly heterogeneous geographically and seasonally: large data gaps were present in the eastern and southern Mediterranean and in non-summer months. We mapped the extent of interpolation versus extrapolation and the proportion of data nearby in environmental space when models calibrated on existing survey data were used for prediction across the entire Mediterranean Sea. Using model predictions to map cetacean densities in the eastern and southern Mediterranean, characterised by warmer, less productive waters, and more intense eddy activity, would lead to potentially unreliable extrapolations. We stress the need for systematic surveys of cetaceans in these environmentally unique Mediterranean waters, particularly in non-summer months.Publisher PDFPeer reviewe