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      Loss of potential bat habitat following a severe wildfire: a model-based rapid assessment

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          Abstract

          Fire is a major disturbance that affects ecological communities, and when fire events increase in frequency or extent, they may jeopardise biodiversity. Although long-term studies are irreplaceable to understand how biological communities respond to wildfires, a rapid, efficient assessment of the consequences of wildfire is paramount to inform habitat management and restoration. Although Species Distribution Models (SDMs) may be applied to achieve this goal, they have not yet been used in that way. In summer 2017, during an extended drought that affected Italy, a severe wildfire occurred in the Vesuvius National Park (southern Italy). We applied SDMs to assess how much potential habitat was lost by the 12 bat species occurring in the area because of the wildfire, and whether habitat fragmentation increased following the event. Our analysis supported the hypotheses we tested (i.e. that the fire event potentially affected all species through habitat reduction and fragmentation) and that the bat species potentially most affected were those adapted to foraging in cluttered habitat (forest). We show that SDMs are a valuable tool for a first, rapid assessment of the effects of large-scale wildfires, and that they may help identify the areas that need to be monitored for animal activity and phenology, and to assist in saving human and financial resources.

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          ORIGINAL ARTICLE: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar

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            Climate-induced variations in global wildfire danger from 1979 to 2013

            Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km2 (25.3%) of the Earth's vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (>1.0 σ above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km2 (53.4%) during the second half of the study period. If these fire weather changes are coupled with ignition sources and available fuel, they could markedly impact global ecosystems, societies, economies and climate.
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              Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

              MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.
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                Author and article information

                Journal
                International Journal of Wildland Fire
                Int. J. Wildland Fire
                CSIRO Publishing
                1049-8001
                2018
                2018
                : 27
                : 11
                : 756
                Article
                10.1071/WF18072
                087708ac-4d8b-4372-a0ef-878881f0e8a4
                © 2018
                History

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