http://hdl.handle.net/10174/54 2026-04-06T19:19:42Z http://hdl.handle.net/10174/28016 Title: Dynamic selection of dispersal pathways for species persistence under climate change Authors: Alagador, Diogo; Cerdeira, Jorge O.; Araújo, Miguel B. Abstract: Ongoing climate change is already affecting distributions of many species. Future impacts of climate change are expected to be even greater. Conservation planning methodologies are usually based on the assumption that species distributions change relatively slowly unless they are directly affected by human activities, but this assumption is inappropriate under climate change. To address this problem, we develop a model that, assuming a fixed budget limiting the selection of areas devoted to conservation, selects areas for each of different periods of time, and indicates how species disperse between selected areas on successive periods. These areas are termed dispersal pathways. Their effectiveness is assessed based on the performance to retain species suitable climates over time, and on the ability of species to disperse between the areas. The model identifies maximum effective dispersal pathways, limited to some given budget. We applied the model to nine Iberian species and considered four climate change and budgetary scenarios. Climate change scenarios assuming reductions of greenhouse gas emissions had relatively modest gains in species retention areas. But larger budgets for area selection translate in significantly better retention levels. Nevertheless, our model identified species that, regardless the high conservation investment attained with unlimited budget, have a very limited ability to disperse to climatically suitable areas. Connectivity enhancement and assisted colonization could be considered for such cases. 2011-07-14T23:00:00Z http://hdl.handle.net/10174/22796 Title: Re-scaling of model evaluation measures to allow direct comparison of their values Authors: Barbosa, A. Márcia Abstract: Species distribution models are increasingly used in ecology, biogeography and climate change research, and are usually complemented with one or more metrics evaluating their performance. Not all metrics vary within the same scale of measurement: for example, Cohen’s kappa and the true skill statistic (TSS) may range between -1 and 1, while most other widely used metrics range only between 0 and 1. Values of different measures are thus not directly comparable, and e.g. a kappa or TSS value of 0.6 does not denote (although it may at first sight suggest) lower discriminative accuracy than an area under the curve (AUC) of 0.8. Yet, these measures are often presented side by side without a clear acknowledgement of this scale difference. I propose clearly acknowledging such difference, or else using a simple formula to standardize these measures so that their values can be compared more directly. The following equation converts an evaluation score that ranges from -1 to 1 into its corresponding value in the 0-to-1 scale: (score+1)/2. Conversion can also be done the other way around with 2(score-0.5). This standardization is implemented in the modEvA* package for R (currently available on R-Forge), both as an independent function and as an option within other functions that compute and compare model evaluation measures. 2015-01-01T00:00:00Z