Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines

Abstract Climate change is projected to alter the geographic distribution of forest ecosystems. This study aimed to evaluate the consequences of climate change on geographical distributions and habitat suitability of 14 threatened forest tree species in the Philippines. Based on the principle of maximum entropy, it utilized a machine algorithm called Maxent to estimate a target probability distribution and habitat suitability of the selected species. Threatened forest tree species occurrence records and sets of biophysical and bioclimatic variables were inputted to Maxent program to predict current and future distribution of the species. The Maxent models of the threatened species were evaluated using Receiver Operating Characteristics Area Under Curve (ROC AUC) and True Skill Statistics (TSS) tests which revealed that the models generated were better than random. The Maxent models ROC AUC values of the 14 species range from 0.70 to 0.972 which is higher than 0.5 of a null model. Based on TSS criteria, Maxent models performed good in two species, very good in ten species, and excellent in two species. Seven species ( Afzelia rhomboidea ; Koordersiodendron pinnatum ; Mangifera altissima ; Shorea contorta ; Shorea palosapis ; Shorea polysperma ; Vitex parviflora ) were found to likely benefit from future climate due to the potential increase in their suitable habitat while the other seven species ( Agathis philippinensis ; Celtis luzonica ; Dipterocarpus grandiflorus ; Shorea guiso ; Shorea negrosensis ; Toona calantas ; Vatica mangachapoi ) will likely experience decline in their suitable habitat. This study provided an initial understanding on how the distribution of threatened forest trees will be affected by climate change in the Philippines. The generated species distribution models and habitat suitability maps could be used as basis in formulating appropriate science-based adaptation policies, strategies and measures that could enhance the resilience of those threatened forest tree species and their natural ecosystems to current and future climate.

[1]  N. Dudley,et al.  Wildlife in a changing climate. , 2012 .

[2]  A. Fischlin,et al.  Ecosystems, their properties, goods and services , 2007 .

[3]  Sunil Kumar,et al.  Spatial heterogeneity influences native and nonnative plant species richness. , 2006, Ecology.

[4]  E. Martínez‐Meyer Climate Change and Biodiversity: Some Considerations in Forecasting Shifts in Species' Potential Distributions , 2005 .

[5]  Andrew Jarvis,et al.  Hole-filled SRTM for the globe Version 4 , 2008 .

[6]  Steven J. Phillips Transferability, sample selection bias and background data in presence‐only modelling: a response to Peterson et al. (2007) , 2008 .

[7]  R. Lasco,et al.  Reducing emissions from deforestation and forest degradation plus (REDD+) in the Philippines: will it make a difference in financing forest development? , 2013, Mitigation and Adaptation Strategies for Global Change.

[8]  Roger A. Baldwin,et al.  Use of Maximum Entropy Modeling in Wildlife Research , 2009, Entropy.

[9]  R. Leemans,et al.  Comparing global vegetation maps with the Kappa statistic , 1992 .

[10]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[11]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[12]  A. Bunn,et al.  Influence of bioclimatic variables on tree‐line conifer distribution in the Greater Yellowstone Ecosystem: implications for species of conservation concern , 2008 .

[13]  Omri Allouche,et al.  Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) , 2006 .

[14]  A. Peterson,et al.  Predicting distributions of known and unknown reptile species in Madagascar , 2003, Nature.

[15]  Sunil Kumar,et al.  Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia , 2009 .

[16]  J. Ragle,et al.  IUCN Red List of Threatened Species , 2010 .

[17]  B. Benito,et al.  Assessing extinction-risk of endangered plants using species distribution models: a case study of habitat depletion caused by the spread of greenhouses , 2009, Biodiversity and Conservation.

[18]  Sam Veloz,et al.  Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models , 2009 .

[19]  J. Palutikof,et al.  Climate change 2007 : impacts, adaptation and vulnerability , 2001 .

[20]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[21]  S. Stuart,et al.  Wildlife in a changing world : an analysis of the 2008 IUCN red list of threatened species , 2009 .

[22]  Catherine H. Graham,et al.  A comparison of methods for mapping species ranges and species richness , 2006 .

[23]  S. Weiss,et al.  GLM versus CCA spatial modeling of plant species distribution , 1999, Plant Ecology.

[24]  P. Hernandez,et al.  The effect of sample size and species characteristics on performance of different species distribution modeling methods , 2006 .

[25]  R. Pearson Species’ Distribution Modeling for Conservation Educators and Practitioners , 2010 .

[26]  T. Weber Maximum entropy modeling of mature hardwood forest distribution in four U.S. states , 2011 .

[27]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[28]  C. J. Huberty,et al.  Applied Discriminant Analysis , 1994 .

[29]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[30]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[31]  M. Araújo,et al.  Five (or so) challenges for species distribution modelling , 2006 .

[32]  R. Kjelgren,et al.  Plant Species vulnerability to climate change in peninsular Thailand. , 2011 .

[33]  A. Peterson,et al.  Effects of sample size on the performance of species distribution models , 2008 .

[34]  R. Pearson,et al.  Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar , 2006 .

[35]  L. Beaumont,et al.  Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions , 2005 .

[36]  Sunil Kumar,et al.  Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US , 2009 .

[37]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[38]  A. Guisan,et al.  Predicting reptile distributions at the mesoscale: relation to climate and topography , 2003 .

[39]  S. Manel,et al.  Evaluating presence-absence models in ecology: the need to account for prevalence , 2001 .

[40]  Robert P. Anderson,et al.  Evaluating predictive models of species’ distributions: criteria for selecting optimal models , 2003 .

[41]  Robert P. Anderson,et al.  Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador , 2004 .

[42]  Monica Papeş,et al.  Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents , 2007 .