Movement distances enhance validity of predictive models

Ko, Chia‐Ying; Root, Terry L.; Lee, Pei-Fen

doi:10.1016/j.ecolmodel.2010.12.001

Cited by 17 publications

(21 citation statements)

References 48 publications

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“…A maximum-entropy approach (i.e., Maxent) (Phillips et al 2006), which individually analyzes the weights of environmental factors and calculates a continuous probability value for each species' distribution and has been estimated its high predictive performance among the Taiwanese species taxa , Ko et al 2011, was used to project the future potential distributions. With five GCMs, two scenarios, and four snapshot years, each species distribution was predicted by a total of 40 models.…”

Section: Model Use and Statistical Analysismentioning

confidence: 99%

Global Change Projections for Taiwan Island Birds: Linking Current and Future Distributions

Lee¹,

Ko²,

Root³

et al. 2012

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The earth is warming rapidly. Species around the world must adapt to the increasing heat and to the rapid rate of temperature change. Decision makers and managers must aid species to adapt and to keep up with the changes if they are not able to do so on their own. Special attention needs to be paid to small islands because they are at high risk for the loss of unique and threatened systems and species, and face habitat loss as a consequence of climate-induced rises in sea level. In this study, we examined 17 endemic avian species on the island of Taiwan. Bird observations from 1993 to 2004 were compared to modeled distributions for 2020, 2050, 2080 and 2100. We used 5 general circulation models (CCCMA, CCSR, EHAM4, GFDL, and HADCM3) for the Intergovernmental Panel for Climate Change A2 and B2 scenarios. Results show that the distributions of 15 out of 17 species are predicted to shift up in elevation with warming. As the lower distributional limits contract to higher elevation, the upper edge of their current distributions cannot shift up in elevation because they were already near or at the tops of the mountains. Consequently, their distributions are predicted to shrink over time. The median elevation of each of these species' distributions is higher than the median elevation of all available habitats on Taiwan. In addition, we find that a few common species are predicted to become rare species under climate change. Two of the 17 species examined are not near the tops of the mountains and are the only species that have median elevations of their distributions lower than the median of all available habitats on Taiwan. These 2 species are predicted to expand the upper-elevation distribution limit but not to contract the lower-elevational ReseARCh ARTICLe Launched to accelerate biodiversity conservationA peer-reviewed open-access journal Chia-Ying Ko et al. / Nature Conservation 2: 21-40 (2012) 22 limit, which results in a widening of their distributions. Hence, we suggest that the median elevation of a species' current distribution plays a key rule and can be further used as an index of the response birds most likely will exhibit as the temperature increases.

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Section: Model Use and Statistical Analysismentioning

confidence: 99%

Global Change Projections for Taiwan Island Birds: Linking Current and Future Distributions

Lee¹,

Ko²,

Root³

et al. 2012

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“…For each validation, we analyzed (i) the area under the ROC curves (AUC; Fawcett 2004;Ko et al 2011), (ii) the correct classification rate (CCR; Ahmadi et al 2013), (iii) the Cohen's kappa (k) (Manel et al 2001), and (iv) the Boyce's index (B) (Boyce et al 2002;Jones-Farrand et al 2011). We tested for residual spatial autocorrelation with Moran's I correlogram (1-predicted values for each location; De Marco et al 2008).…”

Section: Modeling Methodsmentioning

confidence: 99%

Non-invasive genetic sampling to predict wolf distribution and habitat suitability in the Northern Italian Apennines: implications for livestock depredation risk

et al. 2015

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Non-invasive genetic sampling has been used to reconstruct spatial patterns of carnivore distributions, identify regions where conflicts with human activities could threaten the survival of a species, and assess the effectiveness of conservation strategies. In this study, we used detailed information on wolf (Canis lupus) and livestock distributions to infer depredation risks in a wide area of the Italian Apennines. We carried out a General Niche Environment System Factor Analysis (GNESFA) to define the potential distribution of wolves genotyped from 8565 samples collected during 12 years of noninvasive genetic monitoring in 3622 locations. Habitat suitability models indicated that the proportion of meadows, altitude, slope, roughness, and distance from human settlements were the main factors positively related to the potential wolf distribution, in contrast with the extension of cultivated fields and human settlements. Results of GNESFA were used to infer the local depredation risk, which was high in 46.9 % of the pastures, and to rank the areas where prevention tools should be used with priority. In this way, the use of often-limited financial resources for prevention could be promoted in pastures with the highest depredation risk and conflicts between husbandry and wolf presence might be mitigated.

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“…Altitude and slope (only having modest variation across the study area) are expected to be poor predictors at the landscape scale, though both may influence the seasonal distribution of elephant at local scale (de Knegt, 2010;Ngene et al, 2009b). Distribution models that explicitly account for species' ecological traits, such as large body size, wide range size and high vagility (Ko et al, 2011;McPherson and Jetz, 2007), imperfect detectability (Mackenzie et al, 2002) as well as the area surrounding a site (i.e. environmental context; de Knegt et al, 2011;Guisan and Thuiller, 2005), produce more accurate predictions than models based on point data alone, including presence-only models (de Knegt et al, 2011;Ko et al, 2011;McPherson and Jetz, 2007;Rota et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Although in our study the probability of false absences was minimized by repeated total counts with narrow confidence limits coupled to the high detectability of elephant in the open vegetation (Prins and Douglas-Hamilton, 1990), total counts were spatially and temporally discontinuous (seasonal snapshots) and elephant is a large and highly vagile species. Employing coarse-grained predictors (Guisan and Thuiller, 2005) or a buffer around the elephant locations (de Knegt et al, 2011;Ko et al, 2011) was not considered in our study because the grain and buffer size are arbitrarily chosen and the area they include may not have the same likelihood of occupancy as the point where the elephant was observed. We therefore present a modeling approach based on the probability of elephant occupancy to account for elephant vagility, temporal discontinuity of total counts as well as environmental context.…”

Section: Introductionmentioning

confidence: 99%

“…Distribution models that explicitly account for species' ecological traits, such as large body size, wide range size and high vagility (Ko et al, 2011;McPherson and Jetz, 2007), imperfect detectability (Mackenzie et al, 2002) as well as the area surrounding a site (i.e. environmental context; de Knegt et al, 2011;Guisan and Thuiller, 2005), produce more accurate predictions than models based on point data alone, including presence-only models (de Knegt et al, 2011;Ko et al, 2011;McPherson and Jetz, 2007;Rota et al, 2011). Although in our study the probability of false absences was minimized by repeated total counts with narrow confidence limits coupled to the high detectability of elephant in the open vegetation (Prins and Douglas-Hamilton, 1990), total counts were spatially and temporally discontinuous (seasonal snapshots) and elephant is a large and highly vagile species.…”

Section: Introductionmentioning

confidence: 99%

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