Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy and a long-term dataset from Southern Oregon

Yost, Andrew; Petersen, Steven L.; Gregg, Michael A.; Miller, Richard F.

doi:10.1016/j.ecoinf.2008.08.004

Cited by 130 publications

(99 citation statements)

References 18 publications

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“…Both sets of records were used once HMF map was obtained, to compare how well the data fi t such a map. (Yost et al, 2008): (1) MaxEnt (Phillips et al, 2006) was run using the 56 covariates, (2) the gain values were extracted from the Jackknife validation test and a mean test (95% signifi cance level) was applied to determine their confi dence intervals, (3) the covariates with values higher than the upper maximum of the confi dence interval were selected, and (4) a second MaxEnt analysis was run only using the selected covariates (in step 3). Each run in Maxent used the records of the 78 species selected as characteristic of HMF.…”

Section: Methodsmentioning

confidence: 99%

Potential Distribution of Humid Mountain Forest in Mexico

et al. 2012

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Abstract:Humid Mountain Forest is one of the most important Mexican biomes due to the high fl oristic richness and endemism found within its small geographical extension. The distribution of this biome is not well known in the country; accordingly, the goal of this paper is to delimit its distribution by using potential distribution models of species characteristic to the biome as surrogates. Based on herbarium specimens housed at the National Herbarium of Mexico, a database of 78 species restricted or mostly restricted to Humid Mountain Forest was constructed. In addition, 56 environmental covariates (26 climatic, nine soil properties, nine topographic attributes, and 14 remote sensing data) were assembled. Species modeling was done using MaxEnt. Twenty-three covariates defi ned the Humid Mountain Forest distribution in Mexico. Among these, normalized vegetation index for May, total annual precipitation, and organic carbon content in the soil were the most important. According to the models, Humid Mountain Forest in Mexico represents 7% of the total surface of the country and it is found in 25 of the 32 states. Chiapas, Guerrero, Michoacán, and Oaxaca are the most outstanding states since they collectively account for 74% of the total Humid Mountain Forest surface. Keywords: maximum entropy, MODIS remote sensing data, soil properties.Resumen: El Bosque Húmedo de Montaña es uno de los biomas más importantes de México, pues allí se encuentra una gran riqueza fl orística y de endemismos, restringida a una pequeña extensión geográfi ca. Hasta la fecha no se sabe a ciencia cierta la extensión real de este bioma en el país; por tal razón, el objetivo de este trabajo es delimitar su distribución utilizando como variables substitutas la distribución potencial de especies vegetales características de este bioma. Con base en el estudio de especímenes de herbario depositados en el Herbario Nacional de México, se generó una base de datos de 78 especies restringidas o casi restringidas al Bosque Húmedo de Montaña. Por otra parte, se usaron 56 covariables ambientales (26 de clima, nueve propiedades de suelos, nueve atributos topográfi cos y 14 datos de sensores remotos). Los modelos de distribución potencial fueron elaborados utilizando el programa Maxent. Las covariables que resultaron ser signifi cativas para la modelación de la distribución del Bosque Húmedo de Montaña fueron 23; entre las más importantes se encuentran el índice de vegetación normalizada del mes de mayo, la precipitación total anual y el contenido de carbono orgánico en el suelo. De acuerdo con el modelo, el Bosque Húmedo de Montaña en México representa 7% de su territorio y se encuentra en 25 de sus 32 entidades políticas; Chiapas, Guerrero, Michoacán y Oaxaca son los estados más importantes, pues en su territorio se ubica 74% de la superfi cie total del Bosque Húmedo de Montaña. Palabras Clave: datos de sensores remotos de MODIS, máxima entropía, propiedades de suelos. ECOLOGY

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Section: Methodsmentioning

confidence: 99%

Potential Distribution of Humid Mountain Forest in Mexico

et al. 2012

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“…The variable resulting in the lowest drop in gain can be considered the most redundant as its power to fit to the data is compensated for by the remaining variables. Following Yost et al (2008), I ran the jackknife test using 10 random partitions of the dependent data (70% training, 30% testing), with randomised selection of background cells (i.e. predictor data).…”

Section: Methodsmentioning

confidence: 99%

Insights into North Pacific right whale Eubalaena japonica habitat from historic whaling records

Gregr¹

2011

Endang. Species. Res.

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Whaling records from the mid-1800s provide the largest set of observations with which to conduct a basin-scale analysis of potential North Pacific right whale Eubalaena japonica habitat. Since these data lack the concurrent oceanographic data necessary to investigate the species' habitat characteristics I used ocean climate from a 20th century circulation model to create a suitable set of habitat predictors. My goals were to (1) identify regions of suitable habitat and (2) investigate the processes underlying the species-habitat relationship by (3) examining model performance at different spatial and temporal scales. The results show 2 non-overlapping habitat regions in the subarctic North Pacific, supporting the notion of 2 distinct subpopulations. The analysis also implicates surface temperature and temperature variability as strong indicators of potential right whale habitat. Tests of model performance at different scales strongly suggest that at the basin-scale, right whales use regions of cold water with low inter-annual variability and high within-season variability (i.e. areas where high frontal activity occurs predictably from year to year). The significance of these indicators decreased at the regional scale emphasising the coupling of scale and process, and thus the need for different predictors at different scales. Comparisons of models built using different subsets of the dependent data showed how hypotheses can be tested and potential biases in observational data can be explored. Analyses of rare species' habitat such as this can provide guidance for more directed survey efforts and help identify areas and processes of potential biological importance.

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“…An alternative method for assessing variable importance is the jackknife approach [11,12]. This approach excludes one variable at a time when running the model.…”

Section: Variable Responsementioning

confidence: 99%

Use of Maximum Entropy Modeling in Wildlife Research

Baldwin

2009

Entropy

616

415

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Maximum entropy (Maxent) modeling has great potential for identifying distributions and habitat selection of wildlife given its reliance on only presence locations. Recent studies indicate Maxent is relatively insensitive to spatial errors associated with location data, requires few locations to construct useful models, and performs better than other presence-only modeling approaches. Further advances are needed to better define model thresholds, to test model significance, and to address model selection. Additionally, development of modeling approaches is needed when using repeated sampling of known individuals to assess habitat selection. These advancements would strengthen the utility of Maxent for wildlife research and management.

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Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy and a long-term dataset from Southern Oregon

Cited by 130 publications

References 18 publications

Potential Distribution of Humid Mountain Forest in Mexico

Potential Distribution of Humid Mountain Forest in Mexico

Insights into North Pacific right whale Eubalaena japonica habitat from historic whaling records

Use of Maximum Entropy Modeling in Wildlife Research

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