Use of Maximum Entropy Modeling in Wildlife Research

Entropy - Tập 11 Số 4 - Trang 854-866
Roger A. Baldwin1
1Kearney Agricultural Center, University of California, 9240 South Riverbend Avenue, Parlier, CA 93648, USA

Tóm tắt

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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Tài liệu tham khảo

MacKenzie, 2002, Estimating site occupancy rates when detection probabilities are less than one, Ecology, 83, 2248, 10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2

Gu, 2004, Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models, Biol. Conserv., 116, 195, 10.1016/S0006-3207(03)00190-3

Elith, 2006, Novel methods improve prediction of species’ distributions from occurrence data, Ecography, 29, 129, 10.1111/j.2006.0906-7590.04596.x

Hernandez, 2006, The effect of sample size and species characteristics on performance of different species distribution modeling methods, Ecography, 29, 773, 10.1111/j.0906-7590.2006.04700.x

Phillips, 2006, Maximum entropy modeling of species geographic distributions, Ecol. Model., 190, 231, 10.1016/j.ecolmodel.2005.03.026

Phillips, S.J., Dudík, M., and Schapire, R.E. Available online: http://www.cs.princeton.edu/~schapire/maxent/.

Phillips, S.J., Dudík, M., and Schapire, R.E. (2004). Proceedings of the 21st International Conference on Machine Learning, ACM Press.

2001, Maximum entropy: Clearing up mysteries, Entropy, 3, 58, 10.3390/e3020058

Phillips, 2008, Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation, Ecography, 31, 161, 10.1111/j.0906-7590.2008.5203.x

Baldwin, 2008, Den-site characteristics of black bears in Rocky Mountain National Park, Colorado, J. Wildl. Manag., 72, 1717, 10.2193/2007-393

Phillips, S.J. Available online: http://www.cs.princeton.edu/~schapire/maxent/.

Yost, 2008, Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy and a long-term dataset from Southern Oregon, Ecol. Inform., 3, 375, 10.1016/j.ecoinf.2008.08.004

Hoenes, B.D., and Bender, L.C. (2010). Relative habitat and browse use of native desert mule deer and exotic oryx in the greater San Andres Mountains, New Mexico. Human–Wildlife Conflicts, (in press).

Fielding, 1997, A review of methods for the assessment of prediction errors in conservation presence/absence models, Environ. Conserv., 24, 38, 10.1017/S0376892997000088

Engler, 2004, An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data, J. Appl. Ecol., 41, 263, 10.1111/j.0021-8901.2004.00881.x

Pearson, 2007, Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar, J. Biogeogr., 34, 102, 10.1111/j.1365-2699.2006.01594.x

Wisz, 2008, NCEAS Predicting Species Distributions Working Group. Effects of sample size on the performance of species distribution models, Diversity Distrib., 14, 763, 10.1111/j.1472-4642.2008.00482.x

Phillips, 2009, Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data, Ecol. Appl., 19, 181, 10.1890/07-2153.1

Graham, 2008, The NCEAS Predicting Species Distributions Working Group. The influence of spatial errors in species occurrence data used in distribution models, J. Appl. Ecol., 45, 239, 10.1111/j.1365-2664.2007.01408.x

Thorn, 2009, Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus), Divers. Distrib., 15, 289, 10.1111/j.1472-4642.2008.00535.x

Peterson, 2007, Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent, Ecography, 30, 550, 10.1111/j.0906-7590.2007.05102.x

Phillips, 2008, Transferability, sample selection bias and background data in presence-only modelling: A response to Peterson et al. (2007), Ecography, 31, 272, 10.1111/j.0906-7590.2008.5378.x

Swets, 1988, Measuring the accuracy of diagnostic systems, Science, 240, 1285, 10.1126/science.3287615

Elith, 2007, Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines, Divers. Distrib., 13, 265, 10.1111/j.1472-4642.2007.00340.x

Raes, 2007, A null-model for significance testing of presence-only species distribution models, Ecography, 30, 727, 10.1111/j.2007.0906-7590.05041.x

Liu, 2005, Selecting thresholds of occurrence in the prediction of species distributions, Ecography, 28, 385, 10.1111/j.0906-7590.2005.03957.x

Pearson, R.G. Species’ distribution modeling for conservation educators and practitioners. Synthesis. Available online: http://ncep.amnh.org.

Cohen, 1960, A coefficient of agreement for nominal scales, Educ. Psychol. Meas., 20, 37, 10.1177/001316446002000104

Allouche, 2006, Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS), J. Appl. Ecol., 43, 1223, 10.1111/j.1365-2664.2006.01214.x

Pearce, 2000, Evaluating the predictive performance of habitat models developed using logistic regression, Ecol. Model., 133, 225, 10.1016/S0304-3800(00)00322-7

Freeman, 2008, A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa, Ecol. Model., 217, 48, 10.1016/j.ecolmodel.2008.05.015

Osborne, 2008, Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution, Ecol. Model., 219, 17, 10.1016/j.ecolmodel.2008.07.035

Levine, 2009, A method for statistically comparing spatial distribution maps, Int. J. Health Geogr., 8, 7, 10.1186/1476-072X-8-7

Burnham, K.P., and Anderson, D.R. (2002). Model Selection and Inference: A Practical Information-Theoretic Approach, Springer-Verlag. [2nd ed.].

Spiegelhalter, 2003, Bayesian measures of model complexity and fit, J. Roy. Stat. Soc. B Met., 64, 583, 10.1111/1467-9868.00353

DeMatteo, 2008, New data on the status and distribution of the bush dog (Speothos venaticus): Evaluating its quality of protection and directing research efforts, Biol. Conserv., 141, 2494, 10.1016/j.biocon.2008.07.010

Boubli, 2009, Modeling the geographical distribution and fundamental niches of Cacajao spp. and Chiropotes israelita in northwestern Amazonia via a maximum entropy algorithm, Int. J. Primatol., 30, 217, 10.1007/s10764-009-9335-4

Weinsheimer, 2009, Will future anthropogenic climate change increase the potential distribution of the alien invasive Cuban treefrog (Anura: Hylidae)?, J. Nat. Hist., 43, 1207, 10.1080/00222930902783752

Baldwin, R.A. (2008). Population demographics, habitat utilization, critical habitats, and condition of black bears in Rocky Mountain National Park, Colorado. [Ph.D. dissertation, New Mexico State University].

Zar, J.H. (1999). Biostatistical Analysis, Prentice Hall. [4th ed.].