Maximum Entropy-Based Ecological Niche Model and Bio-Climatic Determinants of Lone Star Tick (<i>Amblyomma americanum</i>) Niche

Raghavan, Ram K.; Goodin, Douglas G.; Hanzlicek, Gregg; Zolnerowich, Gregory; Dryden, Michael W.; Anderson, Gary A.; Ganta, Roman R.

doi:10.1089/vbz.2015.1837

Cited by 48 publications

(36 citation statements)

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“…Their identification in the present study as important drivers of RMSF at a county–scale is therefore significant. Optimal humidity conditions are vital for tick survival and it is an important delimiter to their spatial distribution [ 51 ], and relative humidity can often be seen associated with the survival and abundance of ticks in the literature, with higher humidity conditions often favoring the long–term survival of some ticks species’ life stages through dry seasons [ 48 ] among other reasons. The higher daytime land surface temperature identified in this study is likely an indication of the average temperature conditions in some counties that are unfavorable for Dermacentor ticks versus others.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Bayesian Spatio–Temporal Analysis of Climatic and Socio–Economic Determinants of Rocky Mountain Spotted Fever

et al. 2016

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This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio–economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio–temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio–economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main–effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C) in the region, and the relevance of these factors in the context of climate–change impacts on tick–borne diseases are discussed.

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Section: Discussionmentioning

confidence: 99%

Hierarchical Bayesian Spatio–Temporal Analysis of Climatic and Socio–Economic Determinants of Rocky Mountain Spotted Fever

et al. 2016

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“…These methods have been used since the 1920s (Cook, 1925;Sutherst, 2014), but recent years have seen rapid growth in the number of studies employing SDM in fields including ecology, conservation biology, evolutionary biology and epidemiology (Allen & Lendemer, 2016;Coro, Pagano, & Ellenbroek, 2013;Guisan, Thuiller, & Zimmermann, 2017;Gutierrez-Tapia & Palma, 2016;Lezama-Ochoa et al, 2016;Peterson et al, 2011;Raghavan et al, 2016). These methods have been used since the 1920s (Cook, 1925;Sutherst, 2014), but recent years have seen rapid growth in the number of studies employing SDM in fields including ecology, conservation biology, evolutionary biology and epidemiology (Allen & Lendemer, 2016;Coro, Pagano, & Ellenbroek, 2013;Guisan, Thuiller, & Zimmermann, 2017;Gutierrez-Tapia & Palma, 2016;Lezama-Ochoa et al, 2016;Peterson et al, 2011;Raghavan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Species distribution models (SDM, alternatively environmental niche models or ENM) use data on species occurrences in conjunction with environmental data to generate statistical models of species' ecological tolerances, environmental limits and potential to occupy different geographic areas. These methods have been used since the 1920s (Cook, 1925;Sutherst, 2014), but recent years have seen rapid growth in the number of studies employing SDM in fields including ecology, conservation biology, evolutionary biology and epidemiology (Allen & Lendemer, 2016;Coro, Pagano, & Ellenbroek, 2013;Guisan, Thuiller, & Zimmermann, 2017;Gutierrez-Tapia & Palma, 2016;Lezama-Ochoa et al, 2016;Peterson et al, 2011;Raghavan et al, 2016). The primary appeal of SDMs is their tractability; estimating environmental tolerances experimentally is expensive and timeconsuming at best and impractical for many species.…”

Section: Introductionmentioning

confidence: 99%

Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications

Warren

Matzke

Iglesias

2019

Journal of Biogeography

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Aim Species distribution models are used across evolution, ecology, conservation and epidemiology to make critical decisions and study biological phenomena, often in cases where experimental approaches are intractable. Choices regarding optimal models, methods and data are typically made based on discrimination accuracy: a model's ability to predict subsets of species occurrence data that were withheld during model construction. However, empirical applications of these models often involve making biological inferences based on continuous estimates of relative habitat suitability as a function of environmental predictor variables. We term the reliability of these biological inferences ‘functional accuracy.’ We explore the link between discrimination accuracy and functional accuracy. Methods Using a simulation approach we investigate whether models that make good predictions of species distributions correctly infer the underlying relationship between environmental predictors and the suitability of habitat. Results We demonstrate that discrimination accuracy is only informative when models are simple and similar in structure to the true niche, or when data partitioning is geographically structured. However, the utility of discrimination accuracy for selecting models with high functional accuracy was low in all cases. Main conclusions These results suggest that many empirical studies and decisions are based on criteria that are unrelated to models’ usefulness for their intended purpose. We argue that empirical modelling studies need to place significantly more emphasis on biological insight into the plausibility of models, and that the current approach of maximizing discrimination accuracy at the expense of other considerations is detrimental to both the empirical and methodological literature in this active field. Finally, we argue that future development of the field must include an increased emphasis on simulation; methodological studies based on ability to predict withheld occurrence data may be largely uninformative about best practices for applications where interpretation of models relies on estimating ecological processes, and will unduly penalize more biologically informative modelling approaches.

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“…Species distribution models (SDM, alternatively environmental niche models or ENM) use data on species occurrences in conjunction with environmental data to generate statistical models of species’ ecological tolerances, environmental limits, and potential to occupy different geographic areas. These methods have been used since the 1920s (Cook 1925, Sutherst 2014), but recent years have seen rapid growth in the number of studies employing SDM in fields including ecology, conservation biology, evolutionary biology, and epidemiology (Peterson, Soberón et al 2011, Coro, Pagano et al 2013, Allen and Lendemer 2016, Gutierrez-Tapia and Palma 2016, Lezama Ochoa, Murua et al 2016, Raghavan, Goodin et al 2016, Guisan, Thuiller et al 2017). The primary appeal of SDMs is their tractability; estimating environmental tolerances experimentally is expensive and time-consuming at best, and impractical for many species.…”

Section: Figurementioning

confidence: 99%

Evaluating species distribution models with discrimination accuracy is uninformative for many applications

Warren

Matzke

Iglesias

2019

Preprint

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Aim Species distribution models are used across evolution, ecology, conservation, and epidemiology to make critical decisions and study biological phenomena, often in cases where experimental approaches are intractable. Choices regarding optimal models, methods, and data are typically made based on discrimination accuracy: a model’s ability to predict subsets of species occurrence data that were withheld during model construction. However, empirical applications of these models often involve making biological inferences based on continuous estimates of relative habitat suitability as a function of environmental predictor variables. We term the reliability of these biological inferences “functional accuracy.” We explore the link between discrimination accuracy and functional accuracy. Methods Using a simulation approach we investigate whether models that make good predictions of species distributions correctly infer the underlying relationship between environmental predictors and the suitability of habitat. Results We demonstrate that discrimination accuracy is only informative when models are simple and similar in structure to the true niche, or when data partitioning is geographically structured. However, the utility of discrimination accuracy for selecting models with high functional accuracy was low in all cases. Main conclusions These results suggest that many empirical studies and decisions are based on criteria that are unrelated to models’ usefulness for their intended purpose. We argue that empirical modeling studies need to place significantly more emphasis on biological insight into the plausibility of models, and that the current approach of maximizing discrimination accuracy at the expense of other considerations is detrimental to both the empirical and methodological literature in this active field. Finally, we argue that future development of the field must include an increased emphasis on simulation; methodological studies based on ability to predict withheld occurrence data may be largely uninformative about best practices for applications where interpretation of models relies on estimating ecological processes, and will unduly penalize more biologically informative modeling approaches.

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Maximum Entropy-Based Ecological Niche Model and Bio-Climatic Determinants of Lone Star Tick (Amblyomma americanum) Niche

Cited by 48 publications

References 48 publications

Hierarchical Bayesian Spatio–Temporal Analysis of Climatic and Socio–Economic Determinants of Rocky Mountain Spotted Fever

Hierarchical Bayesian Spatio–Temporal Analysis of Climatic and Socio–Economic Determinants of Rocky Mountain Spotted Fever

Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications

Evaluating species distribution models with discrimination accuracy is uninformative for many applications

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