Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes

Shcheglovitova, Mariya; Anderson, Robert P.

doi:10.1016/j.ecolmodel.2013.08.011

Cited by 467 publications

(409 citation statements)

References 30 publications

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“…To parameterize the SDMs properly, we evaluated the performance of various combinations of five feature classes (linear; linear and quadratic; hinge; linear, quadratic and hinge; and linear, quadratic, hinge, product and threshold), and 10 regularization multipliers (from 0.5 to 5, in increments of 0.5) (49). To this end, we evaluated the performance of SDMs built under each combination of model parameters through a geographically structured k-fold cross-validation (i.e., the occurrence records were partitioned into k equal geographically clustered subsamples, here k = 5, and the models were trained with four of the groups and then evaluated with the excluded group until all group combinations were run).…”

Section: Methodsmentioning

confidence: 99%

Inferring responses to climate dynamics from historical demography in neotropical forest lizards

Prates

Xue

Brown

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

105

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We apply a comparative framework to test for concerted demographic changes in response to climate shifts in the neotropical lowland forests, learning from the past to inform projections of the future. Using reduced genomic (SNP) data from three lizard species codistributed in Amazonia and the Atlantic Forest (Anolis punctatus, Anolis ortonii, and Polychrus marmoratus), we first reconstruct former population history and test for assemblage-level responses to cycles of moisture transport recently implicated in changes of forest distribution during the Late Quaternary. We find support for population shifts within the time frame of inferred precipitation fluctuations (the last 250,000 y) but detect idiosyncratic responses across species and uniformity of within-species responses across forest regions. These results are incongruent with expectations of concerted population expansion in response to increased rainfall and fail to detect out-of-phase demographic syndromes (expansions vs. contractions) across forest regions. Using reduced genomic data to infer species-specific demographical parameters, we then model the plausible spatial distribution of genetic diversity in the Atlantic Forest into future climates (2080) under a medium carbon emission trajectory. The models forecast very distinct trajectories for the lizard species, reflecting unique estimated population densities and dispersal abilities. Ecological and demographic constraints seemingly lead to distinct and asynchronous responses to climatic regimes in the tropics, even among similarly distributed taxa. Incorporating such constraints is key to improve modeling of the distribution of biodiversity in the past and future.phylogeography | population genomics | Amazon Forest | Atlantic Forest | climate change

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Section: Methodsmentioning

confidence: 99%

Inferring responses to climate dynamics from historical demography in neotropical forest lizards

Prates

Xue

Brown

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

105

View full text Add to dashboard Cite

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“…Para comprobar la certeza y el aporte de cada variable de forma individual, se utilizó el test Jackknife el cual tiene como función principal correr cada modelo con cada variable por si sola para medir el aporte de la variable particular (Shcheglovitova & Anderson, 2013).…”

Section: Evaluación De Los Modelosunclassified

Modelación de nichos ecológicos basado en tres escenarios de cambio climático para cinco especies de plantas en zonas altas de Costa Rica

Quesada-Quirós

Acosta-Vargas

Arias‐Aguilar

et al. 2016

RFMK

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Modelación de nichos ecológicos basado en tres escenarios de cambio climático para cinco especies de plantas en zonas altas de Costa Rica Climate change generates different responses in the distribution pattern of flora and fauna. According to the predictions of the Intergovernmental Panel on Climate Change (IPCC) through global models, areas that are most affected are the highland areas, due to drastic changes in environmental variables such as temperature and precipitation. That is why ecological niche models with MaxEnt (algorithm based on maximum entropy) were made for the following species: Comarostaphylis

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“…High-quality models should have zero or low omission of evaluation localities and predict evaluation localities statistically better than a random prediction [29]. This approach may be useful for higher sample sizes (up to approximately 25 records) [21], but herein we employed it for species with ≤20 records.…”

Section: Model Evaluationmentioning

confidence: 99%

“…Modeling is also problematic for species that have few records of occurrence. For these cases, maximum entropy modeling (MaxEnt) was ranked among the most effective applications for SDM [21]. MaxEnt is a machine-learning method [22][23][24] that uses principles of Bayesian estimation [25], and it calculates a raw probability value for each pixel of a study region using the maximum likelihood estimation method [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Modeling the distribution of rare and interesting moss species of the family Orthotrichaceae (Bryophyta) in Tajikistan and Kyrgyzstan

2017

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Bryological research carried out from 2008 in Tajikistan and Kyrgyzstan brought interesting data on the occurrence of epiphytic bryophytes which have not been recorded yet there. One of the species was recently described as a new (Orthotrichum pamiricum) and some of the other newly recorded species are considered as rare or endangered in the region of Middle Asia. To make detailed field monitoring of the species with the aim of mapping their distribution in a wild and complex mountainous terrain, it was necessary in the first instance to identify the area with suitable conditions for the occurrence of these species. We present an innovative modeling program MaxEnt (maximum entropy modeling), which have not previously been used for modeling either epiphytic bryophytes or in the Middle Asia region. Using 205 samples (presence-only data), percent tree cover, and seven uncorrelated bioclimatic variables, regions suitable for the occurrence of the studied species were identified. Distribution models for eight most interesting species of Orthotrichum are presented here (O. affine, O. anomalum, O. crenulatum, O. cupulatum, O. pallens, O. pamiricum, O. pumilum, and O. speciosum). They indicated appropriate areas for the most probable occurrence of the species in western Tajikistan, and southwestern and northeastern Kyrgyzstan. These results could serve as guides for future survey expeditions, and aid in the conservation of target species and our understanding of their ecology. Different environmental variables for various species were selected as the most important for modeling. However, for most species higher minimum temperatures and higher precipitation in the wettest month and mean diurnal range were the variables with the greatest contribution to the models.

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Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes

Cited by 467 publications

References 30 publications

Inferring responses to climate dynamics from historical demography in neotropical forest lizards

Inferring responses to climate dynamics from historical demography in neotropical forest lizards

Modelación de nichos ecológicos basado en tres escenarios de cambio climático para cinco especies de plantas en zonas altas de Costa Rica

Modeling the distribution of rare and interesting moss species of the family Orthotrichaceae (Bryophyta) in Tajikistan and Kyrgyzstan

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