Modelling Native and Invasive Woody Species: A Comparison of ENFA and MaxEnt Applied to the Azorean Forest

Silva, Lara Dutra; Costa, Hugo; Azevedo, Eduardo Brito de; Medeiros, Vasco; Alves, Maria Judite; Elias, Rui B.; Silva, Luís

doi:10.1007/978-3-319-55236-1_20

Cited by 12 publications

(21 citation statements)

References 77 publications

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“…It should also be noted that our fixed effects models originated predictions that are globally similar to those previously obtained using other modeling approaches such as maximum entropy and ecological niche factor analysis (e.g., [23,24,67,69]). …”

Section: Discussionsupporting

confidence: 79%

“…Meanwhile, in some cases the effect of particular EGVs became non-significant for very small samples. This should be taken into account when dealing with species represented by a relatively low number of presences [24,69].…”

Section: Discussionmentioning

confidence: 99%

“…Topographical variables were derived based on a digital elevation model (DEM) available in "Clima Insular à Escala Local" (CIELO) model ( [22,23,69,73,74,110]; see http://www.climaat.angra. uac.pt).…”

Section: Ecogeographical Variablesmentioning

confidence: 99%

“…Hill shade is a simulation of the lighting conditions on the surface dictated by the topography and the position of the Sun (the Summer and Winter solstices were considered). For further details see Costa et al [22,23] and Dutra Silva et al [69].…”

Section: Ecogeographical Variablesmentioning

confidence: 99%

“…The second concerns the radiative component and energy balance as affected by the orographic clouds and by topography. The climatic variables selected, were air temperature, precipitation, relative humidity with maximum and minimum extremes, seasonal variation, annual and monthly averages and a summary obtained by Principal Component Analysis (PCA) (see [23,24,69]). Land use variables included one variable expressing the distance to six types of land use (forest; natural vegetation; pastureland; agriculture; barren/bare areas; and urban/industrial areas) [23,24,69]).…”

Section: Climatic Variablesmentioning

confidence: 99%

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Species Distribution Modeling: Comparison of Fixed and Mixed Effects Models Using INLA

Silva

Azevedo

Elias

et al. 2017

IJGI

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Abstract:Invasive alien species are among the most important, least controlled, and least reversible of human impacts on the world's ecosystems, with negative consequences affecting biodiversity and socioeconomic systems. Species distribution models have become a fundamental tool in assessing the potential spread of invasive species in face of their native counterparts. In this study we compared two different modeling techniques: (i) fixed effects models accounting for the effect of ecogeographical variables (EGVs); and (ii) mixed effects models including also a Gaussian random field (GRF) to model spatial correlation (Matérn covariance function). To estimate the potential distribution of Pittosporum undulatum and Morella faya (respectively, invasive and native trees), we used geo-referenced data of their distribution in Pico and São Miguel islands (Azores) and topographic, climatic and land use EGVs. Fixed effects models run with maximum likelihood or the INLA (Integrated Nested Laplace Approximation) approach provided very similar results, even when reducing the size of the presences data set. The addition of the GRF increased model adjustment (lower Deviance Information Criterion), particularly for the less abundant tree, M. faya. However, the random field parameters were clearly affected by sample size and species distribution pattern. A high degree of spatial autocorrelation was found and should be taken into account when modeling species distribution.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Ecogeographical Variablesmentioning

confidence: 99%

Section: Ecogeographical Variablesmentioning

confidence: 99%

Section: Climatic Variablesmentioning

confidence: 99%

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Species Distribution Modeling: Comparison of Fixed and Mixed Effects Models Using INLA

Silva

Azevedo

Elias

et al. 2017

IJGI

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Identification of suitable habitats and priority conservation areas under climate change scenarios for the Chinese alligator (Alligator sinensis)

Yang,

Ling,

et al. 2024

Ecology and Evolution

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Amphibians and reptiles, especially the critically endangered Chinese alligators, are vulnerable to climate change. Historically, the decline in suitable habitats and fragmentation has restricted the distribution of Chinese alligators to a small area in southeast Anhui Province in China. However, the effects of climate change on range‐restricted Chinese alligator habitats are largely unknown. We aimed to predict current and future (2050s and 2070s) Chinese alligator distribution and identify priority conservation areas under climate change. We employed species distribution models, barycenter migration analyses, and the Marxian model to assess current and future Chinese alligator distribution and identify priority conservation areas under climate change. The results showed that the lowest temperature and rainfall seasonality in the coldest month were the two most important factors affecting the distribution of Chinese alligators. Future predictions indicate a reduction (3.39%–98.41%) in suitable habitats and a westward shift in their distribution. Further, the study emphasizes that suitable habitats for Chinese alligators are threatened by climate change. Despite the impact of the Anhui Chinese Alligator National Nature Reserve, protection gaps persist, with 78.27% of the area lacking priority protected area. Our study provides crucial data for Chinese alligator adaptation to climate change and underscores the need for improved conservation strategies. Future research should refine conservation efforts, consider individual plasticity, and address identified limitations to enhance the resilience of Chinese alligator populations in the face of ongoing climate change.

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Predicting Future Climate Change Effects on Biotic Communities: A Species Distribution Modeling Approach

Kumari,

Kotiyal,

Singh

et al. 2024

Forests and Climate Change

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Modelling Native and Invasive Woody Species: A Comparison of ENFA and MaxEnt Applied to the Azorean Forest

Cited by 12 publications

References 77 publications

Species Distribution Modeling: Comparison of Fixed and Mixed Effects Models Using INLA

Species Distribution Modeling: Comparison of Fixed and Mixed Effects Models Using INLA

Identification of suitable habitats and priority conservation areas under climate change scenarios for the Chinese alligator (Alligator sinensis)

Predicting Future Climate Change Effects on Biotic Communities: A Species Distribution Modeling Approach

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