Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy

Mendes, Poliana; Velazco, Santiago José Elías; Andrade, André Felipe Alves de; Marco, Paulo De

doi:10.1016/j.ecolmodel.2020.109180

Cited by 105 publications

(94 citation statements)

References 74 publications

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“…Projection of ENMs throughout the study region can predict suitable areas far from the species’ geographical domain (Velazco et al., 2020). To delimit species distribution under the current condition and avoid models’ overprediction, models were trimmed based on the area encompassed by a minimum convex hull polygon determined by species records plus a buffer zone of 100 km surrounding the edges of convex hull polygons (Kremen et al., 2008; Mendes et al., 2020). We used Mobility‐Oriented Parity to detect and correct models’ extrapolation under future scenarios (see Appendix for details; Owens et al., 2013).…”

Section: Methodsmentioning

confidence: 99%

On opportunities and threats to conserve the phylogenetic diversity of Neotropical palms

Velazco

Svenning

Ribeiro

et al. 2020

Diversity and Distributions

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Aim Palms are an ecologically and societally important plant group, with high diversity in the Neotropics. Here, we estimated the impacts of future climate change on phylogenetic diversity (PD) of Neotropical palms under varying climatic and dispersal scenarios, assessed the effectiveness of the established network of protected areas (PAs) for conserving palms PD today and in 2070, and identified priority areas for the conservation of palm species and their evolutionary history in the face of climate change. Location Neotropics. Methods We used ecological niche modelling to estimate the distribution of 367 species in the present and for 2070 based on two greenhouse gas emission and two dispersal scenarios. We calculated Faith's PD within each five arc‐minute grid cell to evaluate the effectiveness of PAs relative to null models and used phylogenetic spatial prioritisation analysis to detect priority areas. Results We found that even under the most optimistic climatic and dispersal scenarios, the established network of PAs performed poorly in safeguarding palms PD under both current conditions and those projected for 2070. Significant losses in PD inside PAs are expected under future climate conditions, especially if species are unable to disperse to suitable areas. Nevertheless, a modest and strategic increase in the number of PAs could considerably improve the protection of palms PD in the present and 2070. Main conclusions The PD of Neotropical palms is poorly represented within the established network of PAs, at both present and in 2070. A higher realised dispersal rates would diminish PD losses inside the network of PAs. The conservation of palm PD can be improved through the expansion of PAs in strategic regions such as the upper portion of the Amazon Basin, Tropical Andes and Mesoamerica.

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Section: Methodsmentioning

confidence: 99%

On opportunities and threats to conserve the phylogenetic diversity of Neotropical palms

Velazco

Svenning

Ribeiro

et al. 2020

Diversity and Distributions

Self Cite

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“…Besides, a correction for over-prediction using clip models by buffered Minimum Convex Polygon (MCP) was made in ArcGIS SDM Toolbox. The buffered MCP as a posteriori method enables the reduction of over-prediction (Mendes et al 2020). In addition, the ensemble model was evaluated using a weighted mean of all models except the least performing bioclim model.…”

Section: Model Validation and Mappingmentioning

confidence: 99%

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

2021

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Background Species Distribution Modelling (SDM) coupled with freely available multispectral imagery from Sentinel-2 (S2) satellite provides an immense contribution in monitoring invasive species. However, attempts to evaluate the performances of SDMs using S2 spectral bands and S2 Radiometric Indices (S2-RIs) and biophysical variables, in particular, were limited. Hence, this study aimed at evaluating the performance of six commonly used SDMs and one ensemble model for S2-based variables in modelling the current distribution of Prosopis juliflora in the lower Awash River basin, Ethiopia. Thirty-five variables were computed from Sentinel-2B level-2A, and out of the variables, twelve significant variables were selected using Variable Inflation Factor (VIF). A total of 680 presence and absence data were collected to train and validate variables using the tenfold bootstrap replication approach in the R software “sdm” package. The performance of the models was evaluated using sensitivity, specificity, True Skill Statistics (TSS), kappa coefficient, area under the curve (AUC), and correlation. Results Our findings demonstrated that except bioclim all machine learning and regression models provided successful prediction. Among the tested models, Random Forest (RF) performed better with 93% TSS and 99% AUC followed by Boosted Regression Trees (BRT), ensemble, Generalized Additive Model (GAM), Support Vector Machine (SVM), and Generalized Linear Model (GLM) in decreasing order. The relative influence of vegetation indices was the highest followed by soil indices, biophysical variables, and water indices in decreasing order. According to RF prediction, 16.14% (1553.5 km2) of the study area was invaded by the alien species. Conclusions Our results highlighted that S2-RIs and biophysical variables combined with machine learning and regression models have a higher capacity to model invasive species distribution. Besides, the use of machine learning algorithms such as RF algorithm is highly essential for remote sensing-based invasive SDM.

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“…Commonly, a model’s output represents the ecological niches (ENMs). To bring the ENMs closer to SDMs, we performed a posteriori methods based on occurrences–based restriction (OBR)—i.e., using the distance between points to exclude far suitable patches using MSDM R package [ 77 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Hotspots and Prioritizing Protected Areas for Endangered Primate Species in Indonesia under Changing Climate

Condro

Prasetyo

Rushayati

et al. 2021

Biology

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Indonesia has a large number of primate diversity where a majority of the species are threatened. In addition, climate change is conservation issues that biodiversity may likely face in the future, particularly among primates. Thus, species-distribution modeling was useful for conservation planning. Herein, we present protected areas (PA) recommendations with high nature-conservation importance based on species-richness changes. We performed maximum entropy (Maxent) to retrieve species distribution of 51 primate species across Indonesia. We calculated species-richness change and range shifts to determine the priority of PA for primates under mitigation and worst-case scenarios by 2050. The results suggest that the models have an excellent performance based on seven different metrics. Current primate distributions occupied 65% of terrestrial landscape. However, our results indicate that 30 species of primates in Indonesia are likely to be extinct by 2050. Future primate species richness would be also expected to decline with the alpha diversity ranging from one to four species per 1 km2. Based on our results, we recommend 54 and 27 PA in Indonesia to be considered as the habitat-restoration priority and refugia, respectively. We conclude that species-distribution modeling approach along with the categorical species richness is effectively applicable for assessing primate biodiversity patterns.

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Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy

Cited by 105 publications

References 74 publications

On opportunities and threats to conserve the phylogenetic diversity of Neotropical palms

On opportunities and threats to conserve the phylogenetic diversity of Neotropical palms

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

Predicting Hotspots and Prioritizing Protected Areas for Endangered Primate Species in Indonesia under Changing Climate

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