A Test of Species Distribution Model Transferability Across Environmental and Geographic Space for 108 Western North American Tree Species

Predictions from species distribution models (SDMs) are commonly used in support of environmental decision-making to explore potential impacts of climate change on biodiversity. However, because future climates are likely to differ from current climates, there has been ongoing interest in understanding the ability of SDMs to predict species responses under novel conditions (i.e., model transferability). Here, we explore the spatial and environmental limits to extrapolation in SDMs using forest inventory data from 11 model algorithms for 108 tree species across the western United States. Algorithms performed well in predicting occurrence for plots that occurred in the same geographic region in which they were fitted. However, a substantial portion of models performed worse than random when predicting for geographic regions in which algorithms were not fitted. Our results suggest that for transfers in geographic space, no specific algorithm was better than another as there were no significant differences in predictive performance across algorithms. There were significant differences in predictive performance for algorithms transferred in environmental space with GAM performing best. However, the predictive performance of GAM declined steeply with increasing extrapolation in environmental space relative to other algorithms. The results of this study suggest that SDMs may be limited in their ability to predict species ranges beyond the environmental data used for model fitting. When predicting climate-driven range shifts, extrapolation may also not reflect important biotic and abiotic drivers of species ranges, and thus further misrepresent the realized shift in range. Future studies investigating transferability of process based SDMs or relationships between geodiversity and biodiversity may hold promise.

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A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

Remote Sensing ◽

10.3390/rs13081495 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1495

Author(s):

Jehyeok Rew ◽

Yongjang Cho ◽

Eenjun Hwang

Keyword(s):

Neural Networks ◽

Species Distribution ◽

Best Practice ◽

Deep Neural Networks ◽

Species Distribution Models ◽

Species Distribution Model ◽

Environmental Data ◽

Distribution Model ◽

Distribution Models ◽

Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data

Sustainability ◽

10.3390/su10103444 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3444 ◽

Cited By ~ 5

Author(s):

Quanzhong Zhang ◽

Haiyan Wei ◽

Zefang Zhao ◽

Jing Liu ◽

Qiao Ran ◽

...

Keyword(s):

Species Distribution ◽

Habitat Suitability ◽

Species Distribution Model ◽

Environmental Data ◽

Distribution Model ◽

New Approach ◽

Suitability Index ◽

Biological Laws ◽

Experimental Process ◽

Key Variables

Over the years, with the efforts of many researchers, the field of species distribution model (SDM) has been well explored. The model of fuzzy matter elements (FME), which, combined with GIS to predict species distribution, has received extensive attention since its emergence. Based on previous studies, this paper improved FME, extended the scope of the membership degree and habitat suitability index, and explored the unsuitable areas of species. We have enhanced the limitation effect of key variables on species habitats, making the operation of FME more consistent with biological laws. By optimizing the FME, it could avoid the accumulation of predicted errors with multi-variables, and make the predicted results more reasonable. In this study, Gynostemma pentaphyllum (Thunb.) Makino was used as an example. The experimental process used several major environmental variables (climate, soil, and terrain variables) to predict the habitat suitability distribution of G. pentaphyllum in China for its current and future period, which includes the period of 2050s (average for 2041–2060) and 2070s (average for 2061–2080) under representative concentration pathways 4.5 (RCP4.5). The results of the analysis showed that the model performed well with a high accuracy by reducing the redundancy of the environmental data. The study could relieve the reliance on a large database of environmental information and propose a new approach for protecting the G. pentaphyllum in unsuitable areas under climate change.

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Evaluation of species distribution models for estimating animal dark diversity

10.21203/rs.3.rs-1100842/v1 ◽

2021 ◽

Author(s):

Camilo Matus-Olivares ◽

Jaime Carrasco ◽

José Luis Yela ◽

Paula Meli ◽

Andres Weintraub ◽

...

Keyword(s):

Spatial Patterns ◽

Species Distribution ◽

Species Distribution Model ◽

Predictive Performance ◽

The Other ◽

Distribution Model ◽

Distribution Data ◽

Ensemble Model ◽

Distribution Models ◽

Software Environment

Abstract Aim Applying wide and effective sampling of animal communities is rarely possible due to the associated costs and the use of techniques that are not always efficient. Thus, many areas have a faunistic hidden diversity we denote Animal Dark Diversity (ADD), defined as the diversity that is present but not yet detected plus the diversity defined by Pärtel et al. (2011) that is not (yet) present despite the area’s favourable habitat conditions. We evaluated different species distribution model types (SDM techniques) on the basis of three requirements for ADD estimate reliability: 1) estimated spatial patterns of ADD do not differ significantly from other SDM techniques; 2) good predictive performances; and 3) low overfitting. Location Iberian Peninsula. Taxon Chiroptera and Noctuoidea (Lepidoptera) Methods We used distribution data for 25 species of bats and 352 species of moths. We evaluated eleven SDM techniques using biomod2 package implemented in the R software environment. We fitted the various SDM techniques to the data for each species and compared the resulting ADD estimates for the two animal groups under three threshold types. Results The results demonstrated that estimated ADD spatial patterns vary significantly between SDM techniques and depend on the threshold type. They also showed that SDM techniques with overfitting tend to generate smaller ADD sizes, thus reducing the possible species presence estimates. Among the SDMs studied, the ensemble models delivered ADD geographic patterns more like the other techniques while also presenting a high predictive performance for both faunal groups. However, the Ensemble Model Committee Average (ECA) performed much better on the sensitivity metric than all other techniques under any of the thresholds tested. In addition, ECA stood out clearly from the other ensemble model techniques in displaying low-medium overfitting. Main conclusions SDM techniques should no differ among each other in their ADD estimations, have good predictive performances and exhibit low overfitting. Furthermore, to reduce estimate uncertainty it is suggested that the threshold type be one that transforms high values of presences probabilities into binary information and furthermore that the SDM technique have a sensitivity bias, as otherwise the estimates will perform better for species absence in cases where it is not in fact known whether a species is truly absent.

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SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses

PeerJ ◽

10.7717/peerj.4095 ◽

2017 ◽

Vol 5 ◽

pp. e4095 ◽

Cited By ~ 93

Author(s):

Jason L. Brown ◽

Joseph R. Bennett ◽

Connor M. French

Keyword(s):

Species Distribution ◽

Species Distribution Model ◽

Species Distribution Modeling ◽

Environmental Data ◽

Distribution Model ◽

Data Types ◽

Distribution Models ◽

Distribution Modeling ◽

Modeling Software ◽

Cost Paths

SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model’s discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have ‘universal’ analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates—to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user.

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Colonization and extinction dynamics and their link to the distribution of European trees at continental scale

10.1101/748483 ◽

2019 ◽

Author(s):

Arnaud Guyennon ◽

Björn Reineking ◽

Jonas Dahlgren ◽

Aleksi Lehtonen ◽

Sophia Ratcliffe ◽

...

Keyword(s):

Species Distribution ◽

Tree Species ◽

Species Distribution Model ◽

Distribution Model ◽

Metapopulation Dynamics ◽

Occurrence Probability ◽

Distribution Models ◽

Continental Scale ◽

Forest Inventories ◽

National Forest Inventories

AbstractAimProcesses driving current tree species distribution are still largely debated. Attempts to relate species distribution and population demography metrics have shown mixed results. In this context, we would like to test the hypotheses that the metapopulation processes of colonization and extinction are linked to species distribution models.LocationEurope: Spain, France, Germany, Finland, and Sweden.TaxonAngiosperms and Gymnosperms.MethodsFor the 17 tree species analyzed we fitted species distribution model (SDM) relating environmental variables to presence absence data across Europe. Then using independent data from national forest inventories across Europe we tested whether colonization and extinction probabilities are related to occurrence probability estimated by the SDMs. Finally, we tested how colonization and extinction respectively drive probability of presence at the metapopulation equilibrium.ResultsWe found that for most species at least one process (colonization/extinction) is related to the occurrence probability, but rarely both.Main conclusionsOur study supports the view that metapopulation dynamics are partly related to SDM occurrence probability through one of the metapopulation probabilities. However these links are relatively weak and the metapopulation models tend to overestimate the occurrence probability. Our results call for caution in model extrapolating SDM models to metapopulation dynamics.

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Jointly modeling marine species to inform the effects of environmental change on an ecological community in the Northwest Atlantic

Scientific Reports ◽

10.1038/s41598-021-04110-0 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Sarah M. Roberts ◽

Patrick N. Halpin ◽

James S. Clark

Keyword(s):

Marine Fish ◽

Species Distribution ◽

Environmental Effects ◽

Species Distribution Model ◽

Predictive Performance ◽

Single Species ◽

Marine Species ◽

Joint Modeling ◽

Distribution Model ◽

Distribution Models

AbstractSingle species distribution models (SSDMs) are typically used to understand and predict the distribution and abundance of marine fish by fitting distribution models for each species independently to a combination of abiotic environmental variables. However, species abundances and distributions are influenced by abiotic environmental preferences as well as biotic dependencies such as interspecific competition and predation. When species interact, a joint species distribution model (JSDM) will allow for valid inference of environmental effects. We built a joint species distribution model of marine fish and invertebrates of the Northeast US Continental Shelf, providing evidence on species relationships with the environment as well as the likelihood of species to covary. Predictive performance is similar to SSDMs but the Bayesian joint modeling approach provides two main advantages over single species modeling: (1) the JSDM directly estimates the significance of environmental effects; and (2) predicted species richness accounts for species dependencies. An additional value of JSDMs is that the conditional prediction of species distributions can use not only the environmental associations of species, but also the presence and abundance of other species when forecasting future climatic associations.

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