Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution

Global environmental changes are affecting both the distribution and abundance of species at an unprecedented rate. To assess these effects, species distribution models (SDMs) have been greatly developed over the last decades, while species abundance models (SAMs) have generally received less attention even though these models provide essential information for conservation management. With population abundance defined as an essential biodiversity variable (EBV), SAMs could offer spatially explicit predictions of species abundance across space and time. Satellite-derived ecosystem functioning attributes (EFAs) are known to inform on processes controlling species distribution, but they have not been tested as predictors of species abundance. In this study, we assessed the usefulness of SAMs calibrated with EFAs (as process-related variables) to predict local abundance patterns for a rare and threatened species (the narrow Iberian endemic ‘Gerês lily’ Iris boissieri; protected under the European Union Habitats Directive), and to project inter-annual fluctuations of predicted abundance. We compared the predictive accuracy of SAMs calibrated with climate (CLI), topography (DEM), land cover (LCC), EFAs, and combinations of these. Models fitted only with EFAs explained the greatest variance in species abundance, compared to models based only on CLI, DEM, or LCC variables. The combination of EFAs and topography slightly increased model performance. Predictions of the inter-annual dynamics of species abundance were related to inter-annual fluctuations in climate, which holds important implications for tracking global change effects on species abundance. This study underlines the potential of EFAs as robust predictors of biodiversity change through population size trends. The combination of EFA-based SAMs and SDMs would provide an essential toolkit for species monitoring programs.

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Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production

Sustainability ◽

10.3390/su12041481 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1481 ◽

Cited By ~ 1

Author(s):

Xiaobo Xue Romeiko ◽

Zhijian Guo ◽

Yulei Pang ◽

Eun Kyung Lee ◽

Xuesong Zhang

Keyword(s):

Global Warming ◽

Life Cycle ◽

Predictive Accuracy ◽

Regression Tree ◽

Gradient Boosting ◽

Spatially Explicit ◽

Tree Model ◽

Corn Production ◽

County Scale ◽

Regression Tree Model

Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies. However, methods capable of accurately and efficiently calculating spatially explicit life cycle global warming (GW) and eutrophication (EU) impacts at the county scale over a geographic region are lacking. The objective of this study was to determine the most efficient and accurate model for estimating spatially explicit life cycle GW and EU impacts at the county scale, with corn production in the U.S.’s Midwest region as a case study. This study compared the predictive accuracies and efficiencies of five distinct supervised machine learning (ML) algorithms, testing various sample sizes and feature selections. The results indicated that the gradient boosting regression tree model built with approximately 4000 records of monthly weather features yielded the highest predictive accuracy with cross-validation (CV) values of 0.8 for the life cycle GW impacts. The gradient boosting regression tree model built with nearly 6000 records of monthly weather features showed the highest predictive accuracy with CV values of 0.87 for the life cycle EU impacts based on all modeling scenarios. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required the longest training time. ML algorithms demonstrated to be one million times faster than the traditional process-based model with high predictive accuracy. This indicates that ML can serve as an alternative surrogate of process-based models to estimate life-cycle environmental impacts, capturing large geographic areas and timeframes.

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Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change

Journal of Biogeography ◽

10.1111/jbi.12479 ◽

2015 ◽

Vol 42 (5) ◽

pp. 976-988 ◽

Cited By ~ 41

Author(s):

Trisha Distler ◽

Justin G. Schuetz ◽

Jorge Velásquez‐Tibatá ◽

Gary M. Langham

Keyword(s):

Climate Change ◽

Species Richness ◽

Species Distribution ◽

Species Distribution Models ◽

Avian Species ◽

Distribution Models

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A spatially explicit method for evaluating accuracy of species distribution models

Diversity and Distributions ◽

10.1111/j.1472-4642.2010.00707.x ◽

2010 ◽

Vol 16 (6) ◽

pp. 996-1008 ◽

Cited By ~ 10

Author(s):

Mary Smulders ◽

Trisalyn A. Nelson ◽

Dennis E. Jelinski ◽

Scott E. Nielsen ◽

Gordon B. Stenhouse

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Spatially Explicit ◽

Explicit Method ◽

Distribution Models

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How to assess species distribution model accuracy: using internal-aspatial or external-spatial methods?

10.7287/peerj.preprints.27257 ◽

2018 ◽

Author(s):

Chunrong Mi ◽

Falk Huettmann ◽

Yumin Guo

Keyword(s):

Species Distribution ◽

Species Distribution Model ◽

Predictive Accuracy ◽

Landscape Planning ◽

Spatial Prediction ◽

Distribution Model ◽

Model Accuracy ◽

Climate Change Research ◽

Spatial Methods ◽

Occurrence Data

Species distribution models (SDMs) have become an increasingly important tool in ecology, biogeography, evolution and, more recently, in conservation management, landscape planning and climate change research. The assessment of their predictive accuracy is one fundamental issue in the development and application of SDMs. Accuracy assessments for models should have a close connection to the intended use of the model. However, we found that the common evaluation method (we named internal-aspatial) usually ignored how the spatial prediction map actually looks like, and achieves for the real-world species distribution and for application. Therefore, in this research we proposed a spatial method to evaluate model performance by assessing how the prediction maps look like (we named external-spatial). We took Hooded Crane (Grus monacha) as a case, in this research, to compare these two methods (internal-aspatial and external-spatial) performance. Both of the two methods were expressed with three commonly used SDM evaluation criteria (AUC, Kappa and TSS). In addition, model accuracy was also assessed via evaluating the prediction maps with knowledge of the study species and alternative occurrence data assistance. We used two popular data mining algorithms (Random Forest and TreeNet) and ran 8 experiments using 1, 3, 5, 8, 11, 21, 29 and 78 predictors, allowing to develop overall 16 models for this assessment. Results indicated that AUC had a significant linear relationship with Kappa and TSS. Both of interal-aspatial and external-spatial methods could get higher AUC values and they were close. This indicated that internal-aspatial model assessments can serve as powerful assessment-aspatiual metrics without the need of secondary data even! However, internal-aspatial, external-spatial, prediction map evaluation and alternative occurrence data could not distinguish well models with different sets of predictors. This is the first time the concept of spatial assessment criteria is expressed and assessed. Overall, we hope to see more study on meaningful spatial criteria and proposed more and better methods to evaluate SDMs and distribution map in the future.

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