scholarly journals Interactive spatial scale effects on species distribution modeling: The case of the giant panda

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas Connor ◽  
Andrés Viña ◽  
Julie A. Winkler ◽  
Vanessa Hull ◽  
Ying Tang ◽  
...  
Keyword(s):  
Grain Size ◽  
Spatial Scale ◽  
Species Distribution ◽  
Giant Panda ◽  
Species Distribution Model ◽  
Scale Effects ◽  
Distribution Model ◽  
Mountain Range ◽  
Model Accuracy ◽  
Selection Of

Abstract Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km2), we found that the models built at the next-largest extent (500 km2) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species’ habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.

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

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.

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

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.

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

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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