A spatially explicit method for evaluating accuracy of species distribution models

2010 ◽  
Vol 16 (6) ◽  
pp. 996-1008 ◽  
Author(s):  
Mary Smulders ◽  
Trisalyn A. Nelson ◽  
Dennis E. Jelinski ◽  
Scott E. Nielsen ◽  
Gordon B. Stenhouse
Zootaxa ◽  
2010 ◽  
Vol 2426 (1) ◽  
pp. 54 ◽  
Author(s):  
DENNIS RÖDDER ◽  
FRANK WEINSHEIMER ◽  
STEFAN LÖTTERS

Combination of various techniques allows the identification of unique genetic lineages and/or taxa new to science via integrative taxonomy approaches. Next to molecular methods such as DNA ‘barcoding’ and phylogeographic analyses, Species Distribution Models may serve as compliment techniques allowing spatially explicit predictions of a species’ potential distribution even across millennia. They may facilitate the identification of possible recent and historical gene flow pathways. Herein, we highlight advantages of the combination of both molecular and macroecological approaches using the African miniature leaf litter frog Arthroleptis xenodactyloides as example.


2019 ◽  
Vol 25 (5) ◽  
pp. 758-769 ◽  
Author(s):  
Sami Domisch ◽  
Martin Friedrichs ◽  
Thomas Hein ◽  
Florian Borgwardt ◽  
Annett Wetzig ◽  
...  

2014 ◽  
Vol 38 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Jennifer A. Miller

Species distribution models (SDMs) have become a dominant paradigm for quantifying species-environment relationships, and both the models and their outcomes have seen widespread use in conservation studies, particularly in the context of climate change research. With the growing interest in SDMs, extensive comparative studies have been undertaken. However, few generalizations and recommendations have resulted from these empirical studies, largely due to the confounding effects of differences in and interactions among the statistical methods, species traits, data characteristics, and accuracy metrics considered. This progress report addresses ‘virtual species distribution models’: the use of spatially explicit simulated data to represent a ‘true’ species distribution in order to evaluate aspects of model conceptualization and implementation. Simulating a ‘true’ species distribution, or a virtual species distribution, and systematically testing how these aspects affect SDMs, can provide an important baseline and generate new insights into how these issues affect model outcomes.


Ecography ◽  
2019 ◽  
Vol 43 (3) ◽  
pp. 456-466 ◽  
Author(s):  
Nina K. Lany ◽  
Phoebe L. Zarnetske ◽  
Andrew O. Finley ◽  
Deborah G. McCullough

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang

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.


Sign in / Sign up

Export Citation Format

Share Document