scholarly journals Creating virtual species to test species distribution models: the importance of landscape structure, dispersal and population processes

Ecography ◽  
2021 ◽  
Author(s):  
Liam Grimmett ◽  
Rachel Whitsed ◽  
Ana Horta
Keyword(s):  
Species Distribution ◽  
Landscape Structure ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Test Species ◽  
Population Processes ◽  
Virtual Species

A Sensitivity Analysis of the Application of Integrated Species Distribution Models to Mobile Species: A Case Study with the Endangered Baird’s Tapir

2019 ◽  
Vol 46 (03) ◽  
pp. 184-192 ◽  
Author(s):  
Cody J Schank ◽  
Michael V Cove ◽  
Marcella J Kelly ◽  
Clayton K Nielsen ◽  
Georgina O’Farrill ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Animal Movement ◽  
Site Occupancy ◽  
Population Estimates ◽  
Distribution Models ◽  
Test Species ◽  
Modelling Framework ◽  
Data Deficient ◽  
The Individual

SummarySpecies distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. ‘Integrated SDMs’ (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications and predictions for rare species, where data are often limited and prediction errors may have significant management consequences. Despite this potential importance, ISDMs remain largely untested under a variety of scenarios. We performed an exploration of key modelling decisions and assumptions on an ISDM using the endangered Baird’s tapir (Tapirus bairdii) as a test species. We found that site area had the strongest effect on the magnitude of population estimates and underlying intensity surface and was driven by estimates of model intercepts. Selecting a site area that accounted for the individual movements of the species within an average home range led to population estimates that coincided with expert estimates. ISDMs that do not account for the individual movements of species will likely lead to less accurate estimates of species intensity (number of individuals per unit area) and thus overall population estimates. This bias could be severe and highly detrimental to conservation actions if uninformed ISDMs are used to estimate global populations of threatened and data-deficient species, particularly those that lack natural history and movement information. However, the ISDM was consistently the most accurate model compared to other approaches, which demonstrates the importance of this new modelling framework and the ability to combine opportunistic data with systematic survey data. Thus, we recommend researchers use ISDMs with conservative movement information when estimating population sizes of rare and data-deficient species. ISDMs could be improved by using a similar parameterization to spatial capture–recapture models that explicitly incorporate animal movement as a model parameter, which would further remove the need for spatial subsampling prior to implementation.

The basic principle of virtual species and its application to evaluations of species distribution models

2016 ◽  
Vol 36 (9) ◽  
Author(s):  
黄敏毅 HUANG Minyi ◽  
孔晓泉 KONG Xiaoquan ◽  
段仁燕 DUAN Renyan ◽  
吴甘霖 WU Ganlin ◽  
张中信 ZHANG Zhongxin
Keyword(s):  
Basic Principle ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Virtual Species

scholarly journals Inhomogeneous Poisson Point Process for Species Distribution Models: Relative Performance of Methods Accounting for Sampling Bias and Imperfect Detection

2021 ◽  
Author(s):  
Yannick MUGUMAARHAHAMA ◽  
Adandé Belarmain FANDOHAN ◽  
Arsene Ciza MUSHAGALUSA ◽  
Idelphonse Akoeugnigan SODE ◽  
Romain GLELE KAKAÏ
Keyword(s):  
High Precision ◽  
Hierarchical Model ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Sampling Bias ◽  
Abundant Species ◽  
Distribution Models ◽  
Imperfect Detection ◽  
Point Counts ◽  
Virtual Species

Species distribution models have become tools of great importance in ecology since the advanced knowledge of suitable habitat of species is needed in the process of the world's biodiversity conservation. Models that use presence-only data are of great interests and are widely used in ecology due to their easy access. However, these models do not estimate accurately the true spatial species distribution based solely on presence-only data since they do not account for biases induced by the sampling techniques used and imperfect detection. To address this gap, Hierarchical integrated models have been recently introduced. Through this study, we assessed the relative performance of these new SDMs models using simulated data. The performance of the models was tested by comparing the estimates of parameters of the distribution models they provide with parameters used to simulate the distribution of the virtual species. The best model was the one whose estimates were close to the true distribution parameters of the virtual species. Results showed that analyzing Presence-only data in conjunction with Point-counts data through the Dorazio's Hierarchical model produced estimates of the coecients of the species intensity models with high precision and less bias while the Koshkina integrated model showed poor performance. Site-occupancy data, being not informative of species abundance, did not allow reducing biases in Presence-only data. The Dorazio's Hierarchical model produced estimates with high precision even with low detection probability. We have also found that the species rarity tends to in ate the variability of the models' estimates making modelling abundant species to be more accurate than modelling less abundant species. Hence, to model the species distribution with high precision based on Presence-only data, additional Point-counts data are required to account for sampling bias and imperfect detection.

Virtual species distribution models

2014 ◽  
Vol 38 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Jennifer A. Miller
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Empirical Studies ◽  
Simulated Data ◽  
Species Traits ◽  
Spatially Explicit ◽  
Distribution Models ◽  
Climate Change Research ◽  
Dominant Paradigm ◽  
Virtual Species

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.

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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