scholarly journals Effectiveness of Joint Species Distribution Models in the Presence of Imperfect Detection

2021 ◽  
Author(s):  
Stephanie Hogg ◽  
Yan Wang ◽  
Lewi Stone
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Survey Design ◽  
Spatial Modelling ◽  
Probability Of Detection ◽  
List Type ◽  
Distribution Models ◽  
Imperfect Detection ◽  
Biased Estimates

AbstractJoint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise.A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of “collapsed data”. A case study of owls and gliders in Victoria Australia is also illustrated.Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to dis-entangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications.To avoid biased estimates of inter-species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter-dependencies and occupancy.

scholarly journals RUSBoost: A suitable species distribution method for imbalanced records of presence and absence. A case study of twenty-five species of Iberian bats

2021 ◽  
Author(s):  
Jaime Carrasco ◽  
Fugencio Lison ◽  
Andres Weintraub
Keyword(s):  
Species Distribution ◽  
Cross Validation ◽  
Species Distribution Models ◽  
Bayesian Optimization ◽  
List Type ◽  
Distribution Models ◽  
Distribution Method ◽  
Explanatory Variables ◽  
Presence And Absence

Traditional Species Distribution Models (SDMs) may not be appropriate when examples of one class (e.g. absence or pseudo-absences) greatly outnumber examples of the other class (e.g. presences or observations), because they tend to favor the learning of observations more frequently. We present an ensemble method called Random UnderSampling and Boosting (RUSBoost), which was designed to address the case where the number of presence and absence records are imbalanced, and we opened the "black-box" of the algorithm to interpret its results and applicability in ecology. We applied our methodology to a case study of twenty-five species of bats from theIberian Peninsula and we build a RUSBoost model for each species. Furthermore,in order to improve to build tighter models, we optimized their hyperparametersusing Bayesian Optimization. In particular, we implemented a objective function that represents the cross-validation loss: kFoldLoss(z), with z representing the hyper-parameters Maximum Number of Splits, Number of Learners and Learning Rate. The models reached average values for Area Under the ROC Curve (AUC), specificity, sensitivity, and overall accuracy of 0.84±0.05%, 79.5±4.87%, 74.9±6.05%,and 78.8±5.0%, respectively. We also obtained values of variable importance and we analyzed the relationships between explanatory variables and bat presence probability. The results of our study showed that RUSBoost could be a useful tool to develop SDMs with good performance when the presence/absence databases are imbalanced. The application of this algorithm could improve the prediction of SDMs and help in conservation biology and management.

Effects of sample survey design on the accuracy of classification tree models in species distribution models

2006 ◽  
Vol 199 (2) ◽  
pp. 132-141 ◽  
Author(s):  
Thomas C. Edwards ◽  
D. Richard Cutler ◽  
Niklaus E. Zimmermann ◽  
Linda Geiser ◽  
Gretchen G. Moisen
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Survey Design ◽  
Classification Tree ◽  
Sample Survey ◽  
Distribution Models ◽  
Tree Models

scholarly journals The effects of data adequacy and calibration size on the accuracy of presence-only species distribution models

2019 ◽  
Author(s):  
Truly Santika ◽  
Michael F. Hutchinson ◽  
Kerrie A. Wilson
Keyword(s):  
Performance Measures ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Simulated Data ◽  
List Type ◽  
Model Accuracy ◽  
Distribution Models ◽  
Accuracy Measure ◽  
Data Coverage ◽  
True Distribution

ABSTRACTPresence-only data used to develop species distribution models are often biased towards areas that are frequently surveyed. Furthermore, the size of calibration area with respect to the area covered by the species occurrences has been shown to affect model accuracy. However, existing assessments of the effect of data inadequacy and calibration size on model accuracy have predominately been conducted using empirical studies. These studies can give ambiguous results, since the data used to train and test the model can both be biased.These limitations were addressed by applying simulated data to assess how inadequate data coverage and the size of calibration area affect the accuracy of species distribution models generated by MaxEnt and BIOCLIM. The validity of four presence-only performance measures, Contrast Validation Index (CVI), Boyce index, AUC and AUCratio, was also assessed.CVI, AUC and AUCratio ranked the accuracy of univariate models correctly according to the true importance of their defining environmental variable, a desirable property of an accuracy measure. Contrastingly, Boyce index failed to rank the accuracy of univariate models correctly and a high percentage of irrelevant variables produced models with a high Boyce index.Inadequate data coverage and increased calibration area reduced model accuracy by reducing the correct identification of the dominant environmental determinant. BIOCLIM outperformed MaxEnt models in predicting the true distribution of simulated species with a symmetric dominant response. However, MaxEnt outperformed BIOCLIM in predicting the true distribution of simulated species with skew and linear dominant responses. Despite this, the standard performance measures consistently overestimated the performance of MaxEnt models and showed them as always having higher model accuracy than the BIOCLIM models.It has been acknowledged that research should be directed towards testing and improving species distribution modelling tools, particularly how to handle the inevitable bias and scarcity of species occurrence data. Simulated data, as demonstrated here, provides a powerful approach to comprehensively test the performance of modelling tools and to disentangle the effects of data properties and modelling options on model accuracy. This may be impossible to achieve using real-world data.

scholarly journals Combining Deforestation and Species Distribution Models to Improve Measures of Chimpanzee Conservation Impacts of REDD: A Case Study from Ntakata Mountains, Western Tanzania

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1195
Author(s):  
Rebecca Dickson ◽  
Marc Baker ◽  
Noémie Bonnin ◽  
David Shoch ◽  
Benjamin Rifkin ◽  
...  
Keyword(s):  
Biodiversity Conservation ◽  
Species Distribution ◽  
Carbon Emissions ◽  
Species Distribution Models ◽  
Cost Effective ◽  
Climate Mitigation ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Western Tanzania

Projects to reduce emissions from deforestation and degradation (REDD) are designed to reduce carbon emissions through avoided deforestation and degradation, and in many cases, to produce additional community and biodiversity conservation co-benefits. While these co-benefits can be significant, quantifying conservation impacts has been challenging, and most projects use simple species presence to demonstrate positive biodiversity impact. Some of the same tools applied in the quantification of climate mitigation benefits have relevance and potential application to estimating co-benefits for biodiversity conservation. In western Tanzania, most chimpanzees live outside of national park boundaries, and thus face threats from human activity, including competition for suitable habitat. Through a case study of the Ntakata Mountains REDD project in western Tanzania, we demonstrate a combined application of deforestation modelling with species distribution models to assess forest conservation benefits in terms of avoided carbon emissions and improved chimpanzee habitat. The application of such tools is a novel approach that we argue permits the better design of future REDD projects for biodiversity co-benefits. This approach also enables project developers to produce the more manageable, accurate and cost-effective monitoring, reporting and verification of project impacts that are critical to verification under carbon standards.

scholarly journals Joint species distribution models with species correlations and imperfect detection

Ecology ◽  
2019 ◽  
Vol 100 (8) ◽  
Author(s):  
Mathias W. Tobler ◽  
Marc Kéry ◽  
Francis K. C. Hui ◽  
Gurutzeta Guillera‐Arroita ◽  
Peter Knaus ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Imperfect Detection
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