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

Uncertainty associated with survey design in Species Distribution Models

2014 ◽  
Vol 20 (11) ◽  
pp. 1258-1269 ◽  
Author(s):  
Geiziane Tessarolo ◽  
Thiago F. Rangel ◽  
Miguel B. Araújo ◽  
Joaquín Hortal
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Survey Design ◽  
Distribution Models

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 Iterative Models for Early Detection of Invasive Species across Spread Pathways

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 108 ◽  
Author(s):  
Gericke Cook ◽  
Catherine Jarnevich ◽  
Melissa Warden ◽  
Marla Downing ◽  
John Withrow ◽  
...  
Keyword(s):  
Invasive Species ◽  
Early Detection ◽  
Species Distribution ◽  
Gypsy Moth ◽  
Species Distribution Models ◽  
Survey Design ◽  
Area Under The Curve ◽  
Invasion Pathways ◽  
Distribution Models ◽  
Predicted Risk

Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth (Lymantria dispar dispar) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context.

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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