Using occupancy and species distribution models to assess the conservation status and habitat use of the goldline darter(Percina aurolineata) in Georgia, USA

2013 ◽  
Vol 23 (3) ◽  
pp. 347-359 ◽  
Author(s):  
Brett Albanese ◽  
Thomas Litts ◽  
Mieko Camp ◽  
Deborah A. Weiler
Keyword(s):  
Habitat Use ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Conservation Status ◽  
Distribution Models

Combining conservation status and species distribution models for planning assisted colonisation under climate change

2021 ◽  
Author(s):  
Gabriele Casazza ◽  
Thomas Abeli ◽  
Gianluigi Bacchetta ◽  
Davide Dagnino ◽  
Giuseppe Fenu ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Conservation Status ◽  
Distribution Models

scholarly journals Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8059 ◽  
Author(s):  
Benjamin M. Marshall ◽  
Colin T. Strine
Keyword(s):  
Social Media ◽  
North America ◽  
South America ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Conservation Status ◽  
Model Performance ◽  
Distribution Models ◽  
Occurrence Data ◽  
Occurrence Records

A species’ distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurrence records that may present an opportunity to improve species distribution models. We investigated (1) occurrence data of a cryptic group of animals: non-marine snakes, in a biodiversity database (Global Biodiversity Information Facility (GBIF)) and determined (2) whether incidental occurrence records extracted from geo-tagged social media images (Flickr) could improve distribution models for 18 tropical snake species. We provide R code to search for and extract data from images using Flickr’s API. We show the biodiversity database’s 302,386 records disproportionately originate from North America, Europe and Oceania (250,063, 82.7%), with substantial gaps in tropical areas that host the highest snake diversity. North America, Europe and Oceania averaged several hundred records per species; whereas Asia, Africa and South America averaged less than 35 per species. Occurrence density showed similar patterns; Asia, Africa and South America have roughly ten-fold fewer records per 100 km2than other regions. Social media provided 44,687 potential records. However, including them in distribution models only marginally impacted niche estimations; niche overlap indices were consistently over 0.9. Similarly, we show negligible differences in Maxent model performance between models trained using GBIF-only and Flickr-supplemented datasets. Model performance appeared dependent on species, rather than number of occurrences or training dataset. We suggest that for tropical snakes, accessible social media currently fails to deliver appreciable benefits for estimating species distributions; but due to the variation between species and the rapid growth in social media data, may still be worth considering in future contexts.

Putting prey into the picture: improvements to species distribution models for bottlenose dolphins in Doubtful Sound, New Zealand

2020 ◽  
Vol 653 ◽  
pp. 191-204
Author(s):  
S Bennington ◽  
W Rayment ◽  
S Dawson
Keyword(s):  
New Zealand ◽  
Habitat Use ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Bottlenose Dolphins ◽  
Model Performance ◽  
Additive Models ◽  
Distribution Models ◽  
Marine Predators ◽  
Abiotic Variables

Species distribution models (SDMs) often rely on abiotic variables as proxies for biotic relationships. This means that important biotic relationships may be missed, creating ambiguity in our understanding of the drivers of habitat use. These problems are especially relevant for populations of predators, as their habitat use is likely to be strongly influenced by the distribution of their prey. We investigated habitat use of a population of a top predator, bottlenose dolphins Tursiops truncatus, in Doubtful Sound, New Zealand, using generalised additive models, and compared the results of models with and without biotic predictor variables. We found that although habitat use by bottlenose dolphins was significantly correlated with abiotic variables that likely describe foraging areas, introduction of biotic variables describing potential prey almost doubled the deviance explained, from 19.8 to 39.1%. Biotic variables were the most important of the predictors used, and indicated that the dolphins showed a preference for areas with a high abundance of a reef fish, girdled wrasse Notolabrus cinctus. For the dolphins of Doubtful Sound, these results show the importance of prey distribution in driving habitat use. On a broader scale, our results indicate that making an effort to include true biotic descriptors in SDMs can improve model performance, resulting in better understanding of the drivers of distribution of marine predators.

scholarly journals The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0139194 ◽  
Author(s):  
Thibaud Rougier ◽  
Géraldine Lassalle ◽  
Hilaire Drouineau ◽  
Nicolas Dumoulin ◽  
Thierry Faure ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Conservation Status ◽  
Distribution Models ◽  
Combined Use

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.

Sign in / Sign up

Export Citation Format

Share Document