Testing climate-based species distribution models with recent field surveys of pond-breeding amphibians in eastern Missouri

2011 ◽  
Vol 89 (11) ◽  
pp. 1074-1083 ◽  
Author(s):  
D.R. Trumbo ◽  
A.A. Burgett ◽  
J.H. Knouft
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Range ◽  
Environmental Data ◽  
Limited Attention ◽  
Distribution Models ◽  
Field Surveys ◽  
Geographic Ranges ◽  
Using Data ◽  
Study Species

Species distribution models (SDMs) have become an important tool for ecologists by providing the ability to predict the distributions of organisms based on species niche parameters and available habitat across broad geographic areas. However, investigation of the appropriate extent of environmental data needed to make accurate predictions has received limited attention. We investigate whether SDMs developed with regional climate and species locality data (i.e., within Missouri, USA) produce more accurate predictions of species occurrences than models developed with data from across an entire species range. To test the accuracy of the model predictions, field surveys were performed in 2007 and 2008 at 103 study ponds for eight amphibian study species. Models developed using data from across the entire species range did not accurately predict the occurrences of any study species. However, models developed using data only from Missouri produced accurate predictions for four study species, all of which are near the edge of their geographic ranges within the study area. These results suggest that species distribution modeling with regionally focused data may be preferable for local ecological and conservation purposes, and that climate factors may be more important for determining species distributions at the edge of their geographic ranges.

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.

Very high resolution environmental predictors in species distribution models

2013 ◽  
Vol 38 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Jean-Nicolas Pradervand ◽  
Anne Dubuis ◽  
Loïc Pellissier ◽  
Antoine Guisan ◽  
Christophe Randin
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Environmental Data ◽  
Swiss Alps ◽  
Fine Scale ◽  
Distribution Models ◽  
Model Quality ◽  
Vegetation Height ◽  
Very High

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

scholarly journals New findings on protected and strictly protected species confirm the value of the prime hoverfly area network

2018 ◽  
pp. 63-71
Author(s):  
Marina Jankovic ◽  
Marija Milicic ◽  
Dimitrije Radisic ◽  
Dubravka Milic ◽  
Ante Vujic
Keyword(s):  
Protected Areas ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Small Sample Size ◽  
Small Sample ◽  
Area Network ◽  
Distribution Models ◽  
Environmental Pressures ◽  
New Findings ◽  
Using Data

With environmental pressures on the rise, the establishment of pro?tected areas is a key strategy for preserving biodiversity. The fact that many species are losing their battle against extinction despite being within protected areas raises the question of their effectiveness. The aim of this study was to evaluate established Priority Hoverfly Areas (PHAs) and areas that are not yet but could potentially be included in the PHA network, using data from new field surveys. Additionally, species distribution models have been created for two new species recognized as important and added to the list of key hoverfly species. Maps of potential distribution of these species were superimposed on maps of protected areas and PHAs to quantify percentages of overlap. The results of this study are not statisti?cally significant, which could be influenced by a small sample size. However, the results of species distribution models and the extent of overlap with PHAs confirm the utility of these expert-generated designations.

Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)

2011 ◽  
Vol 4 (4) ◽  
pp. 390-401 ◽  
Author(s):  
Gary N. Ervin ◽  
D. Christopher Holly
Keyword(s):  
Invasive Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Imperata Cylindrica ◽  
Environmental Data ◽  
Training Data ◽  
Distribution Data ◽  
Strongly Correlated ◽  
Distribution Models ◽  
Tree Canopy Cover

AbstractSpecies distribution modeling is a tool that is gaining widespread use in the projection of future distributions of invasive species and has important potential as a tool for monitoring invasive species spread. However, the transferability of models from one area to another has been inadequately investigated. This study aimed to determine the degree to which species distribution models (SDMs) for cogongrass, developed with distribution data from Mississippi (USA), could be applied to a similar area in neighboring Alabama. Cogongrass distribution data collected in Mississippi were used to train an SDM that was then tested for accuracy and transferability with cogongrass distribution data collected by a forest management company in Alabama. Analyses indicated the SDM had a relatively high predictive ability within the region of the training data but had poor transferability to the Alabama data. Analysis of the Alabama data, via independent SDM development, indicated that predicted cogongrass distribution in Alabama was more strongly correlated with soil variables than was the case in Mississippi, where the SDM was most strongly correlated with tree canopy cover. Results suggest that model transferability is influenced strongly by (1) data collection methods, (2) landscape context of the survey data, and (3) variations in qualitative aspects of environmental data used in model development.

Predicting the range of a regionally threatened, benthic fish using species distribution models and field surveys

2018 ◽  
Vol 93 (5) ◽  
pp. 972-977 ◽  
Author(s):  
Joshua L. Sherwood ◽  
Andrew J. Stites ◽  
Michael J. Dreslik ◽  
Jeremy S. Tiemann
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Benthic Fish ◽  
Distribution Models ◽  
Field Surveys

scholarly journals REBIOMA Data Portal, Tool for Conservation Planning in Madagascar

2018 ◽  
Vol 2 ◽  
pp. e25864
Author(s):  
Rabetrano Tsiky
Keyword(s):  
Species Distribution ◽  
Conservation Planning ◽  
Species Distribution Models ◽  
Continuous Distribution ◽  
Environmental Data ◽  
Easy Access ◽  
Species Occurrence ◽  
Distribution Models ◽  
Occurrence Data ◽  
Data Portal

Recognizing the abundance and the accumulation of information and data on biodiversity that are still poorly exploited and even unfunded, the REBIOMA project (Madagascar Biodiversity Networking), in collaboration with partners, has developed an online dataportal in order to provide easy access to information and critical data, to support conservation planning and the expansion of scientific and professional activities in Madagascar biodiversity. The mission of the REBIOMA data portal is to serve quality-labeled, up-to-date species occurrence data and environmental niche models for Madagascar’s flora and fauna, both marine and terrestrial. REBIOMA is a project of the Wildlife Conservation Society Madagascar and the University of California, Berkeley. REBIOMA serves species occurrence data for marine and terrestrial regions of Madagascar. Following upload, data is automatically validated against a geographic mask and a taxonomic authority. Data providers can decide whether their data will be public, private, or shared only with selected collaborators. Data reviewers can add quality labels to individual records, allowing selection of data for modeling and conservation assessments according to quality. Portal users can query data in numerous ways. One of the key features of the REBIOMA web portal is its support for species distribution models, created from taxonomically valid and quality-reviewed occurrence data. Species distribution models are produced for species for which there are at least eight, reliably reviewed, non-duplicate (per grid cell) records. Maximum Entropy Modeling (MaxEnt for short) is used to produce continuous distribution models from these occurrence records and environmental data for different eras: past (1950), current (2000), and future (2080). The result is generally interpreted as a prediction of habitat suitability. Results for each model are available on the portal and ready for download as ASCII and HTML files. The REBIOMA Data Portal address is http://data.rebioma.net, or visit http://www.rebioma.netfor more general information about the entire REBIOMA project.

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