Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models

2010 ◽  
Vol 34 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Tim Newbold
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
The Internet ◽  
Limited Information ◽  
Species Occurrence ◽  
Distribution Models ◽  
Museum Data ◽  
Private Collections ◽  
Source Of Information ◽  
Occurrence Of Species

To conserve biodiversity, it is necessary to understand how species are distributed and which aspects of the environment determine distributions. In large parts of the world and for the majority of species, data describing distributions are very scarce. Museums, private collections and the historical literature offer a vast source of information on distributions. Records of the occurrence of species from these sources are increasingly being captured in electronic databases and made available over the internet. These records may be very valuable in conservation efforts. However, there are a number of limitations with museum data. These limitations are dealt with in the first part of this review. Even if the limitations of museum data can be overcome, these data present a far-from-complete picture of the distributions of species. Species distribution models offer a means to extrapolate limited information in order to estimate the distributions of species over large areas. The second part of this paper reviews the challenges of developing species distribution models for use with museum data and describes some of the questions that species distribution models have been used to address. Given the rapidly increasing number of museum records of species occurrence available over the internet, a review of their usefulness in conservation and ecology is timely.

scholarly journals Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0234587
Author(s):  
Mariano J. Feldman ◽  
Louis Imbeau ◽  
Philippe Marchand ◽  
Marc J. Mazerolle ◽  
Marcel Darveau ◽  
...  
Keyword(s):  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Spatial Scales ◽  
Species Conservation ◽  
Innovative Technology ◽  
Data Types ◽  
Species Occurrence ◽  
Distribution Models ◽  
Digital Platforms

Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline of conventional scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 207 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (73%). Papers on birds (49%) and mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata and Hymenoptera received the most attention. Discrepancies between research interest and availability of data were as especially important for amphibians, reptiles and fishes. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of papers are diverse, species conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.

scholarly journals Additions of landscape metrics improve predictions of occurrence of species distribution models

2017 ◽  
Vol 28 (5) ◽  
pp. 963-974 ◽  
Author(s):  
Érica Hasui ◽  
Vinícius X. Silva ◽  
Rogério G. T. Cunha ◽  
Flavio N. Ramos ◽  
Milton C. Ribeiro ◽  
...  
Keyword(s):  
Species Distribution ◽  
Landscape Metrics ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Occurrence Of Species

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.

scholarly journals Trends and gaps in the use of citizen science derived data as input for species distribution models: a quantitative review

2020 ◽  
Author(s):  
Mariano J. Feldman ◽  
Louis Imbeau ◽  
Philippe Marchand ◽  
Marc J. Mazerolle ◽  
Marcel Darveau ◽  
...  
Keyword(s):  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Spatial Scales ◽  
Low Cost ◽  
Innovative Technology ◽  
Data Types ◽  
Species Occurrence ◽  
Distribution Models ◽  
Digital Platforms

AbstractCitizen science (CS) currently refers to some level of volunteer participation in any discipline of scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 224 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (71.2%). Papers on birds (51.2%) and mammals (26.2%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera and Odonata received the most attention. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of SDM papers are diverse, conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.

scholarly journals BioModelos: a collaborative online system to map species distributions

2018 ◽  
Author(s):  
Jorge Velásquez-Tibatá ◽  
María H. Olaya-Rodríguez ◽  
Daniel López-Lozano ◽  
César Gutiérrez ◽  
Iván González ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
National Level ◽  
Species Distribution Modeling ◽  
Risk Assessments ◽  
Species Occurrence ◽  
Distribution Models ◽  
Online System ◽  
Occurrence Data

AbstractInformation on species distribution is recognized as a crucial input for biodiversity conservation and management. To that end, considerable resources have been dedicated towards increasing the quantity and availability of species occurrence data, boosting their use in species distribution modeling and online platforms for their dissemination. Currently, those platforms face the challenge of bringing biology into modeling by making informed decisions that result in meaningful models. Here we describe BioModelos, a modeling approach supported by an online system and a core team, whereby a network of experts contributes to the development of species distribution models by assessing the quality of occurrence data, identifying potentially limiting environmental variables, establishing species’ accessible areas and validating qualitatively modeling predictions. Models developed through BioModelos become publicly available once validated by experts, furthering their use in conservation applications. This approach has been implemented in Colombia since 2013 and it currently consist of a network of nearly 500 experts that collaboratively contribute to enhance the knowledge on the distribution of a growing number of species and where it has aided the development of several decision support products such as national risk assessments and biodiversity compensation manuals. BioModelos is an example of operationalization of an essential biodiversity variable at a national level through the implementation of a research infrastructure that enhances the value of open access species data.

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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