scholarly journals Use of Anecdotal Occurrence Data in Species Distribution Models: An Example Based on the White-Nosed Coati (Nasua narica) in the American Southwest

Animals ◽  
2013 ◽  
Vol 3 (2) ◽  
pp. 327-348 ◽  
Author(s):  
Jennifer Frey ◽  
Jeremy Lewis ◽  
Rachel Guy ◽  
James Stuart
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
American Southwest ◽  
Distribution Models ◽  
Nasua Narica ◽  
Occurrence Data

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.

scholarly journals Evaluating species distribution models with discrimination accuracy is uninformative for many applications

2019 ◽  
Author(s):  
Dan L. Warren ◽  
Nicholas J. Matzke ◽  
Teresa L. Iglesias
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Current Approach ◽  
Data Partitioning ◽  
Empirical Modeling ◽  
Environmental Predictors ◽  
Discrimination Accuracy ◽  
Distribution Models ◽  
Ecological Processes ◽  
Occurrence Data

AbstractAimSpecies distribution models are used across evolution, ecology, conservation, and epidemiology to make critical decisions and study biological phenomena, often in cases where experimental approaches are intractable. Choices regarding optimal models, methods, and data are typically made based on discrimination accuracy: a model’s ability to predict subsets of species occurrence data that were withheld during model construction. However, empirical applications of these models often involve making biological inferences based on continuous estimates of relative habitat suitability as a function of environmental predictor variables. We term the reliability of these biological inferences “functional accuracy.” We explore the link between discrimination accuracy and functional accuracy.MethodsUsing a simulation approach we investigate whether models that make good predictions of species distributions correctly infer the underlying relationship between environmental predictors and the suitability of habitat.ResultsWe demonstrate that discrimination accuracy is only informative when models are simple and similar in structure to the true niche, or when data partitioning is geographically structured. However, the utility of discrimination accuracy for selecting models with high functional accuracy was low in all cases.Main conclusionsThese results suggest that many empirical studies and decisions are based on criteria that are unrelated to models’ usefulness for their intended purpose. We argue that empirical modeling studies need to place significantly more emphasis on biological insight into the plausibility of models, and that the current approach of maximizing discrimination accuracy at the expense of other considerations is detrimental to both the empirical and methodological literature in this active field. Finally, we argue that future development of the field must include an increased emphasis on simulation; methodological studies based on ability to predict withheld occurrence data may be largely uninformative about best practices for applications where interpretation of models relies on estimating ecological processes, and will unduly penalize more biologically informative modeling approaches.

scholarly journals SPATIAL SCALE EFFECTS OF SAMPLING ON THE INTERPOLATION OF SPECIES DISTRIBUTION MODELS IN THE SOUTHWESTERN AMAZON

2016 ◽  
Vol 40 (4) ◽  
pp. 617-625 ◽  
Author(s):  
Symone Maria de Melo Figueiredo ◽  
Eduardo Martins Venticinque ◽  
Evandro Orfanó Figueiredo
Keyword(s):  
Forest Management ◽  
Spatial Scale ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Scale Effects ◽  
Projection Area ◽  
Distribution Models ◽  
Forest Inventories ◽  
Occurrence Data

ABSTRACT Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.

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 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.

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