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.

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 Testing the Influence of Seascape Connectivity on Marine-Based Species Distribution Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Giorgia Cecino ◽  
Roozbeh Valavi ◽  
Eric A. Treml
Keyword(s):  
Species Distribution ◽  
Larval Dispersal ◽  
Species Distribution Models ◽  
Temporal Dynamics ◽  
Explanatory Power ◽  
Model Performance ◽  
Boosted Regression Trees ◽  
Distribution Models ◽  
Flexible Tool ◽  
Species Specific

Species distribution models (SDMs) are commonly used in ecology to predict species occurrence probability and how species are geographically distributed. Here, we propose innovative predictive factors to efficiently integrate information on connectivity into SDMs, a key element of population dynamics strongly influencing how species are distributed across seascapes. We also quantify the influence of species-specific connectivity estimates (i.e., larval dispersal vs. adult movement) on the marine-based SDMs outcomes. For illustration, seascape connectivity was modeled for two common, yet contrasting, marine species occurring in southeast Australian waters, the purple sea urchin, Heliocidaris erythrogramma, and the Australasian snapper, Chrysophrys auratus. Our models illustrate how different species-specific larval dispersal and adult movement can be efficiently accommodated. We used network-based centrality metrics to compute patch-level importance values and include these metrics in the group of predictors of correlative SDMs. We employed boosted regression trees (BRT) to fit our models, calculating the predictive performance, comparing spatial predictions and evaluating the relative influence of connectivity-based metrics among other predictors. Network-based metrics provide a flexible tool to quantify seascape connectivity that can be efficiently incorporated into SDMs. Connectivity across larval and adult stages was found to contribute to SDMs predictions and model performance was not negatively influenced from including these connectivity measures. Degree centrality, quantifying incoming and outgoing connections with habitat patches, was the most influential centrality metric. Pairwise interactions between predictors revealed that the species were predominantly found around hubs of connectivity and in warm, high-oxygenated, shallow waters. Additional research is needed to quantify the complex role that habitat network structure and temporal dynamics may have on SDM spatial predictions and explanatory power.

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 Morphological and molecular evidence support the taxonomic separation of the medically important Neotropical spiders Phoneutria depilata (Strand, 1909) and P. boliviensis (F.O. Pickard-Cambridge, 1897) (Araneae, Ctenidae)

ZooKeys ◽  
2021 ◽  
Vol 1022 ◽  
pp. 13-50
Author(s):  
Nicolas A. Hazzi ◽  
Gustavo Hormiga
Keyword(s):  
Species Distribution ◽  
Species Delimitation ◽  
Species Distribution Models ◽  
Allopatric Speciation ◽  
Molecular Evidence ◽  
Niche Conservatism ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Nucleotide Sequence Data ◽  
Occurrence Records

The species of the genus Phoneutria (Ctenidae), also called banana spiders, are considered amongst the most venomous spiders in the world. In this study we revalidate P. depilata (Strand, 1909), which had been synonymized with P. boliviensisis (F.O. Pickard-Cambridge, 1897), using morphological and nucleotide sequence data (COI and ITS-2) together with species delimitation methods. We synonymized Ctenus peregrinoides, Strand, 1910 and Phoneutria colombiana Schmidt, 1956 with P. depilata. Furthermore, we designated Ctenus signativenter Strand, 1910 as a nomen dubium because the exact identity of this species cannot be ascertained with immature specimens, but we note that the type locality suggests that the C. signativenter syntypes belong to P. depilata. We also provide species distribution models for both species of Phoneutria and test hypotheses of niche conservatism under an allopatric speciation model. Our phylogenetic analyses support the monophyly of the genus Phoneutria and recover P. boliviensis and P. depilata as sister species, although with low nodal support. In addition, the tree-based species delimitation methods also supported the separate identities of these two species. Phoneutria boliviensis and P. depilata present allopatric distributions separated by the Andean mountain system. Species distribution models indicate lowland tropical rain forest ecosystems as the most suitable habitat for these two Phoneutria species. In addition, we demonstrate the value of citizen science platforms like iNaturalist in improving species distribution knowledge based on occurrence records. Phoneutria depilata and P. boliviensis present niche conservatism following the expected neutral model of allopatric speciation. The compiled occurrence records and distribution maps for these two species, together with the morphological diagnosis of both species, will help to identify risk areas of accidental bites and assist health professionals to determine the identity of the species involved in bites, especially for P. depilata.

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.

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