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)

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.

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Combining Deforestation and Species Distribution Models to Improve Measures of Chimpanzee Conservation Impacts of REDD: A Case Study from Ntakata Mountains, Western Tanzania

Forests ◽

10.3390/f11111195 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1195

Author(s):

Rebecca Dickson ◽

Marc Baker ◽

Noémie Bonnin ◽

David Shoch ◽

Benjamin Rifkin ◽

...

Keyword(s):

Biodiversity Conservation ◽

Species Distribution ◽

Carbon Emissions ◽

Species Distribution Models ◽

Cost Effective ◽

Climate Mitigation ◽

Suitable Habitat ◽

Distribution Models ◽

Western Tanzania

Projects to reduce emissions from deforestation and degradation (REDD) are designed to reduce carbon emissions through avoided deforestation and degradation, and in many cases, to produce additional community and biodiversity conservation co-benefits. While these co-benefits can be significant, quantifying conservation impacts has been challenging, and most projects use simple species presence to demonstrate positive biodiversity impact. Some of the same tools applied in the quantification of climate mitigation benefits have relevance and potential application to estimating co-benefits for biodiversity conservation. In western Tanzania, most chimpanzees live outside of national park boundaries, and thus face threats from human activity, including competition for suitable habitat. Through a case study of the Ntakata Mountains REDD project in western Tanzania, we demonstrate a combined application of deforestation modelling with species distribution models to assess forest conservation benefits in terms of avoided carbon emissions and improved chimpanzee habitat. The application of such tools is a novel approach that we argue permits the better design of future REDD projects for biodiversity co-benefits. This approach also enables project developers to produce the more manageable, accurate and cost-effective monitoring, reporting and verification of project impacts that are critical to verification under carbon standards.

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Incorporating movement in species distribution models

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133315580890 ◽

2015 ◽

Vol 39 (6) ◽

pp. 837-849 ◽

Cited By ~ 22

Author(s):

Jennifer A Miller ◽

Paul Holloway

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Suitable Habitat ◽

Age At Maturity ◽

Distribution Models ◽

Migration Rates ◽

Movement Ability ◽

Landscape Factors ◽

Patch Configuration ◽

Realistic Information

Movement in the context of species distribution models (SDMs) generally refers to a species’ ability to access suitable habitat. Movement ability can be determined by some combination of dispersal constraints or migration rates, landscape factors such as patch configuration, disturbance, and barriers, and demographic factors related to age at maturity, mortality, and fecundity. Including movement ability can result in more precise projections that help to distinguish suitable habitat that is or can be potentially occupied, from suitable habitat that is inaccessible. While most SDM studies have ignored movement or conceptualized it in overly simplistic ways (e.g. no dispersal versus unlimited dispersal), it is increasingly important to incorporate realistic information on movement ability, particularly for studies that aim to project future distributions such as climate change forecasting and invasive species applications. This progress report addresses the increasingly complex ways in which movement has been incorporated in SDM and outlines directions for further study.

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

PeerJ ◽

10.7717/peerj.8059 ◽

2019 ◽

Vol 7 ◽

pp. e8059 ◽

Cited By ~ 2

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.

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Species Distribution Models (SDM) – A Strategic Tool for Predicting Suitable Habitats for Conserving the Target Species

Advances in Geospatial Technologies - Handbook of Research on Geographic Information Systems Applications and Advancements ◽

10.4018/978-1-5225-0937-0.ch017 ◽

2017 ◽

pp. 427-440

Author(s):

Balaguru Balakrishnan ◽

Nagamurugan Nandakumar ◽

Soosairaj Sebastin ◽

Khaleel Ahamed Abdul Kareem

Keyword(s):

Species Distribution ◽

Spatial Data ◽

Environmental Variables ◽

Species Distribution Models ◽

Species Distribution Model ◽

Distribution Model ◽

Suitable Habitat ◽

Target Species ◽

Distribution Models ◽

Suitable Habitats

Conservation of the species in their native landscapes required understanding patterns of spatial distribution of species and their ecological connectivity through Species Distribution Models (SDM) by generation and integration of spatial data from different sources using Geographical Information System (GIS) tools. SDM is an ecological/spatial model which combines datasets and maps of occurrence of target species and their geographical and environmental variables by linking various algorithms together, that has been applied to either identify or predict the regions fulfilling the set conditions. This article is focused on comprehensive review of spatial data requirements, statistical algorithms and softwares used to generate the SDMs. This chapter also includes a case study predicting the suitable habitat distribution of Gnetum ula, an endemic and vulnerable plant species using maximum entropy (MaxEnt) species distribution model for species occurrences with inputs from environmental variables such as bioclimate and elevation.

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Bounding species distribution models

Current Zoology ◽

10.1093/czoolo/57.5.642 ◽

2011 ◽

Vol 57 (5) ◽

pp. 642-647 ◽

Cited By ~ 21

Author(s):

Thomas J. Stohlgren ◽

Catherine S. Jarnevich ◽

Wayne E. Esaias ◽

Jeffrey T. Morisette

Keyword(s):

Species Distribution ◽

Honey Bees ◽

Best Practice ◽

Species Distribution Models ◽

Regression Tree ◽

Classification And Regression Tree ◽

Suitable Habitat ◽

Environmental Predictors ◽

Distribution Models ◽

Africanized Honey Bees

Abstract Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for “clamping” model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used.

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Niche conservatism in a plant with long invasion history: the case of the Peruvian peppertree (Schinus molle, Anacardiaceae) in Mexico

Plant Ecology and Evolution ◽

10.5091/plecevo.2020.1562 ◽

2020 ◽

Vol 153 (1) ◽

pp. 3-11

Author(s):

Jorge E. Ramírez-Albores ◽

Gustavo Bizama ◽

Ramiro O. Bustamante ◽

Ernesto I. Badano

Keyword(s):

Invasive Plants ◽

Species Distribution ◽

Environmental Conditions ◽

Species Distribution Models ◽

Large Fraction ◽

Niche Conservatism ◽

Invasion History ◽

Distribution Models ◽

Schinus Molle ◽

Suitable Habitats

Background and aim – Invasive plants should only colonize habitats meeting the environmental conditions included in their native niches. However, if they invade habitats with novel environmental conditions, this can induce shifts in their niches. This may occur in plants with long invasion histories because they interacted with the environmental conditions of invaded regions over long periods of time. We focused on this issue and evaluated whether the niche of the oldest plant invader reported in Mexico, the Peruvian peppertree, is still conserved after almost 500 years of invasion history. Methods – We compared climatic niches of the species between the native and invaded region. We later used species distribution models (SDM) to visualize the geographical expression of both niches in Mexico. Results – The invasive niche of the Peruvian peppertree is fully nested within the native niche. Although this suggests that the niche is conserved, this also indicates that a large fraction of the native niche is empty in the invaded region. The SDM from the native region indicated that Mexico contains habitats meeting the conditions included in this empty fraction of the native niche and, thus, this invasion should continue expanding. Nevertheless, the SDM calibrated with data from the invaded region indicated that peppertrees have colonized all suitable habitats indicated by its invasive niche and, thus, their populations should no longer expand. Conclusion – Our results suggests that the niche of the Peruvian peppertree is partially conserved in Mexico. This may have occurred because individuals introduced into Mexico constituted a small, nonrepresentative sample of the full niche of the species.

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Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models

10.22541/au.163420130.04455423/v1 ◽

2021 ◽

Author(s):

Petra Fuehrding-Potschkat ◽

Holger Kreft ◽

Stefanie Ickert-Bond

Keyword(s):

Species Distribution ◽

Spatial Data ◽

Web Application ◽

Species Distribution Models ◽

Data Cleaning ◽

Distribution Models ◽

Global Biodiversity Information Facility ◽

Wide Range ◽

Occurrence Records ◽

Expert Data

Digital point-occurrence records from the Global Biodiversity Information Facility (GBIF) and other repositories enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time-consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. This study examined the extent to which cleaned data from six pipelines using data cleaning tools (e.g., the GBIF web application, different R packages) affect downstream species distribution models. In addition, we assessed how the pipeline data differ from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false-positives, invalid coordinates, and duplicates, leading to data sets that included between 9,484 (GBIF application) and 5,196 records (manual-guided filtering). The expert data consisted of 703 thoroughly handpicked records, comparable to data from field studies. Although differences in the record numbers were relatively large, stacked species distribution models (sSDM) from the pipelines and the expert data were strongly related (mean Pearson’s r across the pipelines: 0.9986, versus the expert data: 0.9173). The ever-stronger correlations resulted from occurrence information that became increasingly condensed in the course of the workflow (from individual occurrences to collectivized occurrences in grid cells to predicted probabilities in the sSDMs). In sum, our results suggest that the R package-based pipelines reliably identified invalid coordinates. In contrast, the GBIF-filtered data still contained both spatial and taxonomic errors. However, major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of expert taxonomic knowledge. We conclude that application-filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high-quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.

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