scholarly journals Mapping the potential distribution of the Critically Endangered Himalayan Quail Ophrysia superciliosa using proxy species and species distribution modelling

2015 ◽  
Vol 25 (4) ◽  
pp. 466-478 ◽  
Author(s):  
JONATHON C. DUNN ◽  
GRAEME M. BUCHANAN ◽  
RICHARD J. CUTHBERT ◽  
MARK J. WHITTINGHAM ◽  
PHILIP J. K. MCGOWAN
Keyword(s):  
Land Cover ◽  
Species Distribution ◽  
Climate Model ◽  
Habitat Preferences ◽  
Species Distribution Modelling ◽  
Critically Endangered ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Extant Species ◽  
Starting Point

SummaryThe Critically Endangered Himalayan Quail Ophrysia superciliosa has not been reliably recorded since 1876. Recent searches of historical sites have failed to detect the species, but we estimate an extinction year of 2023 giving us reason to believe that the species may still be extant. Species distribution models can act as a guide for survey efforts, but the current land cover in the historical specimen record locations is unlikely to reflect Himalayan Quail habitat preferences due to extensive modifications. Thus, we investigate the use of two proxy species: Cheer Pheasant Catreus wallechi and Himalayan Monal Lophophorus impejanus that taken together are thought to have macro-habitat requirements that encapsulate those of the Himalayan Quail. After modelling climate and topography space for the Himalayan Quail and these proxy species we find the models for the proxy species have moderate overlap with that of the Himalayan Quail. Models improved with the incorporation of land cover data and when these were overlaid with the Himalayan Quail climate model, we were able to identify suitable areas to target surveys. Using a measure of search effort from recent observations of other galliformes, we identify 923 km2 of suitable habitat surrounding Mussoorie in Northern India that requires further surveys. We conclude with a list of five priority survey sites as a starting point.

scholarly journals The value of local community knowledge in species distribution modelling for a threatened Neotropical parrot

2021 ◽  
Author(s):  
Rebecca Biddle ◽  
Ivette Solis-Ponce ◽  
Martin Jones ◽  
Stuart Marsden ◽  
Mark Pilgrim ◽  
...  
Keyword(s):  
Species Distribution ◽  
Field Data ◽  
Species Distribution Modelling ◽  
Data Models ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Community Knowledge ◽  
Occurrence Data ◽  
Community Data

AbstractSpecies distribution models are widely used in conservation planning, but obtaining the necessary occurrence data can be challenging, particularly for rare species. In these cases, citizen science may provide insight into species distributions. To understand the distribution of the newly described and Critically EndangeredAmazona lilacina,we collated species observations and reliable eBird records from 2010–2020. We combined these with environmental predictors and either randomly generated background points or absence points generated from eBird checklists, to build distribution models using MaxEnt. We also conducted interviews with people local to the species’ range to gather community-sourced occurrence data. We grouped these data according to perceived expertise of the observer, based on the ability to identifyA. lilacinaand its distinguishing features, knowledge of its ecology, overall awareness of parrot biodiversity, and the observation type. We evaluated all models using AUC and Tjur R2. Field data models built using background points performed better than those using eBird absence points (AUC = 0.80 ± 0.02, Tjur R2 = 0.46 ± 0.01 compared to AUC = 0.78 ± 0.03, Tjur R2 = 0.43 ± 0.21). The best performing community data model used presence records from people who were able recognise a photograph ofA. lilacinaand correctly describe its distinguishing physical or behavioural characteristics (AUC = 0.84 ± 0.05, Tjur R2 = 0.51± 0.01). There was up to 92% overlap between the field data and community data models, which when combined, predicted 17,772 km2of suitable habitat. Use of community knowledge offers a cost-efficient method to obtain data for species distribution modelling; we offer recommendations on how to assess its performance and present a final map of potential distribution forA. lilacina.

scholarly journals Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change

2018 ◽  
Vol 373 (1761) ◽  
pp. 20170446 ◽  
Author(s):  
Scott Jarvie ◽  
Jens-Christian Svenning
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Climatic Conditions ◽  
First Century ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Major Barrier ◽  
Methodological Choices ◽  
Biodiversity And Ecosystem Function

Trophic rewilding, the (re)introduction of species to promote self-regulating biodiverse ecosystems, is a future-oriented approach to ecological restoration. In the twenty-first century and beyond, human-mediated climate change looms as a major threat to global biodiversity and ecosystem function. A critical aspect in planning trophic rewilding projects is the selection of suitable sites that match the needs of the focal species under both current and future climates. Species distribution models (SDMs) are currently the main tools to derive spatially explicit predictions of environmental suitability for species, but the extent of their adoption for trophic rewilding projects has been limited. Here, we provide an overview of applications of SDMs to trophic rewilding projects, outline methodological choices and issues, and provide a synthesis and outlook. We then predict the potential distribution of 17 large-bodied taxa proposed as trophic rewilding candidates and which represent different continents and habitats. We identified widespread climatic suitability for these species in the discussed (re)introduction regions under current climates. Climatic conditions generally remain suitable in the future, although some species will experience reduced suitability in parts of these regions. We conclude that climate change is not a major barrier to trophic rewilding as currently discussed in the literature.This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’.

scholarly journals Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

2021 ◽  
Author(s):  
Gabriel Dansereau ◽  
Pierre Legendre ◽  
Timothée Poisot
Keyword(s):  
North America ◽  
Species Distribution ◽  
Rare Species ◽  
Beta Diversity ◽  
Species Distribution Modelling ◽  
Distribution Models ◽  
Science Data ◽  
Distribution Modelling ◽  
Ecological Uniqueness ◽  
The Relationship

Aim: Local contributions to beta diversity (LCBD) can be used to identify sites with high ecological uniqueness and exceptional species composition within a region of interest. Yet, these indices are typically used on local or regional scales with relatively few sites, as they require information on complete community compositions difficult to acquire on larger scales. Here, we investigate how LCBD indices can be used to predict ecological uniqueness over broad spatial extents using species distribution modelling and citizen science data. Location: North America. Time period: 2000s. Major taxa studied: Parulidae. Methods: We used Bayesian additive regression trees (BARTs) to predict warbler species distributions in North America based on observations recorded in the eBird database. We then calculated LCBD indices for observed and predicted data and examined the site-wise difference using direct comparison, a spatial autocorrelation test, and generalized linear regression. We also investigated the relationship between LCBD values and species richness in different regions and at various spatial extents and the effect of the proportion of rare species on the relationship. Results: Our results showed that the relationship between richness and LCBD values varies according to the region and the spatial extent at which it is applied. It is also affected by the proportion of rare species in the community. Species distribution models provided highly correlated estimates with observed data, although spatially autocorrelated. Main conclusions: Sites identified as unique over broad spatial extents may vary according to the regional richness, total extent size, and the proportion of rare species. Species distribution modelling can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.

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

Forests ◽  
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.

scholarly journals Making species distribution models available on the web for reuse in biodiversity experiments: Euterpe edulis species case study

2009 ◽  
Vol 21 (1) ◽  
pp. 39-49
Author(s):  
Karla Donato Fook ◽  
Silvana Amaral ◽  
Antônio Miguel Vieira Monteiro ◽  
Gilberto Câmara ◽  
Arimatéa de Carvalho Ximenes ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Modelling ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Urban And Regional Planning ◽  
Community Needs ◽  
Computational Environment ◽  
Decision Making Processes ◽  
The Web

Currently, biodiversity conservation is one of the most urgent and important themes. Biodiversity researchers use species distribution models to make inferences about species occurrences and locations. These models are fundamental for fauna and flora preservation, as well as for decision making processes for urban and regional planning and development. Species distribution modelling tools use large biodiversity datasets which are globally distributed, can be in different computational platforms, and are hard to access and manipulate. The scientific community needs infrastructures in which biodiversity researchers can collaborate and share knowledge. In this context, we present a computational environment that supports the collaboration in species distribution modelling network on the Web. This environment is based on a modelling experiment catalogue and on a set of geoweb services, the Web Biodiversity Collaborative Modelling Services - WBCMS.

scholarly journals Incorporating movement in species distribution models

2015 ◽  
Vol 39 (6) ◽  
pp. 837-849 ◽  
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.

scholarly journals blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models

2018 ◽  
Author(s):  
Roozbeh Valavi ◽  
Jane Elith ◽  
José J. Lahoz-Monfort ◽  
Gurutzeta Guillera-Arroita
Keyword(s):  
Species Distribution ◽  
Cross Validation ◽  
Species Distribution Models ◽  
Predictive Performance ◽  
R Package ◽  
Species Distribution Modelling ◽  
List Type ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Evaluation Approaches

SummaryWhen applied to structured data, conventional random cross-validation techniques can lead to underestimation of prediction error, and may result in inappropriate model selection.We present the R package blockCV, a new toolbox for cross-validation of species distribution modelling.The package can generate spatially or environmentally separated folds. It includes tools to measure spatial autocorrelation ranges in candidate covariates, providing the user with insights into the spatial structure in these data. It also offers interactive graphical capabilities for creating spatial blocks and exploring data folds.Package blockCV enables modellers to more easily implement a range of evaluation approaches. It will help the modelling community learn more about the impacts of evaluation approaches on our understanding of predictive performance of species distribution models.

scholarly journals Integrating Recent Land Cover Mapping Efforts to Update the National Gap Analysis Program's Species Habitat Map

2014 ◽  
Vol XL-1 ◽  
pp. 245-252 ◽  
Author(s):  
A. J. McKerrow ◽  
A. Davidson ◽  
T. S. Earnhardt ◽  
A. L. Benson
Keyword(s):  
Land Cover ◽  
Gap Analysis ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Suitable Habitat ◽  
Vertebrate Species ◽  
Distribution Models ◽  
Terrestrial Vertebrate ◽  
Landscape Characterization ◽  
Land Cover Map

Over the past decade, great progress has been made to develop national extent land cover mapping products to address natural resource issues. One of the core products of the GAP Program is range-wide species distribution models for nearly 2000 terrestrial vertebrate species in the U.S. We rely on deductive modeling of habitat affinities using these products to create models of habitat availability. That approach requires that we have a thematically rich and ecologically meaningful map legend to support the modeling effort. In this work, we tested the integration of the Multi-Resolution Landscape Characterization Consortium's National Land Cover Database 2011 and LANDFIRE's Disturbance Products to update the 2001 National GAP Vegetation Dataset to reflect 2011 conditions. The revised product can then be used to update the species models. <br><br> We tested the update approach in three geographic areas (Northeast, Southeast, and Interior Northwest). We used the NLCD product to identify areas where the cover type mapped in 2011 was different from what was in the 2001 land cover map. We used Google Earth and ArcGIS base maps as reference imagery in order to label areas identified as "changed" to the appropriate class from our map legend. Areas mapped as urban or water in the 2011 NLCD map that were mapped differently in the 2001 GAP map were accepted without further validation and recoded to the corresponding GAP class. We used LANDFIRE's Disturbance products to identify changes that are the result of recent disturbance and to inform the reassignment of areas to their updated thematic label. We ran species habitat models for three species including Lewis's Woodpecker (<i>Melanerpes lewis</i>) and the White-tailed Jack Rabbit (<i>Lepus townsendii</i>) and Brown Headed nuthatch (<i>Sitta pusilla</i>). For each of three vertebrate species we found important differences in the amount and location of suitable habitat between the 2001 and 2011 habitat maps. Specifically, Brown headed nuthatch habitat in 2011 was &minus;14% of the 2001 modeled habitat, whereas Lewis's Woodpecker increased by 4%. The white-tailed jack rabbit (<i>Lepus townsendii</i>) had a net change of &minus;1% (11% decline, 10% gain). For that species we found the updates related to opening of forest due to burning and regenerating shrubs following harvest to be the locally important main transitions. In the Southeast updates related to timber management and urbanization are locally important.

scholarly journals Deep Multi-species Embedding

2017 ◽  
Author(s):  
Di Chen ◽  
Yexiang Xue ◽  
Daniel Fink ◽  
Shuo Chen ◽  
Carla P. Gomes
Keyword(s):  
Neural Network ◽  
Species Distribution ◽  
Species Interactions ◽  
Deep Neural Network ◽  
Species Distribution Models ◽  
Habitat Preferences ◽  
Bird Species ◽  
Single Species ◽  
Additional Contribution ◽  
Distribution Models

Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Applied to bird observational data from the citizen science project eBird, we demonstrate how the DMSE model discovers inter-species relationships to outperform single-species distribution models (random forests and SVMs) as well as competing multi-label models. Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling. An important domain contribution of the DMSE model is the ability to discover and describe species interactions while simultaneously learning the shared habitat preferences among species. As an additional contribution, we provide a graphical embedding of hundreds of bird species in the Northeast US.

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