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 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 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 Challenges and perspectives for species distribution modelling in the neotropics

2011 ◽  
Vol 8 (3) ◽  
pp. 324-326 ◽  
Author(s):  
Luciana H. Y. Kamino ◽  
João Renato Stehmann ◽  
Silvana Amaral ◽  
Paulo De Marco ◽  
Thiago F. Rangel ◽  
...  
Keyword(s):  
Species Distribution ◽  
State Of The Art ◽  
Good Practice ◽  
Species Distribution Modelling ◽  
Distribution Models ◽  
Related Field ◽  
Distribution Modelling ◽  
Field Surveys ◽  
Belo Horizonte ◽  
Species Occurrences

The workshop ‘ Species distribution models: applications, challenges and perspectives ’ held at Belo Horizonte (Brazil), 29–30 August 2011, aimed to review the state-of-the-art in species distribution modelling (SDM) in the neotropical realm. It brought together researchers in ecology, evolution, biogeography and conservation, with different backgrounds and research interests. The application of SDM in the megadiverse neotropics—where data on species occurrences are scarce—presents several challenges, involving acknowledging the limitations imposed by data quality, including surveys as an integral part of SDM studies, and designing the analyses in accordance with the question investigated. Specific solutions were discussed, and a code of good practice in SDM studies and related field surveys was drafted.

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 geographic distribution of Senecio glastifolius in New Zealand: past, current and climatic potential

2021 ◽  
Author(s):  
◽  
Josef Rehua Beautrais
Keyword(s):  
South Africa ◽  
New Zealand ◽  
Species Distribution ◽  
Potential Distribution ◽  
Species Distribution Modelling ◽  
Suitable Habitat ◽  
Native Range ◽  
Data Set ◽  
Distribution Modelling ◽  
Temperature Range

<p>Senecio glastifolius (Asteraceae) is an invasive species in New Zealand, where it threatens rare and vulnerable coastal floristic communities. It has expanded its range dramatically over recent years and continues to spread. It is subject to control programs in parts of its distribution. Uncertainty over its future distribution and invasive impacts in New Zealand contribute to the difficulty of its management. To address this knowledge gap, the potential distribution of S. glastifolius in New Zealand was predicted, based on its bioclimatic niche.  Existing information on its current distribution and historic spread is incomplete, stored in disparate sources, and is often imprecise or inaccurate. In this study, available information on its distribution and spread was synthesised, processed, and augmented with new data collected in the field by the author. This data set was optimised for use in species distribution modelling.  The distribution of S. glastifolius is described in its native range of South Africa, plus invaded regions in Australia, the British Isles and New Zealand. The data set describing its distribution is of higher quality than any known previous data set, is more extensive, and more suitable for use in species distribution modelling. The historic spread of S. glastifolius in New Zealand is presented, illustrating its expansion from sites of introduction in Wellington, Gisborne, plus several subsequent sites, to its now considerable range throughout much of central New Zealand.  A predictive model of the potential distribution of S. glastifolius was created based on the three main climatic variables observed to limit its distribution: mean annual temperature range, aridity, and minimum temperature of the coldest month. MaxEnt models were trained on data from all regions for which georeferenced records of the species were available; South Africa, Australia, New Zealand and the Isles of Scilly. Predictions were evaluated using methods appropriate to the special case of range-expanding species. Models performed well during validation, suggesting good predictive ability when applied to new areas.  Analysis of the realised niche space of S. glastifolius in the two climatic dimensions most influencing its distribution: Annual Temperature Range and Aridity, indicated that it is exploiting almost totally disjunct niche spaces in New Zealand and South Africa. Of the climate space occupied in New Zealand, almost none is available to the species in its native range of South Africa.  Predictions of S. glastifolius’s potential distribution in New Zealand reveal significant areas of suitable habitat yet to be invaded. Much of this suitable habitat is contiguous with the current range and active dispersal front of S. glastifolius, suggesting that invasion is highly likely under a scenario of no management intervention. Specifically, it is suggested that control and surveillance in coastal Taranaki are required to prevent invasion of an area covering most of the northern third of the North Island.</p>

scholarly journals The geographic distribution of Senecio glastifolius in New Zealand: past, current and climatic potential

2021 ◽  
Author(s):  
◽  
Josef Rehua Beautrais
Keyword(s):  
South Africa ◽  
New Zealand ◽  
Species Distribution ◽  
Potential Distribution ◽  
Species Distribution Modelling ◽  
Suitable Habitat ◽  
Native Range ◽  
Data Set ◽  
Distribution Modelling ◽  
Temperature Range

<p>Senecio glastifolius (Asteraceae) is an invasive species in New Zealand, where it threatens rare and vulnerable coastal floristic communities. It has expanded its range dramatically over recent years and continues to spread. It is subject to control programs in parts of its distribution. Uncertainty over its future distribution and invasive impacts in New Zealand contribute to the difficulty of its management. To address this knowledge gap, the potential distribution of S. glastifolius in New Zealand was predicted, based on its bioclimatic niche.  Existing information on its current distribution and historic spread is incomplete, stored in disparate sources, and is often imprecise or inaccurate. In this study, available information on its distribution and spread was synthesised, processed, and augmented with new data collected in the field by the author. This data set was optimised for use in species distribution modelling.  The distribution of S. glastifolius is described in its native range of South Africa, plus invaded regions in Australia, the British Isles and New Zealand. The data set describing its distribution is of higher quality than any known previous data set, is more extensive, and more suitable for use in species distribution modelling. The historic spread of S. glastifolius in New Zealand is presented, illustrating its expansion from sites of introduction in Wellington, Gisborne, plus several subsequent sites, to its now considerable range throughout much of central New Zealand.  A predictive model of the potential distribution of S. glastifolius was created based on the three main climatic variables observed to limit its distribution: mean annual temperature range, aridity, and minimum temperature of the coldest month. MaxEnt models were trained on data from all regions for which georeferenced records of the species were available; South Africa, Australia, New Zealand and the Isles of Scilly. Predictions were evaluated using methods appropriate to the special case of range-expanding species. Models performed well during validation, suggesting good predictive ability when applied to new areas.  Analysis of the realised niche space of S. glastifolius in the two climatic dimensions most influencing its distribution: Annual Temperature Range and Aridity, indicated that it is exploiting almost totally disjunct niche spaces in New Zealand and South Africa. Of the climate space occupied in New Zealand, almost none is available to the species in its native range of South Africa.  Predictions of S. glastifolius’s potential distribution in New Zealand reveal significant areas of suitable habitat yet to be invaded. Much of this suitable habitat is contiguous with the current range and active dispersal front of S. glastifolius, suggesting that invasion is highly likely under a scenario of no management intervention. Specifically, it is suggested that control and surveillance in coastal Taranaki are required to prevent invasion of an area covering most of the northern third of the North Island.</p>

scholarly journals Classification of unlabeled observations in Species Distribution Modelling using Point Process Models

2019 ◽  
Author(s):  
Emy Guilbault ◽  
Ian Renner ◽  
Michael Mahony ◽  
Eric Beh
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distributions ◽  
Species Distribution Modelling ◽  
Process Models ◽  
List Type ◽  
Distribution Models ◽  
Species Identity ◽  
Distribution Modelling ◽  
Unknown Species

1AbstractSpecies distribution modelling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit species distribution models. However, the species observations used in species distribution models can have varying levels of quality and can have incomplete information, such as uncertain species identity.In this paper, we develop two algorithms to reclassify observations with unknown species identities which simultaneously predict different species distributions using spatial point processes. We compare the performance of the different algorithms using different initializations and parameters with models fitted using only the observations with known species identity through simulations.We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that didn’t use the misspecified data.These models represent an helpful and promising tool for opportunistic surveys where misidentification happens or for the distribution of species newly separated in their taxonomy.

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