Speculation versus data-driven conclusions: A response to Gereau et al.’s "Phylogenetic patterns of extinction risk: the need for critical application of appropriate datasets"

10.7287/peerj.preprints.323v1 ◽

2014 ◽

Author(s):

Barnabas Daru ◽

Kowiyou Yessoufou ◽

Jonathan Davies

Keyword(s):

Extinction Risk ◽

Red List ◽

Biodiversity Hotspot ◽

Data Driven ◽

Recent Analysis ◽

Versus Data ◽

Threat Status ◽

Eastern Arc

Gereau et al. (2013) criticized our recent analysis on the phylogenetic patterns of extinction risk in the Eastern Arc biodiversity hotspot (Yessoufou et al. 2012). However, Gereau and colleagues based their critique on preconceptions and speculation rather than data. Here we identify several shortfalls in their lines of argument, and suggest that, given current rates of extinction, it is far more dangerous to wait for complete Red List assessments than to explore patterns of threat using available data. Nonetheless, we agree that all analyses should be based upon the best available data, and we encourage the rapid releases of new data on threat status for the flora of the Eastern Arc.

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Phylogenetic Patterns of Extinction Risk in the Eastern Arc Ecosystems, an African Biodiversity Hotspot

PLoS ONE ◽

10.1371/journal.pone.0047082 ◽

2012 ◽

Vol 7 (10) ◽

pp. e47082 ◽

Cited By ~ 17

Author(s):

Kowiyou Yessoufou ◽

Barnabas H. Daru ◽

T. Jonathan Davies

Keyword(s):

Extinction Risk ◽

Biodiversity Hotspot ◽

Eastern Arc

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Using Red List Indices to measure progress towards the 2010 target and beyond

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2004.1583 ◽

2005 ◽

Vol 360 (1454) ◽

pp. 255-268 ◽

Cited By ~ 156

Author(s):

S.H.M Butchart ◽

A.J Stattersfield ◽

J Baillie ◽

L.A Bennun ◽

S.N Stuart ◽

...

Keyword(s):

Extinction Risk ◽

Relative Rate ◽

Iucn Red List ◽

Red List ◽

World Conservation ◽

Threat Status ◽

2010 Target ◽

Stratified Sample ◽

Objective System ◽

Taxonomic Groups

The World Conservation Union (IUCN) Red List is widely recognized as the most authoritative and objective system for classifying species by their risk of extinction. Red List Indices (RLIs) illustrate the relative rate at which a particular set of species change in overall threat status (i.e. projected relative extinction-risk), based on population and range size and trends as quantified by Red List categories. RLIs can be calculated for any representative set of species that has been fully assessed at least twice. They are based on the number of species in each Red List category, and the number changing categories between assessments as a result of genuine improvement or deterioration in status. RLIs show a fairly coarse level of resolution, but for fully assessed taxonomic groups they are highly representative, being based on information from a high proportion of species worldwide. The RLI for the world's birds shows that that their overall threat status has deteriorated steadily during the years 1988–2004 in all biogeographic realms and ecosystems. A preliminary RLI for amphibians for 1980–2004 shows similar rates of decline. RLIs are in development for other groups. In addition, a sampled index is being developed, based on a stratified sample of species from all major taxonomic groups, realms and ecosystems. This will provide extinction-risk trends that are more representative of all biodiversity.

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A data-driven geospatial workflow to improve mapping species distributions and assessing extinction risk under the IUCN Red List

10.1101/2020.04.27.064477 ◽

2020 ◽

Author(s):

Ruben Dario Palacio ◽

Pablo Jose Negret ◽

Jorge Velásquez-Tibatá ◽

Andrew P. Jacobson

Keyword(s):

Extinction Risk ◽

Conservation Status ◽

Bird Species ◽

Species Distributions ◽

Iucn Red List ◽

Red List ◽

Data Driven ◽

Distribution Maps ◽

Distance Weighted ◽

Extinction Risk Assessment

ABSTRACTSpecies distribution maps are essential for assessing extinction risk and guiding conservation efforts. Here, we developed a data-driven, reproducible geospatial workflow to map species distributions and evaluate their conservation status consistent with the guidelines and criteria of the IUCN Red List. Our workflow follows five automated steps to refine the distribution of a species starting from its Extent of Occurrence (EOO) to Area of Habitat (AOH) within the species range. The ranges are produced with an Inverse Distance Weighted (IDW) interpolation procedure, using presence and absence points derived from primary biodiversity data. As a case-study, we mapped the distribution of 2,273 bird species in the Americas, 55% of all terrestrial birds found in the region. We then compared our produced species ranges to the expert-drawn IUCN/BirdLife range maps and conducted a preliminary IUCN extinction risk assessment based on criterion B (Geographic Range). We found that our workflow generated ranges with fewer errors of omission, commission, and a better overall accuracy within each species EOO. The spatial overlap between both datasets was low (28%) and the expert-drawn range maps were consistently larger due to errors of commission. Their estimated Area of Habitat (AOH) was also larger for a subset of 741 forest-dependent birds. We found that incorporating geospatial data increased the number of threatened species by 52% in comparison to the 2019 IUCN Red List. Furthermore, 103 species could be placed in threatened categories (VU, EN, CR) pending further assessment. The implementation of our geospatial workflow provides a valuable alternative to increase the transparency and reliability of species risk assessments and improve mapping species distributions for conservation planning and decision-making.

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Red List assessment of widespread and long-lived species

Oryx ◽

10.1017/s0030605320001325 ◽

2021 ◽

pp. 1-6

Author(s):

Roderick J. Fensham

Keyword(s):

Extinction Risk ◽

Population Decline ◽

Iucn Red List ◽

Red List ◽

Species Loss ◽

Widespread Species ◽

Criterion A ◽

Threat Status ◽

Eucalypt Species ◽

The World

Abstract The use of criterion A of the IUCN Red List to categorize species as threatened that have undergone recent decline can lead to the listing of relatively common and widespread species. Loss of habitat through deforestation is a common cause of decline throughout much of the world but is often not incorporated into assessments because of uncertainty about the magnitude of change. A recent assessment of eucalypt species in Australia subject to deforestation provides a method for assessment under criterion A and has implications for listing of long-lived, widespread species affected by deforestation. Scenarios for two widespread eucalypt species subject to extensive deforestation are used to demonstrate how the threat status of a species may be recategorized in a lower threat category as declines resulting from a threatening process are mitigated. I argue that criterion A indicates an appropriate assessment of extinction risk and I provide a simple function based on predicted diminishment of the population decline to identify when a species could be disqualified from a threat category under subcriterion A2 (past decline).

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Biological Correlates of Extinction Risk in Resident Philippine Avifauna

Frontiers in Ecology and Evolution ◽

10.3389/fevo.2021.664764 ◽

2021 ◽

Vol 9 ◽

Author(s):

Kyle D. Kittelberger ◽

Montague H. C. Neate-Clegg ◽

J. David Blount ◽

Mary Rose C. Posa ◽

John McLaughlin ◽

...

Keyword(s):

Life History ◽

Life History Traits ◽

Habitat Degradation ◽

Extinction Risk ◽

Strong Support ◽

Iucn Red List ◽

The Philippines ◽

Red List ◽

Forest Dependency ◽

Threat Status

The majority of the world’s biodiversity occurs in the tropics, but human actions in these regions have precipitated an extinction crisis due to habitat degradation, overexploitation, and climate change. Understanding which ecological, biogeographical, and life-history traits predict extinction risk is critical for conserving species. The Philippines is a hotspot of biodiversity and endemism, but it is a region that also suffers from an extremely high level of deforestation, habitat degradation, and wildlife exploitation. We investigated the biological correlates of extinction risk based on the IUCN Red List threat status among resident Philippine birds using a broad range of ecological, biogeographical, and life history traits previously identified as correlates of extinction risk in birds. We found strong support across competing models for endemism, narrower elevational ranges, high forest dependency, and larger body size as correlates significantly associated with extinction risk. Additionally, we compared observed threat status with threat status fitted by our model, finding fourteen species that are not currently recognized by the IUCN Red List as threatened that may be more threatened than currently believed and therefore warrant heightened conservation focus, and predicted threat statuses for the four Philippine Data Deficient bird species. We also assessed species described in recent taxonomic splits that are recognized by BirdLife International, finding 12 species that have a fitted threat status more severe than their IUCN-designated ones. Our findings provide a framework for avian conservation efforts to identify birds with specific biological correlates that increase a species’ vulnerability to extinction both in the Philippine Archipelago and elsewhere on other tropical islands.

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Conservation Status of Marine Biodiversity in Oceania: An Analysis of Marine Species on the IUCN Red List of Threatened Species

Journal of Marine Biology ◽

10.1155/2011/247030 ◽

2011 ◽

Vol 2011 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Beth A. Polidoro ◽

Cristiane T. Elfes ◽

Jonnell C. Sanciangco ◽

Helen Pippard ◽

Kent E. Carpenter

Keyword(s):

Threatened Species ◽

Environmental Changes ◽

Extinction Risk ◽

Conservation Status ◽

Marine Species ◽

Iucn Red List ◽

Red List ◽

Marine Biodiversity ◽

Conservation Priorities ◽

Conservation Action

Given the economic and cultural dependence on the marine environment in Oceania and a rapidly expanding human population, many marine species populations are in decline and may be vulnerable to extinction from a number of local and regional threats. IUCN Red List assessments, a widely used system for quantifying threats to species and assessing species extinction risk, have been completed for 1190 marine species in Oceania to date, including all known species of corals, mangroves, seagrasses, sea snakes, marine mammals, sea birds, sea turtles, sharks, and rays present in Oceania, plus all species in five important perciform fish groups. Many of the species in these groups are threatened by the modification or destruction of coastal habitats, overfishing from direct or indirect exploitation, pollution, and other ecological or environmental changes associated with climate change. Spatial analyses of threatened species highlight priority areas for both site- and species-specific conservation action. Although increased knowledge and use of newly available IUCN Red List assessments for marine species can greatly improve conservation priorities for marine species in Oceania, many important fish groups are still in urgent need of assessment.

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Increasing accuracy: The advantage of using open access species occurrence database in the Red List assessment

Biodiversitas Journal of Biological Diversity ◽

10.13057/biodiv/d210831 ◽

2020 ◽

Vol 21 (8) ◽

Author(s):

Iyan Robiansyah ◽

Wita Wardani

Keyword(s):

Open Access ◽

Extinction Risk ◽

Conservation Status ◽

Iucn Red List ◽

Red List ◽

Suitable Habitat ◽

Herbarium Specimens ◽

Species Occurrence ◽

Extent Of Occurrence ◽

Area Of Occupancy

Abstract. Robiansyah I, Wardani W. 2020. Increasing accuracy: The advantage of using open access species occurrence database in the Red List assessment. Biodiversitas 21: 3658-3664. IUCN Red List is the most widely used instrument to assess and advise the extinction risk of a species. One of the criteria used in IUCN Red List is geographical range of the species assessed (criterion B) in the form of extent of occurrence (EOO) and/or area of occupancy (AOO). While this criterion is presumed to be the easiest to be completed as it is based mainly on species occurrence data, there are some assessments that failed to maximize freely available databases. Here, we reassessed the conservation status of Cibotium arachnoideum, a tree fern distributed in Sumatra and Borneo. This species was previously assessed by Praptosuwiryo (2020, Biodiversitas 21 (4): 1379-1384) which classified the species as Endangered (EN) under criteria B2ab(i,ii,iii); C2a(ii). Using additional data from herbarium specimens recorded in the Global Biodiversity Information Facility (GBIF) website and from peer-reviewed scientific papers, in the present paper we show that C. arachnoideum has a larger extent of occurrence (EOO) and area of occupancy (AOO), more locations and different conservation status compared to those in Praptosuwiryo (2020). Our results are supported by the predicted suitable habitat map of C. arachnoideum produced by MaxEnt modelling method. Based on our assessment, we propose the category of Vulnerable (VU) C2a(i) as the global conservation status for C. arachnoideum. Our study implies the advantage of using open access databases to increase the accuracy of extinction risk assessment under the IUCN Red List criteria in regions like Indonesia, where adequate taxonomical information is not always readily available.

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Spatiophylogenetic modelling of extinction risk reveals evolutionary distinctiveness and brief flowering period as risk factors in a diverse hotspot plant genus

10.1101/496547 ◽

2018 ◽

Author(s):

Russell Dinnage ◽

Alex Skeels ◽

Marcel Cardillo

Keyword(s):

Spatial Data ◽

Extinction Risk ◽

Individual Species ◽

Flowering Period ◽

Spatial Effects ◽

Relative Contribution ◽

Threat Status ◽

Latent Extinction ◽

Evolutionary Distinctiveness ◽

Combine Information

AbstractComparative models used to predict species threat status often combine variables measured at the species level with spatial variables, causing multiple statistical challenges, including phylogenetic and spatial non-independence. We present a novel bayesian approach for modelling threat status that simultaneously deals with both forms of non-independence and estimates their relative contribution, and we apply the approach to modelling threat status in the Australian plant genus Hakea. We find that after phylogenetic and spatial effects are accounted for, species with greater evolutionary distinctiveness and a shorter annual flowering period are more likely to be threatened. The model allows us to combine information on evolutionary history, species biology, and spatial data, to calculate latent extinction risk (potential for non-threatened species to become threatened), and estimate the most important drivers of risk for individual species. This could be of value for proactive conservation decision-making that targets species of concern before they become threatened.

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Addressing Uncertainties in Machine Learning Predictions of Conservation Status

Biodiversity Information Science and Standards ◽

10.3897/biss.3.37147 ◽

2019 ◽

Vol 3 ◽

Cited By ~ 1

Author(s):

Barnaby Walker ◽

Tarciso Leão ◽

Steven Bachman ◽

Eve Lucas ◽

Eimear Nic Lughadha

Keyword(s):

Machine Learning ◽

Natural History ◽

Extinction Risk ◽

Conservation Status ◽

Red List ◽

Learning Models ◽

Species Occurrence ◽

Voucher Specimens ◽

Machine Learning Models ◽

Occurrence Records

Extinction risk assessments are increasingly important to many stakeholders (Bennun et al. 2017) but there remain large gaps in our knowledge about the status of many species. The IUCN Red List of Threatened Species (IUCN 2019, hereafter Red List) is the most comprehensive assessment of extinction risk. However, it includes assessments of just 7% of all vascular plants, while 18% of all assessed animals lack sufficient data to assign a conservation status. The wide availability of species occurrence information through digitised natural history collections and aggregators such as the Global Biodiversity Information Facility (GBIF), coupled with machine learning methods, provides an opportunity to fill these gaps in our knowledge. Machine learning approaches have already been proposed to guide conservation assessment efforts (Nic Lughadha et al. 2018), assign a conservation status to species with insufficient data for a full assessment (Bland et al. 2014), and predict the number of threatened species across the world (Pelletier et al. 2018). The wide range in sources of species occurrence records can lead to data quality issues, such as missing, imprecise, or mistaken information. These data quality issues may be compounded in databases that aggregate information from multiple sources: many such records derive from field observations (78% for plant species in GBIF; Meyer et al. 2016) largely unsupported by voucher specimens that would allow confirmation or correction of their identification. Even where voucher specimens do exist, different taxonomic or geographic information can be held for a single collection event represented by duplicate specimens deposited in different natural history collections. Tools are available to help clean species occurrence data, but these cannot deal with problems like specimen misidentification, which previous work (Nic Lughadha et al. 2019) has shown to have a large impact on preliminary assessments of conservation status. Machine learning models based on species occurrence records have been reported to predict with high accuracy the conservation status of species. However, given the black-box nature of some of the better machine learning models, it is unclear how well these accuracies apply beyond the data on which the models were trained. Practices for training machine learning models differ between studies, but more interrogation of these models is required if we are to know how much to trust their predictions. To address these problems, we compare predictions made by a machine learning model when trained on specimen occurrence records that have benefitted from minimal or more thorough cleaning, with those based on records from an expert-curated database. We then explore different techniques to interrogate machine learning models and quantify the uncertainty in their predictions.

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