Literature DB >> 30065606

Species conservation profiles of a random sample of world spiders II: Gnaphosidae to Nemesiidae.

Sini Seppälä1,2, Sérgio Henriques3,2, Michael L Draney4,2, Stefan Foord5,2, Alastair T Gibbons6,2, Luz A Gomez7,2, Sarah Kariko8,2, Jagoba Malumbres-Olarte9,2,10, Marc Milne11,2, Cor J Vink12,2, Pedro Cardoso1,2.   

Abstract

BACKGROUND: The IUCN Red List of Threatened Species is the most widely used information source on the extinction risk of species. One of the uses of the Red List is to evaluate and monitor the state of biodiversity and a possible approach for this purpose is the Red List Index (RLI). For many taxa, mainly hyperdiverse groups, it is not possible within available resources to assess all known species. In such cases, a random sample of species might be selected for assessment and the results derived from it extrapolated for the entire group - the Sampled Red List Index (SRLI). The current contribution is the second in four papers that will constitute the baseline of a future spider SRLI encompassing 200 species distributed across the world. NEW INFORMATION: A sample of 200 species of spiders were randomly selected from the World Spider Catalogue, an updated global database containing all recognised species names for the group. The 200 selected species where divided taxonomically at the family level and the familes were ordered alphabetically. In this publication, we present the conservation profiles of 45 species belonging to the families alphabetically arranged between Gnaphosidae and Nemesiidae, which encompassed Gnaphosidae, Idiopidae, Linyphiidae, Liocranidae, Lycosidae, Micropholcommatidae, Mysmenidae and Nemesiidae.

Entities:  

Keywords:  Araneae ; Arthropoda ; IUCN.; conservation; endangered species; extinction risk; geographical range

Year:  2018        PMID: 30065606      PMCID: PMC6065607          DOI: 10.3897/BDJ.6.e26203

Source DB:  PubMed          Journal:  Biodivers Data J        ISSN: 1314-2828


Introduction

The IUCN Red List of Threatened Species is the most widely used information source on the extinction risk of species (Lamoreux et al. 2003, Rodrigues et al. 2006, Mace et al. 2008, see also Cardoso et al. 2011, Cardoso et al. 2012). It is based on a number of objective criteria, which are relatively easy to apply when adequate information is available (IUCN 2001). The Red List has been used to raise awareness about threatened species, guide conservation efforts and funding, set priorities for protection, measure site irreplaceability and vulnerability and influence environmental policies and legislation (Gardenfors et al. 2001, Rodrigues et al. 2006, Mace et al. 2008, Martín-López et al. 2009). One of the uses of the Red List is to evaluate and monitor the state of biodiversity and a possible approach for this purpose is the Red List Index (RLI). The RLI helps to develop a better understanding of which taxa, regions or ecosystems are declining or improving their conservation status. It provides policy-makers, stakeholders, conservation practitioners and the general public with sound knowledge of biodiversity status and change and tools with which to make informed decisions. The RLI uses weight scores based on the Red List status of each of the assessed species. These scores range from 0 (Least Concern) to 5 (Extinct/Extinct in the Wild). Summing these scores across all species, relating them to the worst-case scenario - all species extinct and comparing two or more points in time, gives us an indication of how biodiversity is doing. At a global level, the RLI has been calculated for birds (Butchart et al. 2004, Hoffmann et al. 2010), mammals (Hoffmann et al. 2011), amphibians (Hoffmann et al. 2010), corals (Butchart et al. 2010) and cycads (United Nations 2015). For many taxa, mainly hyperdiverse groups, it is not possible within available resources to assess all known species. In such cases, a random sample of species might be selected for assessment and the results derived from it extrapolated for the entire group - the Sampled Red List Index (SRLI, Baillie et al. 2008). The SRLI is now being developed for plants (Brummitt et al. 2015) and efforts towards a SRLI of butterflies (Lewis and Senior 2010) and are also in progress (Clausnitzer et al. 2009). Spiders currently comprise over 47000 species described at a global level (World Spider Catalog 2017). Of these, only 199 species (0.4%) have beed assessed (www.redlist.org), of which the vast majority are from the Seychelles Islands or belong to the golden-orb weavers, (e.g. Kuntner et al. 2017). To these, a large number will be added in the near future, such as 55 species endemic to the Madeira and Selvagens archipelagos and 25 endemic to the Azores, all in Portugal (Cardoso et al. 2017, Borges et al. submitted). The vast majority of spiders assessed to date are therefore either regionally or taxonomically clustered and do not represent the group as a whole. The current contribution is the second in four papers (Seppälä et al. 2018) that will constitute the baseline of a future spider SRLI encompassing 200 species distributed across the world.

Methods

A sample of 200 species of spiders were randomly selected from the World Spider Catalogue (2018), an updated global database containing all recognised species names for the group. The 200 selected species where divided taxonomically to the family level, and those familes were ordered alphabetically. In this publication, we present the conservation profiles of 45 species belonging to the families alphabetically arranged between and , which encompassed , , , , , , and . Species data were collected from all taxonomic bibliography available at the World Spider Catalogue (2018), complemented by data in other publications found through Google Scholar and georeferrenced points made available through the Global Biodiversity Information Facility (www.gbif.org) and also other sources (https://www.biodiversitylibrary.org; https://login.webofknowledge.com; http://srs.britishspiders.org.uk; http://symbiota4.acis.ufl.edu/scan/portal; https://lepus.unine.ch; http://www.tuite.nl/iwg/Araneae/SpiBenelux/?species; https://atlas.arages.de; https://arachnology.cz/rad/araneae-1.html; http://www.ennor.org/iberia/). Whenever possible, with each species record, we also collected additional information, namely habitat type and spatial error of coordinates. For all analyses, we used the R package red - IUCN red-listing tools (Cardoso 2017). This package performs a number of spatial analyses based on either observed occurrences or estimated ranges. Functions include calculating Extent of Occurrence (EOO), Area of Occupancy (AOO), mapping species ranges, species distribution modelling using climate and land cover, calculating the Red List Index for groups of species, amongst others. In this work, the EOO and AOO were calculated in one of two ways: - for extremely range-restricted species for which we assumed knowledge of the full range, these values were classified as observed, the minimum convex polygon encompassing all observations used to calculate the EOO and the 2 km x 2 km cells known to be occupied used to calculate the AOO. When the EOO was smaller than the AOO, it was made equal as per the IUCN guidelines (IUCN Standards and Petitions Subcommittee 2017). - for widespread species or those for which we did not have confidence to know the full range, we performed species distribution modelling (SDM). This was done based on both climatic (Fick and Hijmans 2017) and landcover (Tuanmu and Jetz 2014) datasets, at an approximately 1x1 km resolution. Before modelling, the world layers were cropped to the region of interest for each species and reduced to four layers through a PCA to avoid overfitting. In addition, latitude and longitude were used as two extra layers to avoid the models predicting presences much beyond the known region following the precautionary principle. We then used the Maxent method (Phillips et al. 2006) implemented in the R package red. Isolated patches outside the original distribution polygon were excluded from maps to avoid overestimation of EOO and AOO values. All final maps and values were checked and validated by our own expert opinion. KMLs derived from these maps were also produced using the red package. The cells (2x2 km), predicted to be occupied, were used to calculate the AOO. When the EOO was smaller than the AOO, it was made equal as per the IUCN guidelines (IUCN Standards and Petitions Subcommittee 2017). To infer possible changes in range and/or abundance and for forest species only, we have also consulted the Global Forest Watch portal (World Resources Institute 2014), looking for changes in forest cover during the last 10 years that could have affected the species. Distribution of Caporiacco, 1947 Data type: Distribution File: oo_174617.kml Distribution of (Schenkel, 1963) Data type: Distribution File: oo_170265.kml Distribution of Biswas & Roy, 2008 Data type: Distribution File: oo_170269.kml Distribution of Fox, 1938 Data type: Distribution File: oo_170270.kml Distribution of Dalmas, 1919 Data type: Distribution File: oo_170271.kml Distribution of (Emerton, 1877) Data type: Distribution File: oo_195709.kml Distribution of (Simon, 1895) Data type: Distribution File: oo_170273.kml Distribution of Platnick & Murphy, 1984 Data type: Distribution File: oo_195708.kml Distribution of Chamberlin, 1936 Data type: Distribution File: oo_170275.kml Distribution of Tikader & Gajbe, 1976 Data type: Distribution File: oo_170276.kml Distribution of FitzPatrick, 2007 Data type: Distribution File: oo_170279.kml Distribution of (Todd, 1945) Data type: Distribution File: oo_170280.kml Distribution of (Main, 1985) Data type: Distribution File: oo_170281.kml Distribution of Hewitt, 1916 Data type: Distribution File: oo_170282.kml Distribution of Dupérré, 2013 Data type: Distribution File: oo_199077.kml Distribution of (Loksa, 1965) Data type: Distribution File: oo_170284.kml Distribution of Emerton, 1882 Data type: Distribution File: oo_170285.kml Distribution of (Tanasevitch, 1987) Data type: Distribution File: oo_170286.kml Distribution of (Hogg, 1909) Data type: Distribution File: oo_170287.kml Distribution of (Berland, 1936) Data type: Distribution File: oo_212566.kml Distribution of (Wider, 1834) Data type: Distribution File: oo_212565.kml Distribution of (Crosby, 1929) Data type: Distribution File: oo_170291.kml Distribution of Saito & Ono, 2001 Data type: Distribution File: oo_170292.kml Distribution of (L. Koch, 1872) Data type: Distribution File: oo_170293.kml Distribution of Tanasevitch, 2013 Data type: Distribution File: oo_195707.kml Distribution of Bosselaers, 2009 Data type: Distribution File: oo_170295.kml Distribution of Gertsch, 1941 Data type: Distribution File: oo_170296.kml Distribution of (Lucas, 1846) Data type: Distribution File: oo_199025.kml Distribution of (Blackwall, 1867) Data type: Distribution File: oo_170298.kml Distribution of (Strand, 1916) Data type: Distribution File: oo_170299.kml Distribution of Roewer, 1959 Data type: Distribution File: oo_170300.kml Distribution of Pocock, 1901 Data type: Distribution File: oo_170301.kml Distribution of Pocock, 1901 Data type: Distribution File: oo_170302.kml Distribution of Pococks, 1901 Data type: Distribution File: oo_170303.kml Distribution of Chamberlin & Ivie, 1942 Data type: Distribution File: oo_170304.kml Distribution of (Tikader, 1970) Data type: Distribution File: oo_195706.kml Distribution of (Strand, 1916) Data type: Distribution File: oo_170306.kml Distribution of (Thorell, 1891) Data type: Distribution File: oo_199024.kml Distributon of in, Wang, Peng & Xie, 1995 Data type: Distribution File: oo_212567.kml Distribution of (Walckenaer, 1837) Data type: Distribution File: oo_170310.kml Distribution of Qu, Peng & Yin, 2010 Data type: Distribution File: oo_170311.kml Distribution of Caporiacco, 1941 Data type: Distribution File: oo_170312.kml Distribution of Forster, 1959 Data type: Distribution File: oo_170313.kml Distribution of Miller, Griswold & Yin, 2009 Data type: Distribution File: oo_170314.kml Distribution of Chamberlin, 1937 Data type: Distribution File: oo_170315.kml
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2.  Global biodiversity: indicators of recent declines.

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Journal:  Science       Date:  2010-04-29       Impact factor: 47.728

3.  The value of the IUCN Red List for conservation.

Authors:  Ana S L Rodrigues; John D Pilgrim; John F Lamoreux; Michael Hoffmann; Thomas M Brooks
Journal:  Trends Ecol Evol       Date:  2005-11-02       Impact factor: 17.712

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5.  The changing fates of the world's mammals.

Authors:  Michael Hoffmann; Jerrold L Belant; Janice S Chanson; Neil A Cox; John Lamoreux; Ana S L Rodrigues; Jan Schipper; Simon N Stuart
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2011-09-27       Impact factor: 6.237

6.  Measuring global trends in the status of biodiversity: red list indices for birds.

Authors:  Stuart H M Butchart; Alison J Stattersfield; Leon A Bennun; Sue M Shutes; H Resit Akçakaya; Jonathan E M Baillie; Simon N Stuart; Craig Hilton-Taylor; Georgina M Mace
Journal:  PLoS Biol       Date:  2004-10-26       Impact factor: 8.029

7.  red - an R package to facilitate species red list assessments according to the IUCN criteria.

Authors:  Pedro Cardoso
Journal:  Biodivers Data J       Date:  2017-10-19

8.  Species conservation profiles of endemic spiders (Araneae) from Madeira and Selvagens archipelagos, Portugal.

Authors:  Pedro Cardoso; Luís C Crespo; Isamberto Silva; Paulo Av Borges; Mário Boieiro
Journal:  Biodivers Data J       Date:  2017-10-18

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Journal:  PLoS One       Date:  2015-08-07       Impact factor: 3.240

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Authors:  Sini Seppälä; Sérgio Henriques; Michael L Draney; Stefan Foord; Alastair T Gibbons; Luz A Gomez; Sarah Kariko; Jagoba Malumbres-Olarte; Marc Milne; Cor J Vink; Pedro Cardoso
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1.  Globally distributed occurrences utilised in 200 spider species conservation profiles (Arachnida, Araneae).

Authors:  Pedro Cardoso; Vaughn Shirey; Sini Seppälä; Sergio Henriques; Michael L Draney; Stefan Foord; Alastair T Gibbons; Luz A Gomez; Sarah Kariko; Jagoba Malumbres-Olarte; Marc Milne; Cor J Vink
Journal:  Biodivers Data J       Date:  2019-04-02

2.  Species conservation profiles of a random sample of world spiders IV: Scytodidae to Zoropsidae.

Authors:  Sini Seppälä; Sérgio Henriques; Michael L Draney; Stefan Foord; Alastair T Gibbons; Luz A Gomez; Sarah Kariko; Jagoba Malumbres-Olarte; Marc Milne; Cor J Vink; Pedro Cardoso
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3.  Current GBIF occurrence data demonstrates both promise and limitations for potential red listing of spiders.

Authors:  Vaughn Shirey; Sini Seppälä; Vasco Veiga Branco; Pedro Cardoso
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4.  Species conservation profiles of a random sample of world spiders III: Oecobiidae to Salticidae.

Authors:  Sini Seppälä; Sérgio Henriques; Michael L Draney; Stefan Foord; Alastair T Gibbons; Luz A Gomez; Sarah Kariko; Jagoba Malumbres-Olarte; Marc Milne; Cor J Vink; Pedro Cardoso
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