Upscaling biodiversity: estimating the species-area relationship from small samples

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F18%3A10385363" target="_blank" >RIV/00216208:11310/18:10385363 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11620/18:10385363

Výsledek na webu

<a href="https://doi.org/10.1002/ecm.1284" target="_blank" >https://doi.org/10.1002/ecm.1284</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/ecm.1284" target="_blank" >10.1002/ecm.1284</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Upscaling biodiversity: estimating the species-area relationship from small samples

Popis výsledku v původním jazyce

The challenge of biodiversity upscaling, estimating the species richness of a large area from scattered local surveys within it, has attracted increasing interest in recent years, producing a wide range of competing approaches. Such methods, if successful, could have important applications to multi-scale biodiversity estimation and monitoring. Here we test 19 techniques using a high quality plant data set: the GB Countryside Survey 1999, detailed surveys of a stratified random sample of British landscapes. In addition to the full data set, a set of geographical and statistical subsets was created, allowing each method to be tested on multiple data sets with different characteristics. The predictions of the models were tested against the &quot;true&quot; species-area relationship for British plants, derived from contemporaneously surveyed national atlas data. This represents a far more ambitious test than is usually employed, requiring 5-10 orders of magnitude in upscaling. The methods differed greatly in their performance; while there are 2,326 focal plant taxa recorded in the focal region, up-scaled species richness estimates ranged from 62 to 11,593. Several models provided reasonably reliable results across the 16 test data sets: the Shen and He and the Ulrich and Ollik models provided the most robust estimates of total species richness, with the former generally providing estimates within 10% of the true value. The methods tested proved less accurate at estimating the shape of the species-area relationship (SAR) as a whole; the best single method was Hui&apos;s Occupancy Rank Curve approach, which erred on average by &lt;20%. A hybrid method combining a total species richness estimate (from the Shen and He model) with a downscaling approach (the Sizling model) proved more accurate in predicting the SAR (mean relative error 15.5%) than any of the pure upscaling approaches tested. There remains substantial room for improvement in upscaling methods, but our results suggest that several existing methods have a high potential for practical application to estimating species richness at coarse spatial scales. The methods should greatly facilitate biodiversity estimation in poorly studied taxa and regions, and the monitoring of biodiversity change at multiple spatial scales.

Název v anglickém jazyce

Upscaling biodiversity: estimating the species-area relationship from small samples

Popis výsledku anglicky

The challenge of biodiversity upscaling, estimating the species richness of a large area from scattered local surveys within it, has attracted increasing interest in recent years, producing a wide range of competing approaches. Such methods, if successful, could have important applications to multi-scale biodiversity estimation and monitoring. Here we test 19 techniques using a high quality plant data set: the GB Countryside Survey 1999, detailed surveys of a stratified random sample of British landscapes. In addition to the full data set, a set of geographical and statistical subsets was created, allowing each method to be tested on multiple data sets with different characteristics. The predictions of the models were tested against the &quot;true&quot; species-area relationship for British plants, derived from contemporaneously surveyed national atlas data. This represents a far more ambitious test than is usually employed, requiring 5-10 orders of magnitude in upscaling. The methods differed greatly in their performance; while there are 2,326 focal plant taxa recorded in the focal region, up-scaled species richness estimates ranged from 62 to 11,593. Several models provided reasonably reliable results across the 16 test data sets: the Shen and He and the Ulrich and Ollik models provided the most robust estimates of total species richness, with the former generally providing estimates within 10% of the true value. The methods tested proved less accurate at estimating the shape of the species-area relationship (SAR) as a whole; the best single method was Hui&apos;s Occupancy Rank Curve approach, which erred on average by &lt;20%. A hybrid method combining a total species richness estimate (from the Shen and He model) with a downscaling approach (the Sizling model) proved more accurate in predicting the SAR (mean relative error 15.5%) than any of the pure upscaling approaches tested. There remains substantial room for improvement in upscaling methods, but our results suggest that several existing methods have a high potential for practical application to estimating species richness at coarse spatial scales. The methods should greatly facilitate biodiversity estimation in poorly studied taxa and regions, and the monitoring of biodiversity change at multiple spatial scales.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Ecological Monographs

ISSN

0012-9615

e-ISSN

—

Svazek periodika

88

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

170-187

Kód UT WoS článku

000431631400002

EID výsledku v databázi Scopus

—