Can anthropogenic variables be used as threat proxies for South African plant richness?

Human demographic and socio-economic measures (anthropogenic variables) reflect the detrimental impact of humans on plant diversity globally. The Pretoria (PRE) Computerised Information System (PRECIS) of the South African National Biodiversity Institute (SANBI), provided three sets of South Afncan...

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Main Authors: M. Keith, M. Warren
Format: Article
Language:English
Published: South African National Biodiversity Institut 2007-08-01
Series:Bothalia: African Biodiversity & Conservation
Subjects:
Online Access:https://abcjournal.org/index.php/abc/article/view/305
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spelling doaj-b4437b9ccf034953b0f8a357f0b1eea82020-11-25T03:42:47ZengSouth African National Biodiversity InstitutBothalia: African Biodiversity & Conservation0006-82412311-92842007-08-01371798810.4102/abc.v37i1.305243Can anthropogenic variables be used as threat proxies for South African plant richness?M. Keith0M. Warren1Threatened Species Programme, South African National Biodiversity Institute. Department of Zoology and Entomology, University of PretoriaDepartment of Zoology and Entomology, University of PretoriaHuman demographic and socio-economic measures (anthropogenic variables) reflect the detrimental impact of humans on plant diversity globally. The Pretoria (PRE) Computerised Information System (PRECIS) of the South African National Biodiversity Institute (SANBI), provided three sets of South Afncan plant richness data, overall (OPR), endemic (EPR), and threatened (TPR), to investigate the relationships between richness and six anthropogenic variables. Spearman’s Rank order correlations, Kruskal Wallis Analysis of Variance (ANOVA) and Generalized Linear Models (GLZ) were used. Although all three plant richness measures were correlated with anthropogenic variables, individual anthropogenic variables contributed a small fraction to the explained variation in richness. Differences in spatial and temporal scaling of the datasets, or the response to another causal mechanism, may have contributed to this low explained variation. Because more variation was accounted for in OPR than EPR or TPR, OPR is a more suitable surrogate measure of plant biodiversity when investigating the anthropogenic variables used here. Average human density (HD), infrastructure (degree of urbanization and road cover) (LRU) and percentage land area transformed and degraded (LTD) were identified as useful surrogates of human impacts on OPR. LTD may be a more inclusive human impact measure when conducting analyses of human impacts using OPR. LTD includes the effects of urban expansion, road networks and other land transformation impacts, such as agriculture.https://abcjournal.org/index.php/abc/article/view/305endemic specieshuman population densityland transformationsetting conservation priorities
collection DOAJ
language English
format Article
sources DOAJ
author M. Keith
M. Warren
spellingShingle M. Keith
M. Warren
Can anthropogenic variables be used as threat proxies for South African plant richness?
Bothalia: African Biodiversity & Conservation
endemic species
human population density
land transformation
setting conservation priorities
author_facet M. Keith
M. Warren
author_sort M. Keith
title Can anthropogenic variables be used as threat proxies for South African plant richness?
title_short Can anthropogenic variables be used as threat proxies for South African plant richness?
title_full Can anthropogenic variables be used as threat proxies for South African plant richness?
title_fullStr Can anthropogenic variables be used as threat proxies for South African plant richness?
title_full_unstemmed Can anthropogenic variables be used as threat proxies for South African plant richness?
title_sort can anthropogenic variables be used as threat proxies for south african plant richness?
publisher South African National Biodiversity Institut
series Bothalia: African Biodiversity & Conservation
issn 0006-8241
2311-9284
publishDate 2007-08-01
description Human demographic and socio-economic measures (anthropogenic variables) reflect the detrimental impact of humans on plant diversity globally. The Pretoria (PRE) Computerised Information System (PRECIS) of the South African National Biodiversity Institute (SANBI), provided three sets of South Afncan plant richness data, overall (OPR), endemic (EPR), and threatened (TPR), to investigate the relationships between richness and six anthropogenic variables. Spearman’s Rank order correlations, Kruskal Wallis Analysis of Variance (ANOVA) and Generalized Linear Models (GLZ) were used. Although all three plant richness measures were correlated with anthropogenic variables, individual anthropogenic variables contributed a small fraction to the explained variation in richness. Differences in spatial and temporal scaling of the datasets, or the response to another causal mechanism, may have contributed to this low explained variation. Because more variation was accounted for in OPR than EPR or TPR, OPR is a more suitable surrogate measure of plant biodiversity when investigating the anthropogenic variables used here. Average human density (HD), infrastructure (degree of urbanization and road cover) (LRU) and percentage land area transformed and degraded (LTD) were identified as useful surrogates of human impacts on OPR. LTD may be a more inclusive human impact measure when conducting analyses of human impacts using OPR. LTD includes the effects of urban expansion, road networks and other land transformation impacts, such as agriculture.
topic endemic species
human population density
land transformation
setting conservation priorities
url https://abcjournal.org/index.php/abc/article/view/305
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