Predicting plant species richness in forested landslide zones using geostatistical methods

Landslides, like most natural disturbances, facilitate the evolution of new plant species. Hence, a detail characterization of topographic conditions can improve the prediction and mapping of species in such complex terrains. Within the Outer (Flysch) Upper Carpathian region, south Poland, we analyz...

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Autores principales:	Elvis Tangwa, Wiktor Tracz, Vilém Pechanec, Yisa Ginath Yuh
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Terrain attributes Regression kriging Cokriging Species richness Convergence points density Forested landslides Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/cf5846c0e23f4ef5a52f17a01068b203
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spelling	oai:doaj.org-article:cf5846c0e23f4ef5a52f17a01068b2032021-12-01T05:02:01ZPredicting plant species richness in forested landslide zones using geostatistical methods1470-160X10.1016/j.ecolind.2021.108297https://doaj.org/article/cf5846c0e23f4ef5a52f17a01068b2032021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21009626https://doaj.org/toc/1470-160XLandslides, like most natural disturbances, facilitate the evolution of new plant species. Hence, a detail characterization of topographic conditions can improve the prediction and mapping of species in such complex terrains. Within the Outer (Flysch) Upper Carpathian region, south Poland, we analyze the role of convergence points prepared in a previous study from slope and slope exposition (aspect) data, derived from a 1 m digital elevation model. Convergence points reflected microscale variability in topographic conditions and were analyzed in this study as convergence point density (CPD). Our objective was to use CPD to predict species richness on forested landslides using three geostatistical methods; Ordinary kriging (OK), Ordinary cokriging (OCK), and regression kriging (RK). Our results showed a relatively high correlation (r ∼ 0.65) between species richness and CPD compared to slope or slope exposition or with both. OCK and RK generally improved prediction. However, the application of cokriging in such terrains remains challenging and will not be appropriate, particularly if species richness has a small sample size. RK outperformed OK and OCK, decreasing the root mean square error (RMSE) by 33% and 10%, respectively. RK was also more robust to topographic heterogeneity and the limited number of observations than OCK. We conclude that a denser sampling of species composition or a more robust indicator is needed to improve these results. Notwithstanding these limitations, our results can be used as the first step to support short-term conservation efforts, especially when time-dependent changes in species composition are unimportant.Elvis TangwaWiktor TraczVilém PechanecYisa Ginath YuhElsevierarticleTerrain attributesRegression krigingCokrigingSpecies richnessConvergence points densityForested landslidesEcologyQH540-549.5ENEcological Indicators, Vol 132, Iss , Pp 108297- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Terrain attributes Regression kriging Cokriging Species richness Convergence points density Forested landslides Ecology QH540-549.5
spellingShingle	Terrain attributes Regression kriging Cokriging Species richness Convergence points density Forested landslides Ecology QH540-549.5 Elvis Tangwa Wiktor Tracz Vilém Pechanec Yisa Ginath Yuh Predicting plant species richness in forested landslide zones using geostatistical methods
description	Landslides, like most natural disturbances, facilitate the evolution of new plant species. Hence, a detail characterization of topographic conditions can improve the prediction and mapping of species in such complex terrains. Within the Outer (Flysch) Upper Carpathian region, south Poland, we analyze the role of convergence points prepared in a previous study from slope and slope exposition (aspect) data, derived from a 1 m digital elevation model. Convergence points reflected microscale variability in topographic conditions and were analyzed in this study as convergence point density (CPD). Our objective was to use CPD to predict species richness on forested landslides using three geostatistical methods; Ordinary kriging (OK), Ordinary cokriging (OCK), and regression kriging (RK). Our results showed a relatively high correlation (r ∼ 0.65) between species richness and CPD compared to slope or slope exposition or with both. OCK and RK generally improved prediction. However, the application of cokriging in such terrains remains challenging and will not be appropriate, particularly if species richness has a small sample size. RK outperformed OK and OCK, decreasing the root mean square error (RMSE) by 33% and 10%, respectively. RK was also more robust to topographic heterogeneity and the limited number of observations than OCK. We conclude that a denser sampling of species composition or a more robust indicator is needed to improve these results. Notwithstanding these limitations, our results can be used as the first step to support short-term conservation efforts, especially when time-dependent changes in species composition are unimportant.
format	article
author	Elvis Tangwa Wiktor Tracz Vilém Pechanec Yisa Ginath Yuh
author_facet	Elvis Tangwa Wiktor Tracz Vilém Pechanec Yisa Ginath Yuh
author_sort	Elvis Tangwa
title	Predicting plant species richness in forested landslide zones using geostatistical methods
title_short	Predicting plant species richness in forested landslide zones using geostatistical methods
title_full	Predicting plant species richness in forested landslide zones using geostatistical methods
title_fullStr	Predicting plant species richness in forested landslide zones using geostatistical methods
title_full_unstemmed	Predicting plant species richness in forested landslide zones using geostatistical methods
title_sort	predicting plant species richness in forested landslide zones using geostatistical methods
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/cf5846c0e23f4ef5a52f17a01068b203
work_keys_str_mv	AT elvistangwa predictingplantspeciesrichnessinforestedlandslidezonesusinggeostatisticalmethods AT wiktortracz predictingplantspeciesrichnessinforestedlandslidezonesusinggeostatisticalmethods AT vilempechanec predictingplantspeciesrichnessinforestedlandslidezonesusinggeostatisticalmethods AT yisaginathyuh predictingplantspeciesrichnessinforestedlandslidezonesusinggeostatisticalmethods
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Predicting plant species richness in forested landslide zones using geostatistical methods

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