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**Prediction of butterfly diversity hotspots in Belgium : A comparison of statistically focused and land use-focused models.** / Maes, Dirk; Gilbert, M; Titeux, N; Goffart, P; Dennis, R. L. H.

Onderzoeksoutput: Bijdrage aan tijdschrift › A1: Web of Science-artikel

Maes, D, Gilbert, M, Titeux, N, Goffart, P & Dennis, RLH 2003, 'Prediction of butterfly diversity hotspots in Belgium: A comparison of statistically focused and land use-focused models', *Journal of Biogeography*, vol. 30, nr. 12, blz. 1907-1920. https://doi.org/10.1046/j.0305-0270.2003.00976.x

Maes, D., Gilbert, M., Titeux, N., Goffart, P., & Dennis, R. L. H. (2003). Prediction of butterfly diversity hotspots in Belgium: A comparison of statistically focused and land use-focused models. *Journal of Biogeography*, *30*(12), 1907-1920. https://doi.org/10.1046/j.0305-0270.2003.00976.x

@article{4ae1971944014b089f14620c42f859db,

title = "Prediction of butterfly diversity hotspots in Belgium: A comparison of statistically focused and land use-focused models",

abstract = "AIM: We evaluate differences between and the applicability of three linear predictive models to determine butterfly hotspots in Belgium for nature conservation purposes. LOCATION: The study is carried out in Belgium for records located to Universal Transverse Mercator (UTM) grid cells of 5 · 5 km. METHODS: We first determine the relationship between factors correlated to butterfly diversity by means of modified t-tests and principal components analysis; subsequently, we predict hotspots using linear models based on land use, climate and topographical variables of well-surveyed UTM grid cells (n ¼ 197). The well-surveyed squares are divided into a training set and an evaluation set to test the model predictions. We apply three different models: (1) a ‘statistically focused’ model where variables are entered in descending order of statistical significance, (2) a ‘land use-focused’ model where land use variables known to be related to butterfly diversity are forced into the model and (3) a ‘hybrid’ model where the variables of the ‘land use-focused model’ are entered first and subsequently complemented by the remaining variables entered in descending order of statistical significance. RESULTS: A principal components analyses reveals that climate, and to a large extent, land use are locked into topography, and that topography and climate are the variables most strongly correlated with butterfly diversity in Belgium. In the statistically focused model, biogeographical region alone explains 65{\%} of the variability; other variables entering the statistically focused model are the area of coniferous and deciduous woodland, elevation and the number of frost days; the statistically focused model explains 77{\%} of the variability in the training set and 66{\%} in the evaluation set. In the land use-focused model, biogeographical region, deciduous and mixed woodland, natural grassland, heathland and bog, woodland edge, urban and agricultural area and biotope diversity are forced into the model; the land use-focused model explains 68{\%} of the variability in the training set and 57{\%} in the evaluation set. In the hybrid model, all variables from the land use-focused model are entered first and the covariates elevation, number of frost days and natural grassland area are added on statistical grounds; the hybrid model explains 78{\%} of the variability in the training set and 67{\%} in the evaluation set. Applying the different models to determine butterfly diversity hotspots resulted in the delimitation of spatially different areas. MAIN CONCLUSIONS: The best predictions of butterfly diversity in Belgium are obtained by the hybrid model in which land use variables relevant to butterfly richness are entered first after which climatic and topographic variables were added on strictly statistical grounds. The land use-focused model does not predict butterfly diversity in a satisfactory manner. When using predictive models to determine butterfly diversity, conservationbiologists need to be aware of the consequences of applying such models. Although, in conservation biology, land use-focused models are preferable to statistically focused models, one should always check whether the applied model makes sense on the ground. Predictive models can target mapping efforts towards potentially species-rich sites and permits the incorporation of un-surveyed sites into nature conservancy policies. Speciesrichness distribution maps produced by predictive modelling should therefore be used as pro-active conservation tools.",

author = "Dirk Maes and M Gilbert and N Titeux and P Goffart and Dennis, {R. L. H}",

year = "2003",

doi = "10.1046/j.0305-0270.2003.00976.x",

language = "English",

volume = "30",

pages = "1907--1920",

journal = "Journal of Biogeography",

issn = "0305-0270",

publisher = "Wiley-Blackwell",

number = "12",

}

TY - JOUR

T1 - Prediction of butterfly diversity hotspots in Belgium

T2 - A comparison of statistically focused and land use-focused models

AU - Maes, Dirk

AU - Gilbert, M

AU - Titeux, N

AU - Goffart, P

AU - Dennis, R. L. H

PY - 2003

Y1 - 2003

N2 - AIM: We evaluate differences between and the applicability of three linear predictive models to determine butterfly hotspots in Belgium for nature conservation purposes. LOCATION: The study is carried out in Belgium for records located to Universal Transverse Mercator (UTM) grid cells of 5 · 5 km. METHODS: We first determine the relationship between factors correlated to butterfly diversity by means of modified t-tests and principal components analysis; subsequently, we predict hotspots using linear models based on land use, climate and topographical variables of well-surveyed UTM grid cells (n ¼ 197). The well-surveyed squares are divided into a training set and an evaluation set to test the model predictions. We apply three different models: (1) a ‘statistically focused’ model where variables are entered in descending order of statistical significance, (2) a ‘land use-focused’ model where land use variables known to be related to butterfly diversity are forced into the model and (3) a ‘hybrid’ model where the variables of the ‘land use-focused model’ are entered first and subsequently complemented by the remaining variables entered in descending order of statistical significance. RESULTS: A principal components analyses reveals that climate, and to a large extent, land use are locked into topography, and that topography and climate are the variables most strongly correlated with butterfly diversity in Belgium. In the statistically focused model, biogeographical region alone explains 65% of the variability; other variables entering the statistically focused model are the area of coniferous and deciduous woodland, elevation and the number of frost days; the statistically focused model explains 77% of the variability in the training set and 66% in the evaluation set. In the land use-focused model, biogeographical region, deciduous and mixed woodland, natural grassland, heathland and bog, woodland edge, urban and agricultural area and biotope diversity are forced into the model; the land use-focused model explains 68% of the variability in the training set and 57% in the evaluation set. In the hybrid model, all variables from the land use-focused model are entered first and the covariates elevation, number of frost days and natural grassland area are added on statistical grounds; the hybrid model explains 78% of the variability in the training set and 67% in the evaluation set. Applying the different models to determine butterfly diversity hotspots resulted in the delimitation of spatially different areas. MAIN CONCLUSIONS: The best predictions of butterfly diversity in Belgium are obtained by the hybrid model in which land use variables relevant to butterfly richness are entered first after which climatic and topographic variables were added on strictly statistical grounds. The land use-focused model does not predict butterfly diversity in a satisfactory manner. When using predictive models to determine butterfly diversity, conservationbiologists need to be aware of the consequences of applying such models. Although, in conservation biology, land use-focused models are preferable to statistically focused models, one should always check whether the applied model makes sense on the ground. Predictive models can target mapping efforts towards potentially species-rich sites and permits the incorporation of un-surveyed sites into nature conservancy policies. Speciesrichness distribution maps produced by predictive modelling should therefore be used as pro-active conservation tools.

AB - AIM: We evaluate differences between and the applicability of three linear predictive models to determine butterfly hotspots in Belgium for nature conservation purposes. LOCATION: The study is carried out in Belgium for records located to Universal Transverse Mercator (UTM) grid cells of 5 · 5 km. METHODS: We first determine the relationship between factors correlated to butterfly diversity by means of modified t-tests and principal components analysis; subsequently, we predict hotspots using linear models based on land use, climate and topographical variables of well-surveyed UTM grid cells (n ¼ 197). The well-surveyed squares are divided into a training set and an evaluation set to test the model predictions. We apply three different models: (1) a ‘statistically focused’ model where variables are entered in descending order of statistical significance, (2) a ‘land use-focused’ model where land use variables known to be related to butterfly diversity are forced into the model and (3) a ‘hybrid’ model where the variables of the ‘land use-focused model’ are entered first and subsequently complemented by the remaining variables entered in descending order of statistical significance. RESULTS: A principal components analyses reveals that climate, and to a large extent, land use are locked into topography, and that topography and climate are the variables most strongly correlated with butterfly diversity in Belgium. In the statistically focused model, biogeographical region alone explains 65% of the variability; other variables entering the statistically focused model are the area of coniferous and deciduous woodland, elevation and the number of frost days; the statistically focused model explains 77% of the variability in the training set and 66% in the evaluation set. In the land use-focused model, biogeographical region, deciduous and mixed woodland, natural grassland, heathland and bog, woodland edge, urban and agricultural area and biotope diversity are forced into the model; the land use-focused model explains 68% of the variability in the training set and 57% in the evaluation set. In the hybrid model, all variables from the land use-focused model are entered first and the covariates elevation, number of frost days and natural grassland area are added on statistical grounds; the hybrid model explains 78% of the variability in the training set and 67% in the evaluation set. Applying the different models to determine butterfly diversity hotspots resulted in the delimitation of spatially different areas. MAIN CONCLUSIONS: The best predictions of butterfly diversity in Belgium are obtained by the hybrid model in which land use variables relevant to butterfly richness are entered first after which climatic and topographic variables were added on strictly statistical grounds. The land use-focused model does not predict butterfly diversity in a satisfactory manner. When using predictive models to determine butterfly diversity, conservationbiologists need to be aware of the consequences of applying such models. Although, in conservation biology, land use-focused models are preferable to statistically focused models, one should always check whether the applied model makes sense on the ground. Predictive models can target mapping efforts towards potentially species-rich sites and permits the incorporation of un-surveyed sites into nature conservancy policies. Speciesrichness distribution maps produced by predictive modelling should therefore be used as pro-active conservation tools.

U2 - 10.1046/j.0305-0270.2003.00976.x

DO - 10.1046/j.0305-0270.2003.00976.x

M3 - A1: Web of Science-article

VL - 30

SP - 1907

EP - 1920

JO - Journal of Biogeography

JF - Journal of Biogeography

SN - 0305-0270

IS - 12

ER -

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