Use of a new standardized habitat survey for assessing the habitat preferences and distribution of upland river birds

Buckton, Sebastian T.; Ormerod, Stephen James

doi:10.1080/00063659709461068

Cited by 32 publications

(25 citation statements)

References 25 publications

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“…Such a large array of variables is intended to capture the complex structure of rivers that arises from local geomorphology, natural variations in vegetation and river management. The results provide significant and ecologically meaningful correlates with the distribution of river birds (Buckton & Ormerod 1997; Ormerod et al . 1997).…”

Section: Methodsmentioning

confidence: 79%

“…At each stream, chemical samples were collected for full ionic analysis (Collins & Jenkins 1996), and habitat structure was recorded over a 200‐m reach using the UK Environment Agency RHS. This method records over 120 variables describing the stream channel, flow character and riparian character in addition to measurements of altitude and slope, respectively, by altimeters and clinometers (Buckton & Ormerod 1997; Raven et al . 1997).…”

Section: Methodsmentioning

confidence: 99%

“…In the case of species presence–absence data, discriminant analysis (DA) and logistic regression (LR) have been widely used (Osborne & Tigar 1992; Austin et al . 1994; Green, Osborne & Sears 1994; Austin & Meyers 1996; Buckton & Ormerod 1997; Buckton et al . 1998) but some authors indicate that these methods are limited in assuming a linear response to environmental predictors (Lek et al .…”

Section: Introductionmentioning

confidence: 99%

“…However, rather than evaluating performance solely on prediction success, the number of cases in which species presence or absence is correctly assessed (Mastrorillo et al . 1997; Buckton & Ormerod 1997), we have used more detailed comparisons of model performance as suggested by Fielding & Bell (1997). This involves examining the ability of a given model to predict separately both presence and absence, and represents an advance over more conventional model evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Alternative methods for predicting species distribution: an illustration with Himalayan river birds

Manel

Dias

Buckton

et al. 1999

Journal of Applied Ecology

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266

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Summary1. Current emphasis on species conservation requires the development of speci®c distribution models. Several modelling methods are available, but their performance has seldom been compared. We therefore used discriminant analysis, logistic regression and arti®cial neural networks with environmental data to predict the presence or absence of six river birds along 180 Himalayan streams. We applied each method to calibration sites and independent test sites. With logistic regression, we compared performance in predicting presence±absence using map-derived predictors (river slope and altitude) as opposed to detailed data from a standardized river habitat survey (RHS). 2. Using the entire calibration data, overall success at predicting presence or absence was only slightly greater using arti®cial neural networks (89±100%) than either logistic regression (75±92%) or discriminant analysis (81±95%), and on this criterion all methods gave good performance. 3. When applied to independent test data, overall prediction success averaged 71± 80%, with logistic regression marginally but signi®cantly out-performing the other methods. Encouragingly for researchers with limited data, model performance in jack-knife tests faithfully represented performance in more rigorous validations where calibration (n = 119) and test sites (n = 61) were in separate geographical regions. 4. All three methods predicted true absences (83±92% success) better than true presences (31±44%). Results from logistic regression were the most variable across species, but positive prediction declined with increasing species rarity in each method. 5. Applications with logistic regression illustrated that signi®cant habitat predictors varied between data sets within species. Hypotheses about causal eects by habitat structure on distribution were thus dicult to erect or test. Logistic regression also showed that detailed data from the river habitat survey substantially improved positive prediction by comparison with prediction using slope or altitude alone. 6. We conclude that discriminant analysis, logistic regression and arti®cial neural networks dier only marginally in performance when predicting species distributions. Model choice should therefore depend on the nature of the data, on the needs of any particular analysis, and on whether assumptions for each method are satis®ed. All three methods share drawbacks due to systematic eects by species rarity on performance measures. They also share limitations due to the correlative nature of survey data often used for model development at the spatial scales required in macro-ecology and conservation biology. Tests with independent data, *Correspondence: Dr Steve Ormerod (e-mail: ormerod@cardi.ac.uk). {Present address: Laboratoire de Biologie des Populations d'Altitude, CNRS UMR 5553, UniversiteÂ Joseph Fourier, BP53-38041 Grenoble Cedex 9, France. Journal of Applied Ecology 1999, 36, 734±747# 1999 British Ecological Society using a wider range of performance measures than those used traditionally, will be...

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Section: Methodsmentioning

confidence: 79%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Alternative methods for predicting species distribution: an illustration with Himalayan river birds

Manel

Dias

Buckton

et al. 1999

Journal of Applied Ecology

Self Cite

266

236

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show abstract

“…However, future applications of the three stage framework may be able to utilise existing data. The habitat preferences of some taxa that are of interest to the public have previously been described in detail (Buckton and Ormerod, 1997;Besnard et al 2013), and at many sites the distributions of key species are known (Ross-Smith et al, 2011). The noticeability of organisms and landscape features is relatively straightforward to quantify through field surveys, and when data collection is not feasible it may be possible to make assumptions about noticeability, as was necessarily done for odonates in this study.…”

Section: Discussionmentioning

confidence: 98%

Spatial variation in the impact of dragonflies and debris on recreational ecosystem services in a floodplain wetland

et al. 2015

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a b s t r a c tRecreation is an important ecosystem service. The interaction between people and habitat components is rarely considered in the analyses of recreational experiences, making it difficult to predict what people will experience. In this study we develop a modelling framework that describes three stages of interaction between people and habitats. This framework considers: (1) the distribution of habitat components in the environment, (2) the proportion of the available components that visitors notice, and (3) the net impact of multiple components on the quality of the recreational experience. The model was applied to a case study river floodplain, and was used to estimate visitor exposure to a combination of positive habitat components (dragonflies) and negative components (debris). The model provided an index of net impacts on experience quality that showed spatial variation across the floodplain, and this analysis highlighted areas that would deliver more positive experiences to visitors. The results of a sensitivity analysis indicated that neglecting the noticeability (observation rate) of habitat components resulted in different predictions. It is therefore important that the noticeability of habitat components is considered during analyses of recreational experiences, and recreational ecosystem service valuations.

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Quality assessment using River Habitat Survey data

Raven

Holmes²,

Dawson

et al. 1998

Aquatic Conserv: Mar. Freshw. Ecosyst.

210

118

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Use of a new standardized habitat survey for assessing the habitat preferences and distribution of upland river birds

Cited by 32 publications

References 25 publications

Alternative methods for predicting species distribution: an illustration with Himalayan river birds

Alternative methods for predicting species distribution: an illustration with Himalayan river birds

Spatial variation in the impact of dragonflies and debris on recreational ecosystem services in a floodplain wetland

Quality assessment using River Habitat Survey data

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