Inferring Process From Pattern: Can Territory Occupancy Provide Information About Life History Parameters?

Tyre, Andrew J.; Possingham, Hugh P.; Lindenmayer, David B.

doi:10.1890/1051-0761(2001)011[1722:ipfpct]2.0.co;2

Cited by 111 publications

(38 citation statements)

References 72 publications

(80 reference statements)

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“…Accurate distribution maps of organisms are welcomed in the selection and design of natural parks (Scott et al, 1993), the assessment of human impacts on biodiversity (Lavers and Haines-Young, 1996), or the testing of biogeographical hypotheses (Mourell and Ezcurra, 1996;Leathwick, 1998). However, even a perfect knowledge of the biology of a species cannot guarantee that a static map will reflect dynamic properties of species distribution (Tyre et al, 2001). Moreover, human and logistic limitations make it impractical to survey large areas and, inevitably, our knowledge of the spatial distribution of most species will have many gaps (Osborne and Tigar, 1992).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, human and logistic limitations make it impractical to survey large areas and, inevitably, our knowledge of the spatial distribution of most species will have many gaps (Osborne and Tigar, 1992). A common solution to this problem is to resort to predictive habitat modelling (reviewed in Guisan and Zimmermann, 2000, and see some recent examples in Osborne et al, 2001;Scott et al, 2002;Seoane et al, 2003) and regard the results as potential habitat, able to be reached and colonised by a species (Tyre et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Competing roles for landscape, vegetation, topography and climate in predictive models of bird distribution

Seoane

Bustamante

Dı́az-Delgado

2004

Ecological Modelling

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Predictive habitat models rely on the relationship between a response variable (either occurrence or abundance of a species) and a set of environmental predictors. Vegetation is generally preferred as a source of potential predictors because of having a more direct link with reproductive necessities of species than topography and climate. However, vegetation maps are costly to produce and update and most land-use/land-cover maps are usually made with a general purpose, focused on land management, and not thinking on animal distribution. On the contrary, basic topographic and climatic maps are easier to obtain or to derive, are not so sensitive to legend design, and do not need of such frequent updates. In this study, we compare the predictive ability of different sets of environmental predictors when modelling the distribution of breeding birds. Models were generated for 79 bird species in South-western Spain using Generalised Additive Models (GAMs) with binomial errors and logit link. For each species, several models were created that differed in the set of candidate predictors initially tested (either derived from vegetation or from topo-climatic maps) or in the conditional order in which those predictor sets were tested. Within vegetation predictors, a similar strategy was used to ascertain the relative relevance of vegetation landscape (variables describing the surrounding habitat matrix) compared to vegetation cover (variables describing the type of vegetation found on sampling sites).Vegetation models were significantly more accurate than topo-climatic models, but the difference was due to the higher number of potential predictors in the set of vegetation variables. Vegetation landscape models were significantly more accurate than vegetation cover models, even when controlling for the number of candidate predictors. Models that included both sets of predictors (vegetation and topo-climatic variables) had a slightly superior predictive ability. Our results indicate that, when building predictive models of bird distribution, the best results are obtained using both vegetation and topo-climatic variables as potential predictors. If time or budget constrains compel to concentrate on a single set of predictors, selection should be done on grounds of data availability, because model accuracy is likely to be similar for models derived from vegetation compared to topographic and climatic predictors. In relation to vegetation predictors, vegetation landscape reflects important information not revealed by vegetation cover measures at the sampling site; thus, regional modelling programmes would certainly gain predictive ability by including landscape patterns that are currently disregarded.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Competing roles for landscape, vegetation, topography and climate in predictive models of bird distribution

Seoane

Bustamante

Dı́az-Delgado

2004

Ecological Modelling

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“…It has to be considered that demographic stochasticity and dispersal can prevent a perfect adjustment between predictive models and wildlife distribution as Tyre et al (2001) have shown with computer simulation models. Also, it is interesting to note that when general-purpose land-use/land-cover maps of enough accuracy and resolution are not available there is the alternative of deriving vegetation structural characteristics from satellite images resulting in maps with similar predictive ability of bird distribution.…”

Section: Discussionmentioning

confidence: 99%

“…an species has not occupied all potential adequate habitat because of geographical barriers, or because it has been extirpated by man from otherwise suitable habitat). (c) Unsaturated habitats (small populations are not able to occupy all suitable habitats, but also demographic stochasticity and localised dispersal generate an imperfect correlation between habitat suitability and species distribution; Tyre et al, 2001). (d) Poor quality of the response variable (e.g.…”

Section: Introductionmentioning

confidence: 99%

Are existing vegetation maps adequate to predict bird distributions?

SEOANE

2004

Ecological Modelling

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Bird species are selective on the vegetation types in which they are found but predictive models of bird distribution based on variables derived from land-use/land-cover maps tend to have limited success. It has been suggested that accuracy of existing maps used to derive predictors is in part responsible for the limited success of bird distribution models. In two areas of 4900 km 2 of Western Andalusia, Spain, we compared the predictive ability of bird distribution models derived from two existing general-purpose land-use/land-cover maps, which differ in their resolution and accuracy: a coarse scale vegetation map of Europe, the CORINE land-cover map, and a detailed regional map, the 1995 land-use/land-cover map of Andalusia from the SINAMBA (Consejería de Medio Ambiente, Junta de Andalucía). We compared the bird distribution models derived from these general-purpose vegetation maps with models derived from two more accurate structural vegetation maps built considering directly variables that influence bird habitat selection, one built from satellite images for this study and another obtained by improving the resolution and accuracy of the SINAMBA map with satellite data. We sampled the presence/absence of bird species at 857 points using 15-min point surveys. Predictive models for 54 bird species were built with generalised additive models (GAMs), using as potential predictors the same set of landscape and vegetation structure variables measured on each map. We compared for each bird species the predictive accuracy of the best model derived from each map. Vegetation structure measured at bird sample points was used as ground-truth for comparing the accuracy of vegetation maps. Although maps differed in their resolution and accuracy, the results show that all of them produced similarly accurate bird distribution models, with a mixed map produced with both thematic and satellite information being the best. The models derived from the more accurate vegetation structure maps obtained from satellite data were not more accurate than those derived directly from the SINAMBA or CORINE maps. Our results suggest that some general-purpose land-use/land-cover maps are accurate enough to derive bird distribution models. There is a certain limit to improve vegetation maps above which there is no effect in their power to predict bird distribution.

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“…Nevertheless, HSMs based on land use, topographic and climatic predictors may sometimes show large difference between predicted and observed values (Massimino 2004). This can be due to inadequacy of the modelling technique, unsaturated habitats (Cardillo et al 1999;Tyre et al 2001) or failure to include significant environmental predictors (Crawley 1993;Guisan & Thuiller 2005), whose selection is often critical and strongly dependent on the type of selection strategy used (Araú jo . Moreover, occurrence of many species can be influenced by environmental characteristics or processes that are difficult to measure, or at least have not been measured adequately.…”

Section: Introductionmentioning

confidence: 99%

Usefulness of coarse grain data on forest management to improve bird abundance models

Massimino

Orioli

Pizzardi

et al. 2010

Italian Journal of Zoology

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We tested the use of coarse grain data on forest management systems to improve habitat suitability models in predicting distribution and abundance of five bird species, nesting in broadleaf forests in Lombardy (Italy). They were the long-tailed tit Aegithalos caudatus, marsh tit Poecile palustris, blue tit Cyanistes caeruleus, wood nuthatch Sitta europaea and short-toed tree-creeper Certhia brachydactyla. Bird data were collected from 4552 point counts from 1992 to 2002. To model the habitat suitability for each species, we used generalized linear models with negative binomial distribution and log link function. Environmental predictors were land-use fractional covers, forest isolation, elevation, slope, aspect, and coarse grain data on forest management systems aggregated for administrative provinces. They were selected by a stepwise procedure based on the Bayesian Informative Criterion. Predictors on forest management system were useful to improve model fit for all cavity-nesting species, which was also shown to be affected by forest isolation. The long-tailed tit, which is not an obligate cavity nester, did not exhibit any preference with respect either to forest management or isolation.

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Inferring Process From Pattern: Can Territory Occupancy Provide Information About Life History Parameters?

Cited by 111 publications

References 72 publications

Competing roles for landscape, vegetation, topography and climate in predictive models of bird distribution

Competing roles for landscape, vegetation, topography and climate in predictive models of bird distribution

Are existing vegetation maps adequate to predict bird distributions?

Usefulness of coarse grain data on forest management to improve bird abundance models

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