Predictive mapping of Meadow Pipit density using integrated remote sensing data and an atlas of vascular plants dataset

Kosicki, Jakub Z.; Chylarecki, Przemysław

doi:10.1080/00063657.2013.849656

Cited by 6 publications

(12 citation statements)

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“…The Meadow Pipit is a small (14-23 g) insectivorous passerine that breeds in moorlands and pasturelands across the Palaearctic (over 42°N; Cramp 1988). The European population of the Meadow Pipit has decreased over the last few years (BirdLife International 2014), probably due to land-use changes in breeding areas (Evans et al 2005, Kosicki & Chylarecki 2013. Many Pipits from the British Islands, central Europe and Scandinavia migrate to winter in the Iberian Peninsula and the Maghreb where the species occurs in meadows, fallows, irrigated fields and wooded pasturelands (Tellería et al 1999, Thévenot et al 2003, Vera & Giménez 2012.…”

Section: The Speciesmentioning

confidence: 99%

Using ring records and field surveys to predict the winter distribution of a migratory passerine

2014

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Capsule Ring records can be used to predict spatial changes in habitat suitability in wintering grounds of migratory birds as shown by a case study with the Meadow Pipit (Anthus pratensis). Aims To forecast the winter distribution of the Meadow Pipit in the Iberian Peninsula and the Maghreb (1 700 000 km 2) by combining ring records and fields surveys. Methods We processed winter ring records with Maxent, a popular machine-learning technique used to predict the occurrence probability of species. We then tested whether the occurrence probabilities predicted by the model were significantly positively related to the actual abundance of the Pipit in Spain and Morocco. Results Occurrence probabilities provided by Maxent explained 6% of Pipit abundance distribution in partial least squares regression analyses. There was, in addition, a weak effect of geographical location (1.5%) and a strong effect of local vegetation (17.6%) on Pipit distribution. The occurrence probabilities were used to classify the winter range of the Meadow Pipit in sectors of high, medium and low suitability. The most suitable sector occupied 9% of the region (153 000 km 2) and was restricted to warm and moist lowlands where Pipits gather in grasslands. Conclusion These results support the potential usefulness of cartographic models derived from ring records to describe the wintering range of migratory birds.

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Section: The Speciesmentioning

confidence: 99%

Using ring records and field surveys to predict the winter distribution of a migratory passerine

2014

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“…However, it can be presumed that other aspects of habitat and climate not measured in these models are likely to explain the pattern. For instance, the most important predictor for the Meadow pipit's Anthus pratensis density is the distribution of habitat-specific plant species while a high abundance of the Crested lark Galerida cristata is affected by pigs' density as a measure of the level of farming [21,32]. Furthermore, other study also indicated that coexisting species [7] as well as a quantified co-evolutionary process of a bird parasite (i.e.…”

Section: Gams For Bird Species Density With and Without Landscape Conmentioning

confidence: 97%

“…The Generalised Additive Model method used in this study is often used as a tool for predictive spatial distribution modelling [6,32,33,35,41]. However, despite the advantages of GAMs described by [41], this procedure-being a tool for creating a predictive spatial density pattern-requires evaluation based on an independent dataset [52].…”

Section: Modelling Approachmentioning

confidence: 99%

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Are Landscape Configuration Metrics Worth Including When Predicting Specialist and Generalist Bird Species Density? A Case of the Generalised Additive Model Approach

Kosicki

2017

Environ Model Assess

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Due to the complexity of factors that influence species density on a large geographical scale, the effectiveness of the species distribution model (SDM) is still debatable. That is why the buffer zone (the area within 100 m from the outside edge of the patch), the core, i.e. (patches excluding the 100 m buffer zone from the patch's edge) and patch shape are explored in this study to see how they affect the density of habitat specialist and generalist bird species. Two sets of generalised additive models were generated separately for each of the four bird species: One set of models contained landscape configuration metrics as an additional predictor variable, and the other did not. The results showed that models including the core, the buffer zone and the shape of patches turned out to be definitely better than models without them. Specialist species, the Corn bunting and the Wood nuthatch, are more likely to occur in the core of the preferred patches, and they choose those of a simple shape; while generalist species, the Whinchat and the Tree pipit, are more probable to be present in the buffer zone of a more complicated shape. Thus, the results clearly show that specific landscape configuration models can improve the predictive power of SDMs and can be used as an effective tool for predicting species density and functional bird diversity (specialist and generalist). Furthermore, from the applied ecology perspective, detailed landscape configuration metrics can be considered as a surrogate of elusive habitat conditions.

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“…Many of these studies use publicly available generalized Geographic Information Systems (GIS) datasets (i.e. Corine land cover, NDVI dataset, WordClim) as predictors (e.g., Giordano et al 2010;Kosicki and Chylarecki 2013;Morelli and Tryjanowski 2014). However, such data are only suitable for some opportunistic species that have habitat requirements with sharp boundaries in the landscape, e.g.…”

Section: Introductionmentioning

confidence: 99%

Complex species distribution models of Goldcrests and Firecrests densities in Poland: are remote sensing‐based predictors sufficient?

Kosicki

Stachura

Ostrowska

et al. 2015

Ecological Research

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Species distribution models should identify ecological requirements of species and predict their spatial density. However, data from remote sensing sources are often used alone as predictors in modelling distributions. Such data will only produce accurate models if features that are distinguishable by remote sensing are a good match to the environmental traits that delineate habitat requirements. Both the Goldcrest Regulus regulus and the Firecrest Regulus ignicapilla respond to complex features of habitats that are not described by simple remote sensing data. We tested the usefulness of remote sensing data as a predictor for two Regulus species according to data from 970 study plots sized 1 · 1 km. Predictors were aggregated using the PCAs and related to the Hayne estimator of species density using GAMs. The models based on both remote sensing data and detailed environmental data proved to be better than the model based only on remote sensing data and/or detailed forest structure data. The Goldcrest reached the highest density in areas with a high share of old spruce-dominated forests with a substantial share of the fir, avoiding the pine, and it preferred forests with a low number of tree species. In turn, the Firecrest favoured old forests, dominated by the spruce and the beech, with an admixture of single old fir and larch trees, avoiding the pine, and preferring forests with a high number of tree species. We suggest using not only free data sources, but also more detailed data containing thorough information on forest inventory derived from ground measurements.

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Predictive mapping of Meadow Pipit density using integrated remote sensing data and an atlas of vascular plants dataset

Cited by 6 publications

References 58 publications

Using ring records and field surveys to predict the winter distribution of a migratory passerine

Using ring records and field surveys to predict the winter distribution of a migratory passerine

Are Landscape Configuration Metrics Worth Including When Predicting Specialist and Generalist Bird Species Density? A Case of the Generalised Additive Model Approach

Complex species distribution models of Goldcrests and Firecrests densities in Poland: are remote sensing‐based predictors sufficient?

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