کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
83329 158717 2014 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Conflation and aggregation of spatial data improve predictive models for species with limited habitats: A case of the threatened yellow-billed cuckoo in Arizona, USA
ترجمه فارسی عنوان
تقلید و جمع آوری داده های فضایی باعث بهبود مدل های پیش بینی شده برای گونه هایی با زیستگاه های محدود می شود: مورد زخم زرد بادی تهدید شده در آریزونا، ایالات متحده آمریکا
کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک جنگلداری
چکیده انگلیسی


• The yellow-billed cuckoo is a threatened bird with limited and declining habitat.
• Conflated and aggregated land cover maps are used to develop habitat models.
• Models from aggregated and conflated data are more accurate than original data.
• Cuckoo habitat contains more riparian forest and slightly more invasive cover.
• LULC data quality can influence habitat distribution and conservation planning.

Riparian vegetation provides important wildlife habitat in the southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Geography - Volume 47, February 2014, Pages 57–69
نویسندگان
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