A remote sensing and GIS-based model of habitats and biodiversity in the Greater Yellowstone Ecosystem

Debinski, Diane M.; Kindscher, Kelly; Jakubauskas, Mark E.

doi:10.1080/014311699211336

Cited by 112 publications

(62 citation statements)

References 13 publications

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“…We have built an extensive database of plant, butterfly, and bird community data and remotely sensed data from 1996 to 2002 (e.g., Jakubauskas et al 1998, Debinski et al 1999 in two regions of the ecosystem: the northern ''Gallatins'' region that includes the Gallatin National Forest and northwestern portion of Yellowstone National Park; and the southern ''Tetons'' region that includes Grand Teton National Park and the Bridger-Teton National Forest. The two regions have very distinct landscapes and differ significantly in patch size ), but support similar meadow types and plant, bird, and butterfly diversity.…”

Section: Sampling Sitesmentioning

confidence: 99%

“…' We have focused our analysis here on the birds and butterflies because the plant community is summarized indirectly by changes in NDVI. Further, we analyzed percent cover data of the 25 most dominant plant species at each site using 20 ϫ 20 m 2 sampling units (see Debinski et al [1999] for sampling methods) from 1997, 1998, and 2001, and ordinations did not show any temporal change (D. M. Debinski, unpublished data). Thus, we infer that NDVI change among years was more a reflection of changes in moisture level or productivity than changes in species composition of the plant community.…”

Section: Identifying Species-habitat Associationsmentioning

confidence: 99%

“…We have been studying montane meadow biodiversity of plants, birds, and butterflies annually in the Greater Yellowstone Ecosystem (GYE) since 1992 (Debinski et al 1999(Debinski et al , 2000. We have used satellite imagery to classify meadow types along a moisture gradient (from hydric to mesic to xeric).…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Relationships Between Bird And Butterfly Community Shifts And Environmental Change

Debinski

Vannimwegen

Jakubauskas

2006

Ecological Applications

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Abstract. Quantifying the manner in which ecological communities respond during a time of decreasing precipitation is a first step in understanding how they will respond to longer-term climate change. Here we coupled analysis of interannual variability in remotely sensed data with analyses of bird and butterfly community changes in montane meadow communities of the Greater Yellowstone Ecosystem. Landsat satellite imagery was used to classify these meadows into six types along a hydrological gradient. The northern portion of the ecosystem, or Gallatin region, has smaller mean patch sizes separated by ridges of mountains, whereas the southern portion of the ecosystem, or Teton region, has much larger patches within the Jackson Hole valley. Both support a similar suite of butterfly and bird species. The Gallatin region showed more overall among-year variation in the normalized difference vegetation index (NDVI) when meadow types were pooled within regions, perhaps because the patch sizes are smaller on average. Bird and butterfly communities showed significant relationships relative to meadow type and NDVI. We identified several key species that are tightly associated with specific meadow types along the hydrological gradient. Comparing taxonomic groups, fewer birds showed specific habitat affinities than butterflies, perhaps because birds are responding to differences in habitat structure among meadow types and using the landscape at a coarser scale than the butterflies. Comparing regions, the Teton region showed higher predictability of community assemblages as compared to the Gallatin region. The Gallatin region exhibited more significant temporal trends with respect to butterflies. Butterfly communities in wet meadows showed a distinctive shift along the hydrological gradient during a drought period (1997)(1998)(1999)(2000). These results imply that the larger Teton meadows will show more predictable (i.e., static) species-habitat associations over the long term, but that the smaller Gallatin meadows may be an area that will exhibit the effects of global climate change faster.

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Section: Sampling Sitesmentioning

confidence: 99%

Section: Identifying Species-habitat Associationsmentioning

confidence: 99%

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Quantifying Relationships Between Bird And Butterfly Community Shifts And Environmental Change

Debinski

Vannimwegen

Jakubauskas

2006

Ecological Applications

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“…Moreover, if we are to adopt the coarse filter approach, we also need to be able to establish reliable links between certain habitat types and the occurrence of particular species (Roughgarden et al 1991, Stoms and Estes 1993, Lewis 1998. Methods have been developed for mapping habitats based on spectral reflectance patterns and establishing their correspondence with the occupancy patterns of plant or animal species (Scott et al 1993, Lauver 1997, Nøhr and Jørgensen 1997, Fuller et al 1998, Debinski et al 1999, Nagendra and Gadgil 1999. Nevertheless, the few remote sensing studies that did address the distribution of plant species in wetlands dealt mainly with pure vegetation stands and/or were conducted 1-2 m above the canopy with portable field spectrometers (Budd and Milton 1982, Peñuelas et al 1993, Zhang et al 1997, Spanglet et al 1998.…”

Section: Figmentioning

confidence: 99%

From Satellite Imagery to Peatland Vegetation Diversity: How Reliable Are Habitat Maps?

Poulin¹,

Careau²,

Rochefort³

et al. 2002

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ABSTRACT. Although satellite imagery is becoming a basic component of the work of ecologists and conservationists, its potential and reliability are still relatively unknown for a large number of ecosystems. Using Landsat 7/ETM+ (Enhanced Thematic Mapper Plus) data, we tested the accuracy of two types of supervised classifications for mapping 13 peatland habitats in southern Quebec, Canada. Before classifying peatland habitats, we applied a mask procedure that revealed 629 peatlands covering a total of 18,103 ha; 26% of them were larger than 20 ha. We applied both a simple maximum likelihood (ML) function and a weighted maximum likelihood (WML) function that took into account the proportion of each habitat class within each peatland when classifying the habitats on the image. By validating 626 Global Positioning System locations within 92 peatlands, we showed that both classification procedures provided an accurate representation of the 13 peatland habitat classes. For all habitat classes except lawn with pools, the predominant classified habitat within 45 m of the center of the validation location was of the same type as the one observed in the field. There were differences in the performance of the two classification procedures: ML was a better tool for mapping rare habitats, whereas WML favored the most common habitats. Based on ordinations, peatland habitat classes were as effective as environmental variables such as humidity indicators and water chemistry components at explaining the distribution of plant species and performed 1.6 times better when it came to accounting for vegetation structure patterns. Peatland habitats with pools had the most distinct plant assemblages, and the habitats dominated by herbs were moderately distinct from those characterized by ericaceous shrubs. Habitats dominated by herbs were the most variable in terms of plant species assemblages. Because peatlands are economically valuable wetlands, the maps resulting from the new classification procedure presented here will provide useful information for land managers and conservationists.

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“…According to the IUCN Habitats Classification Scheme [31] and the European Habitats Directive [32], natural habitat types can be mapped at a global scale by means of remote sensing in a harmonized way, avoiding the bias associated with sampling efforts [33][34][35][36][37]. Species data at the global scale might be biased due to differential sampling efforts related to location or taxa, whereas remote sensing data about habitats can be obtained more systematically and globally [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification

et al. 2016

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Protected areas (PAs) need to be assessed systematically according to biodiversity values and threats in order to support decision-making processes. For this, PAs can be characterized according to their species, ecosystems and threats, but such information is often difficult to access and usually not comparable across regions. There are currently over 200,000 PAs in the world, and assessing these systematically according to their ecological values remains a huge challenge. However, linking remote sensing with ecological modelling can help to overcome some limitations of conservation studies, such as the sampling bias of biodiversity inventories. The aim of this paper is to introduce eHabitat+, a habitat modelling service supporting the European Commission's Digital Observatory for Protected Areas, and specifically to discuss a component that systematically stratifies PAs into different habitat functional types based on remote sensing data. eHabitat+ uses an optimized procedure of automatic image segmentation based on several environmental variables to identify the main biophysical gradients in each PA. This allows a systematic production of key indicators on PAs that can be compared globally. Results from a few case studies are illustrated to show the benefits and limitations of this open-source tool.

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A remote sensing and GIS-based model of habitats and biodiversity in the Greater Yellowstone Ecosystem

Cited by 112 publications

References 13 publications

Quantifying Relationships Between Bird And Butterfly Community Shifts And Environmental Change

Quantifying Relationships Between Bird And Butterfly Community Shifts And Environmental Change

From Satellite Imagery to Peatland Vegetation Diversity: How Reliable Are Habitat Maps?

Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification

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