Response of Avian Communities to Historic Habitat Change in the Northern Chihuahuan Desert

Pidgeon, Anna M.; Mathews, Nancy E.; Benoit, Richard J.; Nordheim, Erik V.

doi:10.1046/j.1523-1739.2001.00073.x

Cited by 36 publications

(28 citation statements)

References 43 publications

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“…The study area contains multiple habitat types including two grassland types (black grama (Boutela eriopoda) and mesa grassland), four shrubland types (creosote (Larrea tridentata), mesquite (Prosopis glandulosa or P. pubescens), sandsage (Artemisia filifolia), and whitethorn (Acacia neovernicosa or A. constricta)), and one low tree-dominated habitat, pinyon -juniper (Pinus edulis-Juniperus monosperma or J. deppeana). For a more detailed description of the plant associations occurring in the area, refer to Pidgeon et al [34,35]. The field sites were represented by different soil types according to a digital soil map [36], including deep sand, draw, gravel, gyp upland, limestone hills, limy, loamy, sandstone hill and sandy.…”

Section: Materials and Methods (A) Study Areamentioning

confidence: 99%

Modelling avian biodiversity using raw, unclassified satellite imagery

St-Louis

Pidgeon

Kuemmerle

et al. 2014

Phil. Trans. R. Soc. B

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One contribution of 9 to a Theme Issue 'Satellite remote sensing for biodiversity research and conservation applications'. Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management.

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Section: Materials and Methods (A) Study Areamentioning

confidence: 99%

Modelling avian biodiversity using raw, unclassified satellite imagery

St-Louis

Pidgeon

Kuemmerle

et al. 2014

Phil. Trans. R. Soc. B

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“…For a more complete description of the plant associations occurring in these habitats see Pidgeon et al (2001Pidgeon et al ( , 2003. ), precipitation, and soil types across the Range determine the plant communities (Dick-Peddie 1993).…”

Section: Study Areamentioning

confidence: 99%

Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico

St‐Louis

Pidgeon²,

Clayton³

et al. 2009

Ecography

116

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Predicting broad-scale patterns of biodiversity is challenging, particularly in ecosystems where traditional methods of quantifying habitat structure fail to capture subtle but potentially important variation within habitat types. With the unprecedented rate at which global biodiversity is declining, there is a strong need for improvement in methods for discerning broad-scale differences in habitat quality. Here, we test the importance of habitat structure (i.e. fine-scale spatial variability in plant growth forms) and plant productivity (i.e. amount of green biomass) for predicting avian biodiversity. We used image texture (i.e. a surrogate for habitat structure) and vegetation indices (i.e. surrogates for plant productivity) derived from Landsat Thematic Mapper (TM) data for predicting bird species richness patterns in the northern Chihuahuan Desert of New Mexico. Bird species richness was summarized for forty-two 108 ha plots in the McGregor Range of Fort Bliss Military Reserve between 1996 and 1998. Six Landsat TM bands and the normalized difference vegetation index (NDVI) were used to calculate first-order and second-order image texture measures. The relationship between bird species richness versus image texture and productivity (mean NDVI) was assessed using Bayesian model averaging. The predictive ability of the models was evaluated using leave-one-out cross-validation. Texture of NDVI predicted bird species richness better than texture of individual Landsat TM bands and accounted for up to 82.3% of the variability in species richness. Combining habitat structure and productivity measures accounted for up to 87.4% of the variability in bird species richness. Our results highlight that texture measures from Landsat TM imagery were useful for predicting patterns of bird species richness in semi-arid ecosystems and that image texture is a promising tool when assessing broad-scale patterns of biodiversity using remotely sensed data.

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“…However, it is becoming increasingly clear that community diversity and abundance in arid and semi-arid lands are greatest in vegetation associations that are a mix of grasses and shrubs (Smith et al, 1996b;Whitford, 1997;Pidgeon et al, 2001;Valone and Sauter, 2005).…”

Section: Article In Pressmentioning

confidence: 99%