Dataset Information

Image texture predicts avian density and species richness.

ABSTRACT: For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R(2)?=?0.52, p-value <0.001), and avian species richness among habitats (R(2)=?0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R(2)=?0.13, p-value?=?0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R(2)=?0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas.

SUBMITTER: Wood EM

PROVIDER: S-EPMC3651168 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Image texture predicts avian density and species richness.

Wood Eric M EM Pidgeon Anna M AM Radeloff Volker C VC Keuler Nicholas S NS

PloS one 20130510 5

For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently chara ...[more]

PMID: 23675463

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Project description:BackgroundThe quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover.Methodology/principal findingsWe model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL). Species turnover, or [Formula: see text] diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or [Formula: see text] diversity, based on the Shannon entropy of pixel intensity.To test our approach, we specifically use the green band of Landsat images for a water conservation area in the Florida Everglades. We validate our predictions against data of species occurrences for a twenty-eight years long period for both wet and dry seasons. Our method correctly predicts 73% of species richness. For species turnover, the newly proposed KL divergence prediction performance is near 100% accurate. This represents a significant improvement over the more conventional Shannon entropy difference, which provides 85% accuracy. Furthermore, we find that changes in soil and water patterns, as measured by fluctuations of the Shannon entropy for the red and blue bands respectively, are positively correlated with changes in vegetation. The fluctuations are smaller in the wet season when compared to the dry season.Conclusions/significanceTexture-based statistical multiresolution image analysis is a promising method for quantifying interseasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary. The proposed automated method for quantifying species richness and turnover can also provide analysis at higher spatial and temporal resolution than is currently obtainable from expensive monitoring campaigns, thus enabling more prompt, more cost effective inference and decision making support regarding anomalous variations in biodiversity. Additionally, a matrix-based visualization of the statistical multiresolution analysis is presented to facilitate both insight and quick recognition of anomalous data.

Project description:Studies on the importance of seed arrival for community richness and composition have not considered the number of seeds arriving and its effect on species richness and composition of natural communities is thus unknown. A series of experimental dry grassland communities were established. All communities were composed of the same 44 species in exactly the same proportions on two substrates using three different seed densities.The results showed that seed density had an effect on species richness only at the beginning of the experiment. In contrast, the effects on species composition persisted across the entire study period. The results do not support the prediction that due to higher competition for light in nutrient-rich soil, species richness will be the highest in the treatment with the lowest seed density. However, the prevalence of small plants in the lowest seed density supported the expectation that low seed density guarantees low competition under high soil nutrients. In the nutrient-poor soil, species richness was the highest at the medium seed density, indicating that species richness reflects the balance between competition and limitations caused by the availability of propagules or their ability to establish themselves. This medium seed density treatment also contained the smallest plants.The results demonstrate that future seed addition experiments need to consider the amount of seed added so that it reflects the amount of seed that is naturally found in the field. Differences in seed density, mimicking different intensity of the seed rain may also explain differences in the composition of natural communities that cannot be attributed to habitat conditions. The results also have important implications for studies regarding the consequences of habitat fragmentation suggesting that increasing fragmentation may change species compositions not only due to different dispersal abilities but also due to differential response of plants to overall seed density.

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Image texture predicts avian density and species richness.

Publications

Image texture predicts avian density and species richness.

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