Animal Habitat Quality and Ecosystem Functioning: Exploring Seasonal Patterns Using Ndvi

Abstract

Abstract. Many animal species have developed specific evolutionary adaptations to survive prolonged periods of low energy availability that characterize seasonal environments. The seasonal course of primary production, a major aspect of ecosystem functioning, should therefore be an important factor determining the habitat quality of such species. We tested this hypothesis by analyzing the relationship between habitat quality and ecosystem functioning for brown bears (Ursus arctos), a species showing hyperphagia and hibernation as evolutionary adaptation to seasonal peaks and bottlenecks in ecosystem productivity, respectively. Our unique long-term data set comprised data from two brown bear populations in northern Spain on historical presence, current presence, and reproduction. The data were classified on a grid of 5 3 5 km pixels into five classes: frequent reproduction, sporadic reproduction, frequent presence, sporadic presence, and recent extinction. We used the longterm average of the seasonal course of NDVI (normalized difference vegetation index) as a proxy for ecosystem functioning and investigated the relationship between habitat quality and ecosystem functioning with methods borrowed from statistical point-pattern analysis.We found that brown bears indeed selected habitat with specific ecosystem functioning (i.e., the variance in all habitat classes was smaller than in the landscape overall) and the relationship between habitat quality and ecosystem functioning was ordered. First, the average distance in ecosystem functioning between two habitat classes was larger if the difference in habitat quality was larger. Second, habitat for which there was the greatest need (i.e., breeding habitat) occupied the narrowest niche regarding ecosystem functioning and showed the most pronounced seasonality. Progressively poorer classes occupied wider niches that partly overlapped those of better classes. This indicated that nonbreeding animals are less selective.Our methodology provided new insight into the relationship between ecosystem functioning and habitat quality and could be widely applied to animal species living in seasonal environments. Because NDVI data are continuously collected, our methodology allows for continuous monitoring of changes in habitat quality due to global change.

Ecosystem greenspots pass the first test

“…Given the projections of increasing variability in climatic conditions, locations that remain productive during drought conditions may function as drought micro-refuges. Supporting evidence of the importance of temporal variation in productivity to habitat quality comes from faunal demographic studies (Gunnarsson et al 2005;Wiegand et al 2008). We suggest that conservation planning should explicitly consider temporal variation in habitat quality, particularly when exploring options for mitigating the impacts on biodiversity of projected future climate.…”

“…Mackey et al (2012) demonstrated the potential application of time series of fPAR to the identification of ecosystem greenspots, i.e., locations that maintain relatively high and stable levels of gross primary productivity (GPP) during drought. Integrating the spatial and temporal dimensions of productivity has also been applied to continental scale habitat analysis in relation to dispersive fauna in Australia (Berry et al 2007), biodiversity monitoring in Canada (Coops et al 2008), and seasonal dynamics of habitat quality of brown bears in Spain (Wiegand et al 2008). Mackey et al (2012) proposed that the ecosystem greenspot index could be used to identify locations that currently function as habitat refuges from fire and drought which in some bioregions are likely to become more persistent under future climatic conditions.…”

Testing remotely-sensed predictors of meso-carnivore habitat use in Mediterranean ecosystems

“…Other authors have also used NDVI as measures of seasonal productivity to which wildlife species may or not respond. For example, Wiegand et al (2008) showed that NDVI estimates of seasonal productivity were linked to brown bear (Ursus arctos) population decreases in northern Spain. Despite all the benefits of NDVI, including its wide use, simplicity, easiness to understand, and reliability, it also has the disadvantage of being influenced by the underlying soil reflectance, and at high values of canopy closure NDVI reaches saturation.…”

“…Previous work has shown a relation between vegetation productivity with the canopy reflectance measured by Landsat (Tucker 1979;Huete et al 1997), especially in the red edge region (650-850 nm, Landsat bands 3 and 4; Vogelmann et al 1993;Curran et al 1995). This region of the electromagnetic spectrum is related to the fraction of intercepted photosynthetically active radiation (Gamon et al 1995;Ludeke et al 1996), and is related to the distribution of plant communities, vegetation biomass, land degradation and vegetation quality for herbivores and omnivores (Pettorelli et al 2005;Wiegand et al 2008). To measure productivity we calculated a suite of vegetation indices well established in the remote sensing literature (Table 1).…”

Improving the potential of pixel-based supervised classification in the absence of quality ground truth data

“…This index has also been applied in the estimation of the cumulative effect of rainfall on vegetation -this includes studies on carrying capacity of rangelands and potential crop yields (Flynn, 2006, Kawamura et al, 2005, Wardlow and Egbert, 2008, Wiegand et al, 2008. In a comprehensive account of the sources of variability in canopy reflectance, Ollinger (2011) comments on the fact that NDVI has been applied in more than 2500 studies to identify a wide range of plant traits using a relatively small number of spectral features.…”