Delineating fragmented grassland patches in the tropical region using multi-seasonal SAR and Optical Satellite Images

Globally grasslands are declining and are in highly degraded conditions. In south Asia grasslands are neglected and treated as wastelands. They remain unprotected, highly fragmented, and poorly understood which has led to a loss of unique biodiversity and livelihoods. Mapping grasslands accurately is a challenge and current maps based on optical remote sensing often over- or underestimate grasslands in south Asia due to a prevalant complex landscape matrix, small patch sizes, and obscuring monsoonal clouds. Synthetic Aperture Radar (SAR) fused with moderate spatial resolution has been used to delineate grasslands but, high-resolution, freely available ESA’s sentinel-1(SAR) and -2(optical) provides an opportunity to map small and fragmented patches that were not possible earlier with the publically available moderate or medium spatial resolution remote sensing dataset. Further, high resolution imageries require high computing power which is often limited with stand alone machines. Here we demonstrate that using cloud computing and optimal use of multi-seasonal imagery one can obtain a highly accurate land cover/use classification for a complex habitat matrix. We used freely accessible cloud computing platforms like Google Earth Engine (GEE) and land cover/use classification of sentinel-1 and -2. We compared the accuracy of grassland delineation between 1) seasonal (pre, during, and post-monsoon) sentinel-1, 2) post-monsoon sentinel-2, and 3) combined sentinel-1 and -2. We tested this method at two sites in a highly fragmented habitat matrix in semi-arid areas of Western and Southern India. The classification result has shown the overall accuracy of for the combined image was higher than only sentinel-2 and sentinel-1 alone for both sites. Grasslands habitat accuracy was also consistent with combined image classification across the sites. Our results identified newer grassland areas that coarse landuse management maps used by the government did not. The computation was done on a basic laptop and processing completed very quick. We, therefore, suggest that this novel approach of using cloud computing and optimal use of resource-hungry (computation and storage) high-resolution ESA’s sentinel-1 and -2 data, can be used to identify major land classes and small patchy grassland in the semi-arid regions of Asia and has the potential to map at continent level.

Perceptual Range, Targeting Ability, and Visual Habitat Detection by Greater Fritillary Butterflies Speyeria cybele (Lepidoptera: Nymphalidae) and Speyeria atlantis

Butterflies are widely invoked as model organisms in studies of metapopulation and dispersal processes. Integral to such investigations are understandings of perceptual range; the maximum distance at which organisms are able to detect patches of suitable habitat. To infer perceptual range, researchers have released butterflies at varying distances from habitat patches and observed their subsequent flight behaviors. It is often assumed that butterflies rely on visual senses for habitat detection; however, this assumption has not been explicitly investigated. Here, we assess the extent and sensory determinants of perceptual range for the great spangled fritillary (Speyeria cybele (Fabricius, 1775)) and Atlantis fritillary (Speyeria atlantis (W.H. Edwards, 1862)). This was achieved by experimentally releasing butterflies over open water at various distances from a lake island, representing an isolated habitat patch in a dichotomous habitat-matrix landscape. To infer whether butterflies rely on vision for habitat detection, we exposed a subset of butterflies to a series of intense light flashes before release to induce flash blindness (bleaching of photoreceptive rhodopsins) without affecting olfaction. Flashed individuals were 30.1 times less likely to successfully navigate to the target island after release, suggesting butterflies rely primarily on visual senses to navigate fragmented landscapes. For unflashed butterflies, the likelihood of successful navigation decreased by a factor of 2.1 for every 10 m increase in release distance. However, no specific distance threshold for perceptual range was observed. We therefore suggest that perceptual range is best viewed as a continuum of probabilities (targeting ability), reflecting the likelihood of habitat detection across a range of distances.

Patchy environments, metapopulations, and fugitive species

Populations and species are distributed heterogeneously across the landscape and this has important consequences for their abundance, persistence, and interactions with other species. This chapter introduces the concept of a metapopulation, a “population of populations”, where populations occur in patches of suitable habitat surrounded by areas of unsuitable habitat (“matrix”), and where dispersal serves to connect patch dynamics. Metapopulation theory provides an important conceptual underpinning to the field of conservation biology, fostering the study of corridors and assisted migration as important conservation tools. There also are important parallels between metapopulation theory and epidemiology. The study of patchily distributed populations leads naturally to considerations of species interactions, where it is shown that an inferior competitor may coexist with a superior competitor if the inferior competitor is better a colonizing open patches—a “fugitive species”. This competition-colonization trade-off can be a strong stabilizing mechanism for maintaining biodiversity in a patchy environment.

Correlated evolution of microhabitat, morphology, and behavior in West Indian Anolis lizards: a test of the habitat matrix model

The habitat matrix model (HMM) explains convergence among arboreal animals as a result of the correlated evolution of morphology, locomotor mode, and habitat use. Although the HMM has generated important insights into the ecology of arboreal species, these tests have left a gap in the habitat-behavior-morphology story by focusing primarily on locomotor performance in lab and field experiments and thus failing to include data on locomotor behavior of undisturbed animals in the wild. We combined data on undisturbed locomotion, habitat use, and morphology for 31 species of arboreal lizard in the genusAnolisand used these data to test nine specific predictions arising from the HMM. We find strong support for nearly all aspects of this model. The addition of data on locomotion by undisturbed wild animals offers a more direct and compelling case for the HMM than most previous tests.