Landsats Aff


Bio-D – Solvency/IL – Coral Reefs



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Bio-D – Solvency/IL – Coral Reefs


Mapping of coral reefs provides useful info for managers – habitat variables are correlated with biodiversity
Knudby 7 (Anders, Ellsworth LeDrew and Candace Newman, Dept. of Geography at U Waterloo, Progress in Physical Geography 31(4) pp. 421-434, EBSCO, JMB)

III Habitats as indicators of coral reef biodiversity As noted, several environmental variables have been shown to influence the biodiversity of a given habitat. Mapping such habitat variables could indicate the likely spatial distribution of biodiversity at a local scale and suggest priority areas for conservation, at least for the species for which habitat-biodiversity relationships have been identified. A survey of the literature relating biodiversity and habitat variables yields a complex picture. Studies have focused on a variety of taxonomic or functional groups, have employed different measures of biodiversity, and have measured different habitat variables in different ways. A brief overview is presented in Table I. Detailed relationships are obscured by the number of different variables used, and the different temporal and spatial scales studied (Jones and Syms, 1998). It can thus be argued that a species-specific approach is more appropriate, as habitat associations of many individual species are well known and well defined (Allen et al, 2003). For a few highly significant species such an approach may be useful but, with approximately 10,000 species of described fish, and a total of one million species in all taxonomic groups estimated to exist on coral reefs, a species-specific approach is unfeasible for studies of general biodiversity patterns. Despite these problems, several conclusions can be drawn from the literature: (i) several habitat characteristics influence local biodiversity; (ii) the number of studied biodiversity and habitat variables is large and relationships are not restricted to a few variables: (iii) some habitat variables, namely depth, live coral cover and reef structural complexity, influence more biodiversity variables than others, and show stronger correlations than others; and (iv) common biodiversity measures, including species abundance, richness and diversity, show correlations with these habitat variables. Several causative mechanisms have been proposed for the relations between habitat and biodiversity. The influence of depth has been cited as an example of intermediate disturbance positively influencing species richness at intermediate depth (Huston, 1994: 383), whereas the influence of live coral cover has been related to larval settlement success and to survival of corallivorous and coral-dwelling species (Jones et at., 2004). Reef structural complexity provides physical heterogeneity and refuge for prey species -spaces big enough for them to enter but too small for their predators (Friedlander and Parrish, 1998). This illustrates the important relation between the body sizes of organisms and the spatial scale of the structure providing refuge. Habitat influences on biodiversity also extend to the temporal domain: loss of fish biodiversity can often be attributed to loss of coral cover (Jones et ai, 2004) or loss of physical structure following hurricanes or severe bleaching (Connell et ai, 1997; Garpe era/., 2006: Graham et ai, 2006). Mapping coral reef habitats can therefore provide coral reef managers with important information on the likely spatial distribution of biodiversity in their area and, in the absence of frequent field surveys, can warn about changes in biodiversity to be expected from changing habitats.
Remote sensing key to climate change and coral monitoring
Kerr and Ostrovsky 3 (Jeremy T., and Marsha, both from the Dept. of Biology at U Ottawa, TRENDS in Ecology and Evolution Vol.18 No.6 June, p. 299-305, http://mysite.science.uottawa.ca/jkerr/pdf/tree2003.pdf, accessed 7-6-11, JMB)

Remote sensing data have provided convincing evidence that climate has been changing rapidly [30], complementing ecological discoveries of poleward shifts in the ranges of many species [31,32]. Although the distributions of species have also responded to concurrent land use changes [33], time series AVHRR data demonstrate that substantial alteration to vegetation structure, primary productivity and growing season length have occurred even over the past 20 years. In boreal forests, which studies increasingly indicate to be crucial sinks for carbon dioxide, long-term analysis (1981–1999) of NDVI trends show a general increase in growing season length, annual primary productivity and northward extension of the treeline [34,35]. Integrated NDVI (the sum of NDVI measurements from all AVHRR composites measured throughout the growing season) correlates with fieldbased measurements of net primary productivity, biomass accumulation and temperature. Warming, moistening trends have also been detected with the use of AVHRR and more specialized sensors over marine systems [36], providing important corroborative evidence of climate change. Widespread, synchronous coral bleaching events are due primarily to increasing sea temperatures and can be monitored with the use of Landsat 7 ETM + data [37]. Several biological consequences of climate change can be observed remotely, but field-based research also provides convincing corroboration of biotic consequences of climate change.


Bio-D – Solvency/IL – Conservation


Medium resolution data like Landsat key to preserving bio-d by focusing conservation efforts -- bypasses national boundaries
Fuller and Jessup 8 (D.O., Department of Geography and Regional Studies at U Miami and T.C., AusAID, Australian Embassy in Indonesia, 9/2, http://www.as.miami.edu/geography/research/climatology/Fuller_Jessup_EKal_2008.pdf, accessed 7-6-11, JMB)

Conservationists face many decisions on how to invest scarce resources to maximize conservation of biological diversity. In recent years, international conservation organizations and researchers have conducted a variety of planning exercises to target their investments based on a combination of biological and socioeconomic criteria (O’Connor et al. 2003). The conservation planning process often results in categorical, vector-based maps that reveal units of different conservation value or threat to biodiversity within ecoregions or biodiversity hotspots (e.g., Dinerstein et al. 1995; Olson & Dinerstein 1998). Bailey (1998) defined ecoregions as “major ecosystems resulting from large-scale, predictable patterns of solar radiation and moisture, which in turn affect the kinds of local ecosystems and animals and plants found within,” while Myers et al. (2000) defined the latter as “areas featuring exceptional concentrations of endemic species and experiencing exceptional loss of habitat.” Maps depicting ecoregions and hotspots are often quite coarse spatially and may therefore subsume or ignore areas of high conservation value that lie within homogeneous polygons. Thus, there is growing recognition that finer-scale data are needed to better identify areas within ecoregions that may merit conservation investment (Harris et al. 2005). Despite limitations imposed by scale, ecoregions and hotspots have helped conservation organizations reduce redundancy and achieve complementarity in their selection of sites for conservation investment (Olson & Dinerstein 1998). Because they transcend political boundaries, some authors (e.g., Wikramanayake et al. 2002) argue that conservation planning and investment should target entire ecoregions and hotspots to maintain the functional integrity of large ecological units. Although basing conservation planning on entire ecoregions and hotspots is preferable to planning based on political boundaries, this holistic approach can be problematic when applied to ecoregions and hotspots that span political boundaries where conservation laws, governance and enforcement practices may differ greatly (O’Connor et al. 2003). In addition, biological and socioeconomic data are sometimes collected and compiled using different sampling methods and criteria within different political units. Thus, decisions regarding land use are generally taken at the level of existing political units such as nations, states, provinces and counties and therefore planning exercises generally devolve to the local, political level as the most effective way to engage relevant decision makers. The use of remote sensing to map biodiversity (Rey-Benayas 1995; Gould 2000) holds significant promise for providing fine-scale information on ecosystem condition within specific hotspots and ecoregions. In particular, remote sensing at medium (<100m) to high (0.5-5m) spatial resolutions provides a way to collect common information across political boundaries. For example, Landsat and other optical systems are used to map plant canopy structure and condition, which are key determinants of species richness in forested ecosystems (Ozanne et al. 2003), to extract transportation infrastructure and waterways that provide access to key terrestrial habitats, and to pinpoint recent patterns of degradation caused by land use (Turner et al. 2007). Imagery of moderate-to-high spatial resolution has proven effective for determining threats to biodiversity such as habitat fragmentation, degradation, and reserve integrity (Lambin 1997; Fuller 2001; Sanchez-Azofeifa et al. 2003; Curran et al. 2004; Trigg et al. 2006). The spatial resolution of satellite sensors currently used by conservation biologists to assess habitat and biodiversity generally ranges from 30 m to 1 km, which allows production of maps 5 that can capture subtle gradients of land use, vegetation cover, and habitat type. Since the data structure of satellite images is raster-based, maps derived from these observations generally integrate well with dynamic climate-vegetation models that predict changes in plant functional types in response to changes in climate and other parameters (Kleidon & Mooney 2000; Hannah et al. 2002). Thus, cell or raster-based approaches may facilitate integration of conservation planning with dynamic or transient phenomena such as wildfires and climatic change, which are arguably two of the greatest future threats to biodiversity in the humid lowland tropics (Cochrane 2003)



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