Tracking the effect of climate change on ecosystem functioning using protected areas: Africa as a case study

Pettorelli, Nathalie; Chauvenet, Aliénor L. M.; Duffy, James P.; Cornforth, William A.; Meillere, Alizée; Baillie, Jonathan

doi:10.1016/j.ecolind.2012.02.014

Cited by 74 publications

(48 citation statements)

References 43 publications

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“…For example, the Normalized Difference Vegetation Index (NDVI) has been used to test hypotheses on the recent trends in net primary productivity [23], to relate elephant density and food availability [24], and to predict habitat suitability for the reintroduction of species extinct in the wild [25]. The Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery have been used in regional and global land cover studies to identify hotspots of land cover change [26,27], to monitor forest [28,29] and savannah [30] habitats, to infer their resilience [31] and to assess the impact of habitat loss on birds [32].…”

Section: Introductionmentioning

confidence: 99%

Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology

Marco

Buchanan

Szantoi

et al. 2014

Phil. Trans. R. Soc. B

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Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite-borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.

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Section: Introductionmentioning

confidence: 99%

Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology

Marco

Buchanan

Szantoi

et al. 2014

Phil. Trans. R. Soc. B

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“…Globally, the threats to dryland ecosystems from oil exploration and extraction activities are great [84][85][86], causing reductions in species abundance and biodiversity [87,88]. With much current conservation attention on the relative effectiveness of the global PA network for conservation efforts [89][90][91][92][93][94][95], methods to monitor threats to conservation and management action in PAs are of the utmost importance. If the expansion of oil exploration activities continues to be conducted at the edges of and within desert PAs, they have the potential to increase pressures on the animal populations within through increased edge effects [37,38], reducing the effectiveness of buffer zones around PAs and the resilience of animal populations and ecosystems within from impacts of further processes of land-use and climate change.…”

Section: Discussionmentioning

confidence: 99%

Oil in the Sahara: mapping anthropogenic threats to Saharan biodiversity from space

Duncan

Kretz

Wegmann

et al. 2014

Phil. Trans. R. Soc. B

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Deserts are among the most poorly monitored and understood biomes in the world, with evidence suggesting that their biodiversity is declining fast. Oil exploration and exploitation can constitute an important threat to fragmented and remnant desert biodiversity, yet little is known about where and how intensively such developments are taking place. This lack of information hinders local efforts to adequately buffer and protect desert wildlife against encroachment from anthropogenic activity. Here, we investigate the use of freely available satellite imagery for the detection of features associated with oil exploration in the African Sahelo-Saharan region. We demonstrate how texture analyses combined with Landsat data can be employed to detect ground-validated exploration sites in Algeria and Niger. Our results show that site detection via supervised image classification and prediction is generally accurate. One surprising outcome of our analyses is the relatively high level of site omission errors in Niger (43%), which appears to be due to non-detection of potentially small-scale, temporary exploration activity: we believe the repeated implementation of our framework could reduce the severity of potential methodological limitations. Overall, our study provides a methodological basis for the mapping of anthropogenic threats associated with oil exploitation that can be conducted across desert regions.

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“…Plant cover in grazed rangelands provides livestock fodder, erosion control and biodiversity benefits. The less disturbed regions are of high conservation value because they can provide information on ecosystem structure and function in unmodified landscapes and have potential for monitoring the effects of climate change (Driscoll et al, 2012;Pettorelli et al, 2012;Suppiah et al, 2006). They contain indigenous and endemic flora and fauna and are critical for the reduction of further biodiversity loss (de Groot et al, 2002;Fisher et al, 2009).…”

Section: Introductionmentioning

confidence: 97%

A remote sensing spatio-temporal framework for interpreting sparse indicators in highly variable arid landscapes

Lawley

Lewis

Ostendorf

2016

Ecological Indicators

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a b s t r a c tThe world's extensive and often remote arid landscapes are receiving increasing attention to maintain their ecological and productive values. Monitoring and management of these lands requires indicators and evidence of ecosystem condition and trend, generally derived from widely distributed and infrequently repeated site-based records. However adequate geographic representation and frequent site revisits are difficult to achieve because of the remoteness and vast extent of these landscapes. Interpreting such sparse ecological indicators is difficult, particularly within landscapes that are highly variable in space and time. To interpret ecological indicator data collected in such environments long-term patterns of natural landscape variability need to be understood. This paper presents a framework of landscape spatio-temporal variability within which to interpret ecological indicator data. This framework is based on long-term patterns of vegetation growth across the Australian arid zone, derived from twenty-five years of high temporal resolution National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite imagery. We present a case study of the extensive Alinytjara Wilurara (AW) Natural Resource Management (NRM) region in far western South Australia to illustrate new insights about landscape function gained from this approach, and their implications for collection and interpretation of ecological indicator data. We illustrate how variability in vegetation response is expressed across the region, and how stratification based on active vegetation response differs from more commonly used biogeographic stratifications in this region. Lastly we demonstrate the unique patterns of long-term vegetation response for the major vegetation response classes. Average amount, seasonality, magnitude, timing and variability of vegetation response over time are used to characterise the natural "envelope" of variability of the new landscape classes.The study region showed low vegetation response in summer and higher response in winter. Onset of growth was earlier in the north and in ecosystems dominated by mallee vegetation. Cyclonic influence from the west was evident at the southern margin of the study region. The study demonstrates the landscape functional response of the study region, and presents a method whereby remote sensing reveals the landscape context within which to better interpret ecological indicator data collected in a highly variable landscape.

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Tracking the effect of climate change on ecosystem functioning using protected areas: Africa as a case study

Cited by 74 publications

References 43 publications

Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology

Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology

Oil in the Sahara: mapping anthropogenic threats to Saharan biodiversity from space

A remote sensing spatio-temporal framework for interpreting sparse indicators in highly variable arid landscapes

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