Clare Duncan

Predicting the future impact of droughts on ungulate populations in arid and semi-arid environments.

Droughts can have a severe impact on the dynamics of animal populations, particularly in semi-arid and arid environments where herbivore populations are strongly limited by resource availability. Increased drought intensity under projected climate change scenarios can be expected to reduce the viability of such populations, yet this impact has seldom been quantified. In this study, we aim to fill this gap and assess how the predicted worsening of droughts over the 21st century is likely to impact the population dynamics of twelve ungulate species occurring in arid and semi-arid habitats. Our results provide support to the hypotheses that more sedentary, grazing and mixed feeding species will be put at high risk from future increases in drought intensity, suggesting that management intervention under these conditions should be targeted towards species possessing these traits. Predictive population models for all sedentary, grazing or mixed feeding species in our study show that their probability of extinction dramatically increases under future emissions scenarios, and that this extinction risk is greater for smaller populations than larger ones. Our study highlights the importance of quantifying the current and future impacts of increasing extreme natural events on populations and species in order to improve our ability to mitigate predicted biodiversity loss under climate change.

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

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.

Rehabilitating mangrove ecosystem services: A case study on the relative benefits of abandoned pond reversion from Panay Island, Philippines.

Mangroves provide vital climate change mitigation and adaptation (CCMA) ecosystem services (ES), yet have suffered extensive tropics-wide declines. To mitigate losses, rehabilitation is high on the conservation agenda. However, the relative functionality and ES delivery of rehabilitated mangroves in different intertidal locations is rarely assessed. In a case study from Panay Island, Philippines, using field- and satellite-derived methods, we assess carbon stocks and coastal protection potential of rehabilitated low-intertidal seafront and mid- to upper-intertidal abandoned (leased) fishpond areas, against reference natural mangroves. Due to large sizes and appropriate site conditions, targeted abandoned fishpond reversion to former mangrove was found to be favourable for enhancing CCMA in the coastal zone. In a municipality-specific case study, 96.7% of abandoned fishponds with high potential for effective greenbelt rehabilitation had favourable tenure status for reversion. These findings have implications for coastal zone management in Asia in the face of climate change.

How many remnant gibbon populations are left on Hainan? Testing the use of local ecological knowledge to detect cryptic threatened primates

For Critically Endangered “species of extreme rarity,” there is an urgent need to clarify the potential survival of remnant populations. Such populations can be difficult to detect using standard field methods. Local ecological knowledge (LEK) represents an important alternative source of information, but anecdotal reports of rare or possibly extinct species can contain uncertainty and error. The Hainan gibbon (Nomascus hainanus), the worlds rarest primate species, is confirmed to only survive as a tiny remnant population in Bawangling National Nature Reserve, China, but unverified gibbon sightings have been reported from other forest areas on Hainan. We conducted a large‐scale community interview survey to gather new data on patterns of primate LEK from 709 respondents around seven reserves across Hainan, to investigate the possibility of gibbon survival outside Bawangling and assess whether LEK can provide useful information for conservation management of cryptic remnant populations. Comparative LEK data for gibbons and macaques are consistent with independent data on the relative status of these species across Hainan. Local awareness and experience of gibbons was low across Hainan, including at Bawangling, but we recorded recent anecdotal gibbon reports from most reserves. A follow‐up field survey at Limushan Provincial Nature Reserve did not detect gibbons, however, and documented intensive wildlife exploitation within this reserve. All other surveyed landscapes showed some statistically lower levels of respondent awareness, experience, or sighting histories of gibbons compared to Bawangling, and are therefore considered biologically unlikely to support gibbons. Unverified LEK data can provide important insights into the possible status of cryptic remnant populations when assessed carefully and critically in relation to data from known populations.

Satellite remote sensing to monitor mangrove forest resilience and resistance to sea level rise

1. Coastal ecosystems, such as mangroves, provide key ecosystem services for climate change mitigation and adaptation. However, combined anthropogenic activities and climatic change‐driven sea level rise (SLR) pose a severe threat to their global persistence, and to the continued delivery of these services. Mangrove vulnerability to SLR depends upon capacity for both resilience (landward migration) and resistance (maintained functioning with the existing distribution), which are in turn hindered by extractive activities and coastal infrastructure development. Limited landscape‐scale data availability means existing SLR vulnerability assessment frameworks lack rigorous quantification of these discrete processes. / 2. Here we develop and implement a novel multi‐product (multispectral, microwave, derived‐product) open‐access satellite remote sensing approach to assess both coastal ecosystem SLR resilience and resistance capacity in multiple mangrove sites across the world, and landscape‐level and anthropogenic factors driving these capacities. Our approach allows comparative ranking of resilience and resistance capacities across sites, based on relative observed ecosystem change (biomass, distribution) and in constraints to these two components of SLR vulnerability. / 3. We observe mostly low SLR resilience and resistance across our case study sites. Furthermore, we find that site‐specific resilience and resistance capacities and constraints can be highly incongruent, highlighting the importance of comprehensive SLR vulnerability monitoring for effective management. High within‐site variation was also detected in resilience and resistance capacities and their constraints. This underlines the importance of spatially explicit monitoring at extensive spatial scales to inform decision making. / 4. The methodology developed and repeat‐pass imagery employed adds to the remote monitoring and assessment toolkit for adaptive coastal ecosystem management under SLR, providing a new approach to inform conservation and management priority assessments in data‐deficient regions.

The Role of Herbivory in Structuring Tropical Seagrass Ecosystem Service Delivery

Seagrass meadows support key ecosystem services, via provision of food directly for herbivores, and indirectly to their predators. The importance of herbivores in seagrass meadows has been well-documented, but the links between food webs and ecosystem services in seagrass meadows have not previously been made explicit. Herbivores interact with ecosystem services – including carbon sequestration, cultural values, and coastal protection. Interactions can be positive or negative and depend on a range of factors including the herbivore identity and the grazing type and intensity. There can be unintended consequences from management actions based on a poor understanding of trade-offs that occur with complex seagrass-herbivore interactions. Tropical seagrass meadows support a diversity of grazers spanning the meso-, macro-, and megaherbivore scales. We present a conceptual model to describe how multiple ecosystem services are influenced by herbivore pressure in tropical seagrass meadows. Our model suggests that a balanced ecosystem, incorporating both seagrass and herbivore diversity, is likely to sustain the broadest range of ecosystem services. Our framework suggests the pathway to achieve desired ecosystem services outcomes requires knowledge on four key areas: (1) how size classes of herbivores interact to structure seagrass; (2) desired community and management values; (3) seagrass responses to top–down and bottom–up controls; (4) the pathway from intermediate to final ecosystem services and human benefits. We suggest research should be directed to these areas. Herbivory is a major structuring influence in tropical seagrass systems and needs to be considered for effective management of these critical habitats and their services.

Optimal soil carbon sampling designs to achieve cost-effectiveness: a case study in blue carbon ecosystems

Researchers are increasingly studying carbon (C) storage by natural ecosystems for climate mitigation, including coastal ‘blue carbon’ ecosystems. Unfortunately, little guidance on how to achieve robust, cost-effective estimates of blue C stocks to inform inventories exists. We use existing data (492 cores) to develop recommendations on the sampling effort required to achieve robust estimates of blue C. Using a broad-scale, spatially explicit dataset from Victoria, Australia, we applied multiple spatial methods to provide guidelines for reducing variability in estimates of soil C stocks over large areas. With a separate dataset collected across Australia, we evaluated how many samples are needed to capture variability within soil cores and the best methods for extrapolating C to 1 m soil depth. We found that 40 core samples are optimal for capturing C variance across 1000s of kilometres but higher density sampling is required across finer scales (100–200 km). Accounting for environmental variation can further decrease required sampling. The within core analyses showed that nine samples within a core capture the majority of the variability and log-linear equations can accurately extrapolate C. These recommendations can help develop standardized methods for sampling programmes to quantify soil C stocks at national scales.

Spatial autocorrelation and congruence in the distribution of language and mammal richness: a reply to Cardillo et al. (2015)

Our paper on the relationship between the spatial distribution of language richness and mammal species richness recently argued that little congruence exists between the distribution of threatened languages and threatened mammals in New Guinea, despite high overlap between areas of high language richness and high mammal species richness across this island [1]. In reply to this, Cardillo et al. re-analysed the data at one of the spatial resolutions that we considered in our paper, claiming that the original analyses were statistically flawed [2]. In short, Cardillo et al. argue that the inclusion in our analyses of coastal pixels containing both land and sea, as well as a lack of consideration of spatial autocorrelation, has led us to wrongly derive conclusions about patterns of distribution in language and species richness.