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CURATOR
A pinboard by
Dominic Henry

Postdoctoral Research Fellow, University of Cape Town

PINBOARD SUMMARY

My research focuses on understanding the distribution and abundance of plant and animal species

As the practice of shale gas extraction using hydraulic fracturing expands across the world, there are concerns about the threats this poses to biodiversity and the maintenance of ecological processes. A vast area of approximately 125 000 km² has been earmarked for shale gas exploration in the semi-arid Karoo region of South Africa. Historically, the Karoo region has been understudied and our understanding of biodiversity patterns and the processes that drive these patterns remain limited. In conjunction with the South African National Biodiversity Institute we are working towards quantifying biodiversity patterns in order to support the decision-making process of shale gas development activities. We are attempting to model species distribution and richness patterns of six taxa using an occupancy modelling framework. Our focal groups are birds, butterflies, plants, reptiles, scorpions & mammals.

Occupancy modelling is hierarchical in nature and allows for modelling of the ecological and observation process. Incorporating an observation process is essential because in the large majority of scenarios, the probability of detecting a species in a fieldwork survey is less the one. Traditional occupancy models rely on repeat surveys, multiple observers or spatial subsampling to effectively model the detection process. A relatively less well-known framework, called time-to-detection (TTD) models, relies on data collected in a single survey. In this sampling protocol the time at which a species is encountered after the start of a survey is recorded which in turn provides information on the detection process.

In this paper, we applied multi-species TTD models within a hierarchical Bayesian modelling framework to our focal taxa survey data collected across 50 Karoo study sites. We modelled occupancy as a function of environmental and survey covariates. We illustrate the output of the models with respect to various species distribution and richness patterns across the study area . We also discuss the environmental factors that drive the occurrence and distribution of species. Importantly, the analysis allowed us to identify key biodiversity hotspots within the proposed shale gas development area.

8 ITEMS PINNED

Dynamic occupancy models for analyzing species' range dynamics across large geographic scales.

Abstract: Large-scale biodiversity data are needed to predict species' responses to global change and to address basic questions in macroecology. While such data are increasingly becoming available, their analysis is challenging because of the typically large heterogeneity in spatial sampling intensity and the need to account for observation processes. Two further challenges are accounting for spatial effects that are not explained by covariates, and drawing inference on dynamics at these large spatial scales. We developed dynamic occupancy models to analyze large-scale atlas data. In addition to occupancy, these models estimate local colonization and persistence probabilities. We accounted for spatial autocorrelation using conditional autoregressive models and autologistic models. We fitted the models to detection/nondetection data collected on a quarter-degree grid across southern Africa during two atlas projects, using the hadeda ibis (Bostrychia hagedash) as an example. The model accurately reproduced the range expansion between the first (SABAP1: 1987-1992) and second (SABAP2: 2007-2012) Southern African Bird Atlas Project into the drier parts of interior South Africa. Grid cells occupied during SABAP1 generally remained occupied, but colonization of unoccupied grid cells was strongly dependent on the number of occupied grid cells in the neighborhood. The detection probability strongly varied across space due to variation in effort, observer identity, seasonality, and unexplained spatial effects. We present a flexible hierarchical approach for analyzing grid-based atlas data using dynamical occupancy models. Our model is similar to a species' distribution model obtained using generalized additive models but has a number of advantages. Our model accounts for the heterogeneous sampling process, spatial correlation, and perhaps most importantly, allows us to examine dynamic aspects of species ranges.

Pub.: 24 Jan '14, Pinned: 28 Aug '17

Roles of Spatial Scale and Rarity on the Relationship between Butterfly Species Richness and Human Density in South Africa.

Abstract: Wildlife and humans tend to prefer the same productive environments, yet high human densities often lead to reduced biodiversity. Species richness is often positively correlated with human population density at broad scales, but this correlation could also be caused by unequal sampling effort leading to higher species tallies in areas of dense human activity. We examined the relationships between butterfly species richness and human population density at five spatial resolutions ranging from 2' to 60' across South Africa. We used atlas-type data and spatial interpolation techniques aimed at reducing the effect of unequal spatial sampling. Our results confirm the general positive correlation between total species richness and human population density. Contrary to our expectations, the strength of this positive correlation did not weaken at finer spatial resolutions. The patterns observed using total species richness were driven mostly by common species. The richness of threatened and restricted range species was not correlated to human population density. None of the correlations we examined were particularly strong, with much unexplained variance remaining, suggesting that the overlap between butterflies and humans is not strong compared to other factors not accounted for in our analyses. Special consideration needs to be made regarding conservation goals and variables used when investigating the overlap between species and humans for biodiversity conservation.

Pub.: 29 Apr '15, Pinned: 28 Aug '17

Explaining patterns of avian diversity and endemicity: climate and biomes of southern Africa over the last 140,000 years

Abstract: Test hypotheses that present biodiversity and endemic species richness are related to climatic stability and/or biome persistence.Africa south of 15° S.Seventy eight HadCM3 general circulation model palaeoclimate experiments spanning the last 140,000 years, plus a pre‐industrial experiment, were used to calculate measures of climatic variability for 0.5° grid cells. Models were fitted relating distributions of the nine biomes of South Africa, Lesotho and Swaziland to present climate. These models were used to simulate potential past biome distribution and extent for the 78 palaeoclimate experiments, and three measures of biome persistence. Climatic response surfaces were fitted for 690 bird species regularly breeding in the region and used to simulate present species richness for cells of the 0.5° grid. Species richness was evaluated for residents, mobile species (nomadic or partially/altitudinally migrant within the region), and intra‐African migrants, and also separately for endemic/near‐endemic (hereafter ‘endemic’) species as a whole and those associated with each biome. Our hypotheses were tested by analysing correlations between species richness and climatic variability or biome persistence.The magnitude of climatic variability showed clear spatial patterns. Marked changes in biome distributions and extents were projected, although limited areas of persistence were projected for some biomes. Overall species richness was not correlated with climatic variability, although richness of mobile species showed a weak negative correlation. Endemic species richness was significantly negatively correlated with climatic variability. Strongest correlations, however, were positive correlations between biome persistence and richness of endemics associated with individual biomes.Low climatic variability, and especially a degree of stability enabling biome persistence, is strongly correlated with species richness of birds endemic to southern Africa. This probably principally reflects reduced extinction risk for these species where the biome to which they are adapted persisted.

Pub.: 09 Feb '16, Pinned: 28 Aug '17

Early warning systems for biodiversity in southern Africa – How much can citizen science mitigate imperfect data?

Abstract: It is a hard reality that virtually all countries, no matter how well resourced, take conservation and land use decisions based on highly patchy and imperfect data - if indeed any data at all. Despite a mushrooming of scientific evidence and journals in the past decade, and open-access provision of many expensive global datasets, developing countries in particular often have to make do with inaccurate and coarse-scale global data, in the absence of targeted, local data to solve immediate conservation problems. To what extent can citizen science data compensate for the patchiness of conventional government-gathered scientific data in order to support planning, policy and management? We demonstrate how southern Africa's citizen science-based “early warning system for biodiversity” is used to support land-use planning and conservation decisions, including Red List, strategic and project-based environmental impact assessments and national protected area expansion and implementation strategies. This system integrates volunteer-based species atlases such as the Protea Atlas Project and Southern African Bird Atlas Project (SABAP), species population monitoring such as the Custodians of Rare and Endangered Wildflowers (CREW) project, and site-based rapid assessment and monitoring such as MyBirdPatch and BioBlitz. Countries in southern Africa are on a sharp continuum of research capacity, funding, political engagement and own datasets. Yet there is the capacity for adaptive management systems based in significant part on civil society volunteerism. Crucially, these must be underpinned by statistically sound, simple, repeatable scientific protocols, which are still rare in Africa.

Pub.: 26 Sep '16, Pinned: 28 Aug '17

What determines the importance of a species for ecosystem processes? Insights from tropical ant assemblages.

Abstract: Biodiversity is known to increase ecosystem functioning. However, species vary in their contributions to ecosystem processes. Here, we investigated seven ecosystem functions based on the consumption of different resources in tropical ant communities. We analysed how different species influence site-level resource consumption, and determined how each species influenced performance and stability of these functions. Based on simulated extinctions, we identified 'key species' with significant functional contributions. We then investigated which traits, such as biomass, abundance, and specialisation, characterized them, and compared trait distributions across four sites to analyse differences in functional redundancy. Only few species significantly influenced ecosystem functions. Common generalist species tended to be the most important drivers of many ecosystem functions, though several specialist species also proved to be important in this study. Moreover, species-specific ecological impacts varied across sites. In addition, we found that functional redundancy varied across sites, and was highest in sites where the most common species did not simultaneously have the greatest functional impacts. Furthermore, redundancy was enhanced in sites where species were less specialised and had more even incidence distributions. Our study demonstrates that the ecological importance of a species depends on its functional traits, but also on the community context. It cannot be assessed without investigating its species-specific performance across multiple functions. Hence, to assess functional redundancy in a habitat and the potential for compensation of species loss, researchers need to study species-specific traits that concern functional performance as well as population dynamics and tolerance to environmental conditions.

Pub.: 27 Jul '17, Pinned: 25 Aug '17

Site-occupancy distribution modeling to correct population-trend estimates derived from opportunistic observations.

Abstract: Species' assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection-nondetection records) are generated. Within-season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectability means correcting for observation effort. Second, site-occupancy models are applied directly to the detection-history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site-occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen-science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.

Pub.: 27 Mar '10, Pinned: 25 Aug '17

Biodiversity of man-made open habitats in an underused country: a class of multispecies abundance models for count data

Abstract: Since the 1960s, Japan has become highly dependent on foreign countries for natural resources, and the amount of managed lands (e.g. coppice, grassland, and agricultural field) has declined. Due to infrequent natural and human disturbance, early-successional species are now declining in Japan. Here we surveyed bees, birds, and plants in four human-disturbed open habitats (pasture, meadow, young planted forest, and abandoned clear-cut) and two forest habitats (mature planted forest and natural old-growth). We extended a recently developed multispecies abundance model to accommodate count data, and used the resulting models to estimate species-, functional group-, and community-level state variables (abundance and species richness) at each site, and compared them among the six habitats. Estimated individual-level detection probability was quite low for bee species (mean across species = 0.003; 0.16 for birds). Thirty-two (95% credible interval: 13–64) and one (0–4) bee and bird species, respectively, were suggested to be undetected by the field survey. Although habitats in which community-level abundance and species richness was highest differed among taxa, species richness and abundance of early-successional species were similar in the four disturbed open habitats across taxa except for plants in the pasture habitat which was a good habitat only for several exotic species. Our results suggest that human disturbance, especially the revival of plantation forestry, may contribute to the restoration of early-successional species in Japan.

Pub.: 25 Mar '12, Pinned: 25 Aug '17