Rainfall, geology and landscape position generate large-scale spatiotemporal fire pattern heterogeneity in an African savanna

Fire is considered a critical management tool in fire prone landscapes. Often studies and policies relating to fire focus on why and how the fire regime should be managed, often neglecting to subsequently evaluate management’s ability to achieve these objectives over long temporal and large spatial scales. This study explores to what extent the long-term spatio-temporal fire patterns recorded in the Kruger National Park, South Africa has been influenced by management policies and to what extent it was dictated by underlying variability in the abiotic template. This was done using a spatially explicit fire-scar database from 1941 to 2006 across the 2 million hectare Park. Fire extent (hectares burnt per annum) (i) is correlated with rainfall cycles (ii) exhibits no long-term trend and (iii) is largely non-responsive to prevailing fire management policies. Rainfall, geology and distance from the closest perennial river and the interactions between these variables influence large-scale fire pattern heterogeneity: areas with higher rainfall, on basaltic substrates and far from rivers are more fire prone and have less heterogeneous fire regimes than areas with lower rainfall, on granitic substrates and closer to rivers. This study is the first to illustrate that under a range of rainfall and geological conditions, perennial rivers influence long-term, landscape-scale fire patterns well beyond the riparian zone (typically up to 15 km from the river). It was concluded that despite fire management policies which historically aimed for largely homogeneous fire return regimes, spatially and temporally heterogeneous patterns have emerged. This is primarily because of differences in rainfall, geology and distance from perennial rivers. We postulate that large-scale spatio-temporal fire pattern heterogeneity is implicit to heterogeneous savannas, even under largely homogenizing fire policies. Management should be informed by these patterns, embracing the natural heterogeneity-producing template. We therefore suggest that management actions will be better directed when operating at appropriate scales, nested within the broader implicit landscape patterns, and when focusing on fire regime parameters over which they have more influence (e.g. fire season).http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-0587hb201

Drowning in data, thirsty for information and starved for understanding: A biodiversity information hub for cooperative environmental monitoring in South Africa

The world is firmly cemented in a notitian age (Latin: notitia, meaning data) – drowning in data, yet thirsty for information and the synthesis of knowledge into understanding. As concerns over biodiversity declines escalate, the volume, diversity and speed at which new environmental and ecological data are generated has increased exponentially. Data availability primes the research and discovery engine driving biodiversity conservation. South Africa (SA) is poised to become a world leader in biodiversity conservation. However, continent-wide resource limitations hamper the establishment of inclusive technologies and robust platforms and tools for biodiversity informatics. In this perspectives piece, we bring together the opinions of 37 co-authors from 20 different departments, across 10 SA universities, 7 national and provincial conservation research agencies, and various institutes and private conservation, research and management bodies, to develop a way forward for biodiversity informatics in SA. We propose the development of a SA Biodiversity Informatics Hub and describe the essential components necessary for its design, implementation and sustainability. We emphasise the importance of developing a culture of cooperation, collaboration and interoperability among custodians of biodiversity data to establish operational workflows for data synthesis. However, our biggest challenges are misgivings around data sharing and multidisciplinary collaboration

Drowning in data, thirsty for information and starved for understanding: A biodiversity information hub for cooperative environmental monitoring in South Africa

The world is firmly cemented in a notitian age (Latin: notitia, meaning data) – drowning in data, yet thirsty for information and the synthesis of knowledge into understanding. As concerns over biodiversity declines escalate, the volume, diversity and speed at which new environmental and ecological data are generated has increased exponentially. Data availability primes the research and discovery engine driving biodiversity conservation. South Africa (SA) is poised to become a world leader in biodiversity conservation. However, continent-wide resource limitations hamper the establishment of inclusive technologies and robust platforms and tools for biodiversity informatics. In this perspectives piece, we bring together the opinions of 37 co-authors from 20 different departments, across 10 SA universities, 7 national and provincial conservation research agencies, and various institutes and private conservation, research and management bodies, to develop a way forward for biodiversity informatics in SA. We propose the development of a SA Biodiversity Informatics Hub and describe the essential components necessary for its design, implementation and sustainability. We emphasise the importance of developing a culture of cooperation, collaboration and interoperability among custodians of biodiversity data to establish operational workflows for data synthesis. However, our biggest challenges are misgivings around data sharing and multidisciplinary collaboration

Linking long-term patterns of landscape heterogeneity to changing ecosystem processes in the Kruger National Park, South Africa

Thesis (PhDAgric)--Stellenbosch University, 2018.ENGLISH ABSTRACT: Biodiversity loss is a global threat to ecosystem function and human well-being. Environmental heterogeneity is a recognised driver of biodiversity under a niche-based view of available species habitats. As such, an increase in environmental heterogeneity is expected to promote species coexistence, persistence and diversification. Loss of environmental heterogeneity is therefore considered proximal evidence of biodiversity loss. At a landscape scale, this heterogeneity is defined as the degree of difference between landscape elements and is often described as landscape heterogeneity. Patterns of landscape heterogeneity are generated and maintained by the physical landscape template or abiotic environment (e.g. topography, geology and climate), upon which complex adaptive interactions between landscape pattern (structure and composition) and ecological processes (function) occur. Landscape pattern can therefore be described as the self-organising expression of landscape function which varies not only across space but also through time. Accordingly, observable variations in landscape pattern are conjectured to signify divergence in landscape function. This thesis explores this relationship further within the Kruger National Park (Kruger): a large (~ 20,000 km2 ), longestablished (proclaimed 1898) protected area in South Africa’s semi-arid savanna. Results therefore describe landscape heterogeneity, in terms of the abiotic and biotic components (environmental drivers) that generate and maintain landscape pattern in Kruger, to inform strategic biodiversity planning. Chapter 1 introduces the reader to landscape heterogeneity and its relevance to protected area management and biodiversity conservation. Chapter 2 begins by isolating the effects of ‘stationary’ landscape properties on environmental heterogeneity through their relationship with Landsat spectral variance. Results show this relationship is sensitive to season and rainfall with the effects of dynamic ecosystem processes dominating many areas. Thereafter, Chapters 3 and 4 examine in more detail the nature of selected dynamic drivers in Kruger, namely rainfall and elephants. Results demonstrate the existence of longterm spatiotemporal changes in both rainfall and elephant density and distribution patterns in Kruger from 1985-2015. Together these results feed into chapter 5, where a Structural Equation Model (SEM) is used to investigate the causal structure of landscape heterogeneity with stable landscape properties, rainfall, herbivory and fire. Results are presented as path coefficients and long-term driver dominance maps showing the magnitude and direction of the different cause and effect relationships between heterogeneity, the physical landscape template, rainfall, herbivory and fire return interval. Finally the nature of the environmental-heterogeneity theory is operationalised in Chapter 6 using R, Shiny and Leaflet to provide an interactive web interface for protected area managers to explore heterogeneity differences in context with park specific research questions. Chapter 7 concludes the thesis with a brief synthesis of results in context with current literature and highlights future research opportunities and possible directions.AFRIKAANSE OPSOMMING: Geen opsommingDoctora

Science support within the South African National Parks adaptive management framework

‘Behind all good science is good science support.’ Implementing a successful strategic adaptive management (SAM) framework requires an effective science support structure. This structure must be effective in all areas of data management, starting with data collection and ending with the dissemination of knowledge, to facilitate timeous management decisions and associated actions. Accordingly, South African National Parks has embraced the use of various technologies to enable the effective implementation of a functional support structure. This paper described these technologies and discussed how they benefit the implementation of the SAM framework. Conservation implications: The importance of functional support structures in science and conservation management is frequently undervalued in a system where emphasis is placed on scientific products. In order to promote research and facilitate analysis, sound data management practices are essential to integrating knowledge into an organisation’s institutional memory

Defining optimal sampling effort for large-scale monitoring of invasive alien plants: A Bayesian method for estimating abundance and distribution

1. Monitoring the abundance and spatial structure of invasive alien plant populations is important for designing and measuring the efficacy of long-term management strategies. However, methods for monitoring over large areas with minimum sampling effort, but with sufficient accuracy, are lacking. Although sophisticated sampling techniques are available for increasing sampling efficiency, they are often difficult to implement for large-scale monitoring, thus necessitating a robust yet practical method. 2. We explored this problem over a large area (c.20000km2), using ad hoc presence-absence records routinely collected over 4years in Kruger National Park (KNP), South Africa. Using a Bayesian method designed to solve the pseudo-absence (or false-negative) dilemma, we estimated the abundance and spatial structure of all invasive alien plants in KNP. Five sampling schemes, with different spatially weighted sampling efforts, were assessed and the optimal sampling effort estimated. 3. Although most taxa have very few records (50% of the species have only one record), the more abundant species showed a log-normal species-abundance distribution, with the 29 most abundant taxa being represented by an estimated total of 2·22 million individuals, with most exhibiting positive spatial autocorrelation. 4. Estimations from all sampling schemes approached the real situation with increasing sampling effort. An equal-weighted (uniform) sampling scheme performed best for abundance estimation (optimal efforts of 68 records per km2), but showed no advantage in detecting spatial autocorrelation (247 records per km2 required). With increasing sampling effort, the accuracy of abundance estimation followed an exponential form, whereas the accuracy of distribution estimation showed diverse forms. Overall, a power law relationship between taxon density (as well as the spatial autocorrelation) and the optimal sampling effort was determined. 5. Synthesis and applications. The use of Bayesian methods to estimate optimal sampling effort indicates that for large-scale monitoring, reliable and accurate schemes are feasible. These methods can be used to determine optimal schemes in areas of different sizes and situations. In a large area like KNP, the uniform equal-weighted sampling scheme performs optimally for monitoring abundance and distribution of invasive alien plants, and is recommended as a protocol for large-scale monitoring in other protected areas as well. © 2011 The Authors. Journal of Applied Ecology © 2011 British Ecological Society.Articl