Scale and conservation planning in the real world

Conservation planning is carried out on a variety of geopolitical and biogeographical scales. Whereas considerable consensus is emerging about the most appropriate procedures for identifying conservation areas, the spatial implications of conducting conservation planning at divergent scales have received little attention. Here we explore the consequences of planning at different geopolitical scales, using a database of the mammalian fauna from the Northern Provinces of South Africa. The conservation network resulting from treating the region as one unit is compared with networks generated separately for the provinces nested in that region. These outcomes are evaluated in terms of (i) their land use efficiencies, (ii) their spatial overlap, and (iii) the impact of algorithm attributes. Although land use efficiencies are greater on broader scales, on average the spatial congruence between the broad-scale regional network and fine-scale provincial networks was less than 14%. Algorithms using different selection rules fail to improve this disturbing outcome. Consequently, scale has an overwhelming influence on areas identified as conservation networks in geopolitical units. This should be recognized in conservation planning

Land-cover change in the Kruger to Canyons Biosphere Reserve 1993-2006): A first step towards creating a conservation plan for the subregion.

This paper is a first step towards a conservation plan for the Kruger to Canyons Biosphere Reserve K2C) on the South African Central Lowveld, quantifying the historical land-cover trends 1993-2006). During the analysis period, 36% of the biosphere reserve BR) underwent land-cover change. Settlement areas increased by 39.7%, mainly in rural areas, becoming denser, particularly along roadways. Human-Impacted Vegetation increased by 6.8% and Intact Vegetation declined by 7.3%, predominantly around settlement areas, which is testament to the interdependency between rural communities and the local environment. However, settlement expansion exceeded the rate of rangeland growth; in the long term, this may raise questions for sustainable resource extraction. Similarly, the block losses of intact vegetation are of concern; issues of fragmentation arise, with knock-on effects for ecosystem functioning. In the economic sector, agriculture increased by 51.9%, while forestry and mining declined by 7.1% and 6.3%, respectively. The future of these three sectors may also have significant repercussions for land-cover change in the BR. The identification of historical drivers, along with the chance that existing trends may continue, will have important implications for biodiversity protection in this landscape. Applied within a conservation-planning framework, these land-cover data, together with economic and biodiversity data, will help reconcile the spatial requirements of socio-economic development with those of conservation.SP201

Systematic land-cover change in KwaZulu-Natal, South Africa: Implications for biodiversity.

Land-cover change and habitat loss are widely recognised as the major drivers of biodiversity loss in the world. Land-cover maps derived from satellite imagery provide useful tools for monitoring land-use and land-cover change. KwaZulu-Natal, a populous yet biodiversity-rich province in South Africa, is one of the first provinces to produce a set of three directly comparable land-cover maps (2005, 2008 and 2011). These maps were used to investigate systematic land-cover changes occurring in the province with a focus on biodiversity conservation. The Intensity Analysis framework was used for the analysis as this quantitative hierarchical method addresses shortcomings of other established land-cover change analyses. In only 6 years (2005-2011), a massive 7.6% of the natural habitat of the province was lost to anthropogenic transformation of the landscape. The major drivers of habitat loss were agriculture, timber plantations, the built environment, dams and mines. Categorical swapping formed a significant part of landscape change, including a return from anthropogenic categories to secondary vegetation, which we suggest should be tracked in analyses. Longer-term rates of habitat loss were determined using additional land-cover maps (1994, 2000). An average of 1.2% of the natural landscape has been transformed per annum since 1994. Apart from the direct loss of natural habitat, the anthropogenically transformed land covers all pose additional negative impacts for biodiversity remaining in these or surrounding areas. A target of no more than 50% of habitat loss should be adopted to adequately conserve biodiversity in the province. Our analysis provides the first provincial assessment of the rate of loss of natural habitat and may be used to fulfil incomplete criteria used in the identification of Threatened Terrestrial Ecosystems, and to report on the Convention on Biological Diversity targets on rates of natural habitat loss.SP201

Getting the most out of atlas data

Aim To review some of the applications in ecology and conservation biogeography of datasets derived from atlas projects. We discuss data applications and data quality issues and suggest ways in which atlas data could be improved. Location Southern Africa and worldwide. Methods Atlas projects are broadly defined as collections or syntheses of original, spatially explicit data on species occurrences. We review uses of atlas datasets and discuss data quality issues using examples from atlas projects in southern Africa and worldwide. Results Atlas projects must cope with tradeoffs between data quality and quantity, standardization of sampling methods, quantification of sampling effort, and mismatches in skills and expectations between data collectors and data users. The most useful atlases have a good measure of sampling effort; include data collected at a fine enough resolution to link to habitat variables of potential interest; have a sufficiently large sample size to work with in a multivariate context; and offer clear, quantitative indications of the quality of each record to allow for the needs of users who have specific demands for high-quality data. Main conclusions Atlases have an important role to play in biodiversity conservation and ideally should aim to offer reliable, high quality data that can withstand public, scientific and legal scrutiny.Centre of Excellencel for Invasion Biolog

Sequestration of precious and pollutant metals in biomass of cultured water hyacinth (Eichhornia crassipes)

The aim of this study was to investigate the overall root/shoot allocation of metal contaminants, the amount of metal removal by absorption and adsorption within or on the external root surfaces, the dose-response of water hyacinth metal uptake, and phytotoxicity. This was examined in a single-metal tub trial, using arsenic (As), gold (Au), copper (Cu), iron (Fe), mercury (Hg), manganese (Mn), uranium (U), and zinc (Zn). Iron and Mn were also used in low-, medium-, and high-concentration treatments to test their dose effect on water hyacinth’s metal uptake. Water hyacinth was generally tolerant to metallotoxicity, except for Cu and Hg. Over 80 % of the total amount of metals removed was accumulated in the roots, of which 30–52 % was adsorbed onto the root surfaces. Furthermore, 73–98% of the total metal assimilation by water hyacinth was located in the roots. The bioconcentration factor (BCF) of Cu, Hg, Au, and Zn exceeded the recommended index of 1000, which is used in selection of phytoremediating plants, but those of U, As, and Mn did not. Nevertheless, the BCF for Mn increased with the increase of Mn concentration in water. This suggests that the use of BCF index alone, without the consideration of plant biomass and metal concentration in water, is inadequate to determine the potential of plants for phytoremediation accurately. Thus, this study confirms that water hyacinth holds potential for a broad spectrum of phytoremediation roles. However, knowing whether these metals are adsorbed on or assimilated within the plant tissues as well as knowing their allocation between roots and shoots will inform decisions how to re-treat biomass for metal recovery, or the mode of biomass reduction for safe disposal after phytoremediation

Simulating tick distributions over sub-Saharan Africa: The use of observed and simulated climate surfaces

Aim: A broad suit of climate data sets is becoming available for use in predictive species modelling. We compare the efficacy of using interpolated climate surfaces [Center for Resource and Environmental Studies (CRES) and Climate Research Unit (CRU)] or high-resolution model-derived climate data [Division of Atmospheric Research limited-area model (DARLAM)] for predictive species modelling, using tick distributions from sub-Saharan Africa. Location: The analysis is restricted to sub-Saharan Africa. The study area was subdivided into 3000 grids cells with a resolution of 60 x 60 km. Methods: Species distributions were predicted using an established multivariate climate envelope modelling approach and three very different climate data sets. The recorded variance in the climate data sets was quantified by employing omnidirectional variograms. To further compare the interpolated tick distributions that flowed from using three climate data sets, we calculated true positive (TP) predictions, false negative (FN) predictions as well as the proportional overlaps between observed and modelled tick distributions. In addition, the effect of tick data set size on the performance of the climate data sets was evaluated by performing random draws of known tick distribution records without replacement. Results: The predicted distributions were consistently wider ranging than the known records when using any of the three climate data sets. However, the proportional overlap between predicted and known distributions varied as follows: for Rhipicephalus appendiculatus Neumann (Acari: Ixodidae), these were 60%, 60% and 70%; for Rhipicephalus longus Neumann (Acari: Ixodidae) 60%, 57% and 75%; for Rhipicephalus zambeziensis Walker, Norval & Corwin (Acari: Ixodidae) 57%, 51% and 62%, and for Rhipicephalus capensis Koch (Acari: Ixodidae) 70%, 60% and 60% using the CRES, CRU and DARLAM climate data sets, respectively. All data sets were sensitive to data size but DARLAM performed better when using smaller species data sets. At a 20% data subsample level, DARLAM was able to capture more than 50% of the known records and captured more than 60% of known records at higher subsample levels. Main conclusions: The use of data derived from high-resolution nested climate models (e.g. DARLAM) provided equal or even better species distribution modelling performance. As the model is dynamic and process based, the output data are available at the modelled resolution, and are not hamstrung by the sampling intensity of observed climate data sets (c. one sample per 30,000 km2 for Africa). In addition, when exploring the biodiversity consequences of climate change, these modelled outputs form a more useful basis for comparison with modelled future climate scenarios.Articl

Simulating tick distributions over sub-Saharan Africa: The use of observed and simulated climate surfaces

Aim: A broad suit of climate data sets is becoming available for use in predictive species modelling. We compare the efficacy of using interpolated climate surfaces [Center for Resource and Environmental Studies (CRES) and Climate Research Unit (CRU)] or high-resolution model-derived climate data [Division of Atmospheric Research limited-area model (DARLAM)] for predictive species modelling, using tick distributions from sub-Saharan Africa. Location: The analysis is restricted to sub-Saharan Africa. The study area was subdivided into 3000 grids cells with a resolution of 60 x 60 km. Methods: Species distributions were predicted using an established multivariate climate envelope modelling approach and three very different climate data sets. The recorded variance in the climate data sets was quantified by employing omnidirectional variograms. To further compare the interpolated tick distributions that flowed from using three climate data sets, we calculated true positive (TP) predictions, false negative (FN) predictions as well as the proportional overlaps between observed and modelled tick distributions. In addition, the effect of tick data set size on the performance of the climate data sets was evaluated by performing random draws of known tick distribution records without replacement. Results: The predicted distributions were consistently wider ranging than the known records when using any of the three climate data sets. However, the proportional overlap between predicted and known distributions varied as follows: for Rhipicephalus appendiculatus Neumann (Acari: Ixodidae), these were 60%, 60% and 70%; for Rhipicephalus longus Neumann (Acari: Ixodidae) 60%, 57% and 75%; for Rhipicephalus zambeziensis Walker, Norval & Corwin (Acari: Ixodidae) 57%, 51% and 62%, and for Rhipicephalus capensis Koch (Acari: Ixodidae) 70%, 60% and 60% using the CRES, CRU and DARLAM climate data sets, respectively. All data sets were sensitive to data size but DARLAM performed better when using smaller species data sets. At a 20% data subsample level, DARLAM was able to capture more than 50% of the known records and captured more than 60% of known records at higher subsample levels. Main conclusions: The use of data derived from high-resolution nested climate models (e.g. DARLAM) provided equal or even better species distribution modelling performance. As the model is dynamic and process based, the output data are available at the modelled resolution, and are not hamstrung by the sampling intensity of observed climate data sets (c. one sample per 30,000 km2 for Africa). In addition, when exploring the biodiversity consequences of climate change, these modelled outputs form a more useful basis for comparison with modelled future climate scenarios.Articl