Identifying forest ecosystem regions for agricultural use and conservation

ABSTRACT Balancing agricultural needs with the need to protect biodiverse environments presents a challenge to forestry management. An imbalance in resource production and ecosystem regulation often leads to degradation or deforestation such as when excessive cultivation damages forest biodiversity. Lack of information on geospatial biodiversity may hamper forest ecosystems. In particular, this may be an issue in areas where there is a strong need to reassign land to food production. It is essential to identify and protect those parts of the forest that are key to its preservation. This paper presents a strategy for choosing suitable areas for agricultural management based on a geospatial variation of Shannon's vegetation diversity index (SHDI). This index offers a method for selecting areas with low levels of biodiversity and carbon stock accumulation ability, thereby reducing the negative environmental impact of converting forest land to agricultural use. The natural forest ecosystem of the controversial 1997 Ex-Mega Rice Project (EMRP) in Indonesia is used as an example. Results showed that the geospatial pattern of biodiversity can be accurately derived using kriging analysis and then effectively applied to the delineation of agricultural production areas using an ecological threshold of SHDI. A prediction model that integrates a number of species and families and average annual rainfall was developed by principal component regression (PCR) to obtain a geospatial distribution map of biodiversity. Species richness was found to be an appropriate indicator of SHDI and able to assist in the identification of areas for agricultural use and natural forest management

Conservation planning in spatially and temporally dynamic marine environments

Pelagic ecosystems provide a significant and vital component of the ocean’s productivity and biodiversity. They are also heavily exploited and are currently the focus of numerous ecosystem-based management exercises. Over the past ten years there has been increasing enthusiasm for marine protected areas (MPAs) as a tool for pelagic conservation. However, there remains almost a complete absence of systematic conservation planning in the pelagic realm, both within exclusive economic zones and the high seas. Here we demonstrate the use of a decision support system to guide the implementation of MPAs that consider the physical and biological dynamics typical of the pelagic realm, and propose a method for integrative planning for pelagic and benthic conservation in the Southern Benguela ecosystem. Our approach was to maximize the representation of threatened species and key fisheries species within MPAs closed to fishing. In addition to representation, we consider MPA design to address the dynamics of the system using time series data of key oceanographic characteristics and abundance of small pelagic fish. We also discuss problems associated with offshore conservation, where the features of interest are ephemeral and dynamic. Our approach explicitly involves stakeholders and we incorporate socio-economic data into decision support tools

Environmental and biological monitoring for forecasting anchovy recruitment in the southern Benguela upwelling region

Environmental and biological sampling and monitoring have been carried out in the southern Benguela since 1988. The overall goal of this research is to investigate environmental factors affecting anchovy recruitment and to develop the ability to forecast anchovy recruitment from year-to-year using field data obtained during the spawning season (August to March). Sampling was conducted at three different temporal and spatial scales: during annual (November) broad-scale hydro-acoustic surveys to determine spawner biomass on the entire spawning ground and in the core transport and recruitment areas; during monthly surveys in the core spawning, transport and recruitment regions over two entire spawning seasons (1993/94 and 1994/95); and during weekly sampling (since 1995) along a single transect downstream from the spawning area. Annual surveys provide the best spatial coverage, but are inadequate for representing environmental conditions and anchovy spawning success over a prolonged season. Weekly sampling provides the best temporal coverage, but logistical constraints restrict information to a limited portion of the spawning area and a reduced number of variables. Monthly surveys provide intermediate coverage in time and space, but are expensive and labour-intensive. Forecasting anchovy recruitment has been based on two different approaches: the establishment of empirical relationships, and the development of rule-based expert systems. Forecasts from deterministic expert systems have compared well with final estimates of recruitment strength, and indicate that environmental and biological variables may be used in a structured way to forecast anchovy recruitment

Uninsurable investment risks and capital income taxation

Investment risk, Borrowing constraints, Capital accumulation, Capital income taxation, E21, E22, E62, G32, H24, H25,