Final report : verification of bay productivity measurement by remote sensors

From University of Texas at Austin Marine Science Institute to Texas Water Development BoardInteragency cooperative contract TWDB contract no. IA03-483-003July 2004Ecosystem function in estuarine environments is known to be an important indicator of ecosystem health and productivity. There is a need to quantify estuarine ecosystem function variability and link to freshwater inflow to enable better management of ecosystem health and productivity. An important and quantifiable component of ecosystem function is ecosystem metabolism. Results indicate that open water methods were more appropriate than light-dark bottle methods for measuring net ecosystem metabolism in shallow water estuarine ecosystems because of the large contribution of benthos, which is ignored in water bottles. Spatial and temporal variability in net ecosystem metabolism was found. Spatial variability was attributed to differences in benthic habitats and/or station locations with respect to freshwater inflow point sources. Temporal variability in net ecosystem metabolism may be driven by differences in seasonal temperatures and freshwater inflow differences on seasonal time scales. Net ecosystem metabolism was directly related to amounts of freshwater inflow. The strength of this relationship depended on proximity to freshwater sources. Future studies of whole ecosystem metabolism in shallow estuarine ecosystems should employ open water methods and should strive to link other dynamic environmental conditions, such as temperature or irradiance, to ecosystem health, function, and productivity.Marine Scienc

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures

Biodiversity and ecosystem function in soil

1. Soils are one of the last great frontiers for biodiversity research and are home to an extraordinary range of microbial and animal groups. Biological activities in soils drive many of the key ecosystem processes that govern the global system, especially in the cycling of elements such as carbon, nitrogen and phosphorus. 2. We cannot currently make firm statements about the scale of biodiversity in soils, or about the roles played by soil organisms in the transformations of organic materials that underlie those cycles. The recent UK Soil Biodiversity Programme (SBP) has brought a unique concentration of researchers to bear on a single soil in Scotland, and has generated a large amount of data concerning biodiversity, carbon flux and resilience in the soil ecosystem. 3. One of the key discoveries of the SBP was the extreme diversity of small organisms: researchers in the programme identified over 100 species of bacteria, 350 protozoa, 140 nematodes and 24 distinct types of arbuscular mycorrhizal fungi. Statistical analysis of these results suggests a much greater 'hidden diversity'. In contrast, there was no unusual richness in other organisms, such as higher fungi, mites, collembola and annelids. 4. Stable-isotope (C-13) technology was used to measure carbon fluxes and map the path of carbon through the food web. A novel finding was the rapidity with which carbon moves through the soil biota, revealing an extraordinarily dynamic soil ecosystem. 5. The combination of taxonomic diversity and rapid carbon flux makes the soil ecosystem highly resistant to perturbation through either changing soil structure or removing selected groups of organisms

Development of scenarios for land cover, population density, impervious cover, and conservation in New Hampshire, 2010–2100

Future changes in ecosystem services will depend heavily on changes in land cover and land use, which, in turn, are shaped by human activities. Given the challenges of predicting long-term changes in human behaviors and activities, scenarios provide a framework for simulating the long-term consequences of land-cover change on ecosystem function. As input for process-based models of terrestrial and aquatic ecosystem function, we developed scenarios for land cover, population density, and impervious cover for the state of New Hampshire for 2020–2100. Key drivers of change were identified through information gathered from six sources: historical trends, existing plans relating to New Hampshire’s land-cover future, surveys, existing population scenarios, key informant interviews with diverse stakeholders, and input from subject-matter experts. Scenarios were developed in parallel with information gathering, with details added iteratively as new questions emerged. The final scenarios span a continuum from spatially dispersed development with a low value placed on ecosystem services (Backyard Amenities) to concentrated development with a high value placed on ecosystem services (the Community Amenities family). The Community family includes two population scenarios (Large Community and Small Community), to be combined with two scenarios for land cover (Protection of Wildlands and Promotion of Local Food), producing combinations that bring the total number of scenarios to six. Between Backyard Amenities and Community Amenities is a scenario based on linear extrapolations of current trends (Linear Trends). Custom models were used to simulate decadal change in land cover, population density, and impervious cover. We present raster maps and proportion of impervious cover for HUC10 watersheds under each scenario and discuss the trade-offs of our translation and modeling approach within the context of contemporary scenario projects

Enhancing the function and provisioning of ecosystem services in agriculture: Agroecological principles

Agroecology is essentially based on the use of biodiversity and ecosystem services in agricultural production, and thus represents a true rupture from the way agriculture has been seen and analysed by mainstream science for over a century. Agroecology does not have a consensual definition; it represents a conceptual space to think about agricultural sustainability through strong interactions between science and society with a wealth of new concepts, questions and tools. Among the diverse 'incarnations' of agroecology, the lowest common denominator is found at plot level. The basic and common principle is to increase biomass production by enhancing the services provided by living organisms and by taking the optimal advantage of natural resources, especially those which are abundant and free (e.g. solar radiation, atmospheric carbon and nitrogen, rainfall). Agroecology aims to manage, and in some cases to increase, production in a sustainable and resilient way that will maintain and improve the natural capital in the long term. It will enhance the ecological processes and interactions of functional biodiversity above- and below-ground, over space and in time, by both intensifying biological cycles for nutrients, water and energy, and controlling the aggressors of crops. Because ecosystem services are involved, agroecology has long been working on larger scales (i.e. farms, landscapes, watershed basins, value chains, food systems). Agroecology has had a deep engagement with interdisciplinary research, in particular focusing on some of the drivers of agricultural development such as food industries and distribution, consumer health, public policies, etc. Because agroecology strongly depends on locally available natural resources including agrobiodiversity, it cannot prescribe ready-to-use technical packages to farmers. Rather, agroecological models and solutions are built by mingling scientific and traditional knowledge and by strongly relying on local learning and innovation processes. With the many challenges ahead, agroecology represents a true alternative avenue for agricultural transformation; while it questions the role and practices of agricultural research and calls for a significant renewal

Optimal Pest Control in Agriculture

Based on economic methodology we model an ecosystem with two species in predator-prey relationship: mice feed on grain and grain feeds on a resource. With optimizing behaviour of individual organisms a short-run ecosystem equilibrium is defined and characterized that depends on the farmer’s use of fertilizer and on the mice population which, in turn, is affected by pesticides. In that way, a microfounded agricultural production function is derived. Linking a sequence of short-run ecosystem equilibria yields the growth function of the mice population which is thus derived rather than assumed. In each period the farmer harvests all grain in excess of some given amount of seed. If she maximizes her present-value profits, optimal farming is shown to depend on the prices of pesticide and grain. It is either optimal to use no pesticide or a moderate amount of pesticide or to apply a chattering control. Pest eradication is never optimal. On the other hand, if the farmer takes into account steady state mice populations only, it may be optimal to eradicate mice or to use no or a moderate amount of pesticide depending on prices as well as on the shape of the grain production function which is determined by micro parameters of grain reproduction.pesticides, agriculture, predator-prey, chattering pest control