An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales.

To use models of species distributions effectively in conservation planning, it is important to determine the predictive accuracy of such models. Extensive modelling of the distribution of vascular plant and vertebrate fauna species within north-east New South Wales has been undertaken by linking field survey data to environmental and geographical predictors using logistic regression. These models have been used in the development of a comprehensive and adequate reserve system within the region. We evaluate the predictive accuracy of models for 153 small reptile, arboreal marsupial, diurnal bird and vascular plant species for which independent evaluation data were available. The predictive performance of each model was evaluated using the relative operating characteristic curve to measure discrimination capacity. Good discrimination ability implies that a model's predictions provide an acceptable index of species occurrence. The discrimination capacity of 89% of the models was significantly better than random, with 70% of the models providing high levels of discrimination. Predictions generated by this type of modelling therefore provide a reasonably sound basis for regional conservation planning. The discrimination ability of models was highest for the less mobile biological groups, particularly the vascular plants and small reptiles. In the case of diurnal birds, poor performing models tended to be for species which occur mainly within specific habitats not well sampled by either the model development or evaluation data, highly mobile species, species that are locally nomadic or those that display very broad habitat requirements. Particular care needs to be exercised when employing models for these types of species in conservation planning.     

Assessing state-wide biodiversity in the Florida Gap analysis project

The Florida Gap (Fl-Gap) project provides an assessment of the degree to which native animal species and natural communities are or are not represented in existing conservation lands. Those species and communities not adequately represented in areas being managed for native species constitute 'gaps' in the existing network of conservation lands. The United States Geological Survey Gap Analysis Program is a national effort and so, eventually, all 50 states will have completed it. The objective of Fl-Gap was to provide broad geographic information on the status of terrestrial vertebrates, butterflies, skippers and ants and their respective habitats to address the loss of biological diversity. To model the distributions and potential habitat of all terrestrial species of mammals, breeding birds, reptiles, amphibians, butterflies, skippers and ants in Florida, natural land cover was mapped to the level of dominant or co-dominant plant species. Land cover was classified from Landsat Thematic Mapper (TM) satellite imagery and auxiliary data such as the national wetlands inventory (NWI), soils maps, aerial imagery, existing land use/land cover maps, and on-the-ground surveys. Wildlife distribution models were produced by identifying suitable habitat for each species within that species' range. Mammalian models also assessed a minimum critical area required for sustainability of the species' population. Wildlife species richness was summarized against land stewardship ranked by an area's mandates for conservation protection.     

A Multi-scale Spatial Analysis of Native and Exotic Plant Species Richness Within a Mixed-Disturbance Oak Savanna Landscape

Impacts of human land use pose an increasing threat to global biodiversity. Resource managers must respond rapidly to this threat by assessing existing natural areas and prioritizing conservation actions across multiple spatial scales. Plant species richness is a useful measure of biodiversity but typically can only be evaluated on small portions of a given landscape. Modeling relationships between spatial heterogeneity and species richness may allow conservation planners to make predictions of species richness patterns within unsampled areas. We utilized a combination of field data, remotely sensed data, and landscape pattern metrics to develop models of native and exotic plant species richness at two spatial extents (60- and 120-m windows) and at four ecological levels for northwestern Ohio’s Oak Openings region. Multiple regression models explained 37–77 % of the variation in plant species richness. These models consistently explained more variation in exotic richness than in native richness. Exotic richness was better explained at the 120-m extent while native richness was better explained at the 60-m extent. Land cover composition of the surrounding landscape was an important component of all models. We found that percentage of human-modified land cover (negatively correlated with native richness and positively correlated with exotic richness) was a particularly useful predictor of plant species richness and that human-caused disturbances exert a strong influence on species richness patterns within a mixed-disturbance oak savanna landscape. Our results emphasize the importance of using a multi-scale approach to examine the complex relationships between spatial heterogeneity and plant species richness.     

Modelling the conversion of Colombian lowland ecosystems since 1940: drivers, patterns and rates

In biologically mega-diverse countries that are undergoing rapid human landscape transformation, it is important to understand and model the patterns of land cover change. This problem is particularly acute in Colombia, where lowland forests are being rapidly cleared for cropping and ranching. We apply a conceptual model with a nested set of a priori predictions to analyse the spatial and temporal patterns of land cover change for six 50-100 km(2) case study areas in lowland ecosystems of Colombia. Our analysis included soil fertility, a cost-distance function, and neighbourhood of forest and secondary vegetation cover as independent variables. Deforestation and forest regrowth are tested using logistic regression analysis and an information criterion approach to rank the models and predictor variables. The results show that: (a) overall the process of deforestation is better predicted by the full model containing all variables, while for regrowth the model containing only the auto-correlated neighbourhood terms is a better predictor; (b) overall consistent patterns emerge, although there are variations across regions and time; and (c) during the transformation process, both the order of importance and significance of the drivers change. Forest cover follows a consistent logistic decline pattern across regions, with introduced pastures being the major replacement land cover type. Forest stabilizes at 2-10% of the original cover, with an average patch size of 15.4 (+/-9.2)ha. We discuss the implications of the observed patterns and rates of land cover change for conservation planning in countries with high rates of deforestation.     

Estimation of Evapotranspiration Across the Conterminous United States Using a Regression With Climate and Land‐Cover Data1

Sanford, Ward E. and David L. Selnick, 2012. Estimation of Evapotranspiration Across the Conterminous United States Using a Regression with Climate and Land‐Cover Data. Journal of the American Water Resources Association (JAWRA) 1‐14. DOI: 10.1111/jawr.12010 Abstract: Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water‐balance method was combined with a climate and land‐cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971‐2000 across the U.S. to obtain long‐term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land‐cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land‐cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land‐cover data can also improve those predictions. Using the climate and land‐cover data at an 800‐m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land‐cover data are plentiful.     

The Ecological Modelling System of NELUP

Within the NERC/ESRC Land Use Programme ecological change is assumed to be driven principally by agronomic activity. Land use change is defined as being of two types depending on whether there is a complete change of cover or a modification of an existing cover. Some agronomic activities lead to quantitative changes between land cover types and others to more subtle qualitative changes within land cover types. A modelling system based on three distinct models is described for predicting the effects of the two types of change in land cover on the distribution of plants, invertebrates and birds. The potential use of the system in analysing problems of land use change is illustrated using a simple scenario based on changing nitrogenous fertilizer regimes in lowland agriculture.     

Modelling land use change with generalized linear models--a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana

This paper develops an approach to modelling land use change that links model selection and multi-model inference with empirical models and GIS. Land use change is frequently studied, and understanding gained, through a process of modelling that is an empirical analysis of documented changes in land cover or land use patterns. The approach here is based on analysis and comparison of multiple models of land use patterns using model selection and multi-model inference. The approach is illustrated with a case study of rural housing as it has developed for part of Gallatin County, Montana, USA. A GIS contains the location of rural housing on a yearly basis from 1860 to 2000. The database also documents a variety of environmental and socio-economic conditions. A general model of settlement development describes the evolution of drivers of land use change and their impacts in the region. This model is used to develop a series of different models reflecting drivers of change at different periods in the history of the study area. These period specific models represent a series of multiple working hypotheses describing (a) the effects of spatial variables as a representation of social, economic and environmental drivers of land use change, and (b) temporal changes in the effects of the spatial variables as the drivers of change evolve over time. Logistic regression is used to calibrate and interpret these models and the models are then compared and evaluated with model selection techniques. Results show that different models are 'best' for the different periods. The different models for different periods demonstrate that models are not invariant over time which presents challenges for validation and testing of empirical models. The research demonstrates (i) model selection as a mechanism for rating among many plausible models that describe land cover or land use patterns, (ii) inference from a set of models rather than from a single model, (iii) that models can be developed based on hypothesised relationships based on consideration of underlying and proximate causes of change, and (iv) that models are not invariant over time.     

Predicting Periphyton Cover Frequency Distributions across New Zealand's Rivers

Regression models of mean and mean annual maximum (MAM) cover were derived for two categories of periphyton cover (filaments and mats) using 22 years of monthly monitoring data from 78 river sites across New Zealand. Explanatory variables were derived from observations of water quality variables, hydrology, shade, bed sediment grain size, temperature, and solar radiation. The root mean square errors of these models were large (75‐95% of the mean of the estimated values). The at‐site frequency distributions of periphyton cover were approximated by the exponential distribution, which has the mean cover as its single parameter. Independent predictions of cover distributions at all sites were calculated using the mean predicted by the regression model and the theoretical exponential distribution. The probability that cover exceeds specified thresholds and estimates of MAM cover, based on the predicted distributions, had large uncertainties (~80‐100%) at the site scale. However, predictions aggregated by classes of an environmental classification accurately predicted the proportion of sites for which cover exceeded nominated criteria in the classes. The models are useful for assessing broad‐scale patterns in periphyton cover and for estimating changes in cover with changes in nutrients, hydrological regime, and light.     

Forecasts of habitat loss and fragmentation due to urban growth are sensitive to source of input data

The conversion of natural habitat to urban settlements is a primary driver of biodiversity loss, and species' persistence is threatened by the extent, location, and spatial pattern of development. Urban growth models are widely used to anticipate future development and to inform conservation management, but the source of spatial input to these models may contribute to uncertainty in their predictions. We compared two sources of historic urban maps, used as input for model calibration, to determine how differences in definition and scale of urban extent affect the resulting spatial predictions from a widely used urban growth model for San Diego County, CA under three conservation scenarios. The results showed that rate, extent, and spatial pattern of predicted urban development, and associated habitat loss, may vary substantially depending on the source of input data, regardless of how much land is excluded from development. Although the datasets we compared both represented urban land, different types of land use/land cover included in the definition of urban land and different minimum mapping units contributed to the discrepancies. Varying temporal resolution of the input datasets also contributed to differences in projected rates of development. Differential predicted impacts to vegetation types illustrate how the choice of spatial input data may lead to different conclusions relative to conservation. Although the study cannot reveal whether one dataset is better than another, modelers should carefully consider that geographical reality can be represented differently, and should carefully choose the definition and scale of their data to fit their research objectives.     

Carbon in the Vegetation and Soils of Great Britain

•The total amount of carbon held by vegetation in Great Britain is estimated to be 114 Mtonnes. •Woodlands and forests hold 80% of the G.B. total although they occupy only about 11% of the rural land area. Broadleaf species hold about 50% of the carbon in woodlands and forests. •A map of carbon in the vegetation of Great Britain at 1 km×1 km resolution based on land cover identified in the I.T.E. Land Cover Map is presented. The predominant location of vegetation carbon is the broadleaved woodlands of southern England. •The amount of carbon in the soils of Great Britain is estimated to be 9838 Mt (6948 Mt in Scotland and 2890 Mt in England and Wales). •In Scotland, most soil carbon is in blanket peats, whereas most soil carbon is in stagnogley soils in England and Wales. •The carbon content of the soils of Great Britain is mapped at 1 km×1 km resolution. Scottish peat soils have the greatest density of carbon and in total contain 4523 Mt of carbon, 46% of the G.B. total.     

STREAM HEALTH RANKINGS PREDICTED BY SATELLITE DERIVED LAND COVER METRICS1

ABSTRACT: Land cover and land use change have long been known to influence the chemical, physical, and biological characteristics of streams. This study makes use of land cover maps derived from fine resolution satellite imagery and an extensive stream quality dataset to determine the relationship between small watershed health rankings and land cover composition and configuration. Landscape metrics were derived from digital impervious surface area (ISA), tree cover (percent), and agricultural crop maps within Montgomery County, Maryland. Watershed rankings were developed by state and county collaborators (MD‐DNR and MCDEP) using extensive biological and chemical measurements. In stepwise logistic regression models the factors accounting for the most variation in stream health ranking were the percent ISA, followed by the percent of tree cover. Riparian buffer zone tree cover was also a significant predictor. Of the metrics that considered the spatial configuration of the landscape, a contagion index and the percent of ISA in the flow path from the ISA to the stream were also found to be significant predictors of stream health. Despite limited ability to characterize landscape configuration or narrow riparian buffer zone vegetation with coarser resolution imagery (from Landsat), model results were not significantly different from those based on the use of fine‐resolution ISA information, suggesting that broader area applications of the approach are possible. The results indicate that management practices designed to improve stream water quality should focus on the amount of ISA and tree cover in both the watershed and within the buffer zone.     

Anthropogenic effects on the biodiversity of riparian wetlands of a northern temperate landscape

Land uses such as forestry and agriculture are presumed to degrade the biodiversity of riparian wetlands in the northern temperate regions of the United States. In order to improve land use decision making in this landscape, floral and faunal communities of 15 riparian wetlands associated with low-order streams were related to their surrounding land cover to establish which organismal groups are affected by anthropogenic disturbance and whether these impacts are scale-specific. Study sites were chosen to represent a gradient of disturbance. Vascular plants of wet meadow and shrub carr communities, aquatic macro-invertebrates, amphibians, fish and birds were surveyed, and total abundance, species richness and Shannon diversity were calculated. For each site, anthropogenic disturbances were evaluated at local and landscape scales (500, 1000, 2500 and 5000 m from the site and the site catchment) from field surveys and a geographic information system (GIS). Land use data were grouped into six general land use types: urban, cultivated, rangeland, forest, wetland and water. Shrub carr vegetation, bird and fish diversity and richness generally decrease with increasing cultivation in the landscape. Amphibian abundance decreases and fish abundance increases as the proportions of open water and rangeland increases; bird diversity and richness increase with forest and wetland extent in the landscape. Wet meadow vegetation, aquatic macro-invertebrates, amphibians and fish respond to local disturbances or environmental conditions. Shrub carr vegetation, amphibians and birds are influenced by land use at relatively small landscape scales (500 and 1000 m), and fish respond to land use at larger landscape scales (2500, 5000 m and the catchment). Effective conservation planning for these riparian wetlands requires assessment of multiple organismal groups, different types of disturbance and several spatial scales.1998 Academic Press     

Controls on Nutrients Across a Prairie Stream Watershed: Land Use and Riparian Cover Effects

Nutrient inputs generally are increased by human-induced land use changes and can lead to eutrophication and impairment of surface waters. Understanding the scale at which land use influences nutrient loading is necessary for the development of management practices and policies that improve water quality. The authors assessed the relationships between land use and stream nutrients in a prairie watershed dominated by intermittent stream flow in the first-order higher elevation reaches. Total nitrogen, nitrate, and phosphorus concentrations were greater in tributaries occupying the lower portions of the watershed, closely mirroring the increased density of row crop agriculture from headwaters to lower-elevation alluvial areas. Land cover classified at three spatial scales in each sub-basin above sampling sites (riparian in the entire catchment, catchment land cover, and riparian across the 2 km upstream) was highly correlated with variation in both total nitrogen (r² = 53%, 52%, and 49%, respectively) and nitrate (r² = 69%, 65%, and 56%, respectively) concentrations among sites. However, phosphorus concentrations were not significantly associated with riparian or catchment land cover classes at any spatial scale. Separating land use from riparian cover in the entire watershed was difficult, but riparian cover was most closely correlated with in-stream nutrient concentrations. By controlling for land cover, a significant correlation of riparian cover for the 2 km above the sampling site with in-stream nutrient concentrations could be established. Surprisingly, land use in the entire watershed, including small intermittent streams, had a large influence on average downstream water quality although the headwater streams were not flowing for a substantial portion of the year. This suggests that nutrient criteria may not be met only by managing permanently flowing streams.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

Bird distributions relative to remotely sensed habitats in Great Britain: towards a framework for national modelling

This paper develops a comprehensive and objective picture of bird distributions relative to habitats across Britain. Bird species presence/absence data from an extensive field survey and habitat data from the remotely sensed UK Land Cover Map 2000 were analysed in 36,920 tetrads (2 kmx2 km) across Britain (a 65% sample of Britain's c. 240,000 km2). Cluster analysis linked birds to generalised landscapes based on distinctive habitat assemblages. Maps of the clusters showed strong regional patterns associated with the habitat assemblages. Cluster centroid coordinates for each bird species and each habitat were combined across clusters to derive individualised bird-habitat preference indices and examine the importance of individual habitats for each bird species. Even rare species and scarce habitats showed successful linkages. Results were assessed against published accounts of bird-habitat relations. Objective corroboration strongly supported the associations. Relatively scarce coastal and wetland habitats proved particularly important for many birds. However, extensive arable farmland and woodland habitats were also favoured by many species, despite reported declines in bird numbers in these habitats. The fact that habitat-specialists do not or cannot move habitat is perhaps a reason for declining numbers where habitats have become unsuitable. This study showed that there are unifying principles determining bird-habitat relations which apply and can be quantified at the national scale, and which corroborate and complement the cumulative knowledge of many and varied surveys and ecological studies. This 'generality' suggests that we may be able, reliably and objectively, to integrate and scale up such disparate studies to the national scale, using this generalised framework. It also suggests the potential for a landscape ecology approach to bird-habitat analyses. Such developments will be important steps in building models to develop and test the sustainable management of landscapes for birds.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS

For landslide susceptibility mapping, this study applied and verified a Bayesian probability model, a likelihood ratio and statistical model, and logistic regression to Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite imagery and field surveys; and a spatial database was constructed from topographic maps, soil type, forest cover, geology and land cover. The factors that influence landslide occurrence, such as slope gradient, slope aspect, and curvature of topography, were calculated from the topographic database. Soil texture, material, drainage, and effective depth were extracted from the soil database, while forest type, diameter, and density were extracted from the forest database. Land cover was classified from Landsat TM satellite imagery using unsupervised classification. The likelihood ratio and logistic regression coefficient were overlaid to determine each factors rating for landslide susceptibility mapping. Then the landslide susceptibility map was verified and compared with known landslide locations. The logistic regression model had higher prediction accuracy than the likelihood ratio model. The method can be used to reduce hazards associated with landslides and to land cover planning. Note: This version was published online in June 2005 with the cover date of August 2004.     

Detecting Temporal Change in Watershed Nutrient Yields

Meta-analyses reveal that nutrient yields tend to be higher for watersheds dominated by anthropogenic uses (e.g., urban, agriculture) and lower for watersheds dominated by natural vegetation. One implication of this pattern is that loss of natural vegetation will produce increases in watershed nutrient yields. Yet, the same meta-analyses also reveal that, absent land-cover change, watershed nutrient yields vary from one year to the next due to many exogenous factors. The interacting effects of land cover and exogenous factors suggest nutrient yields should be treated as distributions, and the effect of land-cover change should be examined by looking for significant changes in the distributions. We compiled nutrient yield distributions from published data. The published data included watersheds with homogeneous land cover that typically reported two or more years of annual nutrient yields for the same watershed. These data were used to construct statistical models, and the models were used to estimate changes in the nutrient yield distributions as a result of land-cover change. Land-cover changes were derived from the National Land Cover Database (NLCD). Total nitrogen (TN) yield distributions increased significantly for 35 of 1550 watersheds and decreased significantly for 51. Total phosphorus (TP) yield distributions increased significantly for 142 watersheds and decreased significantly for 17. The amount of land-cover change required to produce significant shifts in nutrient yield distributions was not constant. Small land-cover changes led to significant shifts in nutrient yield distributions when watersheds were dominated by natural vegetation, whereas much larger land-cover changes were needed to produce significant shifts when watersheds were dominated by urban or agriculture. We discuss our results in the context of the Clean Water Act.     

Temporal and Spatial Relationships Between Watershed Land Use and Salt Marsh Disturbance in a Pacific Estuary

Historical and recent remote sensing data can be used to address temporal and spatial relationships between upland land cover and downstream vegetation response at the watershed scale. This is demonstrated for sub-watersheds draining into Elkhorn Slough, California, where salt marsh habitat has diminished because of the formation of sediment fans that support woody riparian vegetation. Multiple regression models were used to examine which land cover variables and physical properties of the watershed most influenced sediment fan size within 23 sub-watersheds (1.4 ha to 200 ha). Model explanatory power increased (adjusted R(2) = 0.94 vs. 0.75) among large sub-watersheds (>10 ha) and historical watershed variables, such as average farmland slope, flowpath slope, and flowpath distance between farmland and marsh, were significant. It was also possible to explain the increase in riparian vegetation by historical watershed variables for the larger sub-watersheds. Sub-watershed area is the overriding physical characteristic influencing the extent of sedimentation in a salt marsh, while percent cover of agricultural land use is the most influential land cover variable. The results also reveal that salt marsh recovery depends on relative cover of different land use classes in the watershed, with greater chances of recovery associated with less intensive agriculture. This research reveals a potential delay between watershed impacts and wetland response that can be best revealed when conducting multi-temporal analyses on larger watersheds.     

Achieving nature conservation objectives: problems and opportunities with economics

This paper considers the use of economic analysis in nature conservation within the Nature Conservancy Council (NCC). The development of official nature conservation in Britain and the relationship to land use changes is described, and the focus on site safeguard illustrated by declines in distributions and viability of populations of some species. A restatement of nature conservation objectives is proposed and the role of economic analysis in developing policies to secure these described. The use of economics by the NCC and the lessons learned from this are outlined. The implication of the commitment to sustainability is then discussed and concerns over how environmental valuation is used in light of this described. The paper concludes that economic analysis is a powerful tool for integrating nature conservation into wider rural policies and for the development of measures to secure contributions by other interests to meeting nature conservation objectives.