Alessandro Gimona

Mapping hotspots of multiple landscape functions: a case study on farmland afforestation in Scotland

Many conservation and restoration efforts in developed countries are increasingly based on the premise of recognising and stimulating more ‘multi-functionality’ in agricultural landscapes. Public policy making is often a pragmatic process that involves efforts to negotiate trade-offs between the potentially conflicting demands of various stakeholders. Conservationists’ efforts to influence policy making, can therefore benefit from any tool that will help them to identify other socio-economic functions or values that coincide with good ecological conservation options. Various types of socio-economic objectives have in recent years been mapped across landscapes and so there are now important opportunities to explore the spatial heterogeneity of these diverse functions across the wider landscape in search of potential spatial synergies, i.e. ‘multiple win locations’ or multifunctional ‘hotspots’.This paper explores the potential occurrence of such synergies within the agricultural landscape of northeast Scotland and evaluates an existing woodland planting policy using and combining three different policy objectives. Our results show that there are indeed broad areas of the studied landscape where multiple objectives (biodiversity, visual amenity and on-site recreation potential) could be achieved simultaneously (hotspots), and that the case study which we evaluate (the Farm Woodland Premium Scheme) could be much better spatially targeted with regards to each individual objective as well as with regards to these hotspots of multifunctionality.

Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes

An integrated methodology, based on linking Bayesian belief networks (BBN) with GIS, is proposed for combining available evidence to help forest managers evaluate implications and trade-offs between forest production and conservation measures to preserve biodiversity in forested habitats. A Bayesian belief network is a probabilistic graphical model that represents variables and their dependencies through specifying probabilistic relationships. In spatially explicit decision problems where it is difficult to choose appropriate combinations of interventions, the proposed integration of a BBN with GIS helped to facilitate shared understanding of the human-landscape relationships, while fostering collective management that can be incorporated into landscape planning processes. Trades-offs become more and more relevant in these landscape contexts where the participation of many and varied stakeholder groups is indispensable. With these challenges in mind, our integrated approach incorporates GIS-based data with expert knowledge to consider two different land use interests - biodiversity value for conservation and timber production potential - with the focus on a complex mountain landscape in the French Alps. The spatial models produced provided different alternatives of suitable sites that can be used by policy makers in order to support conservation priorities while addressing management options. The approach provided provide a common reasoning language among different experts from different backgrounds while helped to identify spatially explicit conflictive areas.

A conditional simulation of acoustic survey data: advantages and potential pitfalls

Standard geostatistical techniques provide effective methods for estimating the global abundance and precision of a variable of interest, for mapping its spatial distribution and for describing its spatial structure. In the case of acoustic survey data, however, obtaining a measure of precision of the global abundance estimate is confounded by the combination of variances from the interpolation of both the acoustic data and the concomitant fish length data. Even if the global estimation variance could be calculated, the distribution of the estimation error is not known and so confidence intervals cannot be determined. Furthermore, kriged distribution maps, in minimising the estimation variance, tend to smooth out local details of the attribute’s spatial variation: small values can be overestimated and larger ones underestimated, such that the kriged map is smoother than reality. This can lead to serious shortcomings when trying to detect patterns of extreme attribute values, such as the high densities encountered in some fish schools. Stochastic geostatistical simulations, conditional on sampled locations, provide a solution to many of these problems. They can deliver a measure of uncertainty for local (density) estimates, a confidence interval estimation for the global mean density, and finally, reproduce global statistics, such as the sample histogram and variogram. In so doing, they also provide maps of the attribute, which are spatially realistic because the variogram is reproduced; these are generated as a number of equiprobable realisations. In the present paper, we apply these techniques to acoustic data from an acoustic survey of North Sea herring. Sequential gaussian simulations are used to generate realisations for fish length and values of the nautical area scattering coefficient. These are then combined to produce realisations of herring density. The combined set of multiple realisations is then used to provide confidence intervals for the global abundance estimate: 95% of the herring abundance estimates are between 5677 and 6271 millions of individuals. Although the method presented in this paper contributes to the assessment of total uncertainty for acoustic surveys, the approach may have suffered from bias due to the use of off-the-shelf data transformation algorithms on fisheries acoustic data, which are often very positively skewed. We discuss this limitation and propose corrections for future work.

Nonlinearities in biodiversity incentive schemes: A study using an integrated agent-based and metacommunity model

We report results from over 20,000 runs of a coupled agent-based model of land use change and species metacommunity model. We explored the effect of increasing government incentive to improve biodiversity, in the context of other influences on land manager decision-making: aspirations, input costs, and price variability. The experiments test the four kinds of policy varying along two dimensions: activity-versus-outcome-based incentive, and individual-versus-collective incentive. The results from the experiments using boundedly rational agents, and comparison with profit-maximisation reveal thresholds in incentive schemes, where a sharp increase in environmental benefit occurs for a small increase in incentive. Further, the context affects the level of incentive at which turning points occur, and the degree of effect. Variability in outcome can also change with incentive and context, and some evidence suggests that environmental benefits are not always monotone increasing functions of incentives. Intuitively, if the incentive signal is large enough, land managers will farm the subsidy; and if the subsidy does not exactly match desired landscape outcomes, deterioration in environmental benefits may occur for higher incentives. Our results, whilst they suggest that outcome-based incentives may be more robust than activity-based, also highlight the importance of context in determining the success of agri-environmental incentive schemes. As such, they lend theoretical support to schemes, such as the Scottish Rural Development Programme, that include a localised component. Highlights? We examine the effect of increasing agri-environmental incentives on biodiversity. ? Experiments are conducted using a coupled agent-based and metacommunity model. ? Results show a nonlinear relationship between incentive and biodiversity. ? Scenarios often show declining biodiversity at high incentives. ? Context-sensitivity supports schemes designed to include a local component.

Effects of landscape configuration on mapping ecosystem service capacity: a review of evidence and a case study in Scotland

ContextHumans structure landscapes for the production of food, fibre and fuel, commonly resulting in declines of non-provisioning ecosystem services (ESs). Heterogeneous landscapes are capable of providing multiple ESs, and landscape configuration—spatial arrangement of land cover in the landscape—is expected to affect ES capacity. However, the majority of ES mapping studies have not accounted for landscape configuration.ObjectivesOur objective is to assess and quantify the relevance of configuration for mapping ES capacity. A review of empirical evidence for configuration effects on the capacity of ten ESs reveals that for four ESs configuration is relevant but typically ignored in ES quantification. For four ESs we quantify the relevance of configuration for mapping ESs using Scotland as a case study.MethodsEach ES was quantified through modelling, respectively ignoring or accounting for configuration. The difference in ES capacity between the two ES models was determined at multiple spatial scales.ResultsConfiguration affected the capacity of all four ESs mapped, particularly at the cell and watershed scale. At the scale of Scotland most local effects averaged out. Flood control and sediment retention responded strongest to configuration. ESs were affected by different aspects of configuration, thus requiring specific methods for mapping each ES.ConclusionsAccounting for configuration is important for the assessment of certain ESs at the cell and watershed scale. Incorporating configuration in landscape management provides opportunities for spatial optimization of ES capacity, but the diverging response of ESs to configuration suggests that accounting for configuration involves trade-offs between ESs.

Rufous-legged Owl (Strix rufipes) and Austral Pygmy Owl (Glaucidium nanum) stand use in a gradient of disrupted and old growth Andean temperate forests, Chile

We studied how human induced structural changes in forests affect stand use of the Rufous-legged Owl (forest-specialist) and the Austral Pygmy Owl (forest-facultative), in a gradient from lowland disrupted forests to protected Andean forests in Chile. We also tested if the calls of one species influenced the calling behaviour of the other. We detected a total of 34 Rufous-legged Owls and 21 Austral Pygmy Owls during the four seasons. Rufous-legged Owls were found principally in old growth Araucaria-Nothofagus stands (32.4%), and Pygmy Owls in old growth evergreen stands (52.4%). For both species there was a seasonal effect on call response, with a drop in responses in autumn and winter. Our models suggested that Rufous-legged Owls inhabit a more specific range of habitat characteristics than Pygmy Owls. The former selected stands with tall trees, relatively low tree density, and high bamboo density. Pygmy Owls selected stands with tall trees and relatively high tree density. There was no evidence that either species influenced the calling behaviour of the other, suggesting no negative association between use of a territory by the two species. Our results emphasize the importance of structural components of old growth forests for both species, but also the relevance of stands surrounding protected areas. Estudiamos cómo los cambios estructurales en el bosque, inducidos por el ser humano, afectan el uso de hábitat del concón (especialista de bosque) y del chuncho (facultativo de bosque), en un gradiente desde bosques perturbados en zonas bajas hasta bosques andinos protegidos de Chile. También evaluamos si los llamados de una especie influencian el comportamiento de vocalización de la otra. Detectamos un total de 34 concones y 21 chunchos para las cuatro estaciones del año. Los concones fueron registrados principalmente en bosques antiguos de Araucaria-Nothofagus (32.4%), y los chunchos en bosques antiguos siempre-verdes (52.4%). Para ambas especies hubo una disminución de la actividad en otoño e invierno. Nuestros modelos sugirieron que el concón tiene requerimientos de hábitat más específicos que el chuncho. El concón seleccionó bosques con árboles altos y en baja densidad relativa, y una alta densidad de quila. Por su parte, el chuncho seleccionó bosques con árboles altos y en alta densidad relativa. No hubo evidencia de que los llamados de una especie afectaran las respuestas de la otra, sugiriendo que no existe una asociación negativa en el uso de un mismo territorio por ellas. Los resultados enfatizan la importancia de los elementos estructurales de los bosques antiguos para ambas especies pero, a su vez, la relevancia de los bosques periféricos a áreas protegidas.

Agent-based modelling of land use effects on ecosystem processes and services

This article introduces the papers in the thematic issue and identifies issues associated with agent-based modelling of land use effects on ecosystem processes and services.

Modelling high resolution RS data with the aid of coarse resolution data and ancillary data

Abstract In environmental applications, the data have a large variety of resolutions carrying information at different scales. Various approaches have been used to include in models information from sources at different scales combining multi-resolution products in order to integrate the spatio-temporal variability of sub-pixel pattern. A methodology is proposed for the integration of the results obtained with a geostatistical downscaling algorithm, based on block-to-point-kriging, in a General Additive Models interpolation framework to enhance the spatio-temporal resolution of remote sensing data. This allows a good reproduction of the overall spatial pattern of the target images and of their local values. The developed framework was tested using MODIS land surface temperature (LST) with the thermal band of Landsat in a situation of high contamination of clouds for the high resolution dataset. The method proved to be flexible and able to blend data from different sensors maintaining the finer spatial structure of the higher resolution data. The method combines strengths from different approaches: (1) it uses of information held in covariates to provide more accurate results; (2) it is applicable to a variety of remote sensing products as the method does not rely on predetermined functional relationships; (3) it can cope with cloud-rich high resolution images as only a subset of high resolution pixels is needed. This approach is general and can be used with numerous combinations of high and low resolution images, such as MODIS-derived variables, using related band ratios from Landsat or other higher resolution sensors. This approach is a valuable addition to space–time measuring and modelling of ecosystems functions from remote sensing.

Exploring robustness of biodiversity policy with a coupled metacommunity and agent-based model

This article reports results using a coupled agent-based model of land-use change and species metacommunity model. We used the coupled model to explore various mechanisms for giving incentives to farmers to manage for better biodiversity, including activity-based, outcome-based and clustered incentives, in which farmers potentially benefit from the activities of neighbours. In so doing, we demonstrate the benefit of using such models to explore ‘in principle’ questions pertaining to biodiversity policy. The results show that the effectiveness of government policies in protecting target vulnerable species can depend on a number of other factors influencing agricultural land-use decision-making, such as input costs, market variability and farmer aspiration levels. They also show that the way these factors influence species persistence can vary from species to species.

Research Article: Estimating local variations in land use statistics

The Scottish agricultural census provides land use statistics as summaries for parish areas. We investigated the dis-aggregation of these parish summaries using the Land Capability for Agriculture and the 1988 Land Cover of Scotland as supporting data sets. It is unlikely that allocation rules to implement the dis-aggregation should be identical across all parishes. Equally, rules for individual parishes are indeterminate. The 891 parishes were classified into nine classes, then each class was regionalised, creating 91 regions overall. Allocation rules were estimated independently for each region and class. Statistical testing identified greater variations in the rules than is expected by random allocation of regions to classes.