Fragmentation and thresholds in hydrological flow‐based ecosystem services

Loss and fragmentation of natural land cover due to expansion of agricultural areas is a global issue. These changes alter the configuration and composition of the landscape, particularly affecting those ecosystem services (benefits people receive from ecosystems) that depend on interactions between landscape components. Hydrological mitigation describes the bundle of ecosystem services provided by landscape features such as woodland that interrupt the flow of runoff to rivers. These services include sediment retention, nutrient retention and mitigation of overland water flow. The position of woodland in the landscape and the landscape topography are both important for hydrological mitigation. Therefore, it is crucial to consider landscape configuration and flow pathways in a spatially explicit manner when examining the impacts of fragmentation. Here we test the effects of landscape configuration using a large number (>7,000) of virtual landscape configurations. We created virtual landscapes of woodland patches within grassland, superimposed onto real topography and stream networks. Woodland patches were generated with user‐defined combinations of patch number and total woodland area, placed randomly in the landscape. The Ecosystem Service model used hydrological routing to map the “mitigated area” upslope of each woodland patch. We found that more fragmented woodland mitigated a greater proportion of the catchment. Larger woodland area also increased mitigation, however, this increase was nonlinear, with a threshold at 50% coverage, above which there was a decline in service provision. This nonlinearity suggests that the benefit of any additional woodland depends on two factors: the level of fragmentation and the existing area of woodland. Edge density (total edge of patches divided by area of catchment) was the best single metric in predicting mitigated area. Distance from woodland to stream was not a significant predictor of mitigation, suggesting that agri‐environment schemes planting riparian woodland should consider additional controls such as the amount of fragmentation in the landscape. These findings highlight the potential benefits of fragmentation to hydrological mitigation services. However, benefits for hydrological services must be balanced against any negative effects of fragmentation or habitat loss on biodiversity and other services

Comparing strengths and weaknesses of three ecosystem services modelling tools in a diverse UK river catchment

Ecosystem services modelling tools can help land managers and policy makers evaluate the impacts of alternative management options or changes in land use on the delivery of ecosystem services. As the variety and complexity of these tools increases, there is a need for comparative studies across a range of settings, allowing users to make an informed choice. Using examples of provisioning and regulating services (water supply, carbon storage and nutrient retention), we compare three spatially explicit tools – LUCI (Land Utilisation and Capability Indicator), ARIES (Artificial Intelligence for Ecosystem Services) and InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs). Models were parameterised for the UK and applied to a temperate catchment with widely varying land use in North Wales. Although each tool provides quantitative mapped output, can be applied in different contexts, and can work at local or national scale, they differ in the approaches taken and underlying assumptions made. In this study, we focus on the wide range of outputs produced for each service and discuss the differences between each modelling tool. Model outputs were validated using empirical data for river flow, carbon and nutrient levels within the catchment. The sensitivity of the models to land-use change was tested using four scenarios of varying severity, evaluating the conversion of grassland habitat to woodland (0–30% of the landscape). We show that, while the modelling tools provide broadly comparable quantitative outputs, each has its own unique features and strengths. Therefore the choice of tool depends on the study question

Spatial patterns and environmental constraints on ecosystem services at a catchment scale

Improved understanding and prediction of the fundamental environmental controls on ecosystem service supply across the landscape will help to inform decisions made by policy makers and land-water managers. To evaluate this issue for a local catchment case study, we explored metrics and spatial patterns of service supply for water quality regulation, agriculture production, carbon storage, and biodiversity for the Macronutrient Conwy catchment. Methods included using ecosystem models such as LUCI and JULES, integration of national scale field survey datasets, earth observation products and plant trait databases, to produce finely resolved maps of species richness and primary production. Analyses were done with both 1x1 km gridded and subcatchment data. A common single gradient characterised catchment scale ecosystem services supply with agricultural production and carbon storage at opposing ends of the gradient as reported for a national-scale assessment. Species diversity was positively related to production due to the below national average productivity levels in the Conwy combined with the unimodal relationship between biodiversity and productivity at the national scale. In contrast to the national scale assessment, a strong reduction in water quality as production increased was observed in these low productive systems. Various soil variables were tested for their predictive power of ecosystem service supply. Soil carbon, nitrogen, their ratio and soil pH all had double the power of rainfall and altitude, each explaining around 45% of variation but soil pH is proposed as a potential metric for ecosystem service supply potential as it is a simple and practical metric which can be carried out in the field with crowd-sourcing technologies now available. The study emphasises the importance of considering multiple ecosystem services together due to the complexity of covariation at local and national scales, and the benefits of exploiting a wide range of metrics for each service to enhance data robustness

Glastir Monitoring & Evaluation Programme. First year annual report

The Welsh Government has commissioned a comprehensive new ecosystem monitoring and evaluation programme to monitor the effects of Glastir, its new land management scheme, and to monitor progress towards a range of international biodiversity and environmental targets. A random sample of 1 km squares stratified by landcover types will be used both to monitor change at a national level in the wider countryside and to provide a backdrop against which intervention measures are assessed using a second sample of 1 km squares located in areas eligible for enhanced payments for advanced interventions. Modelling in the first year has forecast change based on current understanding, whilst a rolling national monitoring programme based on an ecosystem approach will provide an evidence-base for on-going, adaptive development of the scheme by Welsh Government. To our knowledge, this will constitute the largest and most in-depth ecosystem monitoring and evaluation programme of any member state of the European Union

Modelling water and solute transport within vegetated soils using a stochastic framework

Models predicting the fate of water and dissolved chemicals in vegetated soils are required for a wide range of applications. Substantial uncertainty is present due to measurement errors, parametric uncertainty, and structural issues related to model con- ceptualisation. Due to the costs and intrusiveness of subsurface measurements there are limited datasets available to interrogate models against. Furthermore, the models are typically computationally intensive, making it di±cult to fully explore parametric and other uncertainty spaces. Hence there are two pressing needs which must be met to improve the utility of models: more data and constraints are required to quantify the impacts of uncertainty, and e±cient methodologies to explore sensitivities and uncer- tainties are also needed. This dissertation presents and applies a stochastic framework addressing the above concerns. Approaches and underlying assumptions to modelling water °ow and solute transport within soils and plants are examined, and two ex- isting models extended. The problem of uncertainty is investigated, and appropriate approaches suggested. Monte-Carlo techniques, including Markov chain Monte Carlo methods, are developed for application to the models, and tested using a comprehen- sive hydrological and radiological dataset from a plot-scale lysimeter experiment. The integrity of the experimental data is examined. Sensitivity analysis and calibration of the hydrological and radiological data sets is performed, with the ability of the model and framework to recover parameters interrogated. Structural uncertainty and e®ects of erroneous inputs are discussed. Results demonstrate the power of the methods to generate insights into process response and quantify uncertainties. The e±ciency of Markov Chain Monte Carlo techniques is demonstrated, but the advantages of retain- ing simple set search methodologies are also clear. Consideration of model structure also signi¯cantly reduces the uncertain parametric space. However, despite the unusu- ally comprehensive experimental dataset, major issues of uncertainty remain, of which data issues are a dominant component.EThOS - Electronic Theses Online ServiceUnited Kingdom Nirex LimitedGBUnited Kingdo

A viable and cost-effective weather index insurance for rice in Indonesia

The potentially adverse effects of droughts on agricultural output are obvious. Indonesian rice farmers have no financial protection from climate risk via catastrophic weather risk transfer tools. Done well, a weather index insurance (WII) program can not only provide resources that enable recovery, but can also facilitate the adoption of prevention and adaptation measures and incentivise risk reduction. Here, we quantify the applicability, viability, and likely cost of introducing a WII for droughts for rice production in Indonesia. To reduce basis risk, we construct district specific indices that are based on the estimation of Panel Geographically Weighted Regressions models. With these spatial tools, and detailed district level data on past agricultural productivity and weather conditions, we present an algorithm that generates an effective and actuarially sound WII, and measure its effectiveness in reducing income volatility for farmers. We use data on annual paddy production in 428 Indonesian districts, reported over the period 1990-2013, and climate data from 1950-2015. We use the monthly Palmer Drought Severity Index and identify district-specific trigger and exit points for the insurance plan. We quantify the impact of this hypothetical insurance product using past production data to calculate an actuarially-robust and welfare-enhancing price for this scheme

The Effect of Blue-Green Infrastructure on Habitat Connectivity and Biodiversity: A Case Study in the Ōtākaro/Avon River Catchment in Christchurch, New Zealand

The natural capital components in cities (“blue-green infrastructure” BGI) are designed to address long-term sustainability and create multi-benefits for society, culture, business, and ecology. We investigated the added value of BGI through the research question “Can the implementation of blue-green infrastructure lead to an improvement of habitat connectivity and biodiversity in urban environments?” To answer this, the Biological and Environmental Evaluation Tools for Landscape Ecology (BEETLE) within the Land Utilisation and Capability Indicator (LUCI) framework was adopted and applied in Christchurch, New Zealand, for the first time. Three ecologically representative species were selected. The parameterisation was based on ecological theory and expert judgment. By implementation of BGI, the percentages of habitats of interest for kereru and paradise shelduck increased by 3.3% and 2.5%, respectively. This leads to improved habitat connectivity. We suggest several opportunities for regenerating more native patches around the catchment to achieve the recommended minimum 10% target of indigenous cover. However, BGI alone cannot return a full suite of threatened wildlife to the city without predator-fenced breeding sanctuaries and wider pest control across the matrix. The socio-eco-spatial connectivity analysed in this study was formalised in terms of four interacting dimensions

A conceptual framework and practical structure for implementing ecosystem condition accounts

Ecosystem condition is a fundamental component in the ecosystem accounting framework as part of the System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA). Here, we develop a conceptual framework and present a practical structure for implementing ecosystem condition accounts to contribute to the revision process of the SEEA EEA, focussing on six core elements: (1) developing a common definition of ecosystem condition, (2) establishing a conceptual framing for ecosystem condition, (3) portraying the role of condition within the SEEA EEA accounting system, (4) deriving an inclusive multi-purpose approach, (5) describing the components of condition accounts and (6) developing a three-stage structure for reporting accounts. We develop a conceptual framework for an inclusive condition account, building on an ecological understanding of ecosystems upon which definitions, concepts, classifications and reporting structures were based. The framework encompasses the dual perspectives of first, the interdependencies of ecosystem composition, structure and function in maintaining ecosystem integrity and second, the capacity of ecosystems to supply services as benefits for humans. The following components of ecosystem condition accounts are recommended to provide comprehensive, consistent, repeatable and transparent accounts: (1) intrinsic and instrumental values, together with ecocentric and anthropocentric worldviews; (2) a formal typology or classification of characteristics, variables and indicators, based on selection criteria; (3) a reference condition used both to compare past, current and future levels of indicators of condition and as a basis for aggregation of indicators; and (4) a three-stage approach to compiling accounts with increasing levels of information and complexity that are appropriate for different purposes and applications. The recommended broad and inclusive scope of ecosystem condition and the demonstrated practical methods for implementation of accounts will enhance the ecosystem accounting framework and thus support a wider range of current and potential applications and users

A common typology for ecosystem characteristics and ecosystem condition variables

The UN System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA) aims at regular and standardised stocktaking on the extent of ecosystems, their condition and the services they provide to society. Recording the condition of ecosystems is one of the most complex pieces in this exercise, needing to be supported by robust and consistent guidelines. SEEA EEA defines the condition of an ecosystem as its overall quality, measured in terms of quantitative metrics describing both abiotic and biotic characteristics. The main objective of this paper is to propose a simple universal classification (typology) for these ecosystem condition characteristics and metrics, based on long standing ecological concepts and traditions.The proposed SEEA EEA Ecosystem Condition Typology (SEEA ECT) is a hierarchical classification consisting of six classes grouped into three main groups (abiotic, biotic and landscape-level ecosystem characteristics). In order to facilitate practical applications, SEEA ECT is cross-linked to the most relevant existing typologies for ecosystem characteristics currently used for other purposes. To ensure clarity and practicality, we identified potential overlaps between classes and also identified the most important groups of ‘ancillary data’ that should not be considered as ecosystem condition characteristics. We consider that this new typology for ecosystem condition will create a meaningful reporting structure for ecosystem condition accounts, thus facilitating its standardisation and broad application