Natural capital approaches for the optimal design of policies for nature recovery.

By embedding a spatially explicit ecosystem services modelling tool within a policy simulator we examine the insights that natural capital analysis can bring to the design of policies for nature recovery. Our study is illustrated through a case example of policies incentivising the establishment of new natural habitat in England. We find that a policy mirroring the current practice of offering payments per hectare of habitat creation fails to break even, delivering less value in improved flows of ecosystem services than public money spent and only 26% of that which is theoretically achievable. Using optimization methods, we discover that progressively more efficient outcomes are delivered by policies that optimally price activities (34%), quantities of environmental change (55%) and ecosystem service value flows (81%). Further, we show that additionally attaining targets for unmonetized ecosystem services (in our case, biodiversity) demands trade-offs in delivery of monetized services. For some policy instruments it is not even possible to achieve the targets. Finally, we establish that extending policy instruments to offer payments for unmonetized services delivers target-achieving and value-maximizing policy designs. Our findings reveal that policy design is of first-order importance in determining the efficiency and efficacy of programmes pursuing nature recovery. This article is part of the theme issue 'Bringing nature into decision-making'.

What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts.

Reading a visualization is like reading a paragraph. Each sentence is a comparison: the mean of these is higher than those; this difference is smaller than that. What determines which comparisons are made first? The viewer's goals and expertise matter, but the way that values are visually grouped together within the chart also impacts those comparisons. Research from psychology suggests that comparisons involve multiple steps. First, the viewer divides the visualization into a set of units. This might include a single bar or a grouped set of bars. Then the viewer selects and compares two of these units, perhaps noting that one pair of bars is longer than another. Viewers might take an additional third step and perform a second-order comparison, perhaps determining that the difference between one pair of bars is greater than the difference between another pair. We create a visual comparison taxonomy that allows us to develop and test a sequence of hypotheses about which comparisons people are more likely to make when reading a visualization. We find that people tend to compare two groups before comparing two individual bars and that second-order comparisons are rare. Visual cues like spatial proximity and color can influence which elements are grouped together and selected for comparison, with spatial proximity being a stronger grouping cue. Interestingly, once the viewer grouped together and compared a set of bars, regardless of whether the group is formed by spatial proximity or color similarity, they no longer consider other possible groupings in their comparisons.

Attentional Strategies and the Transition From Subitizing to Estimation in Numerosity Perception.

The common view of the transition between subitizing and numerosity estimation regimes is that there is a hard bound on the subitizing range, and beyond this range, people estimate. However, this view does not adequately address the behavioral signatures of enumeration under conditions of attentional load or in the immediate post-subitizing range. The possibility that there might exist a numerosity range where both processes of subitizing and estimation operate in conjunction has so far been ignored. Here, we investigate this new proposal, that people strategically combine the processes of subitizing and estimation to maximize accuracy and precision, given time or attentional constraints. We present a process-level account of how subitizing and estimation can be combined through strategic deployment of attention to maximize the precision of perceived numerosity given time constraints. We then describe a computational model of this account and apply it in two experimental simulations to demonstrate how it can explain key findings in prior enumeration research. While recent modeling work has argued that the behavioral signatures of enumeration can best be explained through a single numerosity system with a single form of representation, we argue that our model demonstrates how the traditional two-systems view of numerical representation accounts for behavioral data through coordination with a unified attentional mechanism, rather than a unified representation.

Novel Miscanthus hybrids: Modelling productivity on marginal land in Europe using dynamics of canopy development determined by light interception.

New biomass crop hybrids for bioeconomic expansion require yield projections to determine their potential for strategic land use planning in the face of global challenges. Our biomass growth simulation incorporates radiation interception and conversion efficiency. Models often use leaf area to predict interception which is demanding to determine accurately, so instead we use low-cost rapid light interception measurements using a simple laboratory-made line ceptometer and relate the dynamics of canopy closure to thermal time, and to measurements of biomass. We apply the model to project the European biomass potentials of new market-ready hybrids for 2020-2030. Field measurements are easier to collect, the calibration is seasonally dynamic and reduces influence of weather variation between field sites. The model obtained is conservative, being calibrated by crops of varying establishment and varying maturity on less productive (marginal) land. This results in conservative projections of miscanthus hybrids for 2020-2030 based on 10% land use conversion of the least (productive) grassland and arable for farm diversification, which show a European potential of 80.7-89.7 Mt year-1 biomass, with potential for 1.2-1.3 EJ year-1 energy and 36.3-40.3 Mt year-1 carbon capture, with seeded Miscanthus sacchariflorus × sinensis displaying highest yield potential. Simulated biomass projections must be viewed in light of the field measurements on less productive land with high soil water deficits. We are attempting to model the results from an ambitious and novel project combining new hybrids across Europe with agronomy which has not been perfected on less productive sites. Nevertheless, at the time of energy sourcing issues, seed-propagated miscanthus hybrids for the upscaled provision of bioenergy offer an alternative source of renewable energy. If European countries provide incentives for growers to invest, seeded hybrids can improve product availability and biomass yields over the current commercial miscanthus variety.

Temporal hydrochemical dynamics of the River Wensum, UK: Observations from long-term high-resolution monitoring (2011-2018).

In 2010, the UK government established the Demonstration Test Catchment (DTC) initiative to evaluate the extent to which on-farm mitigation measures can cost-effectively reduce the impacts of agricultural water pollution on river ecology whilst maintaining food production capacity. A central component of the DTC platform was the establishment of a comprehensive network of automated, web-based sensor technologies to generate high-temporal resolution (30 min) empirical datasets of surface water, groundwater and meteorological parameters over a long period (2011-2018). Utilising 8.9 million water quality measurements generated for the River Wensum, this paper demonstrates how long-term, high-resolution monitoring of hydrochemistry can improve our understanding of the complex temporal dynamics of riverine processes from 30 min to annual timescales. This paper explores the impact of groundwater-surface water interactions on instream pollutant concentrations (principally nitrogen, phosphorus and turbidity) and reveals how varying hydrochemical associations under contrasting flow regimes can elicit important information on the dominant pollution pathways. Furthermore, this paper examines the relationships between agricultural pollutants and precipitation events of varying magnitude, whilst demonstrating how high-resolution data can be utilised to develop conceptual models of hydrochemical processes for contrasting winter and summer seasons. Finally, this paper considers how high-resolution hydrochemical data can be used to increase land manager awareness of environmentally damaging farming operations and encourage the adoption of more water sensitive land management practices.

Selection enables enhancement: An integrated model of object tracking.

The diversity of research on visual attention and multiple-object tracking presents challenges for anyone hoping to develop a unified account. One key challenge is identifying the attentional limitations that give rise to competition among targets during tracking. To address this challenge, we present a computational model of object tracking that relies on two attentional mechanisms: serial selection and parallel enhancement. Selection picks out an object for further processing, whereas enhancement increases sensitivity to stimuli in regions where objects have been selected previously. In this model, multiple target locations can be tracked in parallel via enhancement, whereas a single target can be selected so that additional information beyond its location can be processed. In simulations of two psychological experiments, we demonstrate that spatial competition during enhancement and temporal competition for selection can explain a range of findings on multiple-object tracking, and we argue that the interaction between selection and enhancement captured in the model is critical to understanding attention more broadly.

Topological Relations Between Objects Are Categorically Coded.

How do individuals compare images-for example, two graphs or diagrams-to identify differences between them? We argue that categorical relations between objects play a critical role. These relations divide continuous space into discrete categories, such as "above" and "below," or "containing" and "overlapping," which are remembered and compared more easily than precise metric values. These relations should lead to categorical perception, such that viewers find it easier to notice a change that crosses a category boundary (one object is now above, rather than below, another, or now contains, rather than overlaps with, another) than a change of equal magnitude that does not cross a boundary. We tested the influence of a set of topological categorical relations from the cognitive-modeling literature. In a visual same/different comparison task, viewers more accurately noticed changes that crossed relational category boundaries, compared with changes that did not cross these boundaries. The results highlight the potential of systematic exploration of the boundaries of between-object relational categories.

Extending SME to Handle Large-Scale Cognitive Modeling.

Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine (SME) since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence for SME as a process model, and summarize its role in simulating similarity-based retrieval and generalization. Then we describe five techniques now incorporated into the SME that have enabled it to tackle large-scale modeling tasks: (a) Greedy merging rapidly constructs one or more best interpretations of a match in polynomial time: O(n2 log(n)); (b) Incremental operation enables mappings to be extended as new information is retrieved or derived about the base or target, to model situations where information in a task is updated over time; (c) Ubiquitous predicates model the varying degrees to which items may suggest alignment; (d) Structural evaluation of analogical inferences models aspects of plausibility judgments; (e) Match filters enable large-scale task models to communicate constraints to SME to influence the mapping process. We illustrate via examples from published studies how these enable it to capture a broader range of psychological phenomena than before.

Modeling visual problem solving as analogical reasoning.

We present a computational model of visual problem solving, designed to solve problems from the Raven's Progressive Matrices intelligence test. The model builds on the claim that analogical reasoning lies at the heart of visual problem solving, and intelligence more broadly. Images are compared via structure mapping, aligning the common relational structure in 2 images to identify commonalities and differences. These commonalities or differences can themselves be reified and used as the input for future comparisons. When images fail to align, the model dynamically rerepresents them to facilitate the comparison. In our analysis, we find that the model matches adult human performance on the Standard Progressive Matrices test, and that problems which are difficult for the model are also difficult for people. Furthermore, we show that model operations involving abstraction and rerepresentation are particularly difficult for people, suggesting that these operations may be critical for performing visual problem solving, and reasoning more generally, at the highest level. (PsycINFO Database Record

Potential impacts on ecosystem services of land use transitions to second-generation bioenergy crops in GB.

We present the first assessment of the impact of land use change (LUC) to second-generation (2G) bioenergy crops on ecosystem services (ES) resolved spatially for Great Britain (GB). A systematic approach was used to assess available evidence on the impacts of LUC from arable, semi-improved grassland or woodland/forest, to 2G bioenergy crops, for which a quantitative 'threat matrix' was developed. The threat matrix was used to estimate potential impacts of transitions to either Miscanthus, short-rotation coppice (SRC, willow and poplar) or short-rotation forestry (SRF). The ES effects were found to be largely dependent on previous land uses rather than the choice of 2G crop when assessing the technical potential of available biomass with a transition from arable crops resulting in the most positive effect on ES. Combining these data with constraint masks and available land for SRC and Miscanthus (SRF omitted from this stage due to lack of data), south-west and north-west England were identified as areas where Miscanthus and SRC could be grown, respectively, with favourable combinations of economic viability, carbon sequestration, high yield and positive ES benefits. This study also suggests that not all prospective planting of Miscanthus and SRC can be allocated to agricultural land class (ALC) ALC 3 and ALC 4 and suitable areas of ALC 5 are only minimally available. Beneficial impacts were found on 146 583 and 71 890 ha when planting Miscanthus or SRC, respectively, under baseline planting conditions rising to 293 247 and 91 318 ha, respectively, under 2020 planting scenarios. The results provide an insight into the interplay between land availability, original land uses, bioenergy crop type and yield in determining overall positive or negative impacts of bioenergy cropping on ecosystems services and go some way towards developing a framework for quantifying wider ES impacts of this important LUC.

Assessing the effectiveness of a three-stage on-farm biobed in treating pesticide contaminated wastewater.

Agricultural point source pesticide pollution arising from contaminated machinery washings and accidental spillages pose a significant threat to river water and groundwater quality. In this study, we assess the effectiveness of a three-stage on-farm biobed for treating pesticide contaminated wastewater from a large (20 km(2)) commercial arable estate. The facility consisted of an enclosed machinery wash-down unit (stage 1), a 49 m(2) lined compost-straw-topsoil biobed (stage 2), and a 200 m(2) drainage field with a trickle irrigation system (stage 3). Pesticide concentrations were analysed in water samples collected fortnightly between November 2013 and November 2015 from the biobed input and output sumps and from 20 porous pots buried at 45 cm and 90 cm depth within the drainage field. The results revealed that the biobed removed 68-98% of individual pesticides within the contaminated washings, with mean total pesticide concentrations reducing by 91.6% between the biobed input and output sumps. Drainage field irrigation removed a further 68-99% of individual pesticides, with total mean pesticide concentrations reducing by 98.4% and 97.2% in the 45 cm and 90 cm depth porous pots, respectively. The average total pesticide concentration at 45 cm depth in the drainage field (57 µg L(-1)) was 760 times lower than the mean concentration recorded in the input sump (43,334 µg L(-1)). There was no evidence of seasonality in the efficiency of biobed pesticide removal, nor was there evidence of a decline in removal efficiency over the two-year monitoring period. However, higher mean total pesticide concentrations at 90 cm (102 µg L(-1)) relative to 45 cm (57 µg L(-1)) depth indicated an accumulation of pesticide residues deeper within the soil profile. Overall, the results presented here demonstrate that a three-stage biobed can successfully reduce pesticide pollution risk from contaminated machinery washings on a commercial farm.

Spatially explicit integrated modeling and economic valuation of climate driven land use change and its indirect effects.

We present an integrated model of the direct consequences of climate change on land use, and the indirect effects of induced land use change upon the natural environment. The model predicts climate-driven shifts in the profitability of alternative uses of agricultural land. Both the direct impact of climate change and the induced shift in land use patterns will cause secondary effects on the water environment, for which agriculture is the major source of diffuse pollution. We model the impact of changes in such pollution on riverine ecosystems showing that these will be spatially heterogeneous. Moreover, we consider further knock-on effects upon the recreational benefits derived from water environments, which we assess using revealed preference methods. This analysis permits a multi-layered examination of the economic consequences of climate change, assessing the sequence of impacts from climate change through farm gross margins, land use, water quality and recreation, both at the individual and catchment scale.

Antecedent conditions, hydrological connectivity and anthropogenic inputs: Factors affecting nitrate and phosphorus transfers to agricultural headwater streams.

This paper examines relationships between rainfall-runoff, catchment connectivity, antecedent moisture conditions and fertiliser application with nitrate-N and total phosphorus (TP) fluxes in an arable headwater catchment over three hydrological years (2012-2014). Annual precipitation totals did not vary substantially between years, yet the timing of rainfall strongly influenced runoff generation and subsequent nitrate-N and TP fluxes. The greatest nitrate-N (>250 kg N day(-1)) and TP (>10 kg TP day(-1)) fluxes only occurred when shallow groundwater was within 0.6m of the ground surface and runoff coefficients were greater than 0.1. These thresholds were reached less frequently in 2012 due to drought recovery resulting in lower annual nitrate-N (7.4 kg N ha(-1)) and TP (0.12 kg P ha(-1)) fluxes in comparison with 2013 (15.1 kg N ha(-1); 0.21 kg P ha(-1)). The wet winter of 2013 with elevated shallow groundwater levels led to more frequent activation of sub-surface pathways and tile drain flow. Throughout the period, dry antecedent conditions had a temporary effect in elevating TP loads. Evidence of TP source exhaustion after consecutive storm events can be attributed to the repeated depletion of temporarily connected critical source areas to the river network via impermeable road surfaces. Fertiliser application varied considerably across three years due to differences in crop rotation between farms, with annual N and P fertiliser inputs varying by up to 21% and 41%, respectively. Proportional reductions in annual riverine nitrate-N and TP loadings were not observed at the sub-catchment outlet as loadings were largely influenced by annual runoff. Nitrate loadings were slightly higher during fertiliser application, but there was little relationship between P fertiliser application and riverine TP load. These data indicate that this intensive arable catchment may be in a state of biogeochemical stationarity, whereby legacy stores of nutrients buffer against changes in contemporary nutrient inputs.

The effects of flooding on dioxin and PCB levels in food produced on industrial river catchments.

This research examined the effect of regular flooding upon PCDD/F and PCB levels in milk, beef and lamb, produced on the floodplains of industrial river catchments. Our unique dataset included more than 200 samples analysed for PCDD/Fs and PCBs over two data collection phases (1998-1999 & 2008-2010) from working farms. A robust paired study design was adopted with samples taken from flood-prone farms and nearby control farms not subject to flooding. On industrial river catchments regular flooding is associated with higher PCDD/F and PCB levels in soils and grass. This contamination may be transferred to food but the impact varied by food type. These contrasts may be due to physiological differences between animals, the ages at which they are sent to market and differences in animal husbandry. To minimise the risks of producing food on flood-prone land in industrial river catchments, as well as on any land with elevated PCDD/F and PCB levels, this research suggests a number of options. The choice of livestock may be important and as an example in our study beef cattle accumulated PCDD/Fs to a higher degree than sheep. Land management may also play a role and could include minimising the time that livestock spend on such land or feeding commercial feed, low in PCDD/Fs and PCBs, where appropriate.

Bringing ecosystem services into economic decision-making: land use in the United Kingdom.

Landscapes generate a wide range of valuable ecosystem services, yet land-use decisions often ignore the value of these services. Using the example of the United Kingdom, we show the significance of land-use change not only for agricultural production but also for emissions and sequestration of greenhouse gases, open-access recreational visits, urban green space, and wild-species diversity. We use spatially explicit models in conjunction with valuation methods to estimate comparable economic values for these services, taking account of climate change impacts. We show that, although decisions that focus solely on agriculture reduce overall ecosystem service values, highly significant value increases can be obtained from targeted planning by incorporating all potential services and their values and that this approach also conserves wild-species diversity.

What difference reveals about similarity.

Detecting that two images are different is faster for highly dissimilar images than for highly similar images. Paradoxically, we showed that the reverse occurs when people are asked to describe how two images differ--that is, to state a difference between two images. Following structure-mapping theory, we propose that this disassociation arises from the multistage nature of the comparison process. Detecting that two images are different can be done in the initial (local-matching) stage, but only for pairs with low overlap; thus, "different" responses are faster for low-similarity than for high-similarity pairs. In contrast, identifying a specific difference generally requires a full structural alignment of the two images, and this alignment process is faster for high-similarity pairs. We described four experiments that demonstrate this dissociation and show that the results can be simulated using the Structure-Mapping Engine. These results pose a significant challenge for nonstructural accounts of similarity comparison and suggest that structural alignment processes play a significant role in visual comparison.

Cultural commonalities and differences in spatial problem-solving: a computational analysis.

A fundamental question in human cognition is how people reason about space. We use a computational model to explore cross-cultural commonalities and differences in spatial cognition. Our model is based upon two hypotheses: (1) the structure-mapping model of analogy can explain the visual comparisons used in spatial reasoning; and (2) qualitative, structural representations are computed by people's visual systems and used in these comparisons. We apply our model to a visual oddity task, in which individuals are shown an array of two-dimensional images and asked to the pick the one that does not belong. This task was previously used to evaluate understanding of geometric concepts in two disparate populations: North Americans, and the Mundurukú, a South American indigenous group. Our model automatically generates representations of each hand-segmented image and compares them to solve the task. The model achieves human-level performance on this task, and problems that are hard for the model are also difficult for people in both cultures. Furthermore, ablation studies on the model suggest explanations for cross-cultural differences in terms of differences in spatial representations.

CogSketch: sketch understanding for cognitive science research and for education.

Sketching is a powerful means of working out and communicating ideas. Sketch understanding involves a combination of visual, spatial, and conceptual knowledge and reasoning, which makes it both challenging to model and potentially illuminating for cognitive science. This paper describes CogSketch, an ongoing effort of the NSF-funded Spatial Intelligence and Learning Center, which is being developed both as a research instrument for cognitive science and as a platform for sketch-based educational software. We describe the idea of open-domain sketch understanding, the scientific hypotheses underlying CogSketch, and provide an overview of the models it employs, illustrated by simulation studies and ongoing experiments in creating sketch-based educational software.