Developing a conceptual model of marine farming in New Zealand

Survey and Geographic Information System (GIS) data analysis describes the relative influence of biophysical and human variables on site choices made by marine farmers in New Zealand. Community conflicts have grown in importance in determining farm location and different government planning strategies leave distinct signature patterns. Recent legislation empowers local governments to choose among three strategies for future regional aquaculture development. This paper suggests each strategy could result in different spatial outcomes. Simulation modelling of the type described here can provide a better understanding of farmer responses to management approaches and the range of futures that could result from planning choices made today

Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling

International scientific fishery survey programmes systematically collect samples of target stocks’ biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat conservation and help define harvest control rules based on direct observation of biomass at the sea. However, missed survey locations over the survey years are common in long-term programme data. Currently, modelling approaches to filling gaps in spatiotemporal survey data range from quickly applicable solutions to complex modelling. Most models require setting prior statistical assumptions on spatial distributions, assuming short-term temporal dependency between the data, and scarcely considering the environmental aspects that might have influenced stock presence in the missed locations. This paper proposes a statistical and machine learning based model to fill spatiotemporal gaps in survey data and produce robust estimates for stock assessment experts, decision makers, and regional fisheries management organizations. We apply our model to the SoleMon survey data in North-Central Adriatic Sea (Mediterranean Sea) for 4 stocks: Sepia officinalis, Solea solea, Squilla mantis, and Pecten jacobaeus. We reconstruct the biomass-index (i.e., biomass over the swept area) of 10 locations missed in 2020 (out of the 67 planned) because of several factors, including COVID-19 pandemic related restrictions. We evaluate model performance on 2019 data with respect to an alternative index that assumes biomass proportion consistency over time. Our model’s novelty is that it combines three complementary components. A spatial component estimates stock biomass-index in the missed locations in one year, given the surveyed location’s biomass-index distribution in the same year. A temporal component forecasts, for each missed survey location, biomass-index given the data history of that haul. An environmental component estimates a biomass-index weighting factor based on the environmental suitability of the haul area to species presence. Combining these components allows understanding the interplay between environmental-change drivers, stock presence, and fisheries. Our model formulation is general enough to be applied to other survey data with lower spatial homogeneity and more temporal gaps than the SoleMon dataset

A combinatorial optimisation approach to non-market environmental benefit aggregation

This paper considers the use of spatial microsimulation in the aggregation of regional environmental benefit values. The developed spatial microsimulation model uses simulated annealing to match the Irish Census of Agriculture data to a Contingent Valuation Survey that contains information on Irish farmers’ willingness to pay (WTP) to have the corncrake restored as a common sight in the Irish countryside. We then use this matched farm survey and Census information to produce regional and national total WTP figures, and compare these to figures derived using more standard approaches to calculating aggregate environment benefit values. The main advantage of the spatial microsimulation approach for environmental benefit value aggregation is that it allows one to account for the heterogeneity in the target population. Results indicate that the microsimulation modelling approach provides aggregate WTP estimates of a similar magnitude as those produced using the usual sample mean WTP aggregation at the national level, but yields regional aggregate values which are significantly different

A scoping review of spatial analysis approaches using health survey data in Sub-Saharan Africa

CITATION: Manda, S., Haushona, N. & Bergquist, R. 2020. A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa. International Journal of Environmental Research and Public Health, 17(9). doi:10.3390/ijerph17093070The original publication is available at https://www.mdpi.com/journal/ijerphSpatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review was to conduct an evaluation of studies using spatial statistics approaches for national health survey data in the SSA region. An organized literature search for studies related to spatial statistics and national health surveys was conducted through PMC, PubMed/Medline, Scopus, NLM Catalog, and Science Direct electronic databases. Of the 4,193 unique articles identified, 153 were included in the final review. Spatial smoothing and prediction methods were predominant (n = 108), followed by spatial description aggregation (n = 25), and spatial autocorrelation and clustering (n = 19). Bayesian statistics methods and lattice data modelling were predominant (n = 108). Most studies focused on malaria and fever (n = 47) followed by health services coverage (n = 38). Only fifteen studies employed nonstandard spatial analyses (e.g., spatial model assessment, joint spatial modelling, accounting for survey design). We recommend that for future spatial analysis using health survey data in the SSA region, there must be an improve recognition and awareness of the potential dangers of a naïve application of spatial statistical methods. We also recommend a wide range of applications using big health data and the future of data science for health systems to monitor and evaluate impacts that are not well understood at local levels.https://www.mdpi.com/1660-4601/17/9/3070/htmPublishers versio

Field validation of habitat suitability models for vulnerable marine ecosystems in the South Pacific Ocean:Implications for the use of broad-scale models in fisheries management

AbstractManagement of human activities which impact the seafloor in the deep ocean is becoming increasingly important as bottom trawling and exploration for minerals, oil, and gas continue to extend into regions where fragile ecosystems containing habitat-forming deep-sea corals and sponges may be found. Spatial management of these vulnerable marine ecosystems requires accurate knowledge of their distribution. Predictive habitat suitability modelling, using species presence data and a suite of environmental predictor variables, has emerged as a useful tool for inferring distributions outside of known areas. However, validation of model predictions is typically performed with non-independent data. In this study, we describe the results of habitat suitability models constructed for four deep-sea reef-forming coral species across a large region of the South Pacific Ocean using MaxEnt and Boosted Regression Tree modelling approaches. In order to validate model predictions we conducted a photographic survey on a set of seamounts in an un-sampled area east of New Zealand. The likelihood of habitat suitable for reef-forming corals on these seamounts was predicted to be variable, but very high in some regions, particularly where levels of aragonite saturation, dissolved oxygen, and particulate organic carbon were optimal. However, the observed frequency of coral occurrence in analyses of survey photographic data was much lower than expected, and patterns of observed versus predicted coral distribution were not highly correlated. The poor performance of these broad-scale models is attributed to lack of recorded species absences to inform the models, low precision of global bathymetry models, and lack of data on the geomorphology and substrate of the seamounts at scales appropriate to the modelled taxa. This demonstrates the need to use caution when interpreting and applying broad-scale, presence-only model results for fisheries management and conservation planning in data poor areas of the deep sea. Future improvements in the predictive performance of broad-scale models will rely on the continued advancement in modelling of environmental predictor variables, refinements in modelling approaches to deal with missing or biased inputs, and incorporation of true absence data

Identifying critical source areas using multiple methods for effective diffuse pollution mitigation

Diffuse pollution from agriculture constitutes a key pressure on the water quality of freshwaters and is frequently the cause of ecological degradation. The problem of diffuse pollution can be conceptualised with a source-mobilisation-pathway (or delivery)-impact model, whereby the combination of high source risk and strong connected pathways leads to ‘critical source areas’ (CSAs). These areas are where most diffuse pollution will originate, and hence are the optimal places to implement mitigation measures. However, identifying the locations of these areas is a key problem across different spatial scales within catchments. A number of approaches are frequently used for this assessment, although comparisons of these assessments are rarely carried out. We evaluate the CSAs identified via traditional walkover surveys supported by three different approaches, highlighting their benefits and disadvantages. These include a custom designed smartphone app; a desktop geographic information system (GIS) and terrain analysis-based SCIMAP (Sensitive Catchment Integrated Modelling and Analysis Platform) approach; and the use of a high spatial resolution drone dataset as an improved input data for SCIMAP modelling. Each of these methods captures the locations of the CSAs, revealing similarities and differences in the prioritisation of CSA features. The differences are due to the temporal and spatial resolution of the three methods such as the use of static land cover information, the ability to capture small scale features, such as gateways and the incomplete catchment coverage of the walkover survey. The relative costs and output resolutions of the three methods indicate that they are suitable for application at different catchment scales in conjunction with other methods. Based on the results in this paper, it is recommended that a multi-evidence-based approach to diffuse pollution management is taken across catchment spatial scales, incorporating local knowledge from the walkover with the different data resolutions of the SCIMAP approach

Methodological and empirical challenges in modelling residential location choices

The modelling of residential locations is a key element in land use and transport planning. There are significant empirical and methodological challenges inherent in such modelling, however, despite recent advances both in the availability of spatial datasets and in computational and choice modelling techniques. One of the most important of these challenges concerns spatial aggregation. The housing market is characterised by the fact that it offers spatially and functionally heterogeneous products; as a result, if residential alternatives are represented as aggregated spatial units (as in conventional residential location models), the variability of dwelling attributes is lost, which may limit the predictive ability and policy sensitivity of the model. This thesis presents a modelling framework for residential location choice that addresses three key challenges: (i) the development of models at the dwelling-unit level, (ii) the treatment of spatial structure effects in such dwelling-unit level models, and (iii) problems associated with estimation in such modelling frameworks in the absence of disaggregated dwelling unit supply data. The proposed framework is applied to the residential location choice context in London. Another important challenge in the modelling of residential locations is the choice set formation problem. Most models of residential location choices have been developed based on the assumption that households consider all available alternatives when they are making location choices. Due the high search costs associated with the housing market, however, and the limited capacity of households to process information, the validity of this assumption has been an on-going debate among researchers. There have been some attempts in the literature to incorporate the cognitive capacities of households within discrete choice models of residential location: for instance, by modelling households’ choice sets exogenously based on simplifying assumptions regarding their spatial search behaviour (e.g., an anchor-based search strategy) and their characteristics. By undertaking an empirical comparison of alternative models within the context of residential location choice in the Greater London area this thesis investigates the feasibility and practicality of applying deterministic choice set formation approaches to capture the underlying search process of households. The thesis also investigates the uncertainty of choice sets in residential location choice modelling and proposes a simplified probabilistic choice set formation approach to model choice sets and choices simultaneously. The dwelling-level modelling framework proposed in this research is practice-ready and can be used to estimate residential location choice models at the level of dwelling units without requiring independent and disaggregated dwelling supply data. The empirical comparison of alternative exogenous choice set formation approaches provides a guideline for modellers and land use planners to avoid inappropriate choice set formation approaches in practice. Finally, the proposed simplified choice set formation model can be applied to model the behaviour of households in online real estate environments.Open Acces

Actors and factors - bridging social science findings and urban land use change modeling

Recent uneven land use dynamics in urban areas resulting from demographic change, economic pressure and the cities’ mutual competition in a globalising world challenge both scientists and practitioners, among them social scientists, modellers and spatial planners. Processes of growth and decline specifically affect the urban environment, the requirements of the residents on social and natural resources. Social and environmental research is interested in a better understanding and ways of explaining the interactions between society and landscape in urban areas. And it is also needed for making life in cities attractive, secure and affordable within or despite of uneven dynamics.\ud The position paper upon “Actors and factors – bridging social science findings and urban land use change modeling” presents approaches and ideas on how social science findings on the interaction of the social system (actors) and the land use (factors) are taken up and formalised using modelling and gaming techniques. It should be understood as a first sketch compiling major challenges and proposing exemplary solutions in the field of interest

Impacts of land cover data selection and trait parameterisation on dynamic modelling of species' range expansion

Peer reviewedPublisher PD

The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales

The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process