Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains

Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data-poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data-poor ecosystems based on a space-for-time substitution, using distant, well-studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate-ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains - to investigate ecological response to climate change - allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long-term predictions will be of significant value to natural resource practitioners attempting to manage data-poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where no such method currently exists.

Bayesian classification of catchments using spatial data: a first step to improved modelling of catchment effects on stream ecological condition

A major challenge facing freshwater ecologists and managers is the development of models that link stream ecological condition to catchment scale effects, such as land use. Previous attempts to make such models have followed two general approaches. The bottom-up approach employs mechanistic models, which can quickly become too complex to be useful. The top-down approach employs empirical models derived from large data sets, and has often suffered from large amounts of unexplained variation in stream condition.

We believe that the lack of success of both modelling approaches may be at least partly explained by scientists considering too wide a breadth of catchment type. Thus, we believe that by stratifying large sets of catchments into groups of similar types prior to modelling, both types of models may be improved. This paper describes preliminary work using a Bayesian classification software package, ‘Autoclass’ (Cheeseman and Stutz 1996) to create classes of catchments within the Murray Darling Basin based on physiographic data.

Autoclass uses a model-based classification method that employs finite mixture modelling and trades off model fit versus complexity, leading to a parsimonious solution. The software provides information on the posterior probability that the classification is ‘correct’ and also probabilities for alternative classifications. The importance of each attribute in defining the individual classes is calculated and presented, assisting description of the classes. Each case is ‘assigned’ to a class based on membership probability, but the probability of membership of other classes is also provided. This feature deals very well with cases that do not fit neatly into a larger class. Lastly, Autoclass requires the user to specify the measurement error of continuous variables.

Catchments were derived from the Australian digital elevation model. Physiographic data werederived from national spatial data sets. There was very little information on measurement errors for the spatial data, and so a conservative error of 5% of data range was adopted for all continuous attributes. The incorporation of uncertainty into spatial data sets remains a research challenge.

The results of the classification were very encouraging. The software found nine classes of catchments in the Murray Darling Basin. The classes grouped together geographically, and followed altitude and latitude gradients, despite the fact that these variables were not included in the classification. Descriptions of the classes reveal very different physiographic environments, ranging from dry and flat catchments (i.e. lowlands), through to wet and hilly catchments (i.e. mountainous areas). Rainfall and slope were two important discriminators between classes. These two attributes, in particular, will affect the ways in which the stream interacts with the catchment, and can thus be expected to modify the effects of land use change on ecological condition. Thus, realistic models of the effects of land use change on streams would differ between the different types of catchments, and sound management practices will differ.

A small number of catchments were assigned to their primary class with relatively low probability. These catchments lie on the boundaries of groups of catchments, with the second most likely class being an adjacent group. The locations of these ‘uncertain’ catchments show that the Bayesian classification dealt well with cases that do not fit neatly into larger classes.

Although the results are intuitive, we cannot yet assess whether the classifications described in this paper would assist the modelling of catchment scale effects on stream ecological condition. It is most likely that catchment classification and modelling will be an iterative process, where the needs of the model are used to guide classification, and the results of classifications used to suggest further refinements to models.

Ecological niche modelling and DNA sequencing support a new, morphologically cryptic beetle species unveiled by DNA barcoding

Background: DNA sequencing techniques used to estimate biodiversity, such as DNA barcoding, may reveal cryptic species. However, disagreements between barcoding and morphological data have already led to controversy. Species delimitation should therefore not be based on mtDNA alone. Here, we explore the use of nDNA and bioclimatic modelling in a new species of aquatic beetle revealed by mtDNA sequence data.

Methodology/Principal Findings: The aquatic beetle fauna of Australia is characterised by high degrees of endemism, including local radiations such as the genus Antiporus. Antiporus femoralis was previously considered to exist in two disjunct, but morphologically indistinguishable populations in south-western and south-eastern Australia. We constructed a phylogeny of Antiporus and detected a deep split between these populations. Diagnostic characters from the highly variable nuclear protein encoding arginine kinase gene confirmed the presence of two isolated populations. We then used ecological niche modelling to examine the climatic niche characteristics of the two populations. All results support the status of the two populations as distinct species. We describe the south-western species as Antiporus occidentalis sp.n.

Conclusion/Significance: In addition to nDNA sequence data and extended use of mitochondrial sequences, ecological niche modelling has great potential for delineating morphologically cryptic species. © 2011 Hawlitschek et al.

Modelling the benefits of habitat restoration in socio-ecological systems

Decisions affecting the management of natural resources in agricultural landscapes are influenced by both social and ecological factors. Models that integrate these factors are likely to better predict the outcomes of natural resource management decisions compared to those that do not take these factors into account. We demonstrate how Bayesian Networks can be used to integrate ecological and social data and expert opinion to model the cost-effectiveness of revegetation activities for restoring biodiversity in agricultural landscapes. We demonstrate our approach with a case-study in grassy woodlands of south-eastern Australia. In our case-study, cost-effectiveness is defined as the improvement in native reptile and beetle species richness achieved per dollar spent on a restoration action. Socio-ecological models predict that weed control, the planting of trees and shrubs, the addition of litter and timber, and the addition of rocks are likely to be the most cost-effective actions for improving reptile and beetle species richness. The cost-effectiveness of restoration actions is lower in remnant and revegetated areas than in cleared areas because of the higher marginal benefits arising from acting in degraded habitats. This result is contingent on having favourable landowner attitudes. Under the best-case landowner demographic scenarios the greatest biodiversity benefits are seen when cleared areas are restored. We find that current restoration investment practices may not be increasing faunal species richness in agricultural landscapes in the most cost-effective way, and that new restoration actions may be necessary. Integrated socio-ecological models support transparent and cost-effective conservation investment decisions. Application of these models highlights the importance of collecting both social and ecological data when attempting to understand and manage socio-ecological systems.

Concordance in phylogeography and ecological niche modelling identify dispersal corridors for reptiles in arid Australia

Aim: Using the rock-specialist agamid Ctenophorus caudicinctus as a model, we test hypothesized biogeographical dispersal corridors for lizards in the Australian arid zone (across the western sand deserts), and assess how these dispersal routes have shaped phylogeographical structuring. Location: Arid and semi-arid Australia. Methods: We sequenced a c. 1400 bp fragment of mtDNA (ND2) for 134 individuals of C. caudicinctus as well as a subset of each of the mtDNA clades for five nuclear loci (BDNF, BACH1, GAPD, NTF3, and PRLR). We used phylogenetic methods to assess biogeographical patterns within C. caudicinctus, including relaxed molecular clock analyses to estimate divergence times. Ecological niche modelling (Maxent) was employed to estimate the current distribution of suitable climatic envelopes for each lineage. Results: Phylogenetic analyses identified two deeply divergent mtDNA clades within C. caudicinctus - an eastern and western clade - separated by the Western Australian sand deserts. However, divergences pre-date the Pleistocene sand deserts. Phylogenetic analyses of the nuclear DNA data sets generally support major mtDNA clades, suggesting past connections between the western C. c. caudicinctus populations in far eastern Pilbara (EP) and the lineages to the east of the sand deserts. Ecological niche modelling supports the continued suitability of climatic conditions between the Central Ranges and the far EP for C. c. graafi. Main conclusions: Estimates of lineage ages provide evidence of divergence between eastern and western clades during the Miocene with subsequent secondary contact during the Pliocene. Our results suggest that this secondary contact occurred via dispersal between the Central Ranges and the far EP, rather than the more southerly Giles Corridor. These events precede the origins of the western sand deserts and divergence patterns instead appear associated with Miocene and Pliocene climate change.

Multi-attribute preference modelling and regional land-use planning

Forest management policy decisions are complex due to the multiple-use nature of goods and services from forests, difficulty in monetary valuation of ecological services and the involvement of a large number of stakeholders. Multi-attribute decision techniques can be used to synthesise stakeholder preferences related to regional forest planning because it can accommodate conflicting, multidimensional, incommensurable and incomparable objectives. The objective of this paper is to examine how the Analytical Hierarchy Process (AHP) can be used to incorporate stakeholder preferences in determining optimal forest land-use choices. The Australian Regional Forest Agreement Programme is taken as an illustrative case for the analysis. The results show that the AHP can formalise public participation in decision making and increase the transparency and the credibility of the process.

Decision making to support an integrated infrastructure supply chain industrial ecological approach within public private partnerships

Supply chains are complex adaptive systems for which final performance depends upon numerous interdependent decisions made by numerous firms which synthesise inputs from various resources systems.  The dynamic interdependent behaviour of social, economic, material and informational resource systems within eco-industrial settings that support the built environment life cycle supply chains can be studied at the supply chain level.  The impact of megaprojects is significant and holds promise to explore the impact of decisions on various systems as it combines project and system boundaries.  Megaoprojects considered as major events within systems can produce critical revolutionary impacts on the systems within which they are embedded.  The decisions that are made on megaprojects are central to risk management.  typically major infrastructure projects are procured through a form of public private partnership (PPP).  The core principle of PPP is value for money which refers to the best available outcome attempting to take account of all benefits, costs and risks over the whole life of the procurement.  In this paper the focus is on Australia where there has been considerable acitivity in the use of PPPs.  With recent national infrastucture packages proposed to stimulate the economy due to the global financial crisis, decision modelling on risks is a revelant and critical matter not only in practice but also in the research community.  PPPs encourage the whole-of-lifecycle approach in the procurement and management of public sector assets by transparently recognising the costs and risks associated with the whole life of the required service or facility, thus integrated whole of life supply chains can be considered.  By creating a single point of responsibility for an entire project from inception through operation, a strong incentive is created for thinking about the effects that a design or construction decision will have on the effectiveness and efficiency of managing and maintaining a facility during its operational life.  The decision to procure holistic supply chains becomes a much more viable commercial reality in the PPP environment than previously considered in the usual commercial construction spot transactional approach.  These types of decisions tend to be imprecise, approximate and complex requireing justification and reasoning logic rather than the classical 'truth' logic.  The purpose of this paper is to develop a theoretical decision framework which combines interdependency and multi-values logic for supply chain procurement modelling.

Modelling behavioural and fitness consequences of disturbance for geese along their spring flyway

1. For migratory birds the implications of environmental change may be difficult to predict because they use multiple sites during their annual cycle. Moreover, the migrants’ use of these sites may be interdependent. Along the flyway of the Svalbard pink-footed goose Anser brachyrhynchus population, Norwegian farmers use organized scaring to minimize goose use of their grasslands in spring. We assessed the consequences of this practice for regional site use of pink-footed geese along their spring migration route.

2. We used dynamic programming to find the sequence of migratory decisions that maximizes the fitness of female geese during spring migration, assuming scaring impinges on both food-intake rates and predation risk. The parameterization of the model was based on data gathered from individually marked pink-footed geese between 1991 and 2003.

3. The effect of scaring in terms of fitness and site use was most noticeable regarding food-intake rate. Scaring resulted in a redistribution of geese along the flyway. Furthermore, the outcomes of the modelling exercises were highly dependent on whether or not the geese were omniscient or naive: at moderate scaring levels naive geese were predicted to succumb.

4. On a qualitative basis there was good correspondence between the predictions from the model and the empirical evidence gathered to date.

5. Synthesis and applications. Besides highlighting the importance of learning and changing behaviour in an adaptive fashion, our modelling exercise indicated the potential vulnerability of the geese to abrupt environmental change. In addition, the exercise emphasized the interdependence of site use along the migratory flyway. The model supports the necessity for an integrated flyway management approach. In Norway, discussion is ongoing about the future management of the spring conflict between farming interests and geese. Farmers in north and mid-Norway have announced that they will expand the scaring campaign if a long-term solution, including a compensation scheme, is not forthcoming. If scaring on such a large scale is implemented abruptly, it may have severe consequences for the population: management of both the scaring intensity and its geographical extent is urgently required.

Predicting future ecological degradation based on modelled thresholds

Threshold models are becoming important in determining the ecological consequences of our actions within the environment and have a key role in setting bounds on targets used by natural resource managers. We have been using thresholds and related concepts adapted from the multiple stable-states literature to model ecosystem response in the Coorong, the estuary for Australia’s largest river. Our modelling approach is based upon developing a state-and-transition model, with the states defined by the biota and the transitions defined by a classification and regression tree (CART) analysis of the environmental data for the region. Here we explore the behaviour of thresholds within that model. Managers tend to plan for a set of often arbitrarily-derived thresholds in their natural resource management. We attempt to assess how the precision afforded by analyses such as CART translates into ecological outcomes, and explicitly trial several approaches to understanding thresholds and transitions in our model and how they might be relevant for management. We conclude that the most promising approach would be a mixture of further modelling (using past behaviour to predict future degradation) in conjunction with targeted experiments to confirm the results. Our case study of the Coorong is further developed, particularly for the modelling stages of the protocol, to provide recommendations to improve natural resource management strategies that are currently in use.

Integrated modelling of cost-effective siting and operation of flow-control infrastructure for river ecosystem conservation

Wetland and floodplain ecosystems along many regulated rivers are highly stressed, primarily due to a lack of environmental flows of appropriate magnitude, frequency, duration, and timing to support ecological functions. In the absence of increased environmental flows, the ecological health of river ecosystems can be enhanced by the operation of existing and new flow-control infrastructure (weirs and regulators) to return more natural environmental flow regimes to specific areas. However, determining the optimal investment and operation strategies over time is a complex task due to several factors including the multiple environmental values attached to wetlands, spatial and temporal heterogeneity and dependencies, nonlinearity, and time-dependent decisions. This makes for a very large number of decision variables over a long planning horizon. The focus of this paper is the development of a nonlinear integer programming model that accommodates these complexities. The mathematical objective aims to return the natural flow regime of key components of river ecosystems in terms of flood timing, flood duration, and interflood period. We applied a 2-stage recursive heuristic using tabu search to solve the model and tested it on the entire South Australian River Murray floodplain. We conclude that modern meta-heuristics can be used to solve the very complex nonlinear problems with spatial and temporal dependencies typical of environmental flow allocation in regulated river ecosystems. The model has been used to inform the investment in, and operation of, flow-control infrastructure in the South Australian River Murray.