Appreciating ecological complexity: Habitat contours as a conceptual landscape model

Joern Fischer, David B. Lindermayer, and Ioan Fazey (2004). Appreciating Ecological Complexity: Habitat Contours as a Conceptual Landscape Model. Conservation Biology, 18 (5)pp.1245-1253 RAE2008

Spatial ecological complexity measures in GRASS GIS

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.

Modeling ecological and evolutionary processes in spatially structured populations

Many species inhabit fragmented landscapes, resulting either from anthropogenic or from natural processes. The ecological and evolutionary dynamics of spatially structured populations are affected by a complex interplay between endogenous and exogenous factors. The metapopulation approach, simplifying the landscape to a discrete set of patches of breeding habitat surrounded by unsuitable matrix, has become a widely applied paradigm for the study of species inhabiting highly fragmented landscapes. In this thesis, I focus on the construction of biologically realistic models and their parameterization with empirical data, with the general objective of understanding how the interactions between individuals and their spatially structured environment affect ecological and evolutionary processes in fragmented landscapes. I study two hierarchically structured model systems, which are the Glanville fritillary butterfly in the Åland Islands, and a system of two interacting aphid species in the Tvärminne archipelago, both being located in South-Western Finland. The interesting and challenging feature of both study systems is that the population dynamics occur over multiple spatial scales that are linked by various processes. My main emphasis is in the development of mathematical and statistical methodologies. For the Glanville fritillary case study, I first build a Bayesian framework for the estimation of death rates and capture probabilities from mark-recapture data, with the novelty of accounting for variation among individuals in capture probabilities and survival. I then characterize the dispersal phase of the butterflies by deriving a mathematical approximation of a diffusion-based movement model applied to a network of patches. I use the movement model as a building block to construct an individual-based evolutionary model for the Glanville fritillary butterfly metapopulation. I parameterize the evolutionary model using a pattern-oriented approach, and use it to study how the landscape structure affects the evolution of dispersal. For the aphid case study, I develop a Bayesian model of hierarchical multi-scale metapopulation dynamics, where the observed extinction and colonization rates are decomposed into intrinsic rates operating specifically at each spatial scale. In summary, I show how analytical approaches, hierarchical Bayesian methods and individual-based simulations can be used individually or in combination to tackle complex problems from many different viewpoints. In particular, hierarchical Bayesian methods provide a useful tool for decomposing ecological complexity into more tractable components.

Functional Complexity: The source of value in biodiversity.

Biodiversity may be seen as a scientific measure of the complexity of a biological system, implying an information basis. Complexity cannot be directly valued, so economists have tried to define the services it provides, though often just valuing the services of 'key' species. Here we provide a new definition of biodiversity as a measure of functional information, arguing that complexity embodies meaningful information as Gregory Bateson defined it. We argue that functional information content (FIC) is the potentially valuable component of total (algorithmic) information content (AIC), as it alone determines biological fitness and supports ecosystem services. Inspired by recent extensions to the Noah's Ark problem, we show how FIC/AIC can be calculated to measure the degree of substitutability within an ecological community. Establishing substitutability is an essential foundation for valuation. From it, we derive a way to rank whole communities by Indirect Use Value, through quantifying the relation between system complexity and the production rate of ecosystem services. Understanding biodiversity as information evidently serves as a practical interface between economics and ecological science. © 2012 Elsevier B.V.

Assessing the distribution of social-ecological resilience and risk: Ireland as a case study of the uneven impact of famine

Explanations for the causes of famine and food insecurity often reside at a high level of aggregation or abstraction. Popular models within famine studies have often emphasised the role of prime movers such as population stress, or the political-economic structure of access channels, as key determinants of food security. Explanation typically resides at the macro level, obscuring the presence of substantial within-country differences in the manner in which such stressors operate. This study offers an alternative approach to analyse the uneven nature of food security, drawing on the Great Irish famine of 1845–1852. Ireland is often viewed as a classical case of Malthusian stress, whereby population outstripped food supply under a pre-famine demographic regime of expanded fertility. Many have also pointed to Ireland's integration with capitalist markets through its colonial relationship with the British state, and country-wide system of landlordism, as key determinants of local agricultural activity. Such models are misguided, ignoring both substantial complexities in regional demography, and the continuity of non-capitalistic, communal modes of land management long into the nineteenth century. Drawing on resilience ecology and complexity theory, this paper subjects a set of aggregate data on pre-famine Ireland to an optimisation clustering procedure, in order to discern the potential presence of distinctive social–ecological regimes. Based on measures of demography, social structure, geography, and land tenure, this typology reveals substantial internal variation in regional social–ecological structure, and vastly differing levels of distress during the peak famine months. This exercise calls into question the validity of accounts which emphasise uniformity of structure, by revealing a variety of regional regimes, which profoundly mediated local conditions of food security. Future research should therefore consider the potential presence of internal variations in resilience and risk exposure, rather than seeking to characterise cases based on singular macro-dynamics and stressors alone.

Taylor's power law for ecological communities: an explanation on nonextensive/nonlinear statistical grounds

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An ecological economic interpretation of the Jevons effect

This article develops an ecological economic interpretation of the Jevons effect. Moreover, it is argued that under the neoclassical paradigm there are no elements with which to foresee the long-term existence of this phenomenon. The objective of these arguments is to demonstrate that the Jevons effect can be used to compare the ability of neoclassical and ecological economics describing the social appropriation of nature. This is elaborated in two steps. First, we show the importance of the thesis that the economy cannot be cut off from the biophysical materiality of what is produced to give consistency to the so-called Khazzoom-Brookes postulate. It is made clear that this supposition is exogenous to the neoclassical paradigm. Second, the supposition of the biophysical materiality of what is produced is utilized to make an ecological economic interpretation of the Jevons effect. Afterwards, a comparison is made between the neoclassical and the ecological economic perspectives. This comparison leads to the following conclusions: (i) the persistent presence of the Jevons effect in the long run is an anomaly in the neoclassical paradigm; (ii) the observation of the non-existence of the Jevons effect is a refutation of the supposition that economic growth and biophysical materiality are not separable, a central thesis defended by ecological economists. This situation makes possible to use the Jevons effect as a 'laboratory test' to compare the ability of neoclassical and ecological economic paradigms to describe the social appropriation of nature. (C) 20111 Elsevier B.V. All rights reserved.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.

Transdisciplinary synthesis for ecosystem science, policy and management: The Australian experience

Mitigating the environmental effects of global population growth, climatic change and increasing socio-ecological complexity is a daunting challenge. To tackle this requires synthesis: the integration of disparate information to generate novel insights from heterogeneous, complex situations where there are diverse perspectives. Since 1995, a structured approach to inter-, multi- and trans-disciplinary1 collaboration around big science questions has been supported through synthesis centres around the world. These centres are finding an expanding role due to ever-accumulating data and the need for more and better opportunities to develop transdisciplinary and holistic approaches to solve real-world problems. The Australian Centre for Ecological Analysis and Synthesis (ACEAS ) has been the pioneering ecosystem science synthesis centre in the Southern Hemisphere. Such centres provide analysis and synthesis opportunities for time-pressed scientists, policy-makers and managers. They provide the scientific and organisational environs for virtual and face-to-face engagement, impetus for integration, data and methodological support, and innovative ways to deliver synthesis products. We detail the contribution, role and value of synthesis using ACEAS to exemplify the capacity for synthesis centres to facilitate trans-organisational, transdisciplinary synthesis. We compare ACEAS to other international synthesis centres, and describe how it facilitated project teams and its objective of linking natural resource science to policy to management. Scientists and managers were brought together to actively collaborate in multi-institutional, cross-sectoral and transdisciplinary research on contemporary ecological problems. The teams analysed, integrated and synthesised existing data to co-develop solution-oriented publications and management recommendations that might otherwise not have been produced. We identify key outcomes of some ACEAS working groups which used synthesis to tackle important ecosystem challenges. We also examine the barriers and enablers to synthesis, so that risks can be minimised and successful outcomes maximised. We argue that synthesis centres have a crucial role in developing, communicating and using synthetic transdisciplinary research.

A comparative social network analysis of wasp colonies and classrooms: Linking network structure to functioning

A major question in current network science is how to understand the relationship between structure and functioning of real networks. Here we present a comparative network analysis of 48 wasp and 36 human social networks. We have compared the centralisation and small world character of these interaction networks and have studied how these properties change over time. We compared the interaction networks of (1) two congeneric wasp species (Ropalidia marginata and Ropalidia cyathiformis), (2) the queen-right (with the queen) and queen-less (without the queen) networks of wasps, (3) the four network types obtained by combining (1) and (2) above, and (4) wasp networks with the social networks of children in 36 classrooms. We have found perfect (100%) centralisation in a queen-less wasp colony and nearly perfect centralisation in several other queen-less wasp colonies. Note that the perfectly centralised interaction network is quite unique in the literature of real-world networks. Differences between the interaction networks of the two wasp species are smaller than differences between the networks describing their different colony conditions. Also, the differences between different colony conditions are larger than the differences between wasp and children networks. For example, the structure of queen-right R. marginata colonies is more similar to children social networks than to that of their queen-less colonies. We conclude that network architecture depends more on the functioning of the particular community than on taxonomic differences (either between two wasp species or between wasps and humans).

Relationship between species diversity and ecotope diversity

The relationship between species diversity and ecotope diversity has long been debated. But these debates seem meaningless because most of them were based on different definitions. In this paper, diversity has two components: richness based on the total number and evenness based on the relative abundance. Species diversity is distinguished into individual-counting diversity and biomass-based diversity. Ecotope diversity is divided into individual ecotope-counting diversity and ecotope-area based diversity. Under this definition, we make a comprehensive investigation into Dongzhi tableland of Loess Plateau by cooperating with local technicians. We find that individual-counting diversity is significantly correlated with biomass-based diversity in grassland ecosystems; individual ecotope-counting diversity and ecotope-area based diversity have a significant correlation. Therefore, it is unnecessary to divide species diversity into individual-counting diversity and biomass-based diversity in grassland ecosystems and to distinguish ecotope diversity into individual ecotope-counting and ecotope-area based diversity for the issues that have no special requirement for accuracy. But the analyses of the investigation data demonstrate that species diversity has no significant correlation with ecotope diversity.