Consequences of above-ground invasion by non-native plants into restored vernal pools do not prompt same changes in below-ground processes

Given the frequent overlap between biological plant invasion and ecological restoration efforts it is important to investigate their interactions to sustain desirable plant communities and modify long-Term legacies both above-and below-ground. To address this relationship, we used natural reference, invaded and created vernal pools in the Central Valley of California to examine potential changes in direct and indirect plant effects on soils associated with biological invasion and active restoration ecosystem disturbances. Our results showed that through a shift in vegetation composition and changes in the plant community tissue chemistry, invasion by non-native plant species has the potential to transform plant inputs to soils in vernal pool systems. In particular, we found that while invasive plant litter decomposition was driven by seasonal and interannual variability, associated with changes in precipitation, the overall decomposition rates for invasive litter was drastically lower than native species. This shift has important implications for long-Term alterations in plant-based inputs to soils in an amplifying feedback to nutrient cycling. Moreover, these results were independent of historic active restoration efforts. Despite the consistent shift in plant litter decomposition rates and community composition, we did not detect associated shifts in below-ground function associated with invasion by non-native plants. Instead, soil C:N ratios and microbial biomass did not differ between invaded and naturally occurring reference pools but were reduced in the manipulated created pools independent of invasion levels. Our results suggest that while there is an observed invasive amplifying feedback above-ground this trajectory is not represented below-ground, and restoration legacies dominated 10 years after practices were applied. Restoration practices that limit invasive plant feedbacks and account for soil legacy recovery, therefore offer the best solution for disturbed ephemeral ecosystems

Optimizing resource allocation in computational sustainability: Models, algorithms and tools

The 17 Sustainable Development Goals laid out by the United Nations include numerous targets as well as indicators of progress towards sustainable development. Decision-makers tasked with meeting these targets must frequently propose upfront plans or policies made up of many discrete actions, such as choosing a subset of locations where management actions must be taken to maximize the utility of the actions. These types of resource allocation problems involve combinatorial choices and tradeoffs between multiple outcomes of interest, all in the context of complex, dynamic systems and environments. The computational requirements for solving these problems bring together elements of discrete optimization, large-scale spatiotemporal modeling and prediction, and stochastic models. This dissertation leverages network models as a flexible family of computational tools for building prediction and optimization models in three sustainability-related domain areas: 1) minimizing stochastic network cascades in the context of invasive species management; 2) maximizing deterministic demand-weighted pairwise reachability in the context of flood resilient road infrastructure planning; and 3) maximizing vertex-weighted and edge-weighted connectivity in wildlife reserve design. We use spatially explicit network models to capture the underlying system dynamics of interest in each setting, and contribute discrete optimization problem formulations for maximizing sustainability objectives with finite resources. While there is a long history of research on optimizing flows, cascades and connectivity in networks, these decision problems in the emerging field of computational sustainability involve novel objectives, new combinatorial structure, or new types of intervention actions. In particular, we formulate a new type of discrete intervention in stochastic network cascades modeled with multivariate Hawkes processes. In conjunction, we derive an exact optimization approach for the proposed intervention based on closed-form expressions of the objective functions, which is applicable in a broad swath of domains beyond invasive species, such as social networks and disease contagion. We also formulate a new variant of Steiner Forest network design, called the budget-constrained prize-collecting Steiner forest, and prove that this optimization problem possesses a specific combinatorial structure, restricted supermodularity, that allows us to design highly effective algorithms. In each of the domains, the optimization problem is defined over aspects that need to be predicted, hence we also demonstrate improved machine learning approaches for each.Ph.D

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

Mosquitopia

This edited volume brings together natural scientists, social scientists and humanists to assess if (or how) we may begin to coexist harmoniously with the mosquito. The mosquito is humanity’s deadliest animal, killing over a million people each year by transmitting malaria, yellow fever, Zika and several other diseases. Yet of the 3,500 species of mosquito on Earth, only a few dozen of them are really dangerous—so that the question arises as to whether humans and their mosquito foe can learn to live peacefully with one another. Chapters assess polarizing arguments for conserving and preserving mosquitoes, as well as for controlling and killing them, elaborating on possible consequences of both strategies. This book provides informed answers to the dual question: could we eliminate mosquitoes, and should we? Offering insights spanning the technical to the philosophical, this is the “go to” book for exploring humanity’s many relationships with the mosquito—which becomes a journey to finding better ways to inhabit the natural world. Mosquitopia will be of interest to anyone wanting to explore dependencies between human health and natural systems, while offering novel perspectives to health planners, medical experts, environmentalists and animal rights advocates

A Quantitative Framework for Assessing Ecological Resilience

Quantitative approaches to measure and assess resilience are needed to bridge gaps between science, policy, and management. In this paper, we suggest a quantitative framework for assessing ecological resilience. Ecological resilience as an emergent ecosystem phenomenon can be decomposed into complementary attributes (scales, adaptive capacity, thresholds, and alternative regimes) that embrace the complexity inherent to ecosystems. Quantifying these attributes simultaneously provides opportunities to move from the assessment of specific resilience within an ecosystem toward a broader measurement of its general resilience. We provide a framework that is based on reiterative testing and recalibration of hypotheses that assess complementary attributes of ecological resilience. By implementing the framework in adaptive approaches to management, inference, and modeling, key uncertainties can be reduced incrementally over time and learning about the general resilience of dynamic ecosystems maximized. Such improvements are needed because uncertainty about global environmental change impacts and their effects on resilience is high. Improved resilience assessments will ultimately facilitate an optimized use of limited resources for management

Chapter Threats to Ultraoligotrophic Marine Ecosystems

Applied optic

Valuing biodiversity: Decision support for biosecurity response

This thesis develops a Decision Support System (DSS) to improve biosecurity response decisions affecting indigenous biodiversity. The key elements of the DSS are a synthesis of three components of non-market valuation: choice modelling, a systematic database of values and benefit transfer, with risk simulation to account for uncertainty. The innovative framework is demonstrated in a manual developed for Biosecurity New Zealand analysts for use during the early days of an incursion when time is severely constrained and uncertainty abounds. Theoretical approaches to decision making for environmental resource allocation decisions are reviewed with particular reference to decision support tools including non-market valuation, stated preference techniques, database development and benefit transfer. More complex and time consuming tools such as deliberative support and mediated modelling are also discussed. A framework is developed to quantify biodiversity values for use in Cost Benefit Analysis (CBA). The framework incorporates advanced choice modelling techniques demonstrated through four case studies, a systematic database of biodiversity values and transfer of these values using univariate benefit transfer with theoretical adjustment. The uncertainty in the values captured in the panel version of the Random Parameters Logit (RPL) model is integrated into CBA using risk simulation which utilises the means, standard deviations and correlation coefficients of risky variables to quantify the probability of achieving a positive Net Present Value of different response options. The DSS developed in this thesis has wider application in routine management of pests and diseases and in other resource allocation decisions by public agencies which impact on indigenous biodiversity