Maximizing a new quantity in sequential reserve selection

The fundamental goal of conservation planning is biodiversity persistence, yet most reserve selection methods prioritize sites using occurrence data. Numerous empirical studies support the notion that defining and measuring objectives in terms of species richness (where the value of a site is equal to the number of species it contains, or contributes to an existing reserve network) can be inadequate for maintaining biodiversity in the long term. An existing site-assessment framework that implicitly maximized the persistence probability of multiple species was integrated with a dynamic optimization model. The problem of sequential reserve selection as a Markov decision process was combined with stochastic dynamic programming to find the optimal solution. The approach represents a compromise between representation-based approaches (maximizing occurrences) and more complex tools, like spatially-explicit population models. The method, the inherent problems and interesting conclusions are illustrated with a land acquisition case study on the central Platte River

Principal Response Curve Analysis of Arthropod Community Abundance Data with Sparse Subsets

Principal response curve (PRC) analysis was applied to an assessment of the ecological impact of the genetically-modified (GM), insect-resistant, cotton MON 88702 on predatory Hemiptera communities in the field. The field community was represented by ten taxa collected ten times across the season at six sites, in which individual taxa were not observed in at least 25% of the time (unique site x collection combinations). These complete absences and those nearly so, called sparse subsets of the data in this investigation, were the result of geoclimatic and seasonal variations, which are both independent of the treatment effect for which the PRC analysis is intended. If the sparse subsets were included in the analysis, the treatment effect would be underestimated. Here, a modified analysis is proposed to remove those sparse subsets and to be performed on the incomplete data. In the application to MON 88702, four components (PRC1-4) were significant at the 5% level by the modified method, when more than 50% of the data were excluded due to no- or low responses, and five (PRC1-5) by the classical method. While PRC1-2 was highly consistent between two methods, PRC3-5 was largely different because of sparse subsets of the data. Differences in results between two methods demonstrate that excluding sparse subsets prevented the bias in the estimation of the treatment effect and the relationship with the community from confounding with the environmental variation that caused the sparse data. In this regard, the modification should be considered as a supplement of the classical PRC analysis and recommended when abundance data have sparse subsets

Pollen-Mediated Gene Flow in Maize: Implications for Isolation Requirements and Coexistence in Mexico, the Center of Origin of Maize

Mexico, the center of origin of maize (Zea mays L.), has taken actions to preserve the identity and diversity of maize landraces and wild relatives. Historically, spatial isolation has been used in seed production to maintain seed purity. Spatial isolation can also be a key component for a strategy to minimize pollen-mediated gene flow in Mexico between transgenic maize and sexually compatible plants of maize conventional hybrids, landraces, and wild relatives. The objective of this research was to generate field maize-to-maize outcrossing data to help guide coexistence discussions in Mexico. In this study, outcrossing rates were determined and modeled from eight locations in six northern states, which represent the most economically important areas for the cultivation of hybrid maize in Mexico. At each site, pollen source plots were planted with a yellow-kernel maize hybrid and surrounded by plots with a white-kernel conventional maize hybrid (pollen recipient) of the same maturity. Outcrossing rates were then quantified by assessing the number of yellow kernels harvested from white-kernel hybrid plots. The highest outcrossing values were observed near the pollen source (12.9% at 1 m distance). The outcrossing levels declined sharply to 4.6, 2.7, 1.4, 1.0, 0.9, 0.5, and 0.5% as the distance from the pollen source increased to 2, 4, 8, 12, 16, 20, and 25 m, respectively. At distances beyond 20 m outcrossing values at all locations were below 1%. These trends are consistent with studies conducted in other world regions. The results suggest that coexistence measures that have been implemented in other geographies, such as spatial isolation, would be successful in Mexico to minimize transgenic maize pollen flow to conventional maize hybrids, landraces and wild relatives

New tools for quantitative decision analysis in applied ecology and conservation

Scientists have generated a massive body of theory aimed at predicting and managing the impacts of anthropogenic activities on populations, species, and ecosystems. Transforming this research into knowledge that informs complex decision-making problems remains a major challenge in environmental management and conservation. My dissertation research aims to address this issue through the development and application of mathematical and statistical models. I integrate tools, concepts, and techniques from ecology, applied mathematics, computer science, and statistics to build structured decision-making frameworks for spatial prioritization, resource allocation, and optimal scheduling. I also tackle several of the technical challenges limiting the utility of such tools in practice, and seek to make them accessible to other scientists and decision-makers. Much of my research is motivated by the interest in land acquisition as an in situ conservation strategy. In Chapters 1 and 2, I develop an integrated reserve selection framework for spatial priority-setting and optimal investing. The framework combines Bayesian methods and Markov decision theory in the context of making land acquisition decisions. A second focus of my research focuses on overcoming several of the technical and computational challenges of utilizing Markov decision processes (MDPs) in the context of real-world planning. In Chapter 3, I introduce and test and class of approximation algorithms developed in the artificial intelligence community to simply and solve MDPs with large state spaces. In Chapter 4, I develop a novel method that uses information-gap (radius of stability-type) models to represent uncertainty in the state transition function of an MDP. Rather than requiring information about the extent of parametric uncertainty at the outset, this method addresses the question of how much uncertainty is permissible before the optimal policy would change. Finally, in Chapter 5, I develop a pair of sensitivity metrics for info-gap decision analysis. Both sensitivity metrics are an essential addition to the robust optimization toolkit, providing a systematic approach for identifying weaknesses in an info-gap decision analysis. They are also needed quantities in the effort to make sound, defensible decisions

Observed and predicted outcrossing rates (%) across sites as a function of distance (m).

<p>Observed values (circles) and predicted values (line) based on model (1) with 95% confidence interval (shaded region).</p