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Zeigler S.L.,University of Maryland University College | Che-Castaldo J.P.,National Socio Environmental Synthesis Center | Che-Castaldo J.P.,University of Maryland University College | Neel M.C.,University of Maryland University College
Conservation Biology | Year: 2013

Use of population viability analyses (PVAs) in endangered species recovery planning has been met with both support and criticism. Previous reviews promote use of PVA for setting scientifically based, measurable, and objective recovery criteria and recommend improvements to increase the framework's utility. However, others have questioned the value of PVA models for setting recovery criteria and assert that PVAs are more appropriate for understanding relative trade-offs between alternative management actions. We reviewed 258 final recovery plans for 642 plants listed under the U.S. Endangered Species Act to determine the number of plans that used or recommended PVA in recovery planning. We also reviewed 223 publications that describe plant PVAs to assess how these models were designed and whether those designs reflected previous recommendations for improvement of PVAs. Twenty-four percent of listed species had recovery plans that used or recommended PVA. In publications, the typical model was a matrix population model parameterized with ≤5 years of demographic data that did not consider stochasticity, genetics, density dependence, seed banks, vegetative reproduction, dormancy, threats, or management strategies. Population growth rates for different populations of the same species or for the same population at different points in time were often statistically different or varied by >10%. Therefore, PVAs parameterized with underlying vital rates that vary to this degree may not accurately predict recovery objectives across a species' entire distribution or over longer time scales. We assert that PVA, although an important tool as part of an adaptive-management program, can help to determine quantitative recovery criteria only if more long-term data sets that capture spatiotemporal variability in vital rates become available. Lacking this, there is a strong need for viable and comprehensive methods for determining quantitative, science-based recovery criteria for endangered species with minimal data availability. © 2013 Society for Conservation Biology.

Ahmadia G.N.,World Wildlife Fund | Glew L.,World Wildlife Fund | Provost M.,World Wildlife Fund | Gill D.,National Socio Environmental Synthesis Center | And 6 more authors.
Philosophical Transactions of the Royal Society B: Biological Sciences | Year: 2015

Quasi-experimental impact evaluation approaches, which enable scholars to disentangle effects of conservation interventions from broader changes in the environment, are gainingmomentum in the conservation sector. However, rigorous impact evaluation using statistical matching techniques to estimate the counterfactual have yet to be applied to marine protected areas (MPAs). While there are numerous studies investigating ‘impacts’ of MPAs that have generated considerable insights, results are variable. This variation has been linked to the biophysical and social context in which they are established, as well as attributes of management and governance. To inform decisions about MPA placement, design and implementation, we need to expand our understanding of conditions under which MPAs are likely to lead to positive outcomes by embracing advances in impact evaluation methodologies. Here, we describe the integration of impact evaluation within an MPA network monitoring programme in the Bird’s Head Seascape, Indonesia. Specifically we (i) highlight the challenges of implementation ‘on the ground’ and in marine ecosystems and (ii) describe the transformation of an existing monitoring programme into a design appropriate for impact evaluation. This study offers one potential model for mainstreaming impact evaluation in the conservation sector. ©2015 The Author(s) Published by the Royal Society.

Motesharrei S.,University of Maryland College Park | Motesharrei S.,National Socio Environmental Synthesis Center | Rivas J.,University of Minnesota | Rivas J.,Institute of Global Environment and Society IGES | Kalnay E.,University of Maryland College Park
Ecological Economics | Year: 2014

There are widespread concerns that current trends in resource-use are unsustainable, but possibilities of overshoot/collapse remain controversial. Collapses have occurred frequently in history, often followed by centuries of economic, intellectual, and population decline. Many different natural and social phenomena have been invoked to explain specific collapses, but a general explanation remains elusive.In this paper, we build a human population dynamics model by adding accumulated wealth and economic inequality to a predator-prey model of humans and nature. The model structure, and simulated scenarios that offer significant implications, are explained. Four equations describe the evolution of Elites, Commoners, Nature, and Wealth. The model shows Economic Stratification or Ecological Strain can independently lead to collapse, in agreement with the historical record.The measure "Carrying Capacity" is developed and its estimation is shown to be a practical means for early detection of a collapse. Mechanisms leading to two types of collapses are discussed. The new dynamics of this model can also reproduce the irreversible collapses found in history. Collapse can be avoided, and population can reach a steady state at maximum carrying capacity if the rate of depletion of nature is reduced to a sustainable level and if resources are distributed equitably. © 2014.

Magliocca N.R.,University of Maryland Baltimore County | Magliocca N.R.,National Socio Environmental Synthesis Center | Brown D.G.,University of Michigan | Ellis E.C.,University of Maryland Baltimore County
PLoS ONE | Year: 2014

Local changes in land use result from the decisions and actions of land-users within land systems, which are structured by local and global environmental, economic, political, and cultural contexts. Such cross-scale causation presents a major challenge for developing a general understanding of how local decision-making shapes land-use changes at the global scale. This paper implements a generalized agent-based model (ABM) as a virtual laboratory to explore how global and local processes influence the land-use and livelihood decisions of local land-users, operationalized as settlement-level agents, across the landscapes of six real-world test sites. Test sites were chosen in USA, Laos, and China to capture globally-significant variation in population density, market influence, and environmental conditions, with land systems ranging from swidden to commercial agriculture. Publicly available global data were integrated into the ABM to model cross-scale effects of economic globalization on local land-use decisions. A suite of statistics was developed to assess the accuracy of model-predicted land-use outcomes relative to observed and random (i.e. null model) landscapes. At four of six sites, where environmental and demographic forces were important constraints on land-use choices, modeled land-use outcomes were more similar to those observed across sites than the null model. At the two sites in which market forces significantly influenced land-use and livelihood decisions, the model was a poorer predictor of land-use outcomes than the null model. Model successes and failures in simulating real-world land-use patterns enabled the testing of hypotheses on land-use decision-making and yielded insights on the importance of missing mechanisms. The virtual laboratory approach provides a practical framework for systematic improvement of both theory and predictive skill in land change science based on a continual process of experimentation and model enhancement. © 2014 Magliocca et al.

Cayton H.L.,North Carolina State University | Haddad N.M.,North Carolina State University | Gross K.,North Carolina State University | Diamond S.E.,Case Western Reserve University | And 2 more authors.
Ecology | Year: 2015

Global climate change is causing shifts in phenology across multiple species. We use a geographically and temporally extensive data set of butterfly abundance across the state of Ohio to ask whether phenological change can be predicted from climatological data. Our focus is on growing degree days (GDD), a commonly used measure of thermal accumulation, as the mechanistic link between climate change and species phenology. We used simple calculations of median absolute error associated with GDD and an alternative predictor of phenology, ordinal date, for both first emergence and peak abundance of 13 butterfly species. We show that GDD acts as a better predictor than date for first emergence in nearly all species, and for peak abundance in more than half of all species, especially univoltine species. Species with less ecological flexibility, in particular those with greater dietary specialization, had greater predictability with GDD. The new method we develop for predicting phenology using GDD offers a simple yet effective way to predict species' responses to climate change. © 2015 by the Ecological Society of America.

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