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Fontes, J, Semmens B, Caselle JE, Santos RS, Prakya R.  2016.  Ocean productivity may predict recruitment of the rainbow wrasse (coris julis). Plos One. 11   10.1371/journal.pone.0165648   AbstractWebsite

Predicting recruitment fluctuations of fish populations remains the Holy Grail of fisheries science. While previous work has linked recruitment of reef fish to environmental variables including temperature, the demonstration of a robust relationship with productivity remains elusive. Despite decades of research, empirical evidence to support this critical link remains limited. Here we identify a consistent and strong relationship between recruitment of a temperate wrasse Coris julis, from temperate reefs in the mid-Atlantic region, with Chlorophyll, over contrasting scales, across multiple years. Additionally, we find that the correlation between Chlorophyll and recruitment is not simply masking a temperature-recruitment relationship. Understanding the potential mechanisms underlying recruitment variability, particularly as it relates to changing climate and ocean regimes, is a critical first step towards characterizing species' vulnerability to mismatches between pulsed planktonic production and early pelagic life stages.

Stanton, JC, Semmens BX, McKann PC, Will T, Thogmartin WE.  2016.  Flexible risk metrics for identifying and monitoring conservation-priority species. Ecological Indicators. 61:683-692.   10.1016/j.ecolind.2015.10.020   AbstractWebsite

Region-specific conservation programs should have objective, reliable metrics for species prioritization and progress evaluation that are customizable to the goals of a program, easy to comprehend and communicate, and standardized across time. Regional programs may have vastly different goals, spatial coverage, or management agendas, and one-size-fits-all schemes may not always be the best approach. We propose a quantitative and objective framework for generating metrics for prioritizing species that is straightforward to implement and update, customizable to different spatial resolutions, and based on readily available time-series data. This framework is also well-suited to handling missing-data and observer error. We demonstrate this approach using North American Breeding Bird Survey (NABBS) data to identify conservation priority species from a list of over 300 landbirds across 33 bird conservation regions (BCRs). To highlight the flexibility of the framework for different management goals and timeframes we calculate two different metrics. The first identifies species that may be inadequately monitored by NABBS protocols in the near future (TMT, time to monitoring threshold), and the other identifies species likely to decline significantly in the near future based on recent trends (TPD, time to percent decline). Within the individual BCRs we found up to 45% (mean 28%) of the species analyzed had overall declining population trajectories, which could result in up to 37 species declining below a minimum NABBS monitoring threshold in at least one currently occupied BCR within the next 50 years. Additionally, up to 26% (mean 8%) of the species analyzed within the individual BCRs may decline by 30% within the next decade. Conservation workers interested in conserving avian diversity and abundance within these BCRs can use these metrics to plan alternative monitoring schemes or highlight the urgency of those populations experiencing the fastest declines. However, this framework is adaptable to many taxa besides birds where abundance time-series data are available. Published by Elsevier Ltd.

Scheuerell, MD, Buhle ER, Semmens BX, Ford MJ, Cooney T, Carmichael RW.  2015.  Analyzing large-scale conservation interventions with Bayesian hierarchical models: a case study of supplementing threatened Pacific salmon. Ecology and Evolution. 5:2115-2125.   10.1002/ece3.1509   AbstractWebsite

Myriad human activities increasingly threaten the existence of many species. A variety of conservation interventions such as habitat restoration, protected areas, and captive breeding have been used to prevent extinctions. Evaluating the effectiveness of these interventions requires appropriate statistical methods, given the quantity and quality of available data. Historically, analysis of variance has been used with some form of predetermined before-after control-impact design to estimate the effects of large-scale experiments or conservation interventions. However, ad hoc retrospective study designs or the presence of random effects at multiple scales may preclude the use of these tools. We evaluated the effects of a large-scale supplementation program on the density of adult Chinook salmon Oncorhynchus tshawytscha from the Snake River basin in the northwestern United States currently listed under the U.S. Endangered Species Act. We analyzed 43years of data from 22 populations, accounting for random effects across time and space using a form of Bayesian hierarchical time-series model common in analyses of financial markets. We found that varying degrees of supplementation over a period of 25years increased the density of natural-origin adults, on average, by 0-8% relative to nonsupplementation years. Thirty-nine of the 43year effects were at least two times larger in magnitude than the mean supplementation effect, suggesting common environmental variables play a more important role in driving interannual variability in adult density. Additional residual variation in density varied considerably across the region, but there was no systematic difference between supplemented and reference populations. Our results demonstrate the power of hierarchical Bayesian models to detect the diffuse effects of management interventions and to quantitatively describe the variability of intervention success. Nevertheless, our study could not address whether ecological factors (e.g., competition) were more important than genetic considerations (e.g., inbreeding depression) in determining the response to supplementation.