The rediscovery of remnant Florida panthers (Puma concolor coryi) in southern Florida swamplands prompted a program to protect and stabilize the population. In 1995, conservation managers translocated eight female pumas (P. c. stanleyana) from Texas to increase depleted genetic diversity, improve population numbers, and reverse indications of inbreeding depression. We have assessed the demographic, population-genetic, and biomedical consequences of this restoration experiment and show that panther numbers increased threefold, genetic heterozygosity doubled, survival and fitness measures improved, and inbreeding correlates declined significantly. Although these results are encouraging, continued habitat loss, persistent inbreeding, infectious agents, and possible habitat saturation pose new dilemmas. This intensive management program illustrates the challenges of maintaining populations of large predators worldwide.

Costs and benefits for partners in mutualistic interactions can vary greatly, but surprisingly little is known about the factors that drive this variation across systems. We conducted a meta-analysis of ant-plant protective mutualisms to quantify the effects of ant defenders on plant reproductive output, to evaluate if reproductive effects were predicted from reductions in herbivory and to identify characteristics of the plants, ants and environment that explained variation in ant protection. We also compared our approach with two other recent meta-analyses on ant-plant mutualisms, emphasizing differences in our methodology (using a weighted linear mixed effects model) and our focus on plant reproduction rather than herbivore damage. Based on 59 ant and plant species pairs, ant presence increased plant reproductive output by 49% and reduced herbivory by 62%. The effects on herbivory and reproduction within systems were positively correlated, but the slope of this relationship (0.75) indicated that tolerance to foliar herbivory may be a common plant response to absence of ant guards. Furthermore, the relationship between foliar damage and reproduction varied substantially among systems, suggesting that herbivore damage is not a reliable surrogate for fitness consequences of ant protection. Studies that experimentally excluded ants reported a smaller effect of ant protection on plant reproduction than studies that relied upon natural variation in ant presence, suggesting that study methods can affect results in these systems. Of the ecological variables included in our analysis, only plant life history (i.e., annual or perennial) explained variation in the protective benefit of mutualistic ants: presence of ants benefitted reproduction of perennials significantly more than that of annuals. These results contrast with other quantitative reviews of these relationships that did not include plant life history as an explanatory factor and raise several questions to guide future research on ant-plant protection mutualisms.

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4Models of population dynamics are frequently used for purposes such as testing 5 hypotheses about density dependence and predicting species' responses to future 6 environmental change or conservation actions. Fitting models of population dynamics 7 to field data is challenging because most data sets are characterized by observation 8 error, which can inflate estimates of process variation if ignored. Recently, state-space 9 models have been developed to deal with this problem by directly modeling both the 10 observation error and the ecological process of interest. Conventional state-space 11 models, however, have several important limitations: (1) they assume that random 12 effects are Gaussian distributed, which implies that abundance can be negative and 13 that false positive observation errors are equally likely as false negative errors; (2) they 14 do not admit spatial variation in population dynamics; and (3) some of the parameters 15 of the model are not estimable. We demonstrate how each of these problems can be 16 resolved using a class of hierarchical models proposed by Dail and Madsen (2011, 17 Biometrics) that attributes observation error to imperfect detection. We expand this 18 class of models to accommodate classical growth models (e.g. exponential and 19Ricker-logistic), zero-inflation, and random effects. We also present methods for 20 1 forecasting population size under future environmental conditions. Implementation of 21 these ideas is possible using either frequentist or Bayesian methods, as demonstrated 22 by accompanying R and JAGS code. Results of a simulation study suggest that bias 23 is negligible and coverage nominal for the proposed model extensions. An analysis of 24 data from the North American Breeding Bird Survey highlights how these methods can 25 be readily applied to existing data, but it also suggests that precision will be low when 26 direct information about detection probability (such as is collected using distance 27 sampling or replicated counts) is lacking. 28