Life history and ecology of the elusive European short-snouted seahorse Hippocampus hippocampus.

To improve the understanding of the life history and ecology of one of Europe's most elusive fishes, the short-snouted seahorse Hippocampus hippocampus, data from wild populations in a shallow coastal lagoon in southern Portugal were analysed. The data were collected from 17 tagged seahorses on a focal-study grid as well as from >350 seahorses encountered during underwater visual surveys and a fishery-independent study using beach seines. These populations of settled juveniles and adults had a mean population density of 0·009 m-2 . During the study period (2000-2004), reproduction peaked in July and August. Juveniles recruited to the lagoon at c. 66 mm standard length (LS ) and 0·5 years of age and established small home ranges (0·8 to 18·2 m2 ). First reproduction was estimated at 100 mm and 1 year of age. Based on a fitted von Bertalanffy model, H. hippocampus grew quickly (growth coefficient K = 0·93) to a maximum theoretical size L∞ = 150 mm and have a maximum lifespan of c. 3·2 years. Courtship behaviours were consistent with the maintenance of pair bonds and males brooded multiple batches of young per year. Estimated annual reproductive output averaged 871 young (±632). Together these analyses provide the first life-history parameters for this species and indicate that H. hippocampus bears characteristics of opportunist and intermediate strategists. Such populations are predicted to exhibit large fluctuations in abundance, making them vulnerable to extended periods of poor recruitment.

Sensitivity of population viability to spatial and nonspatial parameters using GRIP.

Metapopulation dynamics are influenced by spatial parameters including the amount and arrangement of suitable habitat, yet these parameters may be uncertain when deciding how to manage species or their habitats. Sensitivity analyses of population viability analysis (PVA) models can help measure relative parameter influences on predictions, identify research priorities for reducing uncertainty, and evaluate management strategies. Few spatial PVAs, however, include sensitivity analyses of both spatial and nonspatial parameters, perhaps because computationally efficient tools for such analyses are lacking or inaccessible. We developed GRIP, a program to facilitate sensitivity analysis of spatial and nonspatial input parameters for PVAs created in RAMAS Metapop, a widely applied software program. GRIP creates random sets of input files by varying parameters specified in the PVA model including vital rates and their correlations among populations, the number and configuration of populations, dispersal rates, dispersal survival, initial population abundances, carrying capacities, and the probability, intensity, and spatial extent of catastrophes, while drawing on specified parameter distributions. We evaluated GRIP's performance as a tool for sensitivity analysis of spatial PVAs and explored the consequences of varying spatial input parameters for predictions of a published PVA model of the sand lizard (Lacerta agilis). We used GRIP output to generate standardized regression coefficients (SRCs) and nonparametric correlation coefficients as indices of the relative sensitivity of predicted conservation status to input parameters. GRIP performed well; with a single analysis we were able to rank the relative influence of input parameters identified as influential by the PVA's original author, S. A. Berglind, who used three separate forms of sensitivity analysis. Our analysis, however, also underscored the value of exploring the relative influence of spatial parameters on PVA predictions; both SRCs and correlation coefficients indicated that the most influential parameters in the sand lizard model were spatial in nature. We provide annotated code so that GRIP may be modified to reflect particular species biology, customized for more complex spatial PVA models, upgraded to incorporate features added in newer versions of RAMAS Metapop, used as a template to develop similar programs, or used as it is for computationally efficient sensitivity analyses in support of conservation planning.