https://boa.unimib.it/bitstream/10281/137284/1/Optimal%20flow.pdf

Optimal flow for brown trout: Habitat - prey optimization.

We present a Bayesian hierarchical model to estimate the abundance and the biomass of brown trout (Salmo trutta fario) by using removal sampling and biometric data collected at several stream sections. The model accounts for (i) variability of the abundance with fish length (as a distribution mixture), (ii) spatial variability of the abundance, (iii) variability of the catchability with fish length (as a logit regression model), (iv) spatial variability of the catchability, and (v) residual variability of the catchability with fish. Model measured variables are the areas of the stream sections as well as the length and the weight of the caught fish. We first test the model by using a simulated dataset before using a 3-location, 2-removal sampling dataset collected in the field. Fifteen model alternatives are compared with an index of complexity and fit by using the field dataset. The selected model accounts for variability of the abundance with fish length and stream section and variability of the catchability with fish length. By using the selected model, 95% credible interval estimates of the abundances at the three stream sections are (0.46,0.59), (0.90,1.07), and (0.56,0.69) fish/m2. Respective biomass estimates are (9.68, 13.58), (17.22, 22.71), and (12.69, 17.31) g/m2.

/pdf/a-hierarchical-model-to-estimate-the-abundance-and-biomass-39k2gax6oy.pdf

A hierarchical model to estimate the abundance and biomass of salmonids by using removal sampling and biometric data from multiple locations

Successful management and protection of wild animal populations relies on good understanding of their life cycles. Because population dynamics depends on intricate interactions of biological and ecological processes at various scales, new approaches are needed that account for the variability of demographic processes and associated parameters in a hierarchy of spatial scales. A hierarchical Bayesian model for the resident brown trout (Salmo trutta) life cycle was built to assess the relative influence of local and general determinants of mortality. The model was fitted to an extensive data set collected in 40 river reaches, combining abundance and environmental data (hydraulics, water temperature). Density-dependent mortality of juveniles increased at low water temperatures and decreased with shelter availability. High water temperature increased density-dependent mortality in adults. The model could help to predict monthly juvenile and adult mortality under scenarios of global warming and changes in shel...

/pdf/understanding-inter-reach-variation-in-brown-trout-salmo-45c1e12ixl.pdf

Understanding inter-reach variation in brown trout (Salmo trutta) mortality rates using a hierarchical Bayesian state-space model

Advantages and insights from a hierarchical Bayesian growth and dynamics model based on salmonid electrofishing removal data

In Italy, the Water Framework Directive introduced major changes to water management, by making water resources more efficient and enforceable, so, this study aims to highlight some of the potential implications of its implementation for lake management. In this respect, the Life+ INHABIT project was crucial in launching the monitoring plan of lake macroinvertebrates, standardized at national level. Quantile regression analysis was focused on nine lakes located in two Italian regions (Piedmont and Sardinia). The studied lakes cover a wide trophic spectrum, from oligotrophy to hyper-trophy. In particular, all Sardinians lakes had higher trophic conditions than Piedmontese lakes, reaching only a meso-eutrophic state. The lakes were sampled through the national standardised protocol taking three replicates for each of the three recognized lake zones. The study lakes showed high chemical variability with conductivity varying between 53 and 561 μS/cm, pH between 6.5 and 9.1, and alkalinity between 14 and 398 mg/L. The bottom sediments were characterized by fine sand (range 5199%), followed by silt (0.6-35%) and clay (0 – 28%). Lake Habitat Survey was also applied and the results of its synthetic Indices highlighted higher values in natural lakes and lower in the reservoirs. In all the lakes, macroinvertebrates mainly consisted of Dipteran chironomids and oligochaetes characterized by relative abundance up to 80% and >90%, respectively. In order to link macroinvertebrate community characteristics to their habitat we measured twenty-one variables that represent chemical, physical and morphological characteristics of the 2. Quantile regression as predictive for lake macroinvertebrate biodiversity __________________________________________________________ 10 environment and for each variable the range of variation was provided to clarify the domain of application. Using quantile regression to evaluate limiting responses, we selected sampling depth and oxygen percent saturation (oxygen content) as the two variables that best explain the number of taxa present and the diversity of a site. The authors also stress the importance of the combination of selected variables in structuring lacustrine macroinvertebrate communities. We provide models able to predict the potential of community diversity as a function of environmental characteristics. All of this evidence, can help water managers in deciding to initially invest resources in those lakes where the biological communities were expected to be impaired and to identify which lakes could be in pristine or in near-pristine conditions.

/pdf/environmental-gradients-and-ecological-processes-as-rtn5uufmcp.pdf

Environmental Gradients and Ecological Processes as Constraints in the Distribution Patterns of Freshwater Biological Communities

We present a hierarchical Bayesian model (HBM) to estimate the growth parameters, production, and production over biomass ratio (P/B) of resident brown trout (Salmo trutta fario) populations. The data which are required to run the model are removal sampling and air temperature data which are conveniently gathered by freshwater biologists. The model is the combination of eight submodels: abundance, weight, biomass, growth, growth rate, time of emergence, water temperature, and production. Abundance is modeled as a mixture of Gaussian cohorts; cohorts centers and standard deviations are related by a von Bertalanffy growth function; time of emergence and growth rate are functions of water temperature; water temperature is predicted from air temperature; biomass, production, and P/B are subsequently computed. We illustrate the capabilities of the model by investigating the growth and production of a brown trout population (Neste d'Oueil, Pyrenees, France) by using data collected in the field from 2005 to 2010.

/pdf/a-length-based-hierarchical-model-of-brown-trout-salmo-1xfylcmkz3.pdf

Jean-Baptiste Lecomte

Papers

A length-based hierarchical model of brown trout (Salmo trutta fario) growth and production