Browsing by Subject "stochasticity"

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  • Vilmi, Annika; Gibert, Corentin; Escarguel, Gilles; Happonen, Konsta; Heino, Jani; Jamoneau, Aurelien; Passy, Sophia I.; Picazo, Felix; Soininen, Janne; Tison-Rosebery, Juliette; Wang, Jianjun (2021)
    Patterns in community composition are scale-dependent and generally difficult to distinguish. Therefore, quantifying the main assembly processes in various systems and across different datasets has remained challenging. Building on the PER-SIMPER method, we propose a new metric, the dispersal-niche continuum index (DNCI), which estimates whether dispersal or niche processes dominate community assembly and facilitates the comparisons of processes among datasets. The DNCI was tested for robustness using simulations and applied to observational datasets comprising organismal groups with different trophic level and dispersal potential. Based on the robustness tests, the DNCI discriminated the respective contribution of niche and dispersal processes in pairwise comparisons of site groups with less than 40% and 30% differences in their taxa and site numbers, respectively. In the observational datasets, the DNCI suggested that dispersal rather than niche assembly was the dominant assembly process which, however, varied in intensity among organismal groups and study contexts, including spatial scale and ecosystem types. The proposed DNCI measures the relative strength of community assembly processes in a way that is simple, easily quantifiable and comparable across datasets. We discuss the strengths and weaknesses of the DNCI and provide perspectives for future research.
  • Malo, Pekka; Tahvonen, Olli; Suominen, Antti; Back, Philipp; Viitasaari, Lauri (2021)
    We solve a stochastic high-dimensional optimal harvesting problem by using reinforcement learning algorithms developed for agents who learn an optimal policy in a sequential decision process through repeated experience. This approach produces optimal solutions without discretization of state and control variables. Our stand-level model includes mixed species, tree size structure, optimal harvest timing, choice between rotation and continuous cover forestry, stochasticity in stand growth, and stochasticity in the occurrence of natural disasters. The optimal solution or policy maps the system state to the set of actions, i.e., clear-cutting, thinning, or no harvest decisions as well as the intensity of thinning over tree species and size classes. The algorithm repeats the solutions for deterministic problems computed earlier with time-consuming methods. Optimal policy describes harvesting choices from any initial state and reveals how the initial thinning versus clear-cutting choice depends on the economic and ecological factors. Stochasticity in stand growth increases the diversity of species composition. Despite the high variability in natural regeneration, the optimal policy closely satisfies the certainty equivalence principle. The effect of natural disasters is similar to an increase in the interest rate, but in contrast to earlier results, this tends to change the management regime from rotation forestry to continuous cover management.
  • Roa-Fuentes, Camilo A.; Heino, Jani; Cianciaruso, Marcus V.; Ferraz, Silvio; Zeni, Jaquelini O.; Casatti, Lilian (2019)
    Freshwater Biology (2019) 64 (3): 447-460
    A multi‐faceted assessment of diversity is needed to improve our understanding of the mechanisms underlying biodiversity patterns and to reveal the impacts of land use alterations on β‐diversity. In this study, we analysed stream fish β‐diversity based on taxonomic, functional, and phylogenetic facets in an intensively cultivated tropical region. We sampled 43 stream reaches in the northwest of São Paulo State, south‐eastern Brazil. Each sampling site was characterised according to catchment‐scale features, landscape dynamic indicators, local‐scale features, and distance between stream reaches as network distance (a proxy for dispersal processes). As response variables, we considered taxonomic, functional, and phylogenetic β‐diversities coupled with a null‐model approach. For each β‐diversity metric, we calculated the mean overall value and tested whether the mean value was different from that expected by chance. To examine variation in β‐diversity for the three facets and determine the relative contributions of predictor variables, we used a distance‐based approach. Taxonomic and functional β‐diversities were higher from the expected value under a null model, suggesting that community assembly of these facets was dominated by deterministic processes. In contrast, phylogenetic β‐diversity was not different from that expected by chance, suggesting that the lineage composition of these assemblages was random. Furthermore, for all three facets, there was a positive environment‐β‐diversity relationship that was determined primarily by local‐scale features, whereas catchment features and landscape dynamic indicators were not important. In addition, none of the β‐diversity facets was correlated with stream network distance, indicating that dispersal processes were not strongly structuring fish assemblages. Our study suggested that although multiple facets of stream fish β‐diversity are ruled mainly by deterministic processes (e.g. species sorting), stochasticity is also important in community assembly. An interesting finding was the mismatch between phylogenetic versus taxonomic and functional β‐diversity. It is likely that the lack of non‐random structure in phylogenetic β‐diversity is due to the variation of phylogenetic signal in some functional traits. Given that landscape dynamic indicators were not correlated with measures of β‐diversity, we suggest that the recent sugarcane expansion in our study area probably has not critically affected stream fish β‐diversity. Also, it is possible that catchment variables presented little variability and did not overwhelm the effect of local environmental variables on β‐diversity. In conclusion, our study suggests that even highly disturbed tropical agroecosystems with a pool of species that is probably decimated, can still display a relatively high β‐diversity determined mainly by species sorting. These findings suggest key environmental features that must be considered in restoration or conservation of β‐diversity in agroecosystems. Specifically, since variation in β‐diversity was explained mainly by local‐scale environmental gradients, conservation schemes would ideally protect enough sites to capture this entire gradient. Overall, the knowledge of multiple facets can foment more effective conservation and restoration actions by providing a more comprehensive view of the structuring factors of assemblages.