CONSERVAÇÃO DE PEIXES DE RIACHO: PLANEJAMENTO E POLÍTICAS PÚBLICAS

Conservation biology has historically been based on principles to protect terrestrial ecosystems, with marine and freshwater ecosystems left behind. As a result, often, protected areas are defined with bases in forest cover and terrestrial characteristics overseeing important components of connectivity of riverine landscapes, such as the connectivity between rivers, lakes, and streams. It is important to emphasize that forest protection is extremely important, but that alone cannot safeguard the protection of freshwater ecosystems. Therefore, our discussion should lead, not to the disregard of terrestrial efforts but to the complementation of existing efforts for forest protection with the addition of areas that can also protect freshwater ecosystems. Fluvial ecosystems are hierarchical and nested systems, with multidimensional connectivity including longitudinal (upstream-downstream), lateral (floodplains and lakes), temporal (seasons) and vertical (groundwaters) connections. Systematic Conservation Planning (SCP) is the most well accepted and used method for designing conservation plans based on cost-effective scenarios that include ecological and socio-economic values resulting in thematic maps of priority areas for conservation. Recently, methods to consider the connectivity of freshwater ecosystems were incorporated into spatial prioritization tools. Maps produced using spatial prioritization tools can help decision making on species management and conservation actions, such as plans for species’ impact reduction (PRIM) and action plans for threatened species (PAN). PRIM and PAN use information about species ecology to focus conservation actions onto target species. These conservation action plans must be viable not only ecologically but also economically. In this context, using SCP to guide designs of PRIM and PAN can help stakeholders to achieve better conservation actions in Brazil. Thus, the SCP can improve the conservation and management of freshwater ecosystems, through the integration of science, society and stakeholder.

How Spatial Attention Affects the Decision Process: Looking through the Lens of Bayesian Hierarchical Diffusion Model & EEG Analysis

Model-based cognitive neuroscience consolidates the cognitive processes and neurophysiological oscillations which are reflections of behavioral performance (e.g., reaction times and accuracy). Here, based on one of well-known sequential sampling models (SSMs), named the diffusion decision model, and the nested model comparison, we explore the underlying latent process of spatial prioritization in perceptual decision processes, so that for estimating the model parameters (i.e. the drift rate, the boundary separation, and the non-decision time), a Bayesian hierarchical approach is considered, which allows inferences to be done simultaneously in the group and individual level. Moreover, well-established neural components of spatial attention which contributed to the latent process and behavioral performance in a visual face-car perceptual decision are detected based on the event-related potential (ERP) analysis. Our cognitive modeling analysis revealed that the non-decision time parameter provides a better fit to the top-down attention with the measures of two powerful weapons, i.e. the deviance information criterion called DIC score and R-square. Also, using multiple regression analysis on the contralateral minus neutral N2 sub-component (N2nc) at central electrodes and contralateral minus neutral alpha power (Anc) at posterior-occipital electrodes in the voluntary attention, it can be concluded that poststimulus N2nc can predict reaction time (RT) and non-decision time parameter relating to spatial prioritization. Whereas, the poststimulus Anc only can predict the RT and not the non-decision time relating to spatial prioritization. The result suggested that the difference of contralateral minus neutral oscillations was more important to reflect the modulation of the top-down spatial attention mechanism in comparison with the difference of ipsilateral minus neutral oscillations.

How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis

Model-based cognitive neuroscience consolidates the cognitive processes and neurophysiological oscillations which are reflections of behavioral performance (e.g., reaction times and accuracy). Here, based on one of the well-known sequential sampling models (SSMs), named the diffusion decision model, and the nested model comparison, we explore the underlying latent process of spatial prioritization in perceptual decision processes, so that for estimating the model parameters (i.e. the drift rate, the boundary separation, and the non-decision time), a Bayesian hierarchical approach is considered, which allows inferences to be done simultaneously in the group and individual level. Moreover, well-established neural components of spatial attention which contributed to the latent process and behavioral performance in a visual face-car perceptual decision are detected based on the event-related potential (ERP) analysis. Our cognitive modeling analysis revealed that the non-decision time parameter provides a better fit to the top-down attention with the measures of two powerful weapons, i.e. the deviance information criterion called DIC score and R-square. Also, using multiple regression analysis on the contralateral minus neutral N2 sub-component (N2nc) at central electrodes and contralateral minus neutral alpha power (Anc) at posterior-occipital electrodes in the voluntary attention, it can be concluded that poststimulus N2nc can predict reaction time (RT) and non-decision time parameter relating to spatial prioritization. Whereas, the poststimulus Anc only can predict the RT and not the non-decision time relating to spatial prioritization. The result suggested that the difference of contralateral minus neutral oscillations was more important to reflect the modulation of the top-down spatial attention mechanism in comparison with the difference of ipsilateral minus neutral oscillations.

Identification of areas of very high biodiversity value to achieve the EU Biodiversity Strategy for 2030 key commitments

Background The European Union strives to increase protected areas of the EU terrestrial surface to 30% by year 2030, of which one third should be strictly protected. Designation of the Natura 2000 network, the backbone of nature protection in the EU, was mostly an expert-opinion process with little systematic conservation planning. The designation of the Natura 2000 network in Romania followed the same non-systematic approach, resulting in a suboptimal representation of invertebrates and plants. To help identify areas with very high biodiversity without repeating past planning missteps, we present a reproducible example of spatial prioritization using Romania’s current terrestrial Natura 2000 network and coarse-scale terrestrial species occurrence. Methods We used 371 terrestrial Natura 2000 Sites of Community Importance (Natura 2000 SCI), designated to protect 164 terrestrial species listed under Annex II of Habitats Directive in Romania in our spatial prioritization analyses (marine Natura 2000 sites and species were excluded). Species occurrences in terrestrial Natura 2000 sites were aggregated at a Universal Traverse Mercator spatial resolution of 1 km2. To identify priority terrestrial Natura 2000 sites for species conservation, and to explore if the Romanian Natura 2000 network sufficiently represents species included in Annex II of Habitats Directive, we used Zonation v4, a decision support software tool for spatial conservation planning. We carried out the analyses nationwide (all Natura 2000 sites) as well as separately for each biogeographic region (i.e., Alpine, Continental, Pannonian, Steppic and Black Sea). Results The results of spatial prioritization of terrestrial Natura 2000 vary greatly by planning scenario. The performance of national-level planning of top priorities is minimal. On average, when 33% of the landscape of Natura 2000 sites is protected, only 20% of the distribution of species listed in Annex II of Habitats Directive are protected. As a consequence, the representation of species by priority terrestrial Natura 2000 sites is lessened when compared to the initial set of species. When planning by taxonomic group, the top-priority areas include only 10% of invertebrate distribution in Natura 2000. When selecting top-priority areas by biogeographical region, there are significantly fewer gap species than in the national level and by taxa scenarios; thusly, the scenario outperforms the national-level prioritization. The designation of strictly protected areas as required by the EU Biodiversity Strategy for 2030 should be followed by setting clear objectives, including a good representation of species and habitats at the biogeographical region level.