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Input, output, impact, and processes are central indicators of the science, technology, and innovation production. The input is usually associated to investments made in science and technology, and it varies among different countries and scientific fields. Thus, the input can influence other impact indicators. Here, we evaluated the effect of the input data (i.e., number of funding) on process (i.e., collaboration) and output (i.e., number of citation) indicators of ecological research. Moreover, we detailed the effect of the number of funding on the collaboration and number of citations by each country (based on the nationality of authors). We found that most of published papers had some degrees of financial support, and that the production of papers with funding increased over the years. Funding had a positive effect on the collaboration and citation of papers; however, we observed that: in countries with higher investments in Science and Technology, the number of funding impacts positively and directly on the number of authors (collaboration) and in countries with low levels of investments in Science and Technology, the number of funding impacts positively and directly on the number of citations. Our models presented a low predictive power, but similar to other informetric studies. Our results indicated that impact indicators evaluated have an integrated structure, and the effects at one level can affect other levels. Nonetheless, the impact of the number of funding on informetric data can vary among countries; therefore, these results are important to the development of national policies and future informetric studies.

Species Cichla piquiti and Cichla kelberi are found in the Serra da Mesa reservoir, Goiás and are sedentary with diurnal habits. This study aimed to evaluate the magnitude and distribution of genetic variability in subpopulations of C. piquiti with specific microsatellite loci and to test transferability in other microsatellite markers for C. kelberi. We analyzed 99 individuals of C. piquiti from seven points to evaluate genetic diversity and structure with 10 microsatellite loci. Transferability of 75 loci was tested in C. kelberi to increase microsatellite markers available. Genetic structure was assessed with Bayesian clustering. Global FST for C. piquiti was weak (0.056), but FIS (0.598) and FIT (0.621) were significantly high, indicating that the mating system has a strong influence on the organization of genetic variability with most mating among related. Two genetic groups were evidenced with most individuals allocated to a single group. Transferability of microsatellite loci for C. kelberi had low polymorphism. The level of genetic diversity was low, increasing inbreeding and suggesting that few individuals of C. piquiti colonized the reservoir during its installation due founder effect. Other factors as reproductive behavior and overfishing can act to decrease genetic diversity. Therefore, we reinforce the need for genetic monitoring to avoid loss of genetic diversity that can be intensified both construction of hydropower plants and ecological and reproductive aspects in some fish species.

Different biological groups can be used for monitoring aquatic ecosystems because they can respond to variations in the environment. However, the evaluation of different bioindicators may demand multiple financial resources and time, especially when abundance quantification and species-level identification are required. In this study, we evaluated whether taxonomic, numerical resolution and cross-taxa can be used to optimize costs and time for stream biomonitoring in Central Brazil (Cerrado biome). For this, we sampled different biological groups (fish, zooplankton, phytoplankton, and periphyton) in stream stretches distributed in a gradient of land conversion dominated by agriculture and livestock. We used the Mantel and Procrustes analyses to test the association among different taxonomic levels (species to class), the association between incidence and abundance data (numerical resolution), and biological groups. We also assessed the relative effect of local environmental and spatial predictors on different groups. The taxonomic levels and numerical resolutions were strongly correlated in all taxonomic groups (r > 0.70). We found no correlations among biological groups. Different sets of environmental variables were the most important to explain the variability in species composition of distinct biological groups. Thus, we conclude that monitoring the streams in this region using bioindicators is more informative through higher taxonomic levels with occurrence data than abundance. However, different biological groups provide complementary information, reinforcing the need for a multi-taxa approach in biomonitoring.