Lessons from the invasion front: Integration of research and management of the lionfish invasion in Brazil.

After successful invasions in the Caribbean and Mediterranean, lionfish (Pterois spp.) have recently invaded another important biogeographical region -the Brazilian Province. In this article, we discuss this new invasion, focusing on a roadmap for urgent mitigation of the problem, as well as focused research and management strategies. The invasion in Brazil is already in the consolidation stage, with 352 individuals recorded so far (2020-2023) along 2766 km of coastline. This includes both juveniles and adults, including egg-bearing females, ranging in length from 9.1 to 38.5 cm. Until now, most of the records in the Brazilian coast occurred in the equatorial southwestern Atlantic (99%), mainly on the Amazon mesophotic reefs (15% of the records), northeastern coast of Brazil (45%), and the Fernando de Noronha Archipelago (41%; an UNESCO World Heritage Site with high endemism rate). These records cover a broad depth range (1-110 m depth), twelve protected areas, eight Brazilian states (Amapá, Pará, Maranhão, Piauí, Ceará, Rio Grande do Norte, Paraíba, and Pernambuco) and multiple habitats (i.e., mangrove estuaries, shallow-water and mesophotic reefs, seagrass beds, artificial reefs, and sandbanks), indicating a rapid and successful invasion process in Brazilian waters. In addition, the lack of local knowledge of rare and/or cryptic native species that are potentially vulnerable to lionfish predation raises concerns regarding the potential overlooked ecological impacts. Thus, we call for an urgent integrated approach with multiple stakeholders and solution-based ecological research, real-time inventories, update of environmental and fishery legislation, participatory monitoring supported by citizen science, and a national and unified plan aimed at decreasing the impact of lionfish invasion. The experience acquired by understanding the invasion process in the Caribbean and Mediterranean will help to establish and prioritize goals for Brazil.

GLOVE: The Global Plastic Ingestion Initiative for a cleaner world.

Plastics are one of the most used materials in the world. Their indiscriminate use and inappropriate disposal have led to inevitable impacts, for instance ingestion, on the environment arousing the attention of the global community. In addition, plastic ingestion studies are often written in scientific jargon or hidden behind paywalls, which makes these studies inaccessible. GLOVE is an online and open-access dashboard database available at gloveinitiative.shinyapps.io/Glove/ to support scientists, decision-makers, and society with information collected from plastic ingestion studies. The platform was created in the R environment, with a web interface developed through Shiny. It already comprises 530 studies, including all biological groups, with 245,366 individual records of 1458 species found in marine, freshwater, and terrestrial environments. The main goal of the GLOVE dashboard database is to improve data accessibility by being a scientifically useful grounded tool for designing effective and innovative actions in the current scenario of upcoming global and local agreements and actions on plastic pollution.

The Bias of combining variables on fish's aggressive behavior studies.

Quantifying animal aggressive behavior by behavioral units, either displays or attacks, is a common practice in animal behavior studies. However, this practice can generate a bias in data analysis, especially when the variables have different temporal patterns. This study aims to use Bayesian Hierarchical Linear Models (B-HLMs) to analyze the feasibility of pooling the aggressive behavior variables of four cichlids species. Additionally, this paper discusses the feasibility of combining variables by examining the usage of different sample sizes and family distributions to aggressive behaviour variables. The subject species were: the angelfish (Pterophyllum scalare), the tiger oscar (Astronotus ocellatus), the Cichlasoma paranaense and the Nile tilapia (Oreochromis niloticus). For each species, 15 groups of 3 individuals were assigned to daily observations (10-min recordings) for 5 days. Aggressive behavior data was labeled according to its aggressive intensity. The variables chase (C), tail beating (TB), push (P), lateral attack (LA) and bite (B) were classified as high intensity. The variables undulation (U), lateral threat (LT) and frontal displays (FD) were classified as low intensity. These behaviors, however, were not present in all species. Model parameters were estimated by Monte Carlo Markov chains using non-informative priors. B-HLMs were performed to assess the impact probability of each variable in the analysis. Results revealed that when combining variables, the resulting distribution is strongly influenced by only one variable in each category. Moreover, in some cases the aggregate values altered the results, which changed the probabilities of the main variables. Species with low aggressive behavior frequencies, such as A. ocellatus, are more sensitive to this bias. LT was the main low intensity variable for all species, while B was the main high intensity variable for the P. scalare and the O. niloticus. LA was the high intensity category variable that was the most relevant for the C. paranaense and A. ocellatus. Moreover, combining the variables did not impact the feasibility of reducing the sample size when compared to using the most quantitative variable. For all species a sample size of 12 did not change the study conclusions. With respect to family distribution, based on DIC values the Gaussian model is more suitable for most of the studied species. However, caution should be taken, because the Gaussian posterior probability distribution overlapped 0 in some cases, which is biologically impossible in aggressive behaviors. The only exception is the A. ocellatus, which, based on DIC values, was the only species better modeled by a Poisson distribution. Bayesian analysis can be therefore considered a strong tool for analyzing aggressive behavior.

Bayesian analysis improves experimental studies about temporal patterning of aggression in fish.

This study aims to describe a Bayesian Hierarchical Linear Model (HLM) approach for longitudinal designs in fish's experimental aggressive behavior studies as an alternative to classical methods In particular, we discuss the advantages of Bayesian analysis in dealing with combined variables, non-statistically significant results and required sample size using an experiment of angelfish (Pterophyllum scalare) species as case study. Groups of 3 individuals were subjected to daily observations recorded for 10min during 5days. The frequencies of attacks, displays and the total attacks (attacks+displays) of each record were modeled using Monte Carlo Markov chains. In addition, a Bayesian HLM was performed for measuring the rate of increase/decrease of the aggressive behavior during the time and to assess the probability of difference among days. Results highlighted that using the combined variable of total attacks could lead to biased conclusions as displays and attacks showed an opposite pattern in the experiment. Moreover, depending of the study, this difference in pattern can happen more clearly or more subtly. Subtle changes cannot be detected when p-values are implemented. On the contrary, Bayesian methods provide a clear description of the changes even when patterns are subtle. Additionally, results showed that the number of replicates (15 or 11) invariant the study conclusions as well that using a small sample size could be more evident within the overlapping days, that includes the social rank stability. Therefore, Bayesian analysis seems to be a richer and an adequate statistical approach for fish's aggressive behavior longitudinal designs.