Research Questions
Background and Interests
My name is Isaac Trindade-Santos, I have a Bachelor (2008 – 2015) in Fish and Fisheries Biology, complemented with a Master`s degree (2015 – 2017) in Ecology and Conservation (both at the Universidade Federal de Sergipe in Brazil) and a PhD in Biology (2017 – 2021) at the University of St Andrews in Scotland. Throughout my academic career I developed skills on answering key ecological questions by using global databases on fish biodiversity. Since 2010 I have been collaborating with FishBase (a global database containing biological, ecological and conservation data for all described fish species), AquaMaps (fish and invertebrates distribution database) and the Sea Around Us Project (global marine fisheries catch exploitation data). Using those global databases, along with BioTIME (the world’s largest compilation of assemblage time series of abundance and biomass data), I have worked with all described marine fishes: Actinopterygii – bony fish species (around 13,000 species) and Elasmobranchii – cartilaginous species (around 900 species of sharks, rays and skates) (Trindade-Santos et al. 2013, Freire et al. 2015, Trindade-Santos and Freire 2015, Trindade-Santos et al. 2020, Trindade-Santos et al. 2022) and some invertebrate species, mainly exploited species of crustaceans (Freire et al. 2021).
My research questions traverse ecological, evolutionary, and conservation perspectives, for example by simulating the effect of an environmental havoc, known as the “Mariana dam disaster”, on beta-diversity (Trindade-Santos et al. 2018) (please see Figure 1 below), measuring the effect of fisheries on global taxonomic and functional diversity (Trindade-Santos et al. 2020), or mapping the global functional rarity of marine fish (Trindade-Santos et al. 2022). An interesting observation from Trindade-Santos et al. 2022 was the detection of a high concentration of rare species towards higher latitudes. I also have experience with scripting languages, especially R and a bit of Python, database management (Access and R), taxonomic name management, remote sensing (ArcGIS), and biodiversity informatics.
Figure 1. Seven hydrographic basins used in this study showing the Doce River (blue line) and its neighbour basins. BA ES coast: this basin comprises part of the coast from Bahia (BA) and Espírito Santo coasts. ES coast: Espírito Santo coast. The blue map shows the variations in species richness and the green map shows the variations in functional richness. The Pfafstetter drainage basin classification has been broadly used to classify freshwater environments for research and management purposes, e.g. “Agência Nacional das Águas – ANA” in Brazil (National Agency of Waters) and by the International Union for Conservation of Nature – IUCN (ANA, 2017; IUCN, 2016).
During my academic career, I have developed skills and experience in measuring the three main biodiversity facets – taxonomic (species richness and abundance evenness distributions), functional (the role that each species plays for ecosystem functioning) and phylogenetic diversity (the evolutionary relationship between species) – and quantifying their change in space and time. For example, in Trindade-Santos et al. (2020), I used temporal regression models to uncover a substantial increase over time in the exploited functional richness of both ray-finned and cartilaginous species of large marine ecosystems, in line with an increase in the extracted taxonomic richness (Figure 2). These trends show that global fisheries are increasingly targeting species that play diverse roles within the marine ecosystem and underline the importance of incorporating functional diversity in ecosystem management. In Trindade-Santos et al. (2022), I also quantified the biogeography of rare fish in the world’s oceans. Concentrations of rarity were found, in excess of what is predicted by a null expectation, near the coasts and at higher latitudes (Figure 3). Similarly, Rabosky et al. (2018) found faster speciation rates outside the tropics, particularly at higher latitudes; it is possible that those regions act as centres for evolutionary and functional novelty (as shown in my PhD research).
"Global change in the functional diversity of marine fisheries exploitation over the past 65 years"
Figure 2. Change in the functional diversity of marine fisheries catches from the Large Marine Ecosystems (LMEs) in the period 1950 to 2014. The map shows, for each LME, the standardized slope of each metric computed against time. Increasing trends (slope greater than 0.2 in the standardized regression) are coloured red–orange, decreasing ones (<−0.2) as green–blue (see scale). 73% of LMEs exhibited a significant trend in Actinopterygii functional richness, with a further 10% showing a nonsignificant increase (see electronic supplementary material, table S4). The equivalent figures for increasing functional richness trends in Elasmobranchii were 72% (significant) plus 15% (nonsignificant). Functional evenness increased in 14% (7% significant + 7% nonsignificant) of Actinopterygii LMEs and 49% (26% + 23%) of Elasmobranchii LMEs; decreasing functional evenness was detected in 71% Actinopterygii LMEs (33% + 38%) and in 53% Elasmobranchii LMEs (21% + 32%). Functional divergence increased in 30% (15% + 15%) of Actinopterygii LMEs and 49% (26% + 23%) of Elasmobranchii LMEs; it declined in 60% (42% + 18%) of Actinopterygii LMEs and 36% (30% + 6%) of Elasmobranchii LMEs. Please note that we use the conventional p < 0.05 threshold to infer significance, and hence the strength of the relationship, to provide information on uncertainty.
"Global patterns in functional rarity of marine fish"
Figure 3. Global biogeography of rarity for bony fishes (a–Actinopterygii) and cartilaginous fishes (c–Elasmobranchii) across Coastal Systems. The functional index used here was distinctiveness. Plots a and c illustrate the numbers of rare species found in each 2° grid cell (species that are both rare taxonomically and functionally (distinct)). Plots b, d shows the Standardized Effect Sizes (SES), where red shaded cells represent an excess of rare species higher than expected by chance and blue cells represent fewer rare species than expected.