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Biology subjects

Jose C Carvalho

Publications and source records attributed to Jose C Carvalho.

2 recordsLinked to original sources

Optimal inventorying and monitoring of taxon, phylogenetic and functional diversity

Comparable data is essential to understand biodiversity patterns. While inventorying requires comprehensive sampling, monitoring focuses on as few components as possible to detect changes. Quantifying species, their evolutionary history, and the way they interact claims for studying changes in taxonomic (TD), phylogenetic (PD) and functional diversity (FD). Here we propose a method for the optimization of sampling protocols for inventorying and monitoring diversity across these three diversity dimensions taking sampling costs into account. We used Iberian spiders, Amazonian bats and Atlantic Forest mammals as three case-studies. The optimal combination of methods for inventorying and monitoring required optimizing the accumulation curve of -diversity and minimizing the difference between sampled and estimated {beta}-diversity (bias), respectively. For Iberian spiders, the optimal combination for TD, PD and FD allowed sampling at least 50% of estimated diversity with 24 person-hours of fieldwork. The optimal combination of six person-hours allowed reaching a bias below 8% for all dimensions. For Amazonian bats, surveying all the 12 sites with mist-nets and 0 or 1 acoustic recorders was the optimal combination for almost all diversity types, resulting in >89% of the diversity and <10% bias with roughly a third of the cost. Only for phylogenetic -diversity, the best solution was less clear and involved surveying both with mist nets and acoustic recorders. For Atlantic Forest mammals the optimal combination to assess all types of - and {beta}-diversity was to walk all the 10 transects and no camera traps, which returned >95% of the diversity and <5% bias with a third of the costs. The widespread use of optimized and standardized sampling protocols and regular repetition in time will radically improve global inventory and monitoring of biodiversity. We strongly advocate for the global adoption of sampling protocols for both inventory and monitoring of taxonomic, phylogenetic and functional diversity.

Ecology

Automated discovery of relationships, models and principles in ecology

O_LIEcological systems are the quintessential complex systems, involving numerous high-order interactions and non-linear relationships. The most commonly used statistical modelling techniques can hardly reflect the complexity of ecological patterns and processes. Finding hidden relationships in complex data is now possible through the use of massive computational power, particularly by means of Artificial Intelligence methods, such as evolutionary computation.\nC_LIO_LIHere we use symbolic regression (SR), which searches for both the formal structure of equations and the fitting parameters simultaneously, hence providing the required flexibility to characterize complex ecological systems.\nC_LIO_LIFirst, we demonstrate how SR can deal with complex datasets for: 1) modelling species richness; and 2) modelling species spatial distributions. Second, we illustrate how SR can be used to find general models in ecology, by using it to: 3) develop species richness estimators; and 4) develop the species-area relationship and the general dynamic model of oceanic island biogeography.\nC_LIO_LIAll the examples suggest that evolving free-form equations purely from data, often without prior human inference or hypotheses, may represent a very powerful tool for ecologists and biogeographers to become aware of hidden relationships and suggest general theoretical models and principles.\nC_LI

Ecology