Ecosystems can be restored by means of biodiversity engineering
January 30, 2019
By Elton Alisson | Agência FAPESP – For many scientists, human activities have had such a significant impact on Earth’s climate and ecosystems since the late eighteenth century that a new geological epoch called the Anthropocene can be deemed to have begun with the Industrial Revolution.
In terms of the number of species that have disappeared, this more recent period of Earth’s history is comparable with others in the remote past when mass extinctions occurred. Existing ecological knowledge must be applied to restore the lost biodiversity and reestablish the provision of many valuable ecosystems services.
A study by Brazilian and British researchers argues that there are now unprecedented theoretical, methodological and technological conditions to address this challenge.
“We’re only a few steps away from being able to ‘engineer’ biodiversity, by which I mean manipulating biodiversity to design the composition of ecological communities and preserve or restore the functions of ecosystems,” Rafael Luís Galdini Raimundo, lead author of the study, told Agência FAPESP. Raimundo is a professor in the Department of Engineering and Environment at the Federal University of Paraíba (DEMA-UFPB), in Brazil.
“We now have the theoretical and methodological conditions to understand and predict the consequences of including species in a community, or of removing species from a community, for the purposes of managing the functional diversity of an ecosystem,” he said.
According to the authors of the study, the manipulation of ecological communities for restoration purposes has a long scientific history. In Europe and the United States, indeed, it has been conducted for more than a century.
However, restoration initiatives have typically focused on including or removing species with the aim of replenishing levels of plant and animal species richness and have overlooked the ecological interactions between populations and species or between predator and prey, for example.
Ecological interactions are determinants of an ecosystem’s level of biodiversity and functioning, molding the strength and modes of natural selection. Changes in the patterns of these interactions brought about by species extinction or the advent of invasive species, for example, affect the evolution of ecologically relevant functional traits, such as beak size in frugivorous (fruit-eating) bird species or the size of the seeds they disperse.
In the Atlantic Rainforest biome found mainly in the South and Southeast regions of Brazil, the loss of abundant bird species such as toucans (Ramphastidae) and the black-fronted piping guan (Pipile jacutinga) has led to a decline in the dispersal of trees with large seeds. The disappearance of species acting as seed dispersal vector for the jussara palm (Euterpe edulis) has led to dispersal of its seeds in fewer areas of the biome, and seed size has diminished as a result, according to the authors of the study.
“Species interactions shape and are shaped by ecological and evolutionary processes that maintain biodiversity and related ecosystem functions,” Raimundo said.
The development of mathematical adaptive network models has enabled ecologists to better understand how changes in the patterns of ecological interactions, which define the structure of interaction networks, are followed by changes in the dynamics and properties of populations of each species, such as species traits and abundances.
These ecological and evolutionary changes in species’ properties can shape new patterns of interactions, creating a complex web of feedback loops. “Application of the network approach to ecology enables us to make predictions about what will happen to ecological and evolutionary processes in these complex networks of interactions and to create testable hypotheses for different management strategies,” Raimundo said. “In this manner, we can build stable communities with all ecosystem functions operating normally.”
Despite the predictive power of adaptive network models in ecosystems management, a lack of data has long prevented their use for restoration. The genome sequencing techniques developed in recent years have cost-effectively provided unprecedented amounts of information on species interactions, giving rise to what the authors call biodiversity big data.
Thanks to these sequencing techniques, the researchers noted, it has become possible to obtain not only data on the ecological structure of networks but also data on the phylogenetic relations among species in a community – a vital element in predicting how ecological networks will reconnect their structures and how new dynamics will remodel species traits and abundances.
“The merging of next-generation genome sequencing techniques with ecological networks provides new tools with which to study the resilience of communities in interacting with environmental change, while at the same time including important properties such as functional diversity,” said Darren Evans, a professor at Newcastle University in the UK and a coauthor of the study.
A number of bottlenecks hinder the use of these evolutionary and ecological predictive models. These bottlenecks include the need for more research collaborations to permit the monitoring of communities as a basis for adaptive network predictions and to increase interaction between theoreticians and researchers engaged in field work and the implementation of restoration practices.
“Successful application of these models requires the establishment of a two-way street between the researchers who develop the models and generate the predictions and the researchers in the field who test restoration practices on the community scale, to improve the models, generate more accurate predictions and, in the long term, refine this biodiversity engineering,” Raimundo said.
The article “Adaptive networks for restoration ecology” (doi: doi.org/10.1016/j.tree.2018.06.002) by Rafael L. G. Raimundo, Paulo R. Guimarães Jr and Darren M. Evans can be read by subscribers to Trends in Ecology & Evolution at www.sciencedirect.com/science/article/pii/S0169534718301393.
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