PhD studentship: Functional diversity as a tool to predict the consequences of
biodiversity loss for coastal ecosystem processes
School of Ocean Sciences, Bangor University: Dr Stuart Jenkins (s.jenkins@bangor.ac.uk), Dr
Jan Hiddink (j.hiddink@bangor.ac.uk)
Department of Biosciences, Swansea University: Dr John Griffin (J.N.Grifffin@swansea.ac.uk)
Justification: Human activities are causing declines in biodiversity on scales from local habitat
patches to the entire globe (Pimm et al. 1995, Barnosky 2012). It is now well-established that the loss
of species from a given trophic level leads to declines in the aggregate ecological functions performed
by those species (reviewed by Cardinale et al. 2006). However, the widely accepted negative
relationship between number of species lost and ecosystem function belies a high level of unexplained
variation (Cardinale et al. 2006). This results in a large margin of error around predictions of how
functioning will change with real-life sequences of species loss – and hence places a major constraint
on the application of this enormous body of work to applied problems such as setting conservation
priorities (Srivastava & Velland 2005). Resolving this issue in coastal ecosystems is particularly
pertinent because not only do these systems provide vital services to human populations concentrated
in coastal areas (Barbier et al. 2011), but at the same time they are suffering high rates of species loss
and environmental change (Lotze et al. 2006).
In theory, the effect of losing a species on functioning should be related to the degree to
which that species uniquely utilizes resources (Martins et al 2012). Changes in functional diversity
(FD; sensu Petchey & Gaston 2002), rather than changes in species richness should provide a better
predictor of how functioning of ecosystems will change since this metric uses traits to capture the
resource-use patterns of species. The consequences of losing a species should be related to the
contribution of that species to FD (Petchey & Gaston 2002, Griffin et al. 2009, Caddotte 2011); FD
may therefore reduce the margin of error around species loss -functioning scenarios. Despite clear
advantages and potential utility in conservation, our understanding of the link between functional
diversity and ecosystem functioning in coastal ecosystems, remains limited by a lack of empirical
investigation.
Objectives: The over-arching aim of this PhD project would be to develop new
understanding of the links between functional diversity and ecosystem functioning within coastal
ecosystems. A particular focus will be investigating the utility of FD to improve predictions of the
effects of real-life scenarios of species losses within coastal ecosystems. The specific objectives of
this proposed PhD are to: 1) Quantify and analyze relationships among functional traits of species
within focal coastal assemblage(s); 2) Evaluate spatial and temporal patterns of FD across gradients
of human impact in coastal systems; and 3) Experimentally manipulate FD, independently of species
richness, to more fully understand how species richness and species traits interact to determine
functioning.
Methods: The successful candidate would use both observational and experimental
approaches in one or more tractable coastal marine assemblages on the north Wales coast (rocky shore
macroalgae, sub-tidal fouling assemblages, salt marsh plants). Fieldwork will be essential, but
controlled outdoor and laboratory mesocosms may also be used to perform experiments. Although the
majority of data collection will be performed by the student, meeting objective two might include
analysis of published data on patterns of extinction vulnerability in local coastal systems. We expect
this project to be conceptually challenging and lead to theoretical advancements in the field and there
will be scope for the student to lead such advances as part of this project.
Literature cited
Barbier EB et al. (2011) Ecol.Monog. 81: 169-193; Barnosky AD et al. (2011) Nature 471: 51-57;
Cardinale BJ et al. (2006) Nature 443: 989-992; Cadotte MW et al. (2011) J. Appl. Ecol. 48:
1079−1087; Griffin JN et al. (2009) Oikos 118,37–44; Lotze HK et al. (2006) Science 312, 1806.
Martins et al (2102) PLoS ONE 7(8): e44297; Petchey OL. & Gaston KJ. (2002) Ecol. Lett., 5: 402–
411; Pimm SL et al. (1995) Science 269: 347-350; Srivastava D & Vellend M. (2005) Annu. Rev.
Ecol. Evol. Syst., 36, 267–294.