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  • Version: Non applicable
  • Publication: Non applicable
  • Rights: Usable if cited
  • Difficulty level of implementation: Moderate
  • Skill required (for method use): Parameterisation and running of the tool requires some knowledge in the R coding environment.
  • Authors: Clement Garcia
  • Tool contributors:
  • Project general coordinator: Jan Marcin Węsławski
  • Project Scientific manager: Julie Bremner
  • Project manager: Gary Saggers (Cefas), Joanna Piwowarczyk (IOPAN)
  • Araújo et al. (2024). Trait–environment relationship of riverine fish assemblages across a human footprint mosaic. Hydrobiologia 851, 1135–1151. https://doi.org/10.1007/s10750-023-05370-9.
  • Beauchard, O.; Bradshaw, C.; Bolam, S.; Tiano, J.C.; Garcia, C.; De Borger, E.; Laffargue, P.; Blomqvist, M.; Tsikopoulou, I.; Papadopoulou, N.K.; Smith, C.J.; Claes, J.; Soetaert, K.; Sciberras, M. (2023). Trawling-induced change in benthic effect trait composition – A multiple case study. Mar. Sci. 10: 1303909. https://dx.doi.org/10.3389/fmars.2023.1303909
  • Borcard, D., Gillet, F., & Legendre, P. (2018). Numerical Ecology with R (2nd ed.). [https://www.academia.edu/37498440/Book_NumericalEcologyWithR]
  • Dray, S., Choler, P., Dolédec, S., Peres-Neto, P. R., Thuiller, W., Pavoine, S., & ter Braak, C. J. (2014). Combining the fourth‐corner and the RLQ methods for assessing trait responses to environmental variation. Ecology, 95(1), 14-21. DOI 10.1890/13-0196.1
  • Gusmao JB, Luna-Jorquera G and Rivadeneira MM (2022) Oceanographic gradients explain changes in the biological traits of nesting seabird assemblages across the south-eastern Pacific. Front. Mar. Sci. 9:897947. doi: 10.3389/fmars.2022.897947
  • Legendre P, Legendre L. Numerical Ecology. 2nd ed. Amsterdam: Elsevier, 1998. ISBN 978-0444892508.
  • Rao et al. (2021). Responses of Functional Traits of Macrobenthic Communities to Human Activities in Daya Bay (A Subtropical Semi-Enclosed Bay), China. Front. Environ. Sci. 9:766580. doi: 10.3389/fenvs.2021.766580
  • Zhu, Y., Liu, Y., Sheng, S. et al. Quantifying the effects of landscape and habitat characteristics on structuring bird assemblages in urban habitat patches. Sci Rep 14, 12707 (2024). https://doi.org/10.1038/s41598-024-63333-z
  • Name: Clement Garcia
  • Organization: Cefas
  • Email:clement.garcia@cefas.gov.uk

Assessing Functional Trait-Based Species Vulnerability Against Environmental and Human Drivers

The vulnerability of individual species to natural or human-driven changes in their environment is dictated by the traits they express. This tool uses specific traits-driver relationships (natural and human) as indicators of the vulnerability of species across communities and of the functional implications of species’ responses to environmental change. The tool uses a method based on 3-tables ordination: the RLQ. The RLQ is a multivariate analysis used to study the relationships between environmental data (table R), species abundance (or biomass or presence/absence) (table L), and species traits (table Q). The analysis provides ordination scores that summarise the joint structure among the three tables, thereby identifying how environmental gradients relate to specific traits. The RLQ starts with the independent ordination of each table. The table R (environment – sites) is generally ordinated using a Principal Component Analysis (PCA), a dimensionality reduction method that reduces large data sets into fewer variables while preserving key data trends. The table L (sites – species) is generally ordinated using a correspondence analysis (CA/COA), a similar dimension-reducing method suited to frequency data, such as count, presence-absence, or biomass data. It seeks ‘correspondence’, i.e. highlights which sites correspond to which species (while PCA maximizes variance explained, CA maximizes the inertia explained, the ‘correspondence’ between the rows and columns of the table). The method used for table Q (species – traits) is strongly linked to the type of trait data: PCA (continuous), Multiple Correspondence Analysis (MCA) (categorical), Hill & Smith Analysis (HillSmith) (mix) or fuzzy-ordination (Fuzzy – PCA or Fuzzy – CA) (fuzzy coded). The RLQ combines the three separate analyses and identifies the main relationships between environmental gradients and trait attributes through species abundance or biomass distribution. It reveals how functional traits are selected for (or against) by environmental drivers subsequently influencing species distribution and community structure and thereby informing on the environmental factors they might be vulnerable to (selecting against) and which traits are driving this vulnerability. The method can show which pressures are most strongly related to environmental variables and how these alter species composition through traits. The association of the RLQ with the 4th Corner method (which tests for individual trait-environment/pressure relationships) can be harnessed to both identify the main relationships between pressures and traits (RLQ) and the most significant association between the two (4th corner). Used for vulnerability assessment, the RLQ/4th corner method can pinpoint which functional traits are strongly selected for (or against) human disturbance.  The analysis can further reveal which community function are the most likely to be affected (for example, in a contaminated area, the analysis might show an increase in species with traits for tolerance). The results are highly visual where a 2-dimensional plot show how environment, pressure and species traits are positioned to show their co-relationships, making the results easier to interpret and communicate to stakeholders. By understanding how human pressures change ecosystems at a functional level, researchers and managers can develop more effective conservation and management plans, such as understanding effects of MPA beyond preserving biodiversity.

 

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