Planktonic functional diversity changes in synchrony with lake ecosystem state

Abstract

Managing ecosystems to effectively preserve function and services requires reliable tools that can infer changes in the stability and dynamics of a system. Conceptually, functional diversity (FD) appears as a sensitive and viable monitoring metric stemming from suggestions that FD is a universally important measure of biodiversity and has a mechanistic influence on ecological processes. It is however unclear whether changes in FD consistently occur prior to state responses or vice versa, with no current work on the temporal relationship between FD and state to support a transition towards traitbased indicators. There is consequently a knowledge gap regarding when functioning changes relative to biodiversity change and where FD change falls in that sequence.We therefore examine the lagged relationship between planktonic FD and abundancebased metrics of system state (e.g. biomass) across five highly monitored lake communities using both correlation and cutting edge non-linear empirical dynamic modelling approaches. Overall, phytoplankton and zooplankton FD display synchrony with lake state but each lake is idiosyncratic in the strength of relationship. It is therefore unlikely that changes in plankton FD are identifiable before changes in more easily collected abundance metrics. These results highlight the power of empirical dynamic modelling in disentangling time lagged relationships in complex multivariate ecosystems, but suggest that FD cannot be generically viable as an early indicator. Individual lakes therefore require consideration of their specific context and any interpretation of FD across systems requires caution. However, FD still retains value as an alternative state measure or a trait representation of biodiversity when considered at the system level.

Early warning signals have limited applicability to empirical lake data

“…We, therefore, applied three deseasoning techniques (averaging, additive decomposition, and STL) factorially with the detrending methods to identify the optimal combination. Averaging simply subtracts the average value for a given month from the current data point of that month 79 , additive decomposition estimates the seasonal cycle from moving averages which is then subtracted from the raw time series 80 , and STL (seasonal trend estimation using loess), which also estimates the average seasonal cycle but uses local polynomials rather than linear/moving averages 81 . All data pre-processing was performed using the EWSmethods R package v1.2.0 82 .…”

Trait-Based Research on Rotifera: The Holy Grail or Just Messy?

“…Interestingly, Jannsson et al [51] assigned to zooplankton, including rotifers, a complexity trait, probably based on behavioral and morphological diversity. Furthermore, O'Brien et al [52] used fuzzy coding for zooplankton, including rotifer traits. Fuzzy coding is based on a scoring system that describes the affinity of a specific taxon to a certain trait category [53].…”

“…Fuzzy coding is based on a scoring system that describes the affinity of a specific taxon to a certain trait category [53]. No information on the coding is provided in O'Brien et al [52], and therefore its appropriateness cannot be assessed. An interesting example of aquatic functional diversity is the study of Neury-Ormanni et al [54] who assembled a detailed trait matrix based on diverse attributes such as chemical preferences, life cycle, morphology, life history, physiology, and diet and feeding behavior for the meiofauna, including eight rotifer species.…”

Functional diversity metrics can perform well with highly incomplete data sets

“…In some cases, assessments of functional diversity provide greater explanatory power than complementary measures of diversity such as species richness (Cadotte et al, 2011; Wilkes et al, 2020), in part due to the mechanistic link between traits and ecosystem functions (Petchey & Gaston, 2006). Assessing functional diversity can contribute advances necessary for tackling human‐induced biodiversity loss (Mason & de Bello, 2013) through producing early warning systems of ecosystem collapse (Frainer et al, 2021, although see O'Brien et al, 2022) and enhancing prioritisation of conservation efforts (Carmona, Bueno, et al, 2021; Carmona, Tamme, et al, 2021; Cooke et al, 2019). Research into how functional diversity has varied across time and space could yield fruitful insights into ecosystem responses to global change (Green et al, 2022; Petchey & Gaston, 2006).…”