Estimating the risk of species interaction loss in mutualistic communities

Funder: Royal Commission for the Exhibition of 1851 (GB)Funder: Cambridge TrustFunder: Cambridge Depatment of ZoologyFunder: Grantham Foundation for the Protection of the Environment; funder-id: http://dx.doi.org/10.13039/100008118Funder: Kenneth Miller TrustFunder: ArcadiaInteractions between species generate the functions on which ecosystems and humans depend. However, we lack an understanding of the risk that interaction loss poses to ecological communities. Here, we quantify the risk of interaction loss for 4,330 species interactions from 41 empirical pollination and seed dispersal networks across 6 continents. We estimate risk as a function of interaction vulnerability to extinction (likelihood of loss) and contribution to network feasibility, a measure of how much an interaction helps a community tolerate environmental perturbations. Remarkably, we find that more vulnerable interactions have higher contributions to network feasibility. Furthermore, interactions tend to have more similar vulnerability and contribution to feasibility across networks than expected by chance, suggesting that vulnerability and feasibility contribution may be intrinsic properties of interactions, rather than only a function of ecological context. These results may provide a starting point for prioritising interactions for conservation in species interaction networks in the future

Simple tipping or complex transition? Lessons from a green Sahara

International audienc

Reply to Carstensen, J., Telford, R.J. and Birks, H.J.B. (2013) Diatom flickering prior to regime shift

Some issues have been raised with regard to our paper, by Carstensen et al. In terms of our data processing, we were aware from the outset of the problems of unevenly spaced temporal data and sediment dating errors. We also wanted to duplicate, as far as possible, the methods published previously that had been used to identify early warning signals in palaeoenvironmental data (for example, ref. 3). Thus, we applied two standard smoothing functions (exponential and Gaussian kernel) to interpolated and non-interpolated (original) diatom data, expressed as three statistical indices (detrended correspondence analysis (DCA), Hill’s diversity index N2 (HDI) and correspondence analysis), using different sliding-window sizes and the two-standard-deviation range of dates for each sample

Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called 'early warning signals', and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data. © 2012 Dakos et al.Peer Reviewe

Recovery rates reflect distance to a tipping point in a living system

Tipping points, at which complex systems can shift abruptly from one state to another, are notoriously difficult to predict1. Theory proposes that early warning signals may be based on the phenomenon that recovery rates from small perturbations should tend to zero when approaching a tipping point2, 3; however, evidence that this happens in living systems is lacking. Here we test such ‘critical slowing down’ using a microcosm in which photo-inhibition drives a cyanobacterial population to a classical tipping point when a critical light level is exceeded. We show that over a large range of conditions, recovery from small perturbations becomes slower as the system comes closer to the critical point. In addition, autocorrelation in the subtle fluctuations of the system’s state rose towards the tipping point, supporting the idea that this metric can be used as an indirect indicator of slowing down4, 5. Although stochasticity prohibits prediction of the timing of critical transitions, our results suggest that indicators of slowing down may be used to rank complex systems on a broad scale from resilient to fragile.

Biotic homogenisation in bird communities leads to large-scale changes in species associations

Abstract The impact of global change on biodiversity is commonly assessed in terms of changes in species distributions, community richness and community composition. Whether and how much associations between species are also changing is much less documented. In this study, we quantify changes in large-scale patterns of species associations in bird communities in relation to changes in species composition. We use network approaches to build three community-aggregated indices reflecting complementary aspects of species association networks. We characterise the spatio–temporal dynamics of these indices using a large-scale and high-resolution dataset of bird co-abundances of 109 species monitored for 17 years (2001–2017) from 1969 sites across France. We finally test whether spatial and temporal changes in species association networks are related to species homogenisation estimated as the spatio–temporal dynamics of species turnover (β-diversity) and community generalism (community generalisation index). The consistency of these relationships is tested across three main habitats, namely woodland, grassland and human settlements. We document a directional change in association-based indices in response to modifications in species turnover and community generalism in space and time. Weaker associations and sparser networks were related to lower spatial species turnover and higher community generalism, suggesting an overlooked aspect of biotic homogenisation affecting species associations and may also have an impact on species interactions. We report that this overall pattern is not constant across habitats, with opposite relationships between biotic homogenisation and change in species association networks in urban versus forest communities suggesting distinct homogenisation processes. Although species associations contain only partial signatures of species interactions, our study highlights that biotic homogenisation translates to finer changes in community structure by affecting the number, strength and type of species associations