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Data from: Demographic traits improve predictions of spatiotemporal changes in community resilience to drought

Cite this dataset

Paniw, Maria; de la Riva, Enrique; Lloret, Francisco (2021). Data from: Demographic traits improve predictions of spatiotemporal changes in community resilience to drought [Dataset]. Dryad.


  1. Communities are increasingly threatened by extreme weather events. The cumulative effects of such events are typically investigated by assessing community resilience, i.e., the extent to which affected communities can achieve pre-event states. However, a mechanistic understanding of the processes underlying resilience is frequently lacking and requires linking various measures of resilience to demographic responses within natural communities.
  2. Using 13 years of data from a shrub community that experienced a severe drought in 2005, we use generalized additive models to investigate temporal changes in three measures of resilience. We assess whether community-weighted, species-specific demographic traits such as longevity and reproductive output can predict changes in resilience and how the performance of these traits compares to more commonly used plant functional traits such as leaf area or root dry-matter content.
  3. We find significant spatiotemporal variation in community dynamics after the drought. Overall, demographic traits are better at predicting absolute and relative resilience in total plant cover, but functional traits outperform demographic traits when resilience in community composition is assessed. All resilience measures show non-linear and context-specific responses to demographic and functional traits; and these responses depend strongly on the severity of initial drought impact.
  4. Synthesis: Our work demonstrates that a full picture of the mechanisms underlying community responses to drought requires the assessment of numerous species-specific characteristics (including demographic and functional traits) and how these characteristics differentially affect complementary measures of community changes through time.  


Quantifying community composition

In order to assess community composition before, during, and after a severe drought event in Doñana National Park, 18 permanent plots of 25 m2 (5 × 5 m) were established in November 2007 (two years after the drought) on a gradient of drought impact. The plots were located at three sites (with six plots per site): Raposo (N 37º0′2″, W 6º30′20″; at 18 m a.s.l.), Marqués (N 37º0′45″, W 6º31′50″; 21 m a.s.l), and Ojillo (N 36º59′40″, W 6º30′50″; 30 m a.s.l.). To avoid spatial autocorrelation, all plots were separated by at least 50 m from each other. Species plant cover was estimated from contacts with branches along transects within plots; these contacts were divided into two categories corresponding to living or dead canopy. Dry organs with signs of old decay (stumps, decomposed stems, branches without thin tips) were excluded. Thus, canopy prior to the episode was considered as the sum of living and dead (i.e., dry) plant canopy in 2007. Relative abundance of each species per plot in years after the extreme drought was calculated as the proportion of their contacts of living canopy relative to the sum of the contacts of living canopy of all species. Similarly, the total vegetation cover per plot was calculated as the summed contacts of living canopy of all species. Relative abundances previous to the drought were calculated as the sum of the living and dead canopy of 2007 of each species relative to the sum of the living and dead canopy of 2007 for all species.

Demographic traits

We obtained four demographic traits for each plant species encountered in the sampling plots: age at first reproduction (AgeFR), maximum longevity (L), minimum size at first reproduction (SizeFR), and ratio of adults to recruits (R/A). To calculate the first three traits for each species, 50 plants growing near the plots were randomly selected in June, 2019, in vegetation patches without signs of drought-induced impact. We estimated plant age from yearly growth scars in the main shoots. We calculated age at first reproduction from inverse prediction (Probability of not flowering = 0.9) obtained from the logistic nominal regression between reproduction stage and plant age following the standard procedure. We obtained minimum size at first reproduction using receiver operating characteristic (ROC) curves. Lastly, we approximated maximum longevity as the 90th percentile of estimated age from yearly growth scales in the 50 plants growing near the plots in patches not impacted by drought; since scars disappear in older plants, 5 to 10 years age intervals - depending on species’ growth form and size - were added to the older scars in larger plants. We calculated ratios of recruits to adults (R/A) from data collected in the years 2007, 2008, 2013 and 2019. Data included the total number of juvenile non-reproductive plants (recruits) and the total number of adults of each species found in plots with low drought impact (6 plots).

Functional traits

We calculated community-weighted means of eight above- and two below-ground traits, which are among the most commonly used to describe plant functional types and have previously been obtained in the study plots in late spring 2013. Above-ground functional traits included plant height (Phg), leaf area per unit of leaf dry mass (SLA), leaf dry matter content (LDMC), stem dry matter content (SDMC), leaf nitrogen concentration (LNC), leaf chlorophyll concentration (LChl), isotopic carbon fraction (δ13C), and seed mass (Smass). Below-ground traits included specific root area (SRA) and root dry matter content (RDMC).

Usage notes

The dataset contains all data and R code to replicate the analyses in Paniw et al. 2021. Demographic traits improve predictions of spatiotemporal changes in community resilience to drought. Journal of Ecology


Ministerio de Ciencia e Innovación, Award: FJCI-2017-32893

Ministerio de Asuntos Económicos y Transformación Digital, Award: CGL2006-01293/BOS

Ministerio de Asuntos Económicos y Transformación Digital, Award: CGL2009-08101

Ministerio de Asuntos Económicos y Transformación Digital, Award: CGL2012-32965

Ministerio de Asuntos Económicos y Transformación Digital, Award: CGL2015-67419-R

Agency for Administration of University and Research, Award: 2017 SGR 1001