Example datasets to characterize and compare EDRs, including abundance data, state, segment, and trajectory dissimilarity matrices for 93 artificial communities belonging to three different EDRs.
EDR_dataList of four nested sublists. Each element of "EDR1", "EDR2", and "EDR3"
is associated with one EDR and includes the following elements:
abundance: Data table with 15 columns and one row for each community state:
EDR: Integer indicating the identifier of the EDR.
traj: Integer containing the identifier of the trajectory for each
artificial community in the corresponding EDR. Each trajectory represents
a different sampling unit.
state: Integer indicating the observations or states of each community.
The sequence of states of a given community forms a trajectory.
sp1, ..., sp12: Vectors containing species abundances for each community
state.
state_dissim: Object of class dist containing Bray-Curtis dissimilarities
between every pair of states in abundance.
segment_dissim: Object of class dist containing the dissimilarities
between every pair of trajectory segments in abundance.
traj_dissim: Object of class dist containing the dissimilarities
between every pair of community trajectories in abundance.
The element EDR3_disturbed represents the dynamics of three disturbed communities
originally associated with EDR3. It includes an abundance matrix with 16 columns
and one row for each community state. The column disturbed_states is a numeric
vector indicating whether the corresponding state represents a state before the
disturbance (0), during or immediately after the release of the disturbance (1),
or a post-disturbance state (> 1).
Artificial data was generated following the procedure explained in Box 1 in Sánchez-Pinillos et al. (2023). The initial state of each community was defined using a hypothetical environmental space with optimal locations for 12 species. Community dynamics were simulated using a general Lotka-Volterra model.
Abundances for EDR3_disturbed were generated following the procedure explained
in Sánchez-Pinillos et al. (2024) for ecological systems affected by pulse
disturbances.
State dissimilarities were calculated using the Bray-Curtis metric. Segment and trajectory dissimilarities were calculated using the package 'ecotraj'.
Sánchez-Pinillos, M., Kéfi, S., De Cáceres, M., Dakos, V. 2023. Ecological Dynamic Regimes: Identification, characterization, and comparison. Ecological Monographs. doi:10.1002/ecm.1589
Sánchez-Pinillos, M., Dakos, V., Kéfi, S. 2024. Ecological Dynamic Regimes: A key concept for assessing ecological resilience. Biological Conservation. doi:10.1016/j.biocon.2023.110409