DOI: 10.5061/dryad.h44j0zpsb
title: Inverse priority effects: A role for historical contingency during species losses

description: Communities worldwide are losing multiple species at an unprecedented
 rate, but how communities reassemble after these losses is often an open
 question. It is well established that the order and timing of species
 arrival during community assembly shapes forthcoming community composition
 and function. Yet, whether the order and timing of species losses can lead
 to divergent community trajectories remains largely unexplored. Here, we
 propose a novel framework that sets testable hypotheses on the effects of
 the order of species losses inverse priority effects and suggests its
 integration into the study of community assembly. We propose that the
 order of species losses within a community can generate alternative
 reassembly trajectories, and suggest mechanisms that may underlie these
 inverse priority effects. To formalize these concepts quantitatively, we
 used a three-species Lotka-Volterra competition model, enabling to
 investigate conditions in which the order of species losses can lead to
 divergent reassembly trajectories. The inverse priority effects framework
 proposed here promotes the systematic study of the dynamics of species
 losses from ecological communities, ultimately aimed to better understand
 community reassembly and guide management decisions in light of rapid
 global change.
description: ## \* File name:
 README_Dataset-Historical-contingency-during-species-losses.txt * Authors:
 Agostina Torres, Sara E. Kuebbing, Katharine L. Stuble, Samantha A.
 Catella, Martin A. Nuñez, and Mariano A. Rodriguez-Cabal * Date created:
 2023-10-30 * Date modified: 2023-12-01 Dataset Attribution and Usage
 \----------------------------- \* Dataset Title: Data for the article
 "Inverse priority effects: A role for historical contingency during
 species losses" \* Persistent Identifier: 10.5061/dryad.h44j0zpsb \*
 Dataset creators: Agostina Torres, Sara E. Kuebbing, Katharine L.Stuble,
 Samantha A. Catella, Martin A. Nuñez, and Mariano A. Rodriguez-Cabal \*
 Suggested Citations: \* Dataset citation: &gt; Torres, A., Kuebbing, S.
 E., Stuble, K. L.,Catella, S. A., Nuñez, M. A., Rodriguez-Cabal, M. A.
 2023. Data for the article " Inverse priority effects: A role for
 historical contingency during species losses", Dryad, Dataset, DOI:
 10.5061/dryad.h44j0zpsb \* Corresponding publication: &gt; Torres, A.,
 Kuebbing, S. E., Stuble, K. L., Catella, S. A., Nuñez, M. A., Rodriguez
 Cabal, M. A. 2023. Inverse priority effects: A role for historical
 contingency during species losses. Ecology Letters. Accepted Contact
 Information ------------------- \* Name: Agostina Torres * Affiliations:
 Instituto de Investigaciones en Biodiversidad y Medioambiente, CONICET,
 Universidad Nacional del Comahue, Bariloche, Argentina. * ORCID ID:
 * Email:
 * Address: e-mail preferred Dataset
 Metadata =========================== Dates and Locations
 ------------------- \* Dates of data collection: Simulated data \*
 Geographic locations of data collection: Simulated data ### Methodological
 Information ========================== \* Methods: we use a
 phenomenological Lotka-Volterra model of three competing species to
 demonstrate how and when inverse priority effects can arise in a community
 based solely on intrinsic dynamics of species interactions, represented as
 competition coefficients. The model takes the form: (dN_i)/dt=r_i N_i
 (1-∑_(j=1)^n▒〖α_ij N_j 〗) where N_i is the abundance of species i
 (i=1,2,3), r_i is the intrinsic growth rate of species i in the absence of
 competition, n is the total number of species in the system, and α_ij is
 the competitive effect of species j on species i for i,j=1,2,3. We chose
 to set all r_i to 1 and limit all α_ij to fall between 0 and 2. We
 designated one species as the focal and varied removal order of the two
 nonfocal species (A and B). Then, we assessed prevalence of inverse
 priority effects that arose specifically when the focal species was
 excluded when nonfocal species B was removed first (i.e., because the
 focal was excluded by nonfocal species A), but persisted when nonfocal
 species A was removed first (i.e., because it could persist with or
 excluded nonfocal species B). Data and File Overview
 ====================== Summary Metrics --------------- \* Code file
 format: .R * Code file count: 4 * Output file formats: .RData * Output
 file count: 3 \* Total file count: 7 (4+3) Naming Conventions
 ------------------ \* File naming scheme: - First part: denotes the type
 of file: Code/Output - Second part: denotes the type of information
 provided in each file. \--&gt; Parts in naming schemes are separated by
 '-'. * Throughout, IPE is an acronym for inverse priority
 effect. Table of Contents ----------------- \* Code files: -
 Code-IPE_LinearStabilityAnalysis.R - Code-IPE_IdentifyIPEs.R -
 Code-IPE_Figure3.R - Code-IPE_Figure4.R \* Output files: -
 Output-GoodAndSkipParams.RData -
 Output-IPE_LinearStabilityAnalysis_Output.RData -
 Output-alldat_stable.RData Setup ----- \* Unpacking instructions: n/a
 File/Folder Details =================== 1\. Code File
 Code__IPE_LinearStabilityAnalysis.R is the code used to find combinations
 of competition coefficients leading to a locally stable 3-species
 equilibrium. There are two output files: 1.1.
 Output-GoodAndSkipParams.RData has the parameter combinations used in the
 analysis saved in the "good" matrix, and all other parameter
 combinations saved in the "skip" matrix (these are parameter
 combinations that do not meet the criteria for a real solution given in
 the Supplemental methods). 1.2.
 Output-IPE_LinearStabilityAnalysis_Output.RData contains three matrices:
 1.2.1. coexistingAlphas * Rows are all parameter combinations we used in
 the analysis (from the "good" matrix described above). * Columns
 1-9 are the competition coefficients with notation α_ij, for species pair
 i,j for all pairwise interactions between i,j, and k, where '1'
 indicates the focal species, '2' indicates nonfocal A, and
 '3' indicates nonfocal B. 1.2.2. coexistingEqpoints * Rows are
 all parameter combinations we used in the analysis (from the
 "good" matrix described above). * Columns are the equilibrium
 abundances of the three species given by equation 2 in the Supplemental
 methods; N1 is the focal, N2 is nonfocal A, and N3 is nonfocal B. 1.2.3.
 STABLE3SpsEq * Rows are all parameter combinations we used in the analysis
 (from the "good" matrix described above). * Column is a binary
 variable indicating whether the real part of all three eigenvalues from
 the Jacobian given by equation 3 in the Supplemental methods was negative
 (1 for yes, 0 for no). 2\. File IPE_IdentifyIPEs.R loads in the data from
 the linear stability analysis and then provides a detailed step-by step
 process of identifying the parameter combinations that yield an inverse
 priority effect, including visualizations along the way. There is one
 output file saved as: 2.1. alldat_stable.RData, which is a matrix. * Rows
 represent a parameter combination that leads to a locally stable 3-species
 coexistence equilibrium. * Columns 1-9 are the competition coefficients
 with notation α_ij, forspecies pair i,j for all pairwise interactions
 between i,j, and k, where'1' indicates the focal species,
 '2' indicates nonfocal A, and '3' indicates nonfocal
 B. * Columns 10-12 ("N1eq", "N2eq", "N3eq")
 are the equilibrium abundances of the three species given by equation 2 in
 the Supplemental methods; N1 is the focal, N2 is nonfocal A, and N3 is
 nonfocal B. * Column 13 ("STABLE3spsEq") is a binary variable
 indicating whether the real part of all three eigenvalues from the
 Jacobian given by equation 3 in the Supplemental methods was negative (1
 for yes, 0 for no; this matrix only contains locally stable combinations
 so all values should =1). * Column 14-19 are values of niche overlap
 ("rho_ij", or ρ_ij in the main text) and relative fitness
 differences ("fit_ij", or f_j/f_i in the main text) for species
 pairs i,j for all pairwise combinations of i, j, and k, where
 '1' indicates the focal species, '2' indicates
 nonfocal A, and '3' indicates nonfocal B. * Columns 20-21
 contain a binary variable indicating whether that parameter combination
 yielded an inverse priority effect (1 for yes, 0 for no).
 "guaranteedIPE1" and "guaranteedIPE2" correspond to
 scenarios 1 and 2, respectively, discussed in the Supplemental methods. *
 Columns 22-24 contain a binary variable indicating whether that parameter
 combination yielded an inverse priority effect (1 for yes, 0 for no).
 "p1IPE", "p2IPE", and "p3IPE" correspond to
 scenarios 3-5, respectively, discussed in the Supplemental methods. *
 Column 25 ("anyIPE") is a summary column indicating whether any
 of the 5 scenarios from the previous columns yielded an inverse priority
 effect (binary, 1 for yes, 0 for no). * Column 26-28 are stabilization
 potential values (1-ρ_ij) for species pairs i,j for all pairwise
 combinations of i, j, and k, where '1' indicates the focal
 species, '2' indicates nonfocal A, and '3' indicates
 nonfocal B. 3\. Code File IPE_Figure3.R is the code that produced Figure
 3. 4\. Code File IPE_Figure4.R is the code that produced Figure 4.