DOI: 10.5061/dryad.p2ngf1vxk
title: Data for: Ecosystem connectivity and configuration can mediate instability at a distance in metaecosystems

description: Ecosystems are connected by flows of nutrients and organisms. Changes to
 connectivity and nutrient enrichment may destabilise ecosystem dynamics
 far from the nutrient source. We used gradostats to examine the effects of
 trophic connectivity (movement of consumers and producers) versus
 nutrient-only connectivity on the dynamics of Daphnia pulex (consumers)
 and algae (resources) in two metaecosystem configurations (linear vs.
 dendritic). We found that Daphnia peak population size and instability
 (coefficient of variation; CV) increased as distance from the nutrient
 input increased, but these effects were lower in metaecosystems connected
 by all trophic levels compared to nutrient-only connected systems and/or
 in dendritic compared to linear systems. We examined the effects of
 trophic connectivity (i.e. both trophic levels move rather than one or the
 other) using a generic model to qualitatively assess whether the
 expectations align with the ecosystem dynamics we observed. Analysis of
 our model shows that increased Daphnia population sizes and fluctuations
 in consumer-resource dynamics are expected with nutrient connectivity,
 with this pattern being more pronounced in linear rather than dendritic
 systems. These results confirm that connectivity may propagate and even
 amplify instability over a metaecosystem to communities distant from the
 source disturbance, and suggest a direction for future experiments, that
 recreate conditions closer to those found in natural systems.
description: Our gradostat flasks contained
 simple communities of the water flea <em>Daphnia</em>
 <em>pulex</em> consuming a mix of three algal species
 (<em>Pseudokirchneriella subcapitata, Scenedesmus quadricauda,
 Ankistrodesmus falcatus</em>). This experiment employed a 2x2x2
 factorial design to test the importance of ecosystem trophic connectivity
 (a treatment considering movement of medium only vs. movement of media,
 phytoplankton and <em>Daphnia</em> between flasks) and
 metaecosystem configuration (linear or dendritic) on the stability of
 <em>Daphnia</em> populations and algal communities with two
 levels of enriched medium input (regular and phosphorus-enriched). Four
 replicates of this whole design were established, for a total of 32
 metaecosystems, run in 9 blocks due to time and space constraints. Each
 metaecosystem consisted of four “nodes” of 500 mL Erlenmeyer flasks with a
 foam stopper to allow for gas exchange (128 flasks total), seeded
 initially with 100 mL algal mix (total average algal density of 2.22 x106
 +/- 1.3x104 cells/mL) to which 50 adult <em>Daphnia</em> with
 eggs (which produce broods of about 15 individuals each week in good
 conditions (Schwartz
 1984) were added before
 topping off the flask to 500 mL with FLAMES media (Celis-Salgado et al. 2008). Configuration was controlled by unidirectionally
 connecting flasks in either a linear configuration (in
 →1→2→3→4→out) or a dendritic configuration (in
 →1, in→2, 1→3, 2→3, 3→4→out). We chose this as the simplest possible
 design in which a linear network could be compared to a branched network,
 with four nodes being the smallest possible number of nodes to create a
 dendritic configuration, and the two nodes branching into a third, similar
 to headwater in a river. Flasks were then connected by Tygon tubing and
 from an inflow reservoir of FLAMES medium (10 μgP/L) or enriched P (70 μgP
 /L) medium which was pumped through the array of flasks using peristaltic
 pumps (Watson-Marlow 503S/RL and Rainin Dynamix RP-1). Pumps were set on
 automatic timers to run for one hour each day at a speed adjusted to move
 a specific volume of media over that hour. The dilution rate was 10% of
 the total volume per for all flasks in the linear configurations and the
 “hub” (3) and “terminal” (4) nodes of the dendritic configurations (50
 mL), and 5% per day (25 mL) for the “upstream” nodes in the dendritic
 configurations (Figure 1). We also controlled functional connectivity,
 contrasting metaecosystem dynamics when only nutrients moved versus the
 case when nutrients, resources and consumers moved. To block the flow of
 organisms in the nutrient-only connectivity treatment, outflow tubing was
 placed inside an 80-µm nylon mesh held in place with the stopper.
 Due to colony formation of
 the phytoplankton and clogging of the mesh, this proved to be an effective
 retention mechanism also for the algal resources, thus we believe flow of
 algae was significantly reduced in these treatments compared the trophic
 connectivity treatments. Though it is possible a small portion of single
 cells were able to pass through, <em>Scenedesmus</em> is known
 to form four-cell colonies in the presence of consumers (which we also
 observed in our algal counts), which are too large to pass through the
 mesh. As <em>D</em>. <em>pulex</em> were unable to
 fit through the tubing or survive moving through the peristaltic
 pumps, in the trophic
 connectivity treatment, <em>D. pulex</em> were manually moved
 using a 2mL transfer pipette at a rate of 10% of the population per day (20% were moved after
 each sampling count as sampling was only done every two days) in all
 linear nodes and the hub and terminal dendritic nodes, and 5% per day (10%
 moved after sampling) in the upstream dendritic nodes, in the same
 downstream direction as media. This type of passive movement at the flow
 rate of the system would be typical of planktonic animals in rivers that
 cannot swim upstream. Inflow stock solutions were prepared using FLAMES
 media (10 μgP/L).  Finally, we modified our inflow reservoirs to
 contain either additionally P-enriched (high P) or regular (low P) FLAMES
 media. To increase P in the additionally nutrient-enriched treatment
 without changing pH, 132 μg/L of
 H<sub>2</sub>KPO<sub>4</sub> and 168
 μg/L H<sub>2</sub>KPO<sub>4</sub> were added to
 our increased P treatment inflow stock solution. For the less-phosphorus
 treatment, no additional phosphorus was added, but 218 μg/L KCl were added
 to control for the K added to the high-P medium. See Figure S1 for a
 photograph of the experimental setup. 
 Experimental
 Sampling The gradostats were sampled every other day for
 30 days. In each node, the concentration of each algal species was
 measured using a haemocytometer. To estimate <em>Daphnia</em>
 population size, a 2mL plastic transfer pipette was used to gently
 agitate, and then sample each node. The number of individuals and two age
 classes (adult or juvenile) in the pipette were determined and then
 replaced to the experimental flask. This process was repeated five times,
 and the average <em>D. pulex</em> count of the five samples
 was used to estimate <em>Daphnia</em> density/2mL (total
 number estimated per flask = sampled count average *250). A pilot
 experiment testing this method proved it had an average error of 17.41 %,
 equating to 2.5 <em>Daphnia</em> more or less than the
 expected count at known densities; there is no reason to believe this
 error was systematic in one direction or the other, or to be
 systematically biased among our treatments. On Day 30 of the experiment, 40mL samples were
 taken from each flask to be analysed for total phosphorus concentration
 (TP). Phosphorus samples were analysed using a standard protocol
 (Wetzel and Likens
 2013) at the
 GRIL-Université du Québec à Montréal analytical laboratory.
 Statistical Analysis
 To quantify the instability of
 <em>Daphnia</em> populations in experimental gradostats, we
 determined the peak total <em>Dapnhia</em> population size (as
 estimated by our density samples) and the coefficient of variation (CV) of
 <em>Daphnia</em> population size over the course of the
 experiment. These variables were calculated for each node within each
 gradostat, as well as in aggregate summed across all nodes for additive
 <em>Daphnia</em> metapopulation peak and CV. Similarly,
 population CV and peak density were calculated for each species of alga
 but we analyse here values based on total algal community density (sum of
 all species present), as <em>Pseudorkirchinella</em> and
 <em>Ankistrodesmus</em> were undetectable in most flasks for
 most of the experiment. <em>Scenedesmus</em> was mostly
 observed in 4-cell colonies, which is common in the presence of consumers,
 but we counted the total number of cells, not
 colonies. All analyses of experimental gradostat data were
 conducted in R version 4 (Team 2020). Statistical tests of the hypothesis were
 two-sided and with a level of significance of
 α=0.05. To determine whether metaecosystem connectivity,
 configuration and nutrient enrichment, as well as node position (1
 upstream to 4 terminal), influenced node <em>Daphnia</em>
 population instability downstream of the nutrient enrichment source, we
 analysed the effects of these factors on mean <em>Daphnia</em>
 population and algal community peak values, on mean Daphnia population and
 algal community CV log-transformed (natural logarithm) values, and on mean
 final TP concentrations values, using linear mixed-effects models with the
 four factors as fixed effects. The mixed model included a random effect
 for ‘system’ which allowed us to account for a possible clustering in the
 response variables since the four nodes were connected as metaecosystems.
 For each of these models, pairwise interactions between factors were
 tested and terms for non-significant interactions were removed from the
 final models we report. Assumptions on the model errors (randomness,
 normality, and homoscedasticity) and the presence of possible influential
 observations or outliers were assessed with diagnostic plots of the model
 residuals. Robust standard errors (Huang and Li 2022) were used to adjust for heteroscedasticity.
 We
 also measured <em>Daphnia</em> metapopulation and algal
 metacommunity instability at the scale of the entire metaecosystem. To
 determine whether metaecosystem connectivity, configuration and nutrient
 enrichment influenced <em>Daphnia</em> metapopulation and
 algal metacommunity instability, we analysed the effects of these factors
 on mean <em>Daphnia</em> metapopulation and algal
 metacommunity peak values, and on mean CV values, using linear
 mixed-effects models with the three factors as fixed effects, using the
 block in which a metaecosystem was run as a random factor. For each of
 these models, pairwise interactions between factors were tested and terms
 for non-significant interactions were removed from the final models we
 present. Assumptions on the model errors (randomness, normality, and
 homoscedasticity) and the presence of possible influential observations or
 outliers were assessed with diagnostic plots of the model residuals.
 Robust standard errors (Huang and Li 2022) were used to adjust for heteroscedasticity.
description: # Data for: Ecosystem connectivity and configuration can mediate
 instability at a distance in metaecosystems
 [https://doi.org/10.5061/dryad.p2ngf1vxk](https://doi.org/10.5061/dryad.p2ngf1vxk) These data describe *Daphnia pulex* and algal metacommunity dynamics in experimental gradostats (flasks connected by the movement of nutrients and organisms in linear and dendritic configurations). Each node was sampled every two days for 30 days to record population densities and estimate their total size. Peak population size and population coefficient of variation was calculated for each flask, and across metacommunities. We also write a mathematical model to describe these dynamics. ## Description of the data and file structure Four data sets are included. The first (dapalg1.csv) is the raw data file with the recorded daily numbers of daphnia, and each species of algae within each experimental flask, as well as our two major outcome variables (peak and CV), and these are the data that were used for node-level analysis. The second (ts gradostats) is these same data but transposed to a time series for creating our first plot. In this data set, we only include the sum of all algal species present in each flask, rather than each species individually. The third file (METAPOP) are the metacommunity-level daphnia and alagal density counts for each day (the sum of all four nodes in the gradostat) with peak and CVs for daphnia and algal populations calculated at the metacommunity level, and was used for statistical analysis at the metacommunity level. The final dataset (model.csv) contains the output from our mathematical model after solving the ODEs in Matlab. it was also used to create figure 1 in which model predictions are compared to experimental results. Below, we describe in detail the names of each variable in each data sheet: FOR dapalg1.csv each row represents 1 flask in an experimental gradostat * Block: block in which flask was run * System: the metaecosystem to which a flask belonged * Replicate: replicate of experiment * Ptreatment: Phosporus-enriched (HP) vs less Phosphorus (LP). this treatment did not result in actual differences * in P concentrations in our systems and was removed from statistical analyses * Etreatment: Connectivity treatment for the system (trophic connectivity, nutrient connectivity, U for * unconnected control flasks) * Ctreatment: Linear or Dendritic configuration * Treatment: overall type of metaecosystem the flask was in * Node: position of flask in gradostat (A=1/upstream through D=4/downstream) * D0-D30: estimated total number of Daphnia in flask * S0-S30:estimated density of Scenedesmus cells/mL in flask, based on hemocytometer count * A0-A30:estimated density of Ankistrodesmus cells/mL in flask, based on hemocytometer count * P0-P30:estimated density of Pseudokirchinella cells/mL in flask, based on hemocytometer count * TA0-TA30:estimated total algal density (cells/mL) in flask (sum of S, A and P) * dmax: daphnia population maximum observed * dcv: coefficient of variation daphnia population * acv: coefficient of variation algal density * TP30: total phosphorus concentration in flask, measured at end of experiment (only available for one replicate) FOR METAPOP.csv. each row represents an entire experimental gradostat (sum of nodes A through D for population counts), with column names having the same meanings as in dapalg1.csv FOR tsgradostats.csv time series data for each node are created by trasnposing the x and y axes of dapalg1.csv such that each column is the number of daphnia or density of algae an experimental flask and each row in a day in order to make time series plots for figure 1. Column names indicate: * Daphnia (D) or total algal density (A) * replicate * number (1-4) * Phosphorus treatment (as in dapalg1.csv) * configuration (as in dapalg1.csv) * connectivity treatment (as in dapalg1.csv) * node position (as in dapalg1.csv) FOR model.csv we obtain time series data up to day 30 for our model simulations in Matlab using ode45 to solve our differential equations with each row representing a day and each column representing daphnia or algae population dynamics in a node in a metaecosystem. Column names indicate whether it is daphnia (D) or algae (A), that these are model simulation results (M) and the configuration (as in all other data frames), connnectivity (as in all other data frames), and node position (as in all other data frames). ## Code/Software We also include the annotated code for all statistical analysis described in the manuscript and create the associated figures as the R script (code for MS.R), which also includes the loading of any packages necessary to run it. Analysis was performed in R V4. We also include the Matlab code written to solve our mathematical model for linear (gradostat\_linear.m) and dendritic (gradostat\_dendritic.m) gradostats.