Peer review of: Untangling the seasonal dynamics of plant-pollinator communities (2020)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
Bramon Mora et al. use a network alignment method to identify rules governing the three stages of 
community dynamics (assembly, intermediate, and disassembly) of a highly resolved plant-pollinator 
network in the Rocky Mountains of Colorado. I think the authors' unique methods and their 
simultaneous assessment of different stages of community dynamics have merit and would contribute 
to a more general understanding of the temporal dynamics of ecological networks. Still, I have many 
comments that I hope the authors will find constructive. 
 
**Interpretation**: The authors suggest that species entering and exiting the community tend to be 
specialists, an inference based on the low relative degree of groups A and C (Fig. 3). However, do 
these species have a low relative degree because they are simply less abundant? If that's true, then 
the assembly and disassembly process is simply due to species increasing and decreasing, 
respectively, in abundance over the course of the season. This would not be surprising and perhaps be 
a trivial result. It would help if the authors provide a clear definition of how degree and relative degree 
were quantified with their weighted network data as well as the sensitivity of these network properties 
to sampling effects (i.e. abundance). In terms of the intermediate dynamics, it appears that there are 
more patterns to unpack (e.g. common transitions include: stay in A; B->A or stay in B; C->A); 
however, the biological significance of these "intermediate dynamics" were not discussed much, 
despite this being framed as one of the novel contributions of this study (Intro. L. 56-58). 
 
**Statistical evidence**: Despite the several assertions made in regard to identifying general 
assembly rules, there is no statistical support for any of these conclusions. For example, the authors 
describe "the position of species entering the network tend to be from group A", but there is no 
evidence that this is "above average" or different than another group. They should be able to infer this 
information from their Bayesian model (which also needs further description, see comments below) 
and should report this in the Results. The authors should also provide statistical evidence supporting 
their descriptions of groups A, B, and C (I've provided one possible way to do this below). 
 
**Bayesian models**: The Bayesian models are not described well at all. What priors did the authors 
use? What statistical software did they use (e.g. they cite McElreath 2018, but its unclear whether 
they use his "rethinking" package in R). Why don't the authors report any statistical evidence to 
support their assertions about the assembly rules they claim to have discovered (e.g. effect sizes with 
credible intervals for each group's transition probability). They could also consider using widely 
applicable information criteria (WAIC) or leave-one-out cross-validation information criteria (LOOIC) to 
inform the relative support of the different models they analyzed (e.g. transition probabilities are 
time-dependent, constant, or density-dependent). The authors could then use these criteria to choose 
which model to highlight in the main text, rather than make what appears to be an arbitrary choice. 
For example, eye-balling the quantiles in Fig. S9 vs. Fig. 4, makes me think that the constant 
probability model may be the best choice (smaller quantile range and fewer parameters; in fact, it 
seems like the authors interpretation of assembly rules follows more from the constant probability 
model rather than the time-dependent one which is highlighted in the main text..). On a related note, 
why was each year analyzed separately? Why not treat each year as a temporal replicate and test 
whether the transition probabilities differ between years (which would suggest assembly rules depend 
on the year, i.e. there are no general rules) or are consistent across years (i.e. there may be general 
rules for this community). 
 
**Identifying node-level properties of each group**: If you're comfortable using Bayesian models, 
one possibility would be to use a multivariate autoregressive model with the brms package in R. 

Sample code: brm(mvbind(species, neighbours-average, neighbours-maximum) ~ 
year + group + (1|species), 
autocor = cor_ar(formula = ~ week, p = 1)). 
This could provide you with information on the effect sizes and credible intervals for the relationship 
between group and node properties that also accounts for confounding factors (e.g. year, species ID, 
temporal autocorrleation within each year, and residual correlation among position properties). This 
approach would also allow you to use all of the information, rather than separating it among years. 
Note that this is a rough sketch and you may find a better alternative. The key point is that you should 
show statistical evidence for the claims you make. 
 
**Static networks**: I feel the authors build a bit of a "straw man" argument in the Intro and 
Discussion regarding theory of "static interaction networks". These networks are only static in regard 
to per-capita parameters (e.g. intrinsic growth rates, inter- and intraspecific interaction rates, 
handling time, to use Bastolla et al. 2009 as an example). In contrast, population-level interaction 
strengths do dynamically change in these models as the abundance of plants and pollinators changes. 
If the authors showed, for example, that per-capita interaction strengths were dynamically changing 
over time, then I think there would be more room for critique. As is though, the dynamic nature of the 
plant-pollinator network analyzed here could simply be due to changes in abundances of interacting 
populations with constant per-capita parameters, which would be consistent with the assumptions of 
the "static interaction networks". Perhaps the "density-dependent" transition probabilities would give 
more insight here; however, the authors haven't provided any details on exactly how density 
dependence was included in that model. 
 
# Minor Comments 
 
Overall, I appreciate the time and effort the authors put into producing an aesthetically pleasing and 
well-written manuscript. 
 
Intro. L. 37: I would only use "evolve" here if you mean "biological evolution" (I assume you aren't 
refering to biological evolution though based on the references given). I would simply use "change" to 
be more clear. 
 
Intro. Fig. 1: I really appreciate the clear description of the 'network alignment' methodology provided 
in this figure. 
 
Intro. L. 95: "then comprise"? This word choice seems odd to me. 
 
Methods (general): For the most part, I think the authors do a good job of clearly describing the 
"network alignment" methodology, which will be unfamiliar to most ecologists. For example, I thought 
the *Measuring the change of position of individual species* section was quite clear and had a good 
example (L. 178-183). Still, there were other places where I needed more help. For example, I got 
confused in the *Position variability across networks* section. It may be helpful to reference Fig. 1 
(which is excellent by the way) at times to help more visual learners understand exactly what your 
methods are doing. For example, the authors could edit the sentence, "For example, given two 
networks At1 and At2 that were collected at two time points t1 and t2, we wanted to test whether or 
not any species i present in both networks---i.e. a species i $\in$ At1 $\cap$ At2 ---changed its 
position over time (e.g. species a3 in Fig. 1b)." 
 
Methods L. 232-234: "Importantly, species are assumed not to re-enter the network once they have 
exited it, making the transition from state y_post to any other state occur with probability zero." 
Although this is a fantastic dataset, my guess is that it is not uncommon for a species to appear to 

exit the network (e.g. not observed one week), and then "re-enter", even though the apparent exit 
from the network could just be due to sampling error. How was this situation treated? Were these 
treated as actual "exits" even though they probably were not? 
 
Methods L. 206-216: Why did the authors focus on the 'short random walks' algorithm? Is there is a 
biological/statistical justification? Or is it because this algorithm produced three groups of plants and 
pollinators, which just felt like a good number of groups for interpretation... 
 
Methods L. 208-211: Please provide a clear description of how you quantified degree and relative 
degree for this weighted network data. 
 
Results Fig. 2b and Fig. 2c: Intuitively, I feel like the results for variability (Fig. 2b) and quality (Fig. 
2c) should be the same, but apparently they give different insights (e.g. plants appear to exhibit less 
variability in their positions across networks, but the low quality suggests they exhibit more variability 
than pollinators). It would be worth clarifying here or in the Methods how these different ways to 
assess change in species position over time have different biological meanings. 
 
Results L. 267: Why highlight the year 2015? It makes me think there is something special about this 
year, but it's just one of the temporal replicates right? 
 
Results L. 281-286: This appears to be a description of the general assembly rules the authors 
discovered. I think it would be much more helpful for the reader if the authors describe this in 
biological terms (e.g. species entering the network tend to be specialists that interact with generalists 
that were already present (i.e. group A)). 
 
Discussion L. 328-329: "Noticeably, our observations on the variability of species positions also agree 
with recent work showcasing constant temporal switching of species' interactions in other empirical 
plant-pollinator communities (CaraDonna et al., 2017; Miele et al., 2019)". One of these studies 
actually contributed the data to this study, so "other" is a bit misleading. 
 
 
 
Reviewer #2: 
Remarks to the Author: 
This is a complex, but extremely well-written paper tackling a very relevant question in community 
ecology. Despite I am not familiar with the methods used, and those are complex, the paper is easy to 
follow. Most of my comments try to link the methods with the empirical observations to enhance the 
story. 
 
My main concern, as always in network ecology, is about sampling effort, and the possibility of having 
unobserved links in the dataset. A couple of sentences about sampling coverage and how robust is 
your analysis to unobserved links may reinforce the conclusions. 
 
A second recommendation would be to give a few more details on how the pairing works. Especially 
related to (i) how the structural roles are defined, which now is only in the supplementary material 
and (ii) how the pairing works when you compare matrices of very different sampling sizes. This is 
something that I still don’t fully understand. For example, in the section starting at line 142, "species 
positions". It is unclear to me if the definition of uniqueness depends on the number of species within 
a network analyzed? I understand uniqueness is a fraction, which may be biased by the number of 
potential pairs that can be analyzed, but I am not sure. If unequal richness is an issue, how do you 
take into account species richness? 

 
The following comments are most of them minor and In Line: 
 
Line 116: "where the weight of all interactions was instead set to 1.” I understand what you mean, but 
you can explain how you binarize the networks in a clearer way. 
Line 232-234: This means NO species was sampled e.g. in week 2, not in week 3, and again in week 
4? This is surprising. 
Line 245: Given the comparison performed, I expected species to resemble the most to themselves 
(be paired with). Another potential question may be how close is the next potential pair is, right? In 
any case, I understand uniqueness is expressed in % and I understand this as the effect sizes of your 
analysis and I would like to see those written here. 
Line 254: I agree that it changes across seasons, but given that weekly networks are most likely 
incomplete, I wonder how sensitive is your analysis to those sampling biases. For example, if you 
compare one network, with the same network jittered, how consistent are the positions? I would love 
to see that small perturbations to the interaction frequencies do not alter drastically the results. 
Line 260. Are plant phonologies larger on average than pollinator phonologies? This may explain the 
discrepancy. 
Line 283 I would suggest refreshing the reader that group A is a specialist- generalist interaction, and 
so on. 
Line 300: I would suggest relating those positions to the nested structure of most networks, or to the 
concept of core species (generalist-generalist), peripheral species (specialist-generalist) and 
specialized interactions. Of course, a discussion on how abundance may be responsible for these 
observed patterns is warranted. 
One key element which may help a lot more empirical readers to add more natural history to the 
Discussion. For example, show me an example of a real species and how it changes roles along the 
season. 
Line 348: But how much this is driven by abundance patterns. The first and last individuals of any 
species are likely under-sampled for its interactions, and hence appear only in a small fraction of their 
potential interaction niche. Hence, what you are observing may be a side effect of a gaussian 
phenology-activity distribution and not an emergent property of the networks. I am just playing devil's 
advocate here but would be a discussion worth having. 
Figure 4. Effect sizes look minor in several panels. This may be better reflected in text and mention 
the probabilities of each transition (and associated errors) explicitly.