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REVIEW 4 major objections 5 minor 58 references

Uncovering complementary information sharing in spider monkey collective foraging using higher-order spatial networks

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper finds persistent topological holes in the overlapping core ranges of spider monkeys and interprets them as evidence that different individuals hold complementary foraging knowledge, so the group as a whole tracks its patchy…

desk verdict A real theoretical benchmark and a novel TDA application in ecology, but the complementarity claim needs a null model before it carries weight. read the letter →

arxiv 2505.01167 v3 pith:O2J6FHBY submitted 2025-05-02 q-bio.QM cond-mat.dis-nncond-mat.stat-mechcs.SIphysics.soc-ph

Gabriel Ramos-Fernandez , Ross S. Walker , Matthew J. Silk , Denis Boyer , Sandra E. Smith-Aguilar This is my paper · ORCID
classification q-bio.QMcond-mat.dis-nncond-mat.stat-mechcs.SIphysics.soc-ph
keywords fission-fusiondynamicsspidermonkeysforaginginformationsharingsimplicialcomplexespersistenthomologyBettinumberscorerangeoverlapcollectivecognition
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that fission-fusion spider monkeys pool foraging knowledge in a complementary way, so that different individuals know different parts of the forest and the group as a whole knows more than any single monkey. Its evidence is geometrical: the partial overlaps of individual core ranges, treated as higher-order spatial networks, contain topological holes that persist as the overlaps become less redundant. These holes are interpreted as pockets of unique knowledge held by subsets of individuals, surrounded by shared areas where that knowledge can be exchanged. If this interpretation holds, the group's constantly splitting and merging social structure is a form of collective information processing that lets it track a patchy, seasonal food supply.

What carries the argument

The machinery is a filtered simplicial complex: a higher-order network in which a simplex joins any set of individuals whose core ranges intersect, with each simplex weighted by its intersection/union ratio w and filtered by a parameter alpha measuring how far w sits from the predicted optimum w* = 1/(n+1). Persistent homology computes the Betti numbers, which count holes in each dimension, and their persistence across the filtration; maximal simplicial degree centrality identifies which subsets participate in the most connections.

What would settle it

A permutation null model that randomly rotates the observed core ranges around their centroids while preserving shape, then recomputes the simplicial complexes: if holes of dimension 1 and higher still persist in the randomized data, the holes cannot be evidence of knowledge complementarity.

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Extended reading notes

Core claim

The paper's discovery is that the overlap geometry of spider monkey core ranges is balanced between redundancy and uniqueness in a way that maximizes information transfer. For any set of n individuals, an optimization model with uniform, independent movement predicts that the intersection/union ratio of their core ranges should be w* = 1/(n+1); the observed ratios follow this decreasing curve, especially for small sets. Filtering the resulting simplicial complexes from more to less redundant overlaps reveals persistent holes of dimensions 0 through 4, meaning that even as low-redundancy subsets are added, some areas are used by a subset of individuals and not by others. The authors read these holes as complementarity: shared areas provide the meeting ground where uniquely known foraging spots can be transmitted, and the persistence of the holes shows that this complementary knowledge structure is a stable feature across seasons.

Load-bearing premise

The load-bearing premise is that a monkey's core range is a map of what it knows about fruiting-tree locations; if the ranges instead reflect social attraction, shared sleeping sites, or habitat geometry, the holes in the overlap structure would not be evidence of complementary information sharing at all.

Editorial extensions

If this is right

  • If the complementarity is real, the group's collective knowledge of fruiting-tree locations is larger than any member's, and fission-fusion social dynamics are a mechanism for maintaining that distributed memory.
  • Subsets of intermediate size (roughly 4 to 6 individuals) should be the most important brokers of foraging information, since their overlap ratios are the most sensitive to membership changes and their simplicial centrality rises most steeply.
  • The persistence barcodes of the overlap complex give a season-by-season readout of how much of the group's knowledge is held uniquely, which could be monitored as the environment changes.
  • Because the same balance between redundant and unique area applies to any number of individuals, the w* = 1/(n+1) prediction gives a quantitative target for testing other fission-fusion species.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An alternative reading of the holes, based on shared sleeping sites or social attraction rather than knowledge, could be tested by comparing simplicial complexes built from daytime foraging locations against those from nighttime sleeping locations; only the foraging-based complexes should show the complementarity signal.
  • The optimality argument assumes uniform and independent movement inside core ranges; relaxing that to movement along known travel routes would shift w*, so the observed fit to 1/(n+1) is a baseline that should be rechecked against more detailed movement data.
  • If the interpretation is right, the same hole-detection pipeline could be applied to other fission-fusion species, and the persistence of holes should predict the group's success at finding scarce fruit in the dry season, which the paper's small sample could not confirm.
  • The paper's lack of a significant dry/wet season difference, despite a visible trend, suggests the relevant environmental variable is spatial rather than temporal patchiness; testing against spatial fruit distribution is the natural next step.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper applies persistent homology to the partial overlaps of individual spider-monkey core ranges across six years, split into dry and wet seasons. The authors define an intersection/union ratio w for every subset of n core ranges, derive a parameter-free optimal benchmark w* = 1/(n+1) from an information-transfer model in the Supplementary Information, and use deviations from w* as a filtration parameter α. They report persistent holes (Betti numbers) in the resulting simplicial complexes, interpret them as "pockets of unique knowledge" reflecting complementary foraging information, and introduce a filtration complementarity index (FCI) to compare seasons. They also examine maximal simplicial degree centrality across simplex sizes and find no robust demographic correlates. Code and data are deposited in a Zenodo repository.

Significance. If the central inference were supported, the paper would provide one of the first empirical demonstrations that higher-order overlap geometry in a fission-fusion society encodes complementary information, linking topological data analysis to collective intelligence. The derivation of w* = 1/(n+1) is a genuine parameter-free benchmark, the Supplementary Information proof is detailed, and the descriptive pipeline is reproducible from the deposited code and data. The paper is also honest about the proxy assumption regarding core ranges and about the low statistical power of the seasonal comparison. However, the leap from "holes exist in core-range overlap complexes" to "the group shares complementary foraging information" is currently untested against any null model or behavioural validation, which is the central weakness.

major comments (4)
  1. [Results: 'The structure of simplicial complexes shows holes at various dimensions'; Methods: 'Filtration procedure'] The claim that persistent holes 'reveal complementarity in the foraging information' is not tested against any null model for how independently located, geographically constrained core ranges would overlap. A null model that randomly places or relabels the observed core-range polygons within the study area, or permutes spatial locations while preserving polygon shapes and habitat boundaries, would show whether the observed Betti numbers and FCI values exceed chance level. Without such a baseline, the holes could be produced by the lake boundary, the shape of the forest fragment, or sleeping-site centrality rather than by sharing of foraging information.
  2. [Introduction, third paragraph] The entire interpretation rests on the unvalidated assumption that a 60% a-LoCoH core range represents the area where an individual 'knows the location of available fruiting trees relatively well.' The paper cites previous work ([11], [20]) but does not validate the proxy with the available behavioural data, such as visits to fruiting trees or the phenology records described in the Methods. Because holes are interpreted as 'pockets of unique knowledge,' the central conclusion is conditional on this assumption; the authors should either test it against known fruiting-tree locations or explicitly reframe the claims as being about overlap geometry rather than cognition.
  3. [Methods, 'Simplical complex construction'] The maximal simplex size nmax = 6 was selected post hoc as 'the largest which consistently resulted in non-trivial structures across all seasons.' The headline persistence barcodes and the FCI (Eq. 5) are computed on structures truncated at this data-dependent cutoff, and no robustness analysis is presented for Betti numbers or FCI across nmax (the Supplementary Information only checks centrality). The authors should show that the main qualitative results, including the multi-dimensional holes and the seasonal FCI comparison, are stable for nmax in, say, {4, 5, 6, 7, 8, 9}; otherwise the central results are conditional on a parameter chosen after seeing the data.
  4. [Supplementary Information, Section 1.2; Eq. (3)] The benchmark w* is derived under the assumption that all n core ranges have identical area (N_k = N), but the empirical core ranges vary substantially in area within seasons (Figure 3). Because w is the ratio of intersection to union, a single small core range can drive low w values for large n, and the filtration defined by Eq. (4) will then classify such sets as 'less redundant' even under a null of random overlap. The authors should test whether the persistence results are robust to using an area-corrected overlap measure or to restricting comparisons to sets with comparable range sizes.
minor comments (5)
  1. [Throughout] The term 'simplical' is misspelled in several places, including the Methods heading 'Simplical complex construction'; it should be 'simplicial.'
  2. [Figure 4] The axis labels 'n.d12' and 'w.d12' are cryptic; the figure would be clearer with labels such as 'number of individuals n' and 'intersection/union ratio w' for each panel.
  3. [Results, 'The extent of overlap varies predictably...'] The ANOVA on observed w values treats each subset of individuals as an independent observation, but subsets overlap heavily (the same individual appears in many sets). This should be acknowledged, and a permutation-based or mixed-effects approach would be more appropriate.
  4. [Methods, 'Filtration procedure'] The choice of f(alpha) = 5^{-alpha} is arbitrary, and while the authors correctly note that a monotone reparametrization preserves the filtration order, the FCI values and statements about persistence 'for several units of alpha' are not invariant to that choice. A brief statement that conclusions are robust to alternative decreasing functions f would strengthen the presentation.
  5. [Discussion] The sentence beginning 'Conversely, values below the predicted line are rare' is slightly ambiguous because Figure 4 shows many points both above and below the w* curve; it would help to specify that the statement refers to the bulk of the distribution or to a particular subset of seasons.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the w* = 1/(n+1) prediction is derived from an explicit optimization model and the persistence analysis is a descriptive application of that model, not a fitted-input prediction.

full rationale

I walked the paper's derivation chain and found no step where a claimed prediction or first-principles result reduces, by the paper's own equations or by self-citation, to its own inputs. The central theoretical quantity w* = 1/(n+1) is derived in the Supplementary Information from a stated optimization problem: an information-transfer objective T(O) is constructed from explicit assumptions (uniform space use, independent movement, cell-based knowledge), and the global maximizer is proven to be O* = (0,...,0,N/2), which yields w*_n = 1/(n+1). This is a genuine derivation, not a fit or a renamed empirical observation. The observed w values are then compared against this curve qualitatively, and the paper explicitly notes discrepancies (multimodality, values above prediction), so the comparison is not forced by construction. The filtration index alpha is defined relative to w* through wi = f(alpha)/(n(i)+1), but this is a reparametrization of the same overlap data and the paper notes that monotonic changes in f would not change the topological features. The existence of holes and their persistence are computed from the spatial overlaps, and the interpretation of those holes as 'complementarity' is an interpretive step explicitly flagged as conditional ('If we assume that a hole of any dimension... is the result of unique areas...'), not a derivation that assumes the conclusion. The choice of nmax = 6 is described as chosen upon observation, which is a methodological caveat, but it is not a parameter fitted to the data and then used to predict the same data; it is a stated modelling cutoff. Self-citations (e.g., refs. 11, 20, 51) provide empirical background for the proxy assumption that core ranges reflect foraging knowledge and for the 60% utilization distribution; these are prior empirical studies, and the current paper's central topological analysis does not reduce to them. No uniqueness theorem, ansatz smuggled via citation, or renamed known result was found. Therefore the paper is self-contained with respect to the w* derivation and the persistence analysis, and the circularity score is 0.

Assumptions & free parameters 6 free parameters · 7 assumptions · 2 invented entities

The central claim rests on a chain of assumptions: the knowledge proxy (core range equals knowledge), the hole-complementarity mapping, the downward closure of spatial overlaps, and the idealizing assumptions (uniform use, independence, equal range sizes) behind the w* = 1/(n+1) derivation. Free parameters are mostly methodological thresholds (nmax = 6, 60% isopleth, a = 15000, f(alpha) = 5-alpha, alpha = 4 for centrality, and the FCI dimension weighting). No physical entities are invented, but the filtration complementarity index is a bespoke measure whose interpretation is assumption-dependent.

free parameters (6)
  • nmax (maximal simplex size) = 6
    Chosen post hoc as "the largest which consistently resulted in non-trivial structures across all seasons" (Methods, Simplicial complex construction). The main Betti-number results depend on this cutoff; robustness is checked only for the centrality analysis.
  • f(alpha) filtration function = 5 - alpha
    Described as "some arbitrary positive and decreasing function" (Methods, Filtration procedure). It affects the alpha-axis and the filtration complementarity index values, although not the order in which simplices enter the filtration.
  • Core range isopleth = 60% utilization distribution
    Choice of utilization-distribution level for defining knowledge areas, validated by prior work [51] but still a threshold that affects all overlap values.
  • a parameter (a-LoCoH) = 15000
    Constant hull parameter in T-LoCoH used across seasons (Methods, Individual core range estimation). A method choice from the LoCoH family, not fitted to the present data.
  • alpha threshold for centrality = 4
    Maximal simplicial degree centrality is computed at alpha <= 4; the paper states other values gave similar results (Methods, Maximal simplicial degree centrality).
  • FCI dimension weighting = d + 1
    The filtration complementarity index weights d-th Betti numbers by (d+1); the paper reports that alternative definitions gave similar results (Methods, Filtration complementarity index).
assumptions (7)
  • domain assumption An individual's 60% core range represents its knowledge of fruiting-tree locations during a season.
    Introduction, paragraph 3: "we assume that an individual's core range represents, for a given season, the area where it knows the location of available fruiting trees relatively well." This is the load-bearing proxy for knowledge.
  • ad hoc to paper A topological hole in the overlap complex corresponds to areas used uniquely by some individuals, i.e., holes equal complementarity.
    Results section on simplicial complex structure: "If we assume that a hole of any dimension in the simplicial complex is the result of unique areas being used by some individuals... then we can interpret holes... as evidence of complementarity." This maps pure geometry onto an information-sharing meaning.
  • domain assumption Downward closure: whenever n core ranges overlap, all subsets of those ranges also overlap.
    Methods, Filtration procedure: the authors add lower-order simplices to satisfy downward closure, justified by overlaps concentrating near the center of the group range, which "makes it unlikely that the areas of n individuals will overlap significantly but the overlaps between subsets of n will not."
  • domain assumption Individuals use their core ranges uniformly and move independently (for the w* derivation).
    SI section 1.2.1, Assumptions 1 and 3: "Individuals utilise the area in their core ranges uniformly" and "Movements of different individuals are independent." These idealizations drive the optimality proof.
  • domain assumption Homogeneous foraging ability, equal core-range sizes Nk = N, for all individuals (for the w* derivation).
    SI section 1.2.1: "Suppose that the population is homogeneous in foraging ability. Meaning, Nk = N > 0 for k in [n]." The empirical core ranges are unequal in size, so the benchmark w* = 1/(n+1) is derived under a violated idealization.
  • standard math The global optimality proof of the quadratic information-transfer objective (Theorem 1 in SI) is correct.
    The SI provides a full argument using quadratic-programming optimality conditions, but the proof is not machine-checked, and not every algebraic step could be independently verified here.
  • standard math Persistent homology computations in Gudhi are faithful implementations of simplicial homology.
    Betti numbers and persistence barcodes are computed with the standard GUDHI library [54], treated as correct software.
invented entities (2)
  • Filtration complementarity index (FCI)
    purpose: Single scalar per season quantifying how many holes persist and at what dimension across the alpha-filtration, used for dry/wet and fruit-variation comparisons.
    Bespoke index (Eq. 5). Its meaning as complementarity rests on the assumed hole-complementarity link, and no null distribution or external validation is provided.
  • Optimal overlap benchmark w* = 1/(n+1) independent evidence
    purpose: Theoretical reference for a balanced redundancy/uniqueness trade-off in n individuals; used to define alpha and to compare against observed overlaps.
    Derived parameter-free from an explicit optimization problem with stated assumptions that do not include the target result; it makes a falsifiable scaling prediction (Figure 4) that is not used to fit constants.

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Pith. "Pith review of Uncovering complementary information sharing in spider monkey collective foraging using higher-order spatial networks." pith.science (2026). https://pith.science/paper/O2J6FHBY

@misc{pith2026250501167,
  author       = {Pith},
  title        = {Pith review of: Uncovering complementary information sharing in spider monkey collective foraging using higher-order spatial networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O2J6FHBY}},
  note         = {Machine review of arXiv:2505.01167}
}
read the original abstract

Collectives are often able to process information in a distributed fashion, surpassing each individual member's processing capacity. In fission-fusion dynamics, where group members come together and split from others often, sharing complementary information about uniquely known foraging areas could allow a group to track a heterogenous foraging environment better than any group member on its own. We analyse the partial overlaps between individual spider monkey core ranges, which we assume represent the knowledge of an individual during a given season. Sets of individuals with complementary overlaps are identified, showing a balance between redundantly and uniquely known portions, and we use simplicial complexes to represent these higher-order interactions. The structure of the simplicial complexes shows holes in various dimensions, revealing complementarity in the foraging information that is being shared. We propose that the complex spatial networks arising from fission-fusion dynamics allow for adaptive, collective processing of foraging information in dynamic environments.

Figures

Figures reproduced from arXiv: 2505.01167 by the authors.

Figure 1
Figure 1. Strategy to analyse spatial overlaps in individual core ranges as higher-order interactions [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Maps of overlaid individual core ranges, for different years and seasons (top row: dry [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Density plot of individual core ranges, split by year and season (magenta: dry seasons; [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Scaling relationship between the number of individual core ranges and its intersec [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Persistence barcodes for the simplicial complexes in each season, including the simplices of [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Maximal simplicial degree centrality values for simplices of different size. Each dot [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Relationship between the confidence intervals in the biweekly index of fruit abundance [PITH_FULL_IMAGE:figures/full_fig_p031_7.png]
Figure 8
Figure 8. Figure 8: Maximal simplicial degree centrality values for simplices composed of adult individuals [PITH_FULL_IMAGE:figures/full_fig_p031_8.png]
Figure 9
Figure 9. Figure 9: Maximal simplicial degree centrality values for simplices with different proportions of [PITH_FULL_IMAGE:figures/full_fig_p032_9.png]
Figure 10
Figure 10. Figure 10: Maximal simplicial degree centrality values for simplices composed of different propor [PITH_FULL_IMAGE:figures/full_fig_p032_10.png]
Figure 11
Figure 11. Figure 11: Maximal simplicial degree centrality values for simplices of different size with maximum [PITH_FULL_IMAGE:figures/full_fig_p032_11.png]
Figure 12
Figure 12. Figure 12: Maximal simplicial degree centrality values for simplices of different size with maximum [PITH_FULL_IMAGE:figures/full_fig_p033_12.png]
Figure 13
Figure 13. Figure 13: Maximal simplicial degree centrality values for simplices of different size with maximum [PITH_FULL_IMAGE:figures/full_fig_p033_13.png]
Figure 14
Figure 14. Figure 14: Maximal simplicial degree centrality values for simplices of different size with maximum [PITH_FULL_IMAGE:figures/full_fig_p033_14.png]
Figure 15
Figure 15. Figure 15: Maximal simplicial degree centrality values for simplices of different size with maximum [PITH_FULL_IMAGE:figures/full_fig_p034_15.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.