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Title: Food Web Construction for Paleo Communities
Version: 1.0.2
Description: R tools for reconstructing paleo food webs from species traits and size rules.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (≥ 3.5)
LazyData: true
Imports: dplyr, stats, tibble, tidyr, tidyselect
Suggests: igraph, knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
URL: https://beckslab.github.io/pfwim/, https://github.com/BecksLab/pfwim
NeedsCompilation: no
Packaged: 2026-04-25 10:15:13 UTC; tanyastrydom
Author: Tanya Strydom ORCID iD [aut, cre], Jack O. Shaw ORCID iD [ctb], Alexander M. Dunhill ORCID iD [ctb], Andrew Beckerman ORCID iD [ctb], Baran Karapunar ORCID iD [ctb], Jennifer A. Dunne ORCID iD [ctb], Pincelli M. Hull ORCID iD [ctb]
Maintainer: Tanya Strydom <t.strydom@sheffield.ac.uk>
Repository: CRAN
Date/Publication: 2026-04-28 19:30:24 UTC

pfwim: Food Web Construction for Paleo Communities

Description

logo

R tools for reconstructing paleo food webs from species traits and size rules.

Author(s)

Maintainer: Tanya Strydom t.strydom@sheffield.ac.uk (ORCID)

Other contributors:

See Also

Useful links:

Example feeding rules for traits

Description

A mock trait dataset using four trait classes specified in traits to infer feeding interactions

A mock trait dataset using four trait classes specified in traits to infer feeding interactions

Usage

feeding_rules

feeding_rules

Format

feeding_rules

A data frame with 7,240 rows and 60 columns:

trait_type_resource

broader resource trait class - i.e. column name in traits

trait_resource

specific resource trait class - i.e. row entry in trait column

trait_type_consumer

broader consumer trait class - i.e. column name in traits

trait_consumer

specific consumer trait class - i.e. row entry in trait column

...

feeding_rules

A data frame with 7,240 rows and 60 columns:

trait_type_resource

broader resource trait class - i.e. column name in traits

trait_resource

specific resource trait class - i.e. row entry in trait column

trait_type_consumer

broader consumer trait class - i.e. column name in traits

trait_consumer

specific consumer trait class - i.e. row entry in trait column

...

Source

NA

NA

Infer trophic interactions using PFWIM trait rules

Description

Infers a consumer–resource edgelist using categorical trait matching rules based on the PFWIM (Paleo Food Web Inference Model) described in Shaw (2024). Interactions are inferred by comparing resource and consumer trait combinations against a set of allowed trait rules.

Usage

infer_edgelist(
  data,
  cat_combo_list,
  col_taxon = "taxon",
  col_num_size = NULL,
  cat_trait_types = NULL,
  num_size_rule = NULL,
  certainty_req = "all",
  allow_self = TRUE,
  return_full_matrix = FALSE,
  print_dropped_taxa = FALSE,
  hide_printout = FALSE,
  ...
)

Arguments

data

A data.frame containing taxa and associated trait values. Each row represents a taxon and each column represents a trait.

cat_combo_list

A data.frame defining allowed consumer–resource trait combinations. Must contain columns:

trait_type_resource

Resource trait category

trait_resource

Resource trait value

trait_type_consumer

Consumer trait category

trait_consumer

Consumer trait value

col_taxon

Character string indicating the column containing taxon names in data. Default "taxon".

col_num_size

Optional column name containing numerical size values for taxa. Used when applying a numeric predator–prey size rule.

cat_trait_types

Optional character vector specifying a subset of categorical trait columns to use. If NULL, all traits appearing in cat_combo_list are used.

num_size_rule

Optional function defining the predator–prey size feasibility rule. The function must accept two numeric vectors: ⁠(resource_size, consumer_size)⁠ and return 1 for feasible interactions and 0 otherwise.

Example:

function(res_size, con_size) { ifelse(res_size <= con_size, 1, 0) }

certainty_req

Defines how many trait rules must be satisfied for an interaction to be considered feasible.

"all"

All trait types must match

numeric

Minimum number of matching trait types required

allow_self

Logical. If TRUE, allows interactions where the resource and consumer are the same taxon (self-loops). Default is FALSE.

return_full_matrix

Logical. If TRUE, returns the full matrix of taxon pairs and the number of matching trait rules. If FALSE, returns only inferred interactions as an edgelist.

print_dropped_taxa

Logical. If TRUE, prints taxa that were removed from the inferred food web because they have no feasible interactions.

hide_printout

Logical. If TRUE, suppresses progress messages.

...

Additional arguments reserved for future extensions.

Details

Trait matching is performed across all trait types present in cat_combo_list. For each potential taxon pair, the number of satisfied trait rules is calculated. An interaction is inferred when the number of satisfied rules meets the required threshold defined by certainty_req.

Optionally, a numerical predator–prey size rule can also be applied using num_size_rule.

The function evaluates all possible consumer–resource taxon pairs and determines interaction feasibility using categorical trait rules and, optionally, a numerical size constraint. The final edgelist includes only interactions meeting the certainty requirement.

Value

If return_full_matrix = FALSE:

A two-column matrix containing:

taxon_resource

Resource taxon

taxon_consumer

Consumer taxon

If return_full_matrix = TRUE:

A matrix containing all taxon pairs and the number of trait rules satisfied.

References

Shaw, J. (2024). PFWIM: Paleo Food web Inference Model. Preprint.

Examples

infer_edgelist(
  data = traits,
  cat_combo_list = feeding_rules,
  col_taxon = "species",
  certainty_req = "all"
)

Generate hypothetical realised webs using a power-law link distribution

Description

PFWIM generates a series of replicate hypothetical realised food webs by reducing the feasible links for each consumer to match a target link distribution. The default distribution is a mixed exponential–power law in-degree distribution as described in Shaw (2024) and Roopnarine (2006).

Usage

powerlaw_prey(
  el,
  n_samp = 50,
  y = 2.5,
  func = function(r, M, y) exp(-r/(exp((y - 1) * (log(M)/y))))
)

Arguments

el

Data frame or matrix containing a feasible consumer–resource edgelist. Column 1 = resource, Column 2 = consumer.

n_samp

Integer. Number of replicate realised webs to generate. Default = 50.

y

Numeric. Parameter controlling the shape of the power-law distribution. Default = 2.5.

func

Function. Probability function of the in-degree r, total prey richness M, and parameter y. Must return a numeric value > 0. Default: function(r, M, y) exp(-r / (exp((y - 1) * (log(M) / y)))).

Details

For each consumer in el, the number of prey links in a realised web is sampled according to the distribution defined by func. The sampled prey are drawn randomly without exceeding the maximum feasible prey for that consumer.

Value

A list of length n_samp. Each element is a data frame representing a realised food web edgelist with two columns:

resource

Resource species

consumer

Consumer species

References

Shaw, J. (2024). PFWIM: Paleo Food-web Inference Model. Preprint. Roopnarine, P. (2006). Palaeoecology and food-web structure in fossil communities.

Examples

# Infer a minimal edgelist
edgelist <- infer_edgelist(
  data = data.frame(
    species = c("plankton","plant_1","plant_2","cod","rat","deer"),
    feeding = c("primary","primary","primary","secondary","secondary","secondary")
  ),
  cat_combo_list = data.frame(
    trait_type_resource = c("feeding","feeding","feeding"),
    trait_resource = c("primary","primary","primary"),
    trait_type_consumer = c("feeding","feeding","feeding"),
    trait_consumer = c("secondary","secondary","secondary")
  ),
  col_taxon = "species",
  certainty_req = "all"
)

# Generate realized webs
webs <- powerlaw_prey(edgelist, n_samp = 3, y = 2.5)

Sample integers according to a discrete probability distribution

Description

Generates random draws from 1:M with probabilities defined by a user-specified function (default is the PFWIM mixed exponential–power law distribution).

Usage

sample_pdf(
  M = 100,
  y = 2.5,
  func = function(r, M, y) exp(-r/(exp((y - 1) * (log(M)/y)))),
  n_samp = 100
)

Arguments

M

Integer. Maximum value to sample (upper bound of 1:M).

y

Numeric. Parameter controlling the distribution shape.

func

Function. Probability function of r (1:M), M, and y. Default is the PFWIM exponential–power law function: function(r, M, y) exp(-r / (exp((y - 1) * (log(M) / y)))).

n_samp

Integer. Number of random draws.

Value

Integer vector of length n_samp with values in 1:M.

Examples

# Generate 3 replicate webs from a small edgelist
edgelist <- data.frame(
  res_node_node_name_inferred = c("plankton","plant_1","plant_2"),
  con_node_node_name_inferred = c("cod","rat","deer")
)
webs <- powerlaw_prey(edgelist, n_samp = 3, y = 2.5)

Example species traits data to infer feeding rules

Description

A mock trait dataset using four trait classes to determine interactions as specified in feeding_rules

Usage

traits

Format

traits

A data frame with 7,240 rows and 60 columns:

species

species name

motility

motility class of species

habitat

habitat species found in

feeding

trophic level of species

size

categorical size classes

...

Source

NA

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.