We are therefore we interact. This is random.

ou as a prey item fit in my gob

Here it may be meaningful to contextualise the different ‘types’ of food webs within the larger research programmes (or even practical needs) that have been driving the construction of them.

generates a unique representation of the mechanisms

Putting the parts together; what does it mean?

the magnitude of the edges

As highlighted in Poisot, Stouffer, and Kéfi

discussion

predict

‘code’/define

their goal

This will allow us to ensure the right models are being used to answer the right questions, particularly within the context of trying to accelerate cross-cutting research in the face of global change.

[do we need an e.g., ref??]

Navigating food web prediction

Most attempts

acking in discussions

the development of these different models have carved out the path for constructing either synthetic, ecologically plausible networks (Poisot, Gravel, et al. 2016), or providing ‘first draft’ networks that can be utilised in real world settings (Strydom et al. 2022)

Although

and coded.

models

construct and build

and, within the context of this manuscript, specifically food webs

causes dissonance even within the field (Dormann 2023).

understand

ecosystems

direction (expressed as degrees)

gradeint/‘continuous’ way to view the landscape

more discrete

Froma

boundaries.

randomly

regularly

0.1

then the spatial and temporal biases induced by data collection would further impact the realized false negative rate, as in this case the probability of false negative would not be constant for each pair of species across sites

seems both pertinent and necessary

We suggest using neutral models of species abundances to design the number of observations sufficient to say an interaction doesn’t exist.

Introduction

(x-axis)

connectance C = 0.1

It’s the birthday paradox, but backwards

Results/summary

almost entirely known

40, 60, and 80%

Figure 1:

code availability

Transfer learning

Our results show that SDM mo

he variation in the LCBD-richness relationsh

The regional variatio

Our results show that this negative LCBD-richness relationship is indeed not constant and displays regional variation, as the profile was different in our species poor Southwest subregion.

Our result for our species-rich Northeast subregion showed a decreasing, slightly curvilinear relationship between LCBD values and species richness.

Spatial distribution of PCD components. Again, a distinct PCDc cluster (as seen on the third map of the left column) matches the cluster for which βs metric is more important.

Equator

clear

Poisot, Timothée, Benjamin Baiser, Jennifer A Dunne, Sonia Kéfi, François Massol, Nicolas Mouquet, Tamara N Romanuk, Daniel B Stouffer, Spencer A Wood, and Dominique Gravel. 2016. “Mangal - Making Ecological Network Analysis Simple.” Ecography 39 (4): 384–90.

Poisot, Timothée, and Daniel B. Stouffer. 2018a. “Interactions Retain the Co-Phylogenetic Matching That Communities Lost.” Oikos 127 (2): 230–38. https://doi.org/10.1111/oik.03788. Poisot, Timothée, and Daniel B Stouffer. 2018b. “Interactions Retain the Co-Phylogenetic Matching That Communities Lost.” Oikos 127 (2): 230–38.

but rarely do so with more distant communities.

βwn - i.e., those which differences between them are significantly smaller than the differences in relation to the metaweb

Results

where one group is ancestral to the other two.

respond to environmental gradients in space and time

ks

The dissimilarity of networks is not described in the same way for local networks across a metacommunity. Because of particular characteristics such as communities’ species composition and relationship with local environment, the differences in ecological networks can be due to species turnover, links established by shared species or a combination of both. In our case these differences were very prominent, making it possible to group communities by their interactions dissimilarity decomposition.

clearly

Poisot et al. 2019

βos can not assume values higher than βwn.

kmeans

m

Jarrod D.

an respond to different environmental gradients.

The differences between communities related to interactions may be, but not necessarily are, correspondent to those related to their species composition (Poisot, Stouffer, and Gravel 2014).

All these factors contribute to the fact that the differences between interactions are more prone to variability and are always equal or greater than the differences in species composition, and, therefore, are more informative than the number of species or functional diversity alone (Poisot et al. 2017).

(Poisot, Cirtwill, et al. 2016).

(Tylianakis and Morris 2017)

such as the phylogenetic signal of interactions

connections between them and their evolutive history.

the indexes that measure characteristics of ecological networks can also respond to environmental gradients in space and time

are much more variable than the occurrence of species

ies no

betadiversity

Only very-large food we

We can derive a measure of departure from expected number of links

by . Th

In addition to use as a prior,

Distribution of connectance

oposed using a Beta distribution for the probability of any specific edge in a food web. The prior defined above may be used in this way.

Connectance is constant (for large enough food webs)

A maximum likelihood estimate of each can be calculated by rearranging equation {#eq:lhat} and fitting a Beta distribution to the result:

divergent iterations

iterations per chain

prior predictive checks

The prior we use here can be thought of as beginning with a uniform prior and observing only one interaction among nine species.

first

We have rephrased the question of connectance in food webs as the proportion of links realized above the minimum. There are several ways of writing down this model; we compare two possibilities below. In both cases, we use a discrete probability distribution as the likelihood, with the number of observed links above the minimum as ‘successes’ and the number of possible links as ‘trials’. Each model tries to capture variation in link number greater than would be predicted by alone.

question of connectance in food webs

onships in the m

In this paper we will descri

When making predictions is it often helpful to use generative models,

Historical efforts to predict link number have produced numerous candidate models, of which the power law is the most general. - Early predictions differ in whether this is a linear of exponential relationship - Has consequences for spatial scaling (Brose) While flexible, the power law relationship is limited because the parameters are difficult to reason about ecologically. This is in part because many mechanisms can produce power-law shaped relationships.

Power laws are very flexible, and indeed this function matches empirical data well.

We seek to assess the fitness for pur

MARINE FOODWEB

Main question Here we address the question of what we can and cannot do with this large store of ecological network data. A major challenge to ecological synthesis is generalizing from samples to the behaviour of ecological systems two obstacles to such generalizing in ecological systems: data coverage and data quality data coverage: are data collected from every relevant system? data quality: are data fit-for-purpose? Two particular aspects of quality taxonomic resolution sampling effort Synthesizing ecological data presents important challenges and also some exciting opportunities. Mangal is well suited to offer such opportunities in the study of ecological networks.

ogical data as we could find

Data quality: sampling effort and taxonomy Jordano (2016b) – importance of taxonomic resolution Sampling effort and taxonomic detail are two very challenging but important part of any ecological dataset. The datasets in Mangal represent some of the most detailed studies of ecological networks available. * measures of network structure may be particularly sensitive to the amount of sampling effort * repeat sampling may be necessary to capture a “saturation” of interactions. * we present some visualization of the sampling coverage of Mangal [tk] * High taxonomic resolution is difficult to achieve in ecology, especially depending on the sampling method used (e.g. gut contents vs observations). We present a breakdown of the taxonomic resolution of Mangal. * Ecological networks occur in various kinds, but they are not all equally well sampled. We present a breakdown of the number of parasitic, mutualistic and predator-prey networks sampled in Mangal

This begs the question of what can be achieved with our current knowledge of ecological networks. TK