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arxiv: 2605.04807 · v1 · submitted 2026-05-06 · 🧬 q-bio.PE

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A process-based dynamic occupancy model to study range dynamics under non-equilibrium conditions

C\'ecile Kauffmann, Olivier Gimenez, S\'ebastien Devillard, Simon Lacombe

Pith reviewed 2026-05-08 16:27 UTC · model grok-4.3

classification 🧬 q-bio.PE
keywords dynamic occupancy modelrange dynamicsdispersal pressurecolonization processnon-equilibrium conditionsgrey wolfEurasian otterspatial connectivity
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The pith

A dispersal-pressure dynamic occupancy model disentangles connectivity from environment to identify limits on species distributions under non-equilibrium conditions.

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

The paper introduces a process-based dynamic occupancy model that represents colonization via a dispersal-pressure mechanism and uses sparse distance matrices to handle large spatial scales. This setup lets researchers study how species expand their ranges while explicitly separating the roles of dispersal history and local environment. Simulation tests across varied scenarios recover true parameter values without bias. When fitted to data on grey wolves and Eurasian otters, the model shows wolves strongly limited by altitude and forest cover but otters limited mainly by prior dispersal rather than current habitat. The result is clearer inference about what actually constrains distributions when systems are not at equilibrium.

Core claim

We propose a dynamic occupancy model whose colonization probability is formulated as a function of dispersal pressure from nearby occupied sites, implemented with sparse matrices for national-scale computation. Simulations confirm unbiased recovery of parameters under multiple ecological conditions. Application to grey wolf and Eurasian otter data reveals contrasting drivers: wolves constrained by altitude and forest, otters constrained by dispersal history rather than environment.

What carries the argument

The dispersal-pressure formulation of the colonization process, which models the influence of dispersing individuals from occupied neighboring sites in a flexible, interpretable way.

If this is right

  • Environmental effects on occupancy can be estimated separately from dispersal effects.
  • Species-specific limiting factors (habitat versus dispersal history) become identifiable.
  • The approach scales to national and transnational data sets.
  • Predictive performance for range dynamics improves when connectivity is modeled explicitly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The framework could improve forecasts of range shifts under climate change by treating dispersal as an explicit, measurable process.
  • It may help prioritize connectivity conservation where dispersal history, not habitat, is the binding constraint.
  • The same structure could be adapted to model range contractions or invasions without new model classes.

Load-bearing premise

The dispersal-pressure formulation accurately captures real colonization without introducing bias into estimates of environmental effects.

What would settle it

Independent field measurements of dispersal rates or distances that deviate markedly from the colonization parameters estimated by the model on the same species would falsify the claim.

Figures

Figures reproduced from arXiv: 2605.04807 by C\'ecile Kauffmann, Olivier Gimenez, S\'ebastien Devillard, Simon Lacombe.

Figure 1
Figure 1. Figure 1: Summary of the four simulation scenarios. (A) dispersal kernel showing the dispersal pressure as a function of the distance to a single occupied cell for theoretical short- and long￾distance dispersers. (B) Installation probability as a function of the value of x for a theoretical habitat generalist and habitat specialist. (C - F) Example of a simulated dataset for each scenario. The value of the covariate… view at source ↗
Figure 2
Figure 2. Figure 2: Summary of the simulation study. The first two rows show the relative bias of the posterior mean for each parameter and the last two the coefficient of variation. Panels represent model parameters (αξ: intercept of the installation probability, ω: extinction probability, ρ: detec￾tion probability, βξ slope of the installation probability, λ dispersal rate, σ: scale parameter for the dispersal distance). Sc… view at source ↗
Figure 3
Figure 3. Figure 3: Estimated dispersal pressure exerted by a single occupied cell as a function of the distance for wolves and otters. Solid lines represent median values while shaded ribbons represent 95% credible intervals. protocol (Reuther et al., 2000)) and opportunistic observations. We defined our sites as pixels in a 10-by-10 km grid covering the whole mainland France (N = 5478) to match the resolution at which otter… view at source ↗
Figure 4
Figure 4. Figure 4: Maps of estimated installation probabilities for wolves (left) and otters (right). 3 Results 3.1 Simulation study For ω, ρ and βξ, the 50% credible interval of the relative bias overlapped zero for all scenarios, showing unbiased estimation (Fig. 2A). Parameter αξ was slightly overestimated for scenario 1 (short dispersal distance, generalist species) with an estimated relative bias of 0.07 [-0.08, 0.19] (… view at source ↗
Figure 5
Figure 5. Figure 5: Estimated installation probability (ξ) for wolves (top row) and otters (bottom row) as a function of altitude, proportion of forest cover, proportion of high-altitude, proportion of intensive agricultural area, proportion of riparian forest and proportion of urban area. Only variables with an inclusion probability > 0.66 are shown. Solid lines represent median values while shaded ribbons represent 95% cred… view at source ↗
Figure 6
Figure 6. Figure 6: Maps showing the posterior mean of the occupancy probability at each site for the grey wolf (top two rows) and Eurasian otter (bottom two rows). 19 view at source ↗
read the original abstract

Failing to account for ecological processes such as dispersal and connectivity when modeling distributions can lead to biased inference about environmental drivers and reduced predictive performance. Spatial dynamic occupancy models are promising to study range dynamics while accounting for dispersal and connectivity, but they currently rely on restrictive formulations of the colonization process, and computational constraints prevent their application at large spatial scales. Here, we propose a process-based dynamic occupancy model to study the distribution of range-expanding species while accounting for connectivity and effects of the environment. We introduce a formulation based on dispersal-pressure that provides a flexible and ecologically interpretable representation of the colonization process, and develop a computational approach based on sparse distance matrices that enables its application to national and transnational scales. We conducted a simulation study that showed unbiased parameter estimation across various ecological scenarios. We also applied our model to two range-expanding carnivores offering complementary insights: the grey wolf and the Eurasian otter. Our model revealed contrasting colonization dynamic, with wolves primarily constrained by altitude and forest cover while otters where only marginally affected by the environment, suggesting that their distribution is limited by dispersal history rather than habitat preferences. By explicitly disentangling the influence of dispersal and environment on distributions, our model provides better insight into occupancy-environment relationships under non-equilibrium conditions, and help identifies what limits species distributions. In light of the increasing availability of large-scale biodiversity data, our framework offers opportunities to study range dynamics using mechanistic approaches across entire landscapes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a process-based dynamic occupancy model incorporating a dispersal-pressure formulation for the colonization process to account for connectivity and environmental effects when modeling range dynamics of expanding species under non-equilibrium conditions. It develops a sparse distance matrix approach for computational tractability at large scales, reports unbiased parameter recovery in simulations across scenarios, and applies the model to grey wolf and Eurasian otter data, concluding that wolves are primarily limited by environmental factors (altitude and forest cover) while otters are limited by dispersal history rather than habitat.

Significance. If the central claims hold, the work advances the field by providing a mechanistic framework that explicitly disentangles dispersal history from environmental drivers in occupancy models, addressing a known limitation of standard approaches. The simulation validation across scenarios and the contrasting real-data insights for two species are strengths that support its potential utility. The large-scale computational method is also a practical contribution given increasing availability of biodiversity data.

major comments (2)
  1. [Simulation study (as referenced in abstract)] The dispersal-pressure formulation is presented as flexible and ecologically interpretable for separating colonization from environmental effects, but the simulation study does not appear to have tested scenarios in which the environment simultaneously influences both occupancy and dispersal processes; this leaves open the possibility of residual confounding in the occupancy-environment relationships reported for the real-data applications.
  2. [Application section] In the real-data application, the conclusion that otters are dispersal-limited (while wolves are environment-limited) depends on the specific kernel and independence assumptions in the dispersal-pressure term; without sensitivity analyses varying the kernel form or including environment-dispersal interactions, it is unclear whether the contrasting insights reflect biological differences or model structure.
minor comments (2)
  1. [Abstract] The abstract contains a minor grammatical error ('help identifies' should read 'helps identify').
  2. [Methods] Additional details on data exclusion rules, error propagation, and exact implementation of the sparse matrix approach would improve reproducibility and clarity in the methods.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and positive review of our manuscript. We have carefully addressed each major comment below and revised the manuscript to incorporate additional analyses where appropriate.

read point-by-point responses

  1. Referee: [Simulation study (as referenced in abstract)] The dispersal-pressure formulation is presented as flexible and ecologically interpretable for separating colonization from environmental effects, but the simulation study does not appear to have tested scenarios in which the environment simultaneously influences both occupancy and dispersal processes; this leaves open the possibility of residual confounding in the occupancy-environment relationships reported for the real-data applications.

    Authors: We thank the referee for highlighting this point. Our simulation study examined multiple scenarios with environmental effects on occupancy, environmental effects on colonization, and different dispersal kernels, demonstrating unbiased recovery in each case. However, we did not include an explicit scenario in which the identical environmental covariates simultaneously and directly influence both the occupancy and dispersal-pressure processes. We agree this is a useful addition for ruling out residual confounding. In the revised manuscript, we will add this simulation scenario and report the results to confirm that the model structure continues to separate the processes effectively. revision: yes

  2. Referee: [Application section] In the real-data application, the conclusion that otters are dispersal-limited (while wolves are environment-limited) depends on the specific kernel and independence assumptions in the dispersal-pressure term; without sensitivity analyses varying the kernel form or including environment-dispersal interactions, it is unclear whether the contrasting insights reflect biological differences or model structure.

    Authors: We agree that robustness to kernel choice and potential environment-dispersal interactions merits explicit testing. The original analyses used species-specific kernels grounded in the literature, with the independence assumption inherent to the dispersal-pressure formulation. To address the concern, the revised manuscript will include sensitivity analyses that (i) vary the kernel form (e.g., exponential versus Gaussian) and (ii) allow for environment-dispersal interactions where computationally feasible. These results will be presented alongside the main findings so that readers can evaluate whether the contrasting conclusions for wolves and otters are robust to these modeling choices. revision: yes

Circularity Check

0 steps flagged

No significant circularity: model derived from process assumptions with independent simulation validation

full rationale

The paper introduces a dispersal-pressure formulation for the colonization process in dynamic occupancy models, supported by ecological process assumptions and validated through simulation studies that recover parameters without bias across scenarios. Application to wolf and otter data provides contrasting insights based on fitted results rather than tautological reduction. No load-bearing derivation step reduces by construction to its own inputs, fitted parameters renamed as predictions, or self-citation chains. The derivation chain remains self-contained against external benchmarks like simulation recovery and real-data application.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the model rests on standard occupancy modeling assumptions plus a new domain-specific representation of colonization; no explicit free parameters or invented entities are detailed in the provided text.

axioms (1)
  • domain assumption Colonization can be represented via a flexible dispersal-pressure process that incorporates connectivity and environment
    This is the core innovation stated in the abstract as overcoming restrictive prior formulations.

pith-pipeline@v0.9.0 · 5569 in / 1215 out tokens · 35531 ms · 2026-05-08T16:27:32.545026+00:00 · methodology

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