A landscape experiment of spatial network robustness and space‐use reorganization following habitat fragmentation
Bibliographic record
Abstract
Abstract Network theory increasingly informs wildlife conservation in disturbed landscapes, but with concern increasingly expressed about its application to real‐world situations. The theory predicts that the connectivity of scale‐free networks should be particularly sensitive to the disturbance of highly connected nodes (i.e. hubs). This expectation relies on complete patch removal, thus restraining its application to the last of several steps involved in habitat fragmentation, and overlooks potential reconnection of patches after disturbance (i.e. rewiring). We performed a landscape‐scale experiment to evaluate the robustness of scale‐free spatial networks of woodland caribou ( Rangifer tarandus caribou ) to logging activity. We built caribou networks before and after disturbance using a mechanistic model of inter‐patch movements and differentiated networks disturbed in their hubs and non‐hubs. We applied a reaction–advection–diffusion model to networks before and after disturbance to account for the spatio‐temporal dynamics of caribou movement within the networks and anticipate their space use. We validated network and space‐use predictions using empirical estimates from GPS relocations of caribou. Using the validated predictions, we compared topological network measures before and after disturbance to quantify changes in connectivity within the networks according to the type of disturbed nodes (i.e. hubs or non‐hubs) and assessed space‐use reorganization. We used control networks, for which no disturbance occurred in the before–after timeframe of the study, in the latter analysis to get a baseline rate of change. Disturbances due to logging activity typically resulted in fragmentation and shrinkage instead of complete patch removal. Independently to the type of disturbed nodes, caribou rewired their network using remnant patches from the fragmentation process. Consequently, topological network measures generally did not differ between before and after disturbance, such that caribou networks displayed some robustness to logging activity due to the rewiring process. Space‐use reorganization was greater, however, when hubs were disturbed in comparison with non‐hubs and controls. Even though caribou rewired their networks, they revisited less patches after the disturbance of hubs. A naive application of network theory (i.e. without potential rewiring and using complete patch removal), to assess spatial network robustness, may be inappropriate during most steps of the fragmentation process because of the rewiring process. Indeed, network rewiring facilitated by the presence of remnant patches can enhance spatial network robustness, in comparison with no rewiring. In addition, species‐specific functional connectivity should be accounted for when anticipating the rewiring process and animal space use within disturbed networks. A free Plain Language Summary can be found within the Supporting Information of this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".