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Abstract Human impacts have led to dramatic biodiversity change which can be highly scale‐dependent across space and time. A primary means to manage these changes is via passive (here, the removal of disturbance) or active (management interventions) ecological restoration. The recovery of biodiversity, following the removal of disturbance, is often incomplete relative to some kind of reference target. The magnitude of recovery of ecological systems following disturbance depends on the landscape matrix and many contingent factors. Inferences about recovery after disturbance and biodiversity change depend on the temporal and spatial scales at which biodiversity is measured.We measured the recovery of biodiversity and species composition over 33 years in 17 temperate grasslands abandoned after agriculture at different points in time, collectively forming a chronosequence since abandonment from 1 to 80 years. We compare these abandoned sites with known agricultural land‐use histories to never‐disturbed sites as relative benchmarks. We specifically measured aspects of diversity at the local plot‐scale (α‐scale, 0.5 m2) and site‐scale (γ‐scale, 10 m2), as well as the within‐site heterogeneity (β‐diversity) and among‐site variation in species composition (turnover and nestedness).At our α‐scale, sites recovering after agricultural abandonment only had 70% of the plant species richness (and ~30% of the evenness), compared to never‐ploughed sites. Within‐site β‐diversity recovered following agricultural abandonment to around 90% after 80 years. This effect, however, was not enough to lead to recovery at our γ‐scale. Richness in recovering sites was ~65% of that in remnant never‐ploughed sites. The presence of species characteristic of the never‐disturbed sites increased in the recovering sites through time. Forb and legume cover declines in years since abandonment, relative to graminoid cover across sites.Synthesis.We found that, during the 80 years after agricultural abandonment, old fields did not recover to the level of biodiversity in remnant never‐ploughed sites at any scale. β‐diversity recovered more than α‐scale or γ‐scale. Plant species composition recovered, but not completely, over time, and some species groups increased their cover more than others. Patterns of ecological recovery in degraded ecosystems across space and long time‐scales can inform targeted active restoration interventions and perhaps, lead to better outcomes.
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Graphical representation of landscape heterogeneity identification through unsupervised acoustic analysis
Abstract Changes in land use and climate change threaten global biodiversity and ecosystems, calling for the urgent development of effective conservation strategies. Recognizing landscape heterogeneity, which refers to the variation in natural features within an area, is crucial for these strategies. While remote sensing images quantify landscape heterogeneity, they might fail to detect ecological patterns in moderately disturbed areas, particularly at minor spatial scales. This is partly because satellite imagery may not effectively capture undergrowth conditions due to its resolution constraints. In contrast, soundscape analysis, which studies environmental acoustic signals, emerges as a novel tool for understanding ecological patterns, providing reliable information on habitat conditions and landscape heterogeneity in complex environments across diverse scales and serving as a complement to remote sensing methods.We propose an unsupervised approach using passive acoustic monitoring data and network inference methods to analyse acoustic heterogeneity patterns based on biophony composition. This method uses sonotypes, unique acoustic entities characterized by their specific time‐frequency spaces, to establish the acoustic structure of a site through sonotype occurrences, focusing on general biophony rather than specific species and providing information on the acoustic footprint of a site. From a sonotype composition matrix, we use the Graphical Lasso method, a sparse Gaussian graphical model, to identify acoustic similarities across sites, map ecological complexity relationships through the nodes (sites) and edges (similarities), and transform acoustic data into a graphical representation of ecological interactions and landscape acoustic diversity.We implemented the proposed method across 17 sites within an oil palm plantation in Santander, Colombia. The resulting inferred graphs visualize the acoustic similarities among sites, reflecting the biophony achieved by characterizing the landscape through its acoustic structures. Correlating our findings with ecological metrics like the Bray–Curtis dissimilarity index and satellite imagery indices reveals significant insights into landscape heterogeneity.This unsupervised approach offers a new perspective on understanding ecological and biological interactions and advances soundscape analysis. The soundscape decomposition into sonotypes underscores the method's advantage, offering the possibility to associate sonotypes with species and identify their contribution to the similarity proposed by the graph.
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- Award ID(s):
- 2213568
- PAR ID:
- 10593929
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 16
- Issue:
- 6
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 1255-1272
- Size(s):
- p. 1255-1272
- Sponsoring Org:
- National Science Foundation
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