250-Year reconstruction of pollarding events reveals sharp management changes in Iberian ash woodlands

Candel-Pérez, David; Hernández‐Alonso, Héctor; Castro, Federico de; Sangüesa‐Barreda, Gabriel; Mutke, Sven; García‐Hidalgo, Miguel; Rozas, Vicente; Olano, José Miguel

doi:10.1007/s00468-022-02343-8

Cited by 7 publications

(2 citation statements)

References 49 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process is recognized as a major force in forest regeneration ( Matula et al., 2019 ) and significantly impacts forest dynamics ( Martini et al., 2008 ). Humans recognized and harnessed these phenomena from early times ( Petit and Watkins, 2003 ; Candel-Pérez et al., 2022 ). A famous example is pollarding, where all the shoots of a tree crown are regularly cut off to encourage the growth of new sprouts, which were used as firewood and material for weaving baskets.…”

Section: Introductionmentioning

confidence: 99%

Predicting resprouting of Platanus × hispanica following branch pruning by means of machine learning

Shu,

Yazdi,

Rötzer

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

IntroductionResprouting is a crucial survival strategy following the loss of branches, being it by natural events or artificially by pruning. The resprouting prediction on a physiological basis is a highly complex approach. However, trained gardeners try to predict a tree’s resprouting after pruning purely based on their empirical knowledge. In this study, we explore how far such predictions can also be made by machine learning.MethodsTable-topped annually pruned Platanus × hispanica trees at a nursery were LiDAR-scanned for two consecutive years. Topological structures for these trees were abstracted by cylinder fitting. Then, new shoots and trimmed branches were labelled on corresponding cylinders. Binary and multiclass classification models were tested for predicting the location and number of new sprouts.ResultsThe accuracy for predicting whether having or not new shoots on each cylinder reaches 90.8% with the LGBMClassifier, the balanced accuracy is 80.3%. The accuracy for predicting the exact numbers of new shoots with the GaussianNB model is 82.1%, but its balanced accuracy is reduced to 42.9%.DiscussionThe results were validated with a separate dataset, proving the feasibility of resprouting prediction after pruning using this approach. Different tree species, tree forms, and other variables should be addressed in further research.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting resprouting of Platanus × hispanica following branch pruning by means of machine learning

Shu,

Yazdi,

Rötzer

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…This process is recognized as a major force in forest regeneration (Matula et al, 2019) and has significant impacts on forest dynamics (Martini et al, 2008). Humans recognized and harnessed these phenomena from early times (Candel-Pérez et al, 2022;Petit & Watkins, 2003). A famous example is pollarding, where all the shoots of a tree crown are regularly cut off to encourage the growth of new sprouts, which were used as firewood and material for weaving baskets.…”

Section: Introductionmentioning

confidence: 99%

Predicting resprouting ofPlatanus×hispanicafollowing branch pruning by means of machine learning

Shu,

Yazdi,

Rötzer

et al. 2023

Preprint

View full text Add to dashboard Cite

Resprouting is a crucial survival strategy following the loss of branches, being it by natural events or artificially by pruning. The prediction of resprouting patterns on a physiological basis is a highly complex approach. However, trained gardeners try to predict a tree's resprouting after pruning purely based on their empirical knowledge and a visual check of the tree's geometry. In this study, we explore in how far such predictions can also be made by algorithms, especially using machine learning. Table-topped annually pruned Platanus × hispanica trees at a nursery were documented with terrestrial LiDAR scanners in two consecutive years. Topological structures for these trees were abstracted from point clouds by cylinder fitting. Then, new shoots and trimmed branches were labelled on corresponding cylinders. Binary and multiclass classification models were tested for predicting the location and number of new sprouts. The accuracy for predicting whether having or not new shoots on each cylinder reaches 90.8% with the LGBMClassifier, the balanced accuracy is 80.3%. The accuracy for predicting the exact numbers of new shoots with GaussianNB model is 82.1% but its balanced accuracy is reduced to 42.9%. The results were validated with a separate evaluation dataset. It proves a feasibility in predicting resprouting patterns after pruning using this approach. Different tree species, tree forms, and other variables should be addressed in further research.

show abstract

An ecological perspective for analysing rural depopulation and abandonment

Lloret,

Escudero,

Lloret

et al. 2024

People and Nature

View full text Add to dashboard Cite

Population loss in rural areas is rapidly increasing in high‐income countries, raising concerns and debate, given its socio‐economic consequences. Despite the evident environmental dimension of the phenomenon, ecological knowledge has been neglected in the analysis of actions aiming to reverse rural depopulation. Particularly, cultural landscapes reflect memories of ecological processes that have configured current patterns of biodiversity and ecosystem services. Based on ecological principles, we present a conceptual procedure to assess the behaviour of social–ecological systems subjected to depopulation, projecting their expected trajectories through time within a framework defined jointly by demographic and environmental–ecological dimensions (in our case, biodiversity, carbon storage, pollution control, water resources and soil conservation). We applied this procedure to various alternative interventions designed to confront depopulation in Spain: (1) non‐intervention, (2) maintenance of the historical landscape configuration, (3) active conservation, (4) extensive, sustainable land use and (5) intensified land use. We conclude that extensive, sustainable land use better optimizes criteria of demographic consolidation, environmental impact, resilience and implementation of actions confronting depopulation. We highlight the need to incorporate ecological knowledge into the assessment and application of actions confronting rural depopulation. Read the free Plain Language Summary for this article on the Journal blog.

show abstract

250-Year reconstruction of pollarding events reveals sharp management changes in Iberian ash woodlands

Cited by 7 publications

References 49 publications

Predicting resprouting of Platanus × hispanica following branch pruning by means of machine learning

Predicting resprouting of Platanus × hispanica following branch pruning by means of machine learning

Predicting resprouting ofPlatanus×hispanicafollowing branch pruning by means of machine learning

An ecological perspective for analysing rural depopulation and abandonment

Contact Info

Product

Resources

About