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12 results for “silviculture”

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edi56/100

National Phenology Network tree phenology at Crosby Farm Adaptive Silviculture for Climate Change study, 2021-2025

Phenology is the study of relations between climate and periodic biological phenomena, such as bud break or leaf drop in deciduous trees. Phenology is a leading indicator of climate change, and the response of urban tree species to climate can help inform how to manage for a more resilient, and adaptive urban tree canopy. This dataset contains tree phenology data from the The Mississippi National River and Recreation Area (MNRRA) Urban Affiliate Adaptive Silviculture for Climate Change (ASCC) project located at Crosby Farm Regional Park. This dataset includes Individual Phenometrics, Site Phenomentrics, Status and Intensity, and Magnitude Phenometrics. This data was collected through mobile app submissions to Nature's Notebook and downloaded from the National Phenology Network Observation Portal, filtered by date range 01/01/2021 to 02/26/2024 and for Crosby Farm ASCC. Data Attribution: USA National Phenology Network. 2024. Plant and Animal Phenology Data. Data type: Status & Intensity, Individual Phenometrics, Site Phenometrics, Magnitude Phenometricts. 01/01/2021-02/26/2024 for Region: 45.221627°, -92.554965° (UR); 44.599185°, -93.5712° (LL). USA-NPN, St. Paul, Minnesota, USA. Data set accessed 03/19/2024 at http://doi.org/10.5066/F78S4N1

openCC (other)Feb 2026View details →
zenodo44/100

Tree mortality risks under climate change in Europe: assessment of silviculture practices and genetic conservation networks

<p>General context: Climate change can positively or negatively affect abiotic and biotic drivers of tree mortality. Process-based models integrating these climatic effects are only seldom used at species distribution scale.</p> <p>Objective: The main objective of this study was to investigate the multi-causal mortality risk of five major European forest tree species across their distribution range from an ecophysiological perspective, to quantify the impact of forest management practices on this risk and to identify threats on the genetic conservation network.</p> <p><br> Methods: We used the process-based ecophysiological model CASTANEA to simulate the mortality risk of \textit{Fagus sylvatica}, \textit{Quercus petraea}, \textit{Pinus sylvestris}, \textit{Pinus pinaster} and \textit{Picea abies} under current and future climate conditions, while considering local silviculture practices. The mortality risk was assessed by a composite risk index \textit{(CRIM)} integrating the risks of carbon starvation, hydraulic failure and frost damage. We took into account extreme climatic events with the \textit{CRIM$_{max}$}, computed as the maximum annual value of the \textit{CRIM}.</p> <p><br> Results: The physiological processes&#39; contributions to \textit{CRIM} differed among species: it was mainly driven by hydraulic failure for \textit{P. sylvestris} and \textit{Q. petraea}, by frost damage for \textit{P. abies}, by carbon starvation for \textit{P. pinaster}, and by a combination of hydraulic failure and frost damage for \textit{F. sylvatica}. Under future climate, projection showed an increase of \textit{CRIM} for \textit{P. pinaster} but a decrease for \textit{P. abies}, \textit{Q. petraea} and \textit{F. sylvatica}, and little variation for \textit{P. sylvestris}. Under the harshest future climatic scenario, forest management decreased the mean \textit{CRIM} for \textit{P. sylvestris}, increased it for \textit{P. abies} and \textit{P. pinaster} and had no major impact for the two broadleaved species. By the year 2100, 38\% to 90\% of the conservation units are at extinction threat (\textit{CRIM$_{max}$}=1), depending on the species.</p> <p><br> Conclusions: Using a process-based ecophysiological model allowed us to disentangle the multiple drivers of tree mortality under current and future climate. Taking into account the positive effect of increased CO$_2$ on fertilization and water use efficiency, the average risks may increase or decrease in the future depending on species and sites. However, considering extreme climatic events, future projections are as pessimistic than those obtained with bioclimatic niche models.</p> <p>&nbsp;</p> <p>Abbreviation for column:</p> <p>X&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Longitude<br> Y&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Latitude<br> LAImax&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Leaf area index max reach<br> Nha&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Density per hectar<br> Vha&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Volume per hectar<br> NEE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Net ecosystem exchange<br> NPP&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;net primary production<br> Reco&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Respiration ecosystem<br> GPP&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Gross primary production<br> Etveg&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Evapotranspiration canopy<br> Etsol&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Evapotranspiration sol<br> TR&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;tree transpiration<br> ETP&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;evapotranspiration potentiel<br> BiomassOfReserves&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Biomass of reserve<br> rw&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;ring width<br> dbh&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;diameter at breast heast<br> height&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;height<br> BBday&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Budburst date<br> rFD&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;risk of frost<br> CRIM_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Maximum combined risk index of mortality reach<br> rNSC&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;risk of carbon starvation<br> rPLC&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;risk of embolism<br> rPLC_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Maximum risk of embolism reach<br> CRIM&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;combined risk index of mortality<br> Climate&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Climatic model<br> rNSC_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;maximum risk of carbon starvation reach<br> rFD_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Maximum risk of frost&nbsp; reach<br> Scenario_Sylvicol&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;null means no silvulcture simulated<br> species&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;species<br> Country&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Country<br> alt_watch&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;altitude of climate simulated<br> grid_watch&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;number of the pixel point of WATCH<br> grid_eurocordex&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;number of the pixel point of Eurocordex<br> Pinus_sylvestris&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Fagus_sylvatica&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Quercus_petraea&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Picea_abies&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Pinus_pinaster&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2020View details →
zenodo40/100

Silvicultural regime shapes understory functional structure in European forests

<p>This is the dataset used for the article "Silvicultural regime shapes understory functional structure in European forests" by Francesco Chianucci, Francesca Napoleone et al., which has been accepted in Journal of Applied Ecology.</p> <p>Attached is also an R code to illustrate the statistical analyses performed in the study.</p>

opencc-by-4.0Jun 2024View details →
zenodo40/100

Supplementary material for research article "Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history"

<p>This public repository contains mainly datasets generated and analyzed during the current study, closely linked to the research article &quot;Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history&quot;. Furthermore, the repository contains&nbsp;additional figures and deep dives on the methodological background the research article was&nbsp;built on.</p>

opencc-by-4.0Apr 2019View details →
zenodo36/100

Silvicultural and economic dataset for even-aged and coppice-with-standards managements in France.

<p>Data set from&nbsp;DEFIFORBOIS project &quot;Development and sustainability of the forest-wood sector in the Centre region&quot;, PSDR 4 project Centre-Val de Loire Region, France. This study has been carried out also with financial support from the French National Research Agency (ANR) in the frame of the Investments for the future Programme, within the Cluster of Excellence COTE (ANR-10-LABX-45) through the Project LUCAS.</p>

opencc-by-4.0Sep 2020View details →
dryad36/100

Ungulate occurrence in forest harvest blocks is influenced by forage availability, surrounding habitat, and silviculture practices

<p>Forest harvesting causes habitat loss and alteration and can change predator-prey dynamics. In Canada, forest harvesting has shifted the distribution and abundance of ungulates (deer, elk, and moose) that prefer early seral forest, resulting in unsustainable caribou predation by shared predators (bears, cougars, and wolves). Long-term solutions for caribou recovery require management to reduce ungulate prey species within caribou ranges. Silviculture practices applied after forest harvesting directly affect the amount of forage available in harvested areas, and therefore influence ungulate distribution, but few studies have completed detailed assessments on how specific treatments of site preparation, planting, and stand tending influence ungulate use of harvest blocks.</p> <p>We used camera traps, silviculture data, GIS-derived habitat and disturbance data, and detailed vegetation data collected at field sites to investigate ungulate occurrence in harvest blocks in west-central Alberta, Canada. We compared seasonal ungulate occurrence and investigated how site-specific characteristics, the surrounding habitat and disturbance density, and fine-scale silviculture treatments influenced ungulate occurrence in blocks. </p> <p>Deer, elk, and moose occurrence was higher in summer compared to winter. Elk, moose, and white-tailed deer occurrence were higher in blocks with greater availability of specific forage species. Moose occurrence was higher in blocks with a lower road density in the surrounding area, and white-tailed deer occurrence was higher in blocks further from seismic lines and with a lower proportion of harvest blocks in the surrounding area.</p> <p>Deer, elk, and moose occurrence were higher in younger harvest blocks. Mule deer and white-tailed deer occurrence were lower in blocks with higher planting densities of lodgepole pine, and mule deer occurrence was also lower in blocks that had been stand-tended.</p> <p>Our study provides detailed information on ungulate response to fine-scale silviculture methods used in Alberta, directly linking wildlife occurrence to forestry practices, and providing practical scientific information to inform sustainable forestry. Translating this research into practical landscape management decisions could benefit boreal biodiversity, including threatened species like caribou, and culturally and economically important species like deer, elk, and moose. </p>

opencc-zeroMar 2023View details →
dryad36/100

Silviculture simplifies anuran–prey networks and increases niche partitioning in the Brazilian Atlantic Forest

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publicNov 2025View details →
dryad36/100

Ungulate occurrence in forest harvest blocks is influenced by forage availability, surrounding habitat, and silviculture practices

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publicMar 2023View details →
dryad32/100

Data from: Structural and compositional dynamics of strictly protected woodland communities with silvicultural implications, using Białowieża Forest as an example

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publicAug 2019View details →
dryad32/100

Plant and insect assemblages in mixedwood Boreal forest at the Zama silvicultural experiment site in northwestern Alberta, Canada

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publicApr 2025View details →
dryad28/100

Data from: Direct and indirect effects of pine silviculture on the larval occupancy and breeding of declining amphibian species

1. Plantation silviculture is increasing globally and is particularly intensive in temperate coniferous forests, where densely planting trees requires practices common to non-conifer systems that can alter forest floor microhabitat, and potentially threaten amphibian persistence. Most declining amphibian species depend on specific forest microhabitats as terrestrial refugia, but amphibian extirpation associated with tree harvest alone appears unlikely, suggesting that impacts of planting forests on groundcover might better predict recent declines in amphibian occupancy. 2. We repeatedly sampled larval presence or absence of 10 amphibian species native to temperate coniferous forest in the Southeastern United States for one year at 62 isolated wetlands located in either naturally regenerating or planted forest (plantation) to assess three direct ways that planted forests might reduce amphibian breeding site occupancy by: 1) increasing conifer densities, 2) decreasing groundcover, and 3) an indirect pathway, whereby increased tree densities at plantations might reduce groundcover and thus amphibian site occupancy. 3. After controlling for wetland traits and accounting for differences in detection, breeding site occupancy for 8/10 amphibian species was dependent upon whether forests were planted surrounding wetlands (within 300 m). Herbaceous groundcover, not canopy, most commonly influenced occupancy and increased occupancy for declining surface active or fossorial amphibians. 4. Path analyses showed that, by directly and indirectly reducing groundcover (via conifer densities), plantations had significantly lower occupancy of two declining surface active or fossorial frog species, whereas two common aquatic frog species were tolerant to planting conifers. Among declining species, salamanders showed a greater reduction in occupancy than anurans, likely because of greater vulnerability to the drier forest floor conditions of plantation than naturally regenerating forests. 5. Synthesis and applications: Direct negative impacts of coniferous plantation on amphibians can be addressed by limiting groundcover and soil impacts, including switching from high intensity practices, such as mechanical chopping vegetation or bedding soil, to lower intensity site preparation treatments that are less likely to significantly disturb groundcover. Indirect negative effects of dense canopy cover at planted forests could be lowered by periodically thinning canopies prior to final harvest, thus increasing intact forest groundcover and the conservation of both common and declining amphibians.

opencc-zeroSep 2019View details →
dryad28/100

Data from: Direct and indirect effects of pine silviculture on the larval occupancy and breeding of declining amphibian species

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publicSep 2019View details →

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