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8 results for “tree microhabitats”
Tree size, microhabitat diversity and landscape structure determine the value of isolated trees for bats in farmland
<p>Isolated trees are increasingly recognised as playing a vital role in supporting biodiversity in agricultural landscapes, yet their occurrence has declined substantially in recent decades. Most bats in Europe are tree-dependent species that rely on woody elements in order to persist in farmlands. However, isolated trees are rarely considered in conservation programs and landscape planning. Further investigations are therefore urgently required to identify which trees – based on both their intrinsic characteristics and their location in the landscape – are particularly important for bats. We acoustically surveyed 57 isolated trees for bats to determine the relative and interactive effects of size, tree-related microhabitat (TreM) diversity and surrounding landscape context on bat activity. Tall trees with large diameter at breast height and crown area positively influenced the activity of <em>Pipistrellus pipistrellus</em> and small Myotis bats (<em>Myotis</em> spp.) while smaller and thinner trees favoured <em>M. myotis</em> activity. The diversity of TreMs that can be used as roosts had a positive effect on (i) <em>Barbastella barbastellus</em> activity only when trees were relatively close (10% within 100 radius scale). The potential benefits of isolated trees for bats result from ecological mechanisms operating at both tree and landscape scales, underlining the crucial need for implementing a multi-scale approach in conservation programs. Maintaining the largest and most TreM-diversified trees located in the most heterogeneous agricultural landscapes will provide the greatest benefits.</p>
Tree census data associated to 'Large-scale informative priors to better predict the local occurrence rate of a rare tree-related microhabitat'
<p>This repository contains two excel spreadsheets providing data about the trees studied in Cottais et al. study (doi : 10.1101/2024.11.28.625900):</p> <ul> <li>df_TreM.xlsx has 1462 lines (headers not included); each line corresponds to one oak tree sampled in 2021 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id : the id of the sampling plot where the tree is located</li> <li>Tree_Id : a unique id of the tree used in the BloBiForM project</li> <li>Species_Latin_Name : the latin name of the tree species ('Quercus_sp' for all trees given than only oaks are reported here)</li> <li>DBH_cm : the diameter at breast height of the tree in cm</li> <li>TreM : whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Life_Stage : whether the tree is a living tree or a snag</li> <li>Last_Logging_Year : the year of the last logging event in the stand where the tree is located</li> <li>conv : whether the last logging event occured less that 120 years before 2024 (conv=0; the stand is now classic high forest) or is more ancient (conv=1, the stand is being converted to high forest through sprout thinning)</li> </ul> </li> <li>DMH_2022.xlsx has 1190 lines (headers not included); each line corresponds to one tree sampled in 2022 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id: the id of the sampling plot where the tree is located</li> <li>Species_Latin_Name: the latin name of the tree species, obtained as a translation from french using the dictionnary in page 2; oaks are not the only species reported, but only oaks are used in Cottais et al. study;</li> <li>Species_French_Name : the french name of the tree species;</li> <li>DBH_cm: the diameter at breast height of the tree in cm</li> <li>TreM: whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Tree_Id: when the tree belongs to the cohort of the BloBiForM project, the tree id is reported; left empty otherwise</li> <li>Nb_brin: number of stems on the tree stump;</li> <li>Commentaire: any comments during fieldwork</li> </ul> </li> </ul>
Data for "Trait-based response of deadwood and tree-related microhabitats to decline in temperate lowland and montane forests"
<p><strong>Sampling design and case studies</strong></p> <p>The study was conducted in two French regions, the Loire valley and the French Pyrenees, and one German region, the Bavarian mountains. In the Loire valley, we studied two lowland sites in oak-dominated (both <em>Quercus petraea</em> (Matt.) Liebl. and <em>Quercus robur</em> L.) forests, one in the Orleans State Forest (107-174 m a.s.l.) and one in the Vierzon State Forest (120-190 m a.s.l.). The main secondary species in these forests were hornbeam (<em>Carpinus betulus</em> L.) and Scots pine (<em>Pinus sylvestris</em> L.). In 2020, we selected nine plots to represent a decline gradient in each of these forests. While the Orleans Forest was healthy overall, the Vierzon Forest had undergone several decline events due to successive droughts aggravated by edaphic factors. In the Pyrenees, we studied two sites in montane forests dominated by silver fir (<em>Abies alba</em> Mill.), whose decline is mainly the result of successive droughts occurring since the 1980’s, and with Norway spruce (<em>Picea abies</em> (L.) H. Karst) and European beech (<em>Fagus sylvatica</em> L.) as secondary species. In 2017, we selected 43 plots: (i) 21 plots in the Aure Valley (854-1570 m a.s.l.) and (ii) 22 plots on the Sault Plateau (705-1557 m a.s.l.). The severe summer drought of 2003 had significant effects on tree mortality in oak and fir forests (Cours and others, 2022). Finally, we studied 19 plots of montane forest in the Bavarian Forest National Park, dominated by Norway spruce (<em>Picea abies</em> (L.) H. Karst) with European beech and silver fir as the main secondary species (Bässler and others, 2009). The dieback results from several cycles of windstorms followed by bark beetle (<em>Ips typographus</em> (L.)) outbreaks (Müller and others, 2010), the dominant drivers of forest dynamics in Norway spruce forests in temperate Europe (Zemlerová and others, 2023). This dieback phenomenon was more severe than either of the aforementioned drought-induced declines, and resulted in greater tree mortality (Cours and others, 2021). In the fir and oak forests in France, our plots were set up in managed forests, and the surrounding forest was also predominantly managed. On the other hand, in the German spruce forest, our plots were set up both within the core area of the Bavarian Forest National Park, and in the surrounding zone (BIOKLIM project), with little or no human intervention (Müller and others, 2010).</p> <p><strong>Field measurements</strong></p> <p>Plots were set up with a Bitterlich relascope with an opening angle corresponding to counting factor n° 1 (ratio 1/50), and mean plot area was about 0.3 ha. For each tree within the plot, we recorded its status (i.e. dead, living, snag, log), tree-species and diameter at breast height (DBH; minimum DBH recorded = 17.5 cm for living trees and logs, 7.5 cm for snags, 67.5 cm for very large trees). We took the proportion of dead trees in basal area (i.e. the ratio of the cumulative basal area of standing and lying dead trees to the basal area of all the trees in the plot), hereinafter referred to as “mortality rate”, as a proxy for the level of local stand decline. Note that this “mortality rate” does not reflect true overall mortality rate in managed oak forests, as foresters removed most valuable declining trees. We visually inventoried TreMs on living trees, logs and snags, and included the 47 types described by Larrieu et al. (2018).</p> <p>For each deadwood item (length > 1 m) in the plot, we measured its decay stage (from 1 = hard dead wood fully covered with bark to 4 = soft wood without bark), length, diameter at mid-length for logs and snags < 4 m long, and DBH for dead trees and snags > 4 m. Deadwood was classified in the following categories: ground-lying (logs and uprooted dead trees) vs standing (snags and standing dead trees); small and mid-size (less than 40 cm in diameter) vs large and very large (more than 40 cm in diameter); and fresh (decay class 1 and 2) vs decayed (decay stage 3 and 4). We calculated the total number of items per hectare by allocating a coefficient N<sub>d</sub> related to diameter (d) to each item observed in the relascope sampling: (N<sub>d</sub> = π 10<sup>8</sup> [ArcTan(1/50)/(π d)]<sup>2</sup>). We estimated TreM diversity and the number of deadwood types per plot.</p> <p>We compiled a list of eco-morphological traits for woody elements (i.e., life status (living, dead) and vertical position (downed, standing), decay stage and diameter) and for TreMs detected in the field (TreM nature, association with deadwood (saproxylic, epixylic, mould), type of bearing substrate (i.e., living tree, dead tree or snag, and log), position in the tree (i.e. base, trunk, crown), degree of wetness, life span or ontogenesis).</p>
Data from: The indicator side of tree microhabitats: a multi-taxon approach based on bats, birds and saproxylic beetles
1. National and international forest biodiversity assessments largely rely on indirect indicators, based on elements of forest structure that are used as surrogates for species diversity. These proxies are reputedly easier and cheaper to assess than biodiversity. Tree microhabitats – tree-borne singularities such as cavities, conks of fungi or bark characteristics – have gained attention as potential forest biodiversity indicators. However, as with most biodiversity indicators, there is a lack of scientific evidence documenting their quantitative link with the biodiversity they are supposed to assess. 2. We explored the link between microhabitat indices and the richness and abundance of three taxonomic groups: bats, birds, and saproxylic beetles. Using a nation-wide multi-taxon sampling design in France, we compared 213 plots located inside and outside strict forest reserves. We hypothesized that the positive effect setting aside forest reserves has on biodiversity conservation is indirectly due to an increase in the proportion of large structural elements (e.g. living trees, standing and lying deadwood). These, in turn, are likely to favour the quantity and diversity of microhabitats. We analysed the relationship between the abundance and species richness of different groups and guilds (e.g. red-listed species, forest specialists, cavity dwellers) and microhabitat density and diversity. We then used confirmatory structural equation models to assess the direct and indirect effects of management abandonment, large structural elements and microhabitats on the biodiversity of the target species. 3. For several groups of birds and bats, the indirect effect of management abandonment and large structural elements on biodiversity was mediated by microhabitats. However, the magnitude of the link between microhabitat indices and biodiversity was moderate. In particular, saproxylic beetles' biodiversity was poorly explained by microhabitats, large structural elements or management abandonment. 4. Synthesis and applications: Tree microhabitats may serve as indicators for bats and birds, but they are not a universal biodiversity indicator. Rather, compared to large structural elements, they most likely have a complementary role to biodiversity. In terms of forest management and conservation, preserving diversity of microhabitats at the local scale benefits several groups of both bats and birds.
Data from: Modelling the probability of microhabitat formation on trees using cross-sectional data
1. Context: Tree-related microhabitats (TreMs), such as trunk cavities, peeled bark, cracks or sporophores of lignicolous fungi, are essential to support forest biodiversity because they are used as substrate, foraging, roosting or breeding places by bryophytes, fungi, invertebrates and vertebrates. Biodiversity conservation requires the continuous presence of TreMs in a forest. However, little is known about their dynamics. Moreover, we usually have only cross-sectional TreM data (observations of many trees at a single time), making it difficult to estimate TreM formation rates. 2. Method: This study adapted the methods of survival and reliability analysis to model the rate of TreM formation per unit of diameter increment as a function of tree diameter at breast height (DBH). We tested three variants of this model: the TreM formation rate independent of, proportional to or increasing non-linearly with DBH. We calculated the likelihood of the models, considering cross-sectional observations either of TreM presence/absence or TreM number on trees of different sizes. We calibrated the models in six sub-natural montane forests dominated by European beech (Fagus sylvatica) and silver fir (Abies alba) – in the French Pyrenees. Assuming an annual DBH increment value, the annual formation rate of TreMs was predicted both at the level of the tree and at the level of the forest stand. 3. Results: This method provided a coherent framework to model the probability that a TreM forms on a tree during a unit growth step and produces realistic predictions of TreM accumulation on trees. TreM formation accelerated as trees grew for A. alba but not for F. sylvatica. The TreM formation rate was twice as fast on F. sylvatica as on A. alba. We estimated a formation of 0.82–1.28 TreMs/ha per year and 0.5–0.9 TreM bearing trees/ha per year in the sub-natural forests studied. 4. Synthesis and applications: This method makes rigorous modelling of the formation of TreMs possible during the growth of trees and forest stands. The quantitative evaluation of TreM fluxes will help to design forest biodiversity conservation strategies favouring the development and temporal continuity of TreMs.
Data from: The indicator side of tree microhabitats: a multi-taxon approach based on bats, birds and saproxylic beetles
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Data from: Modelling the probability of microhabitat formation on trees using cross-sectional data
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Nest microhabitats and tree size mediate shifts in ant community structure across elevation in tropical rainforest canopies
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