Observer and relocation errors matter in resurveys of historical vegetation plots

Aim: Revisits of non-permanent, relocatable plots first surveyed several decades ago offer a direct way to observe vegetation change and form a unique and increasingly used source of information for global change research. Despite the important insights that can be obtained from resurveying these quasi-permanent vegetation plots, their use is prone to both observer and relocation errors. Studying the combined effects of both error types is important since they will play out together in practice and it is yet unknown to what extent observed vegetation changes are influenced by these errors. Methods: We designed a study that mimicked all steps in a resurvey study and that allowed determination of the magnitude of observer errors only vs the joint observer and relocation errors. Communities of vascular plants growing in the understorey of temperate forests were selected as study system. Ten regions in Europe were covered to explore generality across contexts and 50 observers were involved, which deliberately differed in their experience in making vegetation records. Results: The mean geographic distance between plots in the observer+relocation error data set was 24m. The mean relative difference in species richness in the observer error and the observer+relocation data set was 15% and 21%, respectively. The mean pseudo-turnover between the five records at a quasi-permanent plot location was on average 0.21 and 0.35 for the observer error and observer+relocation error data sets, respectively. More detailed analyses of the compositional variation showed that the nestedness and turnover components were of equal importance in the observer data set, whereas turnover was much more important than nestedness in the observer+relocation data set. Interestingly, the differences between the observer and the observer+relocation data sets largely disappeared when looking at temporal change: both the changes in species richness and species composition over time were very similar in these data sets. Conclusions: Our results demonstrate that observer and relocation errors are non-negligible when resurveying quasi-permanent plots. A careful interpretation of the results of resurvey studies is warranted, especially when changes are assessed based on a low number of plots. We conclude by listing measures that should be taken to maximally increase the precision and the strength of the inferences drawn from vegetation resurveys

Tree diversity does not always improve resistance of forest ecosystems to drought

Climate models predict an increase in the intensity and frequency of drought episodes in the Northern Hemisphere. Among terrestrial ecosystems, forests will be profoundly impacted by drier climatic conditions, with drastic consequences for the functions and services they supply. Simultaneously, biodiversity is known to support a wide range of forest ecosystem functions and services. However, whether biodiversity also improves the resistance of these ecosystems to drought remains unclear. We compared soil drought exposure levels in a total of 160 forest stands within five major forest types across Europe along a gradient of tree species diversity. We assessed soil drought exposure in each forest stand by calculating the stand-level increase in carbon isotope composition of late wood from a wet to a dry year (Δδ(13)CS). Δδ(13)CS exhibited a negative linear relationship with tree species diversity in two forest types, suggesting that species interactions in these forests diminished the drought exposure of the ecosystem. However, the other three forest types were unaffected by tree species diversity. We conclude that higher diversity enhances resistance to drought events only in drought-prone environments. Managing forest ecosystems for high tree species diversity does not necessarily assure improved adaptability to the more severe and frequent drought events predicted for the future

Identifying the tree species compositions that maximize ecosystem functioning in European forests

1. Forest ecosystem functioning generally benefits from higher tree species richness, but variation within richness levels is typically large. This is mostly due to the contrasting performances of communities with different compositions. Evidencebased understanding of composition effects on forest productivity, as well as on multiple other functions will enable forest managers to focus on the selection of species that maximize functioning, rather than on diversity per se. 2. We used a dataset of 30 ecosystem functions measured in stands with different species richness and composition in six European forest types. First, we quantified whether the compositions that maximize annual above-ground wood production (productivity) generally also fulfil the multiple other ecosystem functions (multifunctionality). Then, we quantified the species identity effects and strength of interspecific interactions to identify the “best” and “worst” species composition for multifunctionality. Finally, we evaluated the real-world frequency of occurrence of best and worst mixtures, using harmonized data from multiple national forest inventories. 3. The most productive tree species combinations also tended to express relatively high multifunctionality, although we found a relatively wide range of compositions with high- or low-average multifunctionality for the same level of productivity. Monocultures were distributed among the highest as well as the lowest performing compositions. The variation in functioning between compositions was generally driven by differences in the performance of the component species and, to a lesser extent, by particular interspecific interactions. Finally, we found that the most frequent species compositions in inventory data were monospecific stands and that the most common compositions showed below-average multifunctionality and productivity. 4. Synthesis and applications. Species identity and composition effects are essential to the development of high-performing production systems, for instance in forestry and agriculture. They therefore deserve great attention in the analysis and design of functional biodiversity studies if the aim is to inform ecosystem management. A management focus on tree productivity does not necessarily trade-off against other ecosystem functions; high productivity and multifunctionality can be combined with an informed selection of tree species and species combinations

Biodiversity and ecosystem functioning relations in European forests depend on environmental context

The importance of biodiversity in supporting ecosystem functioning is generally well accepted. However, most evidence comes from small-scale studies, and scaling-up patterns of biodiversity-ecosystem functioning (B-EF) remains challenging, in part because the importance of environmental factors in shaping B-EF relations is poorly understood. Using a forest research platform in which 26 ecosystem functions were measured along gradients of tree species richness in six regions across Europe, we investigated the extent and the potential drivers of context dependency of B-EF relations. Despite considerable variation in species richness effects across the continent, we found a tendency for stronger B-EF relations in drier climates as well as in areas with longer growing seasons and more functionally diverse tree species. The importance of water availability in driving context dependency suggests that as water limitation increases under climate change, biodiversity may become even more important to support high levels of functioning in European forests

The functional significance of tree species diversity in European forests - the FunDivEUROPE dataset

<p><span>The FunDivEUROPE project, short for "Functional Significance of Forest Biodiversity in Europe," aimed at exploring the intricate relationships between forest biodiversity and ecosystem functionality, focusing specifically on European forests. The project was a collaborative effort involving scientists from multiple disciplines and institutions. It entailed a comprehensive, large-scale assessment of forest biodiversity and its impact on ecosystem functions in a network of observational plots spanning the European continent. This extensive network enabled us to systematically examine how variations in tree species diversity and functional traits influenced key ecosystem functions.</span></p> <p><span>In total, 209 mature forest plots measuring 30 x 30 meters were located</span> <span>in six European countries, ranging from boreal to Mediterranean zones, with each representing a major European forest type: Finland (boreal forest), Poland (hemiboreal forest), Germany (temperate deciduous forest), Romania (mountainous deciduous forest), Italy (thermophilous deciduous forest), and Spain Mediterranean mixed forest). Richness levels of one, two, three, four, and five target species were replicated within and across regions.</span></p> <p><span>A major strength of the FunDivEUROPE project was the general philosophy to measure all ecosystem functions in all plots, following the same protocol by the same observers across the six forest types. In each of the 209 plots, 27 ecosystem functions were measured.</span></p> <p><span>Here, we present data on a high number of basic data for each of the 209 plots, describing geographic and geomorphological, as well as soil and bedrock characteristics, climate variables, and several measures of tree diversity. We further show data of the 27 ecosystem functions, which were classified into six groups reflecting basic ecological processes, and which have established links to supporting, provisioning, regulating or cultural ecosystem services. Details about the measurement protocols are provided.</span></p> <p><span>Major results from the FunDivEUROPE project shed light on the fundamental importance of biodiversity in European forests. The project revealed that diverse forests tend to be more resilient to disturbances, sequester more carbon, and provide enhanced diversity of forest-associated taxa. Moreover, the study highlighted the crucial role of particular tree species and functional traits in shaping ecosystem services and functions. The findings of FunDivEUROPE thus offer insights for forest management and conservation practices, advocating for the preservation and restoration of diverse forest ecosystems.</span></p><p>Funding provided by: European Commission<br>Crossref Funder Registry ID: https://ror.org/00k4n6c32<br>Award Number: 265171</p><h3>General design</h3> <p>The FunDivEUROPE project, short for "Functional Significance of Forest Biodiversity in Europe," aimed at exploring the intricate relationships between forest biodiversity and ecosystem functioning, focusing specifically on European forests (Baeten et al., 2019; Baeten et al., 2013; Ratcliffe et al., 2017; van der Plas et al., 2016a; van der Plas et al., 2016b; van der Plas et al., 2018). In total, 209 mature forest plots measuring 30 x 30 meters were located in six European countries, ranging from boreal to Mediterranean zones, and with each representing a major European forest type: Finland (28 plots, boreal forest), Poland (43 plots, hemiboreal forest), Germany (38 plots, temperate deciduous forest), Romania (28 plots, mountainous deciduous forest), Italy (36 plots, thermophilous deciduous forest), and Spain (36 plots, Mediterranean mixed forest). These plots were primarily established to investigate the role of the richness of regionally common and economically important 'target' species on ecosystem functioning and were hence selected to differ as much as possible in the richness of these. Plot selection was aimed at mimicking the design of a biodiversity experiment, in which variation in environment is minimized and diversity is not confounded with composition, as in most observational studies of diversity. Hence, plots were carefully selected so that correlations between tree species richness and community composition, topography (slope, altitude), and potentially confounding soil factors (texture, depth, pH) were minimized, thus ensuring robust tests of diversity-ecosystem function relationships (comparative study design). Most forest plots were historically used for timber production but are now managed by low-frequency thinning or with minimal intervention. Hence, species compositions and diversity patterns in forests are predominantly management-driven and/or are the result of random species assembly, from the regional species pool. All sites are considered as mature forests.</p> <p>In total, there were 15 target species across all 209 plots, and plots were selected so that almost all possible combinations of these target species were realized. Target species contributed to more than 90% of the tree biomass in the plots and therefore we expected them to be most important for ecosystem functioning. Richness levels of one, two, three, four, and five target species were replicated 56, 67, 54, 29, and 3 times, respectively, across countries, and most possible target species compositions were realized. For the majority of species combinations, we included two or more "realizations" (not strict replicates, because species abundances differ), which allows for comparing the importance of species diversity with that of species composition for this subset of plots. At each richness level, each target tree species was present in at least one plot, allowing us to statistically test for the effects of presence/absence of species on ecosystem functioning. Since species evenness might also affect ecosystem functioning, all plots were selected to have target species with similar abundances (with Pielou's evenness values above 0.6 in > 91% of the plots). To reach this goal, we <em>a priori</em> decided to exclude locally rare target species (<2 individuals per plot) in richness measures. To describe community composition and to estimate biomass values of each tree in each plot, we identified all stems ≥7.5 cm in diameter to species and permanently marked them (12,939 stems in total). More details about the design of the FunDivEUROPE plot network can be found in Baeten et al. (2013).</p> <p>We determined a high number of basic data for each of the 209 plots, describing geographic and geomorphological, as well as soil and bedrock characteristics, see also Ratcliffe et al (2017). Soil pH was determined in the same samples used for C and N determination (see below) with a 0.01M CaCl<sub>2</sub> solution at a ratio of 1:2.5 using a 827 pH labs Metrohm AG, Herisau, Switzerland; see details in Dawud et al. (2017). For each plot, we extracted mean annual temperature, temperature seasonality (standard deviation of mean monthly temperatures), annual precipitation, and precipitation seasonality (standard deviation of mean monthly precipitation) from the WorldClim dataset (interpolated from measurements taken between 1960 and to 1990 and at a spatial resolution of one square kilometer) and the slope from the GTOPO30—digital elevation model with a spatial resolution of one square kilometer (data available from the U.S. Geological Survey); see details in Kambach et al. (2019). We further quantified several measures of tree diversity, based on the initial inventory made in each plot, see Baeten et al. (2013). Short description of all these variables are available in the "Metadata" sheet of the data file.</p> <h3 class="MsoNormal"><span>Ecosystem functions methodology</span></h3> <p class="MsoNormal">A major strength of the FunDivEUROPE project was the general philosophy to measure all ecosystem functions in all plots, following the same protocol by the same observers across the six forest types. Measurements are thus directly comparable across plots and show high coverage.</p> <p class="MsoNormal">In each of the 209 plots, 27 ecosystem functions were measured. The functions were <em>a priori</em> classified into six groups reflecting basic ecological processes (groups 1 to 5 below), and which have established links to supporting, provisioning, regulating, or cultural ecosystem services. These functions were also used in Chao et al. (in press): Hill-Chao numbers allow decomposing gamma-multifunctionality into alpha and beta components. Ecology Letters. In addition, we quantified timber quality as an additional ecosystem service.  </p> <p class="MsoNormal">In the following, we describe the methodology for each measured ecosystem function/service. (For more details, see also Baeten et al., 2019; Ratcliffe et al., 2017; van der Plas et al., 2016a; van der Plas et al., 2016b; van der Plas et al., 2018), and other FunDivEUROPE publications that focus on specific ecosystem properties and functions. Additional datasets are stored in the FunDivEUROPE data portal (https://data.botanik.uni-halle.de/fundiveurope/, logon required to view most data; all metadata is publicly available).</p> <p class="MsoNormal">1. Nutrient and carbon cycling-related drivers (header in the data table in parentheses):</p> <p class="MsoNormal">a.       Earthworm biomass:  Biomass of all earthworms [g m<sup>-2</sup>] (earthworm_biomass)</p> <p class="MsoNormal">Earthworm sampling was carried out in spring 2012 in Italy, Germany, and Finland, and in autumn 2012 in Poland, Romania, and Spain. Plots were divided in nine (10 x 10) m subplots. One sample per plot was taken in the center subplot. Sampling close to tree stems was avoided and whenever possible performed, in between multiple, different tree species. At each sampling point, earthworms were sampled by means of a combined method. First litter was handsorted over an area of (25 x 25) cm<sup>2</sup>. After litter removal over an enlarged area of 0.5 m², ethological extraction using a mustard suspension was applied. Finally, hand sorting of a soil sample of (25 × 25) cm<sup>2</sup> and 20 cm depth was performed in the middle of the 0.5 m² area. Earthworms were preserved in ethanol (70%) for two weeks, and transferred to a 5% formaldehyde solution for fixation (until constant weight), after which they were transferred to ethanol (70%) again for further preservation and identification. All worms were individually weighed, including gut content, and identified to species level.  Results per unit area of the three sampling techniques were summed to determine the total earthworm biomass per m². <span>For details on earthworm biomass measurements, we refer to De Wandeler et al. (2018; 2016).</span></p> <p class="MsoNormal">b.      Fine woody debris: Number of snags and standing dead trees shorter than 1.3 m and thinner than 5 cm DBH, and all stumps and other dead wood pieces lying on the forest floor (fine_woody_debris)</p> <p class="MsoNormal">Fine woody debris (FWD) was measured in two circular subplots (radius of 7 m) located in the opposite corners of each plot. All standing dead trees thinner than 5 cm diameter at breast height and snags shorter than 1.3 m, and all stumps and other dead wood pieces lying on the forest floor, were surveyed. In this study, we used the number of FWD pieces in each plot.</p> <p class="MsoNormal">c.       Microbial biomass: Mineral soil (0–5cm layer) microbial biomass carbon [mg C kg<sup>-1</sup>] (microbial_biomass_mineral)</p> <p class="MsoNormal">For soil sampling, each of the 209 plots was divided into nine 10x10m subplots. A soil sample was taken from five of the nine subplots and mixed to obtain one representative composite sample from each plot. Forest floor and mineral soil horizons (0-5 cm) were sampled separately. Soils were sieved fresh (4mm), stored at 4°C and analyzed within two weeks. Sampling was performed in spring 2012 in Italy, Germany, and Finland, and in autumn 2012 in Poland, Romania, and Spain. No forest floor was collected from the plots in Germany.</p> <p class="MsoNormal">Soil microbial biomass C was determined by the chloroform fumigation extraction method, of 10g and 15g (organic and mineral soil, respectively) soil, followed by 0.5 M K<sub>2</sub>SO<sub>4</sub> extraction of both fumigated and unfumigated soils (soil:solution ratio, 1:5). Fumigations were carried out for three days in vacuum desiccators with alcohol-free chloroform. Extracts were filtered (Whatman n° 42), and dissolved organic carbon in fumigated and unfumigated extracts was measured with a Total Organic Carbon analyser (Labtoc, Pollution and Process Monitoring Limited, UK). Soil microbial biomass C was calculated by dividing the difference of total extract between fumigated and unfumigated samples with a kEC (extractable part of microbial biomass C after fumigation) of 0.45 for biomass C (Joergensen and Mueller, 1996).</p> <p class="MsoNormal">d.      Soil carbon stocks:  Total soil carbon stock in forest floor and 0–10 cm mineral soil layer combined [Mg ha<sup>-1</sup>] (soil_c_ff_10)</p> <p class="MsoNormal">Soil sampling was carried out from May 2012 to October 2012 (i.e. Poland in May 2012, Spain in June 2012, Finland and Germany in August 2012, Romania in September 2012 and Italy in October 2012). Nine forest floor samples and nine cores of mineral soil were collected from each plot and these were subsequently pooled into one sample per plot by each soil layer, i.e. forest floor, 0–10cm and 10–20cm depths for samples from Germany, Finland, Italy, and Romania. For Poland, the fixed depth was extended to 20–30cm and 30–40 cm whereas for Spain it was only possible to sample up to the 0–10cm layer due to the stoniness of the site. We oven-dried the samples at 55°C to constant weight, sorted out stones and other materials, ground the forest floor first with a heavy-duty SM 2000-Retsch cutting mill, and we then took subsamples and ground it further into finer particles with a planetary ball mill (PM 400-Retsch) for six minutes at 280rpm. The mineral soil samples were sieved through 2mm diameter mesh. We carried out carbonate removal treatments for those soil samples whose pH value exceeded the threshold point and proved presence of carbonates when tested with a 4N HCl fizz test. We used 6% (w/v) H<sub>2</sub>SO<sub>3</sub> solution and followed the carbonate removal procedure described by (Skjemstad and Baldock, 2007). We took subsamples and further ground it into finer particles with a planetary ball mill (PM 400-Retsch) for six minutes at 280 rpm before analyzing soil organic carbon (SOC) with a Thermo Scientific FLASH 2000 soil CN analyzer. Soil organic C stocks were estimated by multiplying the SOC concentrations with soil bulk density, relative root volume and relative stone volume using the formula described in Vesterdal et al., (2008). We also determined the moisture content of the soil samples by oven-dried subsamples at 105°C and the reported SOC stock is thus on 105°C dry weight basis. </p> <p class="MsoNormal">2. Nutrient cycling related processes</p> <p class="MsoNormal">a.       Litter decomposition: Decomposition of leaf litter using the litterbag methodology [% daily rate] (litter_decomp_day)</p> <p class="MsoNormal">Litter collection and litterbag construction</p> <p class="MsoNormal">Leaf litter from all target tree species of the cross-region exploratory platform was collected at tree species-specific peak leaf litter fall between October 2011 and November 2012. Except for the Finnish forests, where freshly fallen leaf litter was collected from the forest floor, litter was collected using suspended litter traps, which were regularly harvested at one to two-week intervals. In all cases, litter was collected nearby, but not within the experimental plots. Litter was then air-dried and stored until the preparation of the litterbags.</p> <p class="MsoNormal">Litterbags (15 x 15 cm) were constructed using polyethylene fabrics of two different mesh sizes. For the bottom side of the litterbags, we used a small mesh width of 0.5 x 0.5 mm in order to minimize losses of litter fragments, while for the upper side, we used a large mesh width of 5 x 8 mm to allow soil macrofauna access to the litter within bags. Litterbags were filled with 10 g of litter. For litter mixtures, litterbags were filled with equivalent proportion of each litter species. Subsamples of all litter species were weighed, dried at 65°C for 48 h and reweighed to get a 65°C dry mass correction factor.</p> <p class="MsoNormal">Litterbag incubation</p> <p class="MsoNormal">Within each experimental plot, three litterbags with the plot-specific litter type (either single litter species or specific mixtures) were placed on bare soil after the natural litter layer had been removed, and fixed to the soil by placing chicken wire on top of it. The litterbags were removed from the field when 50–60% of the initial litter mass of the region's fastest decomposing species was remaining (evaluated with an extra set of litterbags that were harvested regularly). As a consequence, the duration of litter decomposition varied among regions. This procedure ensured that litter was sampled at similar decomposition stages across all sites, facilitating meaningful comparisons of litter diversity effects.</p> <p class="MsoNormal">Litter processing</p> <p class="MsoNormal">Harvested litterbags were sent to Montpellier where they were dried at 65°C. Litter was cleaned of pieces of wood, stones or other foreign material that occasionally got into the litterbags. Litter was then weighed, ground to a particle size of 1 mm with a Cyclotec Sample Mill (Tecator, Höganäs, Sweden). To correct for potential soil contamination during decomposition in the field, we determined the ash content of initial and final litter material on all samples and expressed litter mass loss on ash-free litter mass. </p> <p class="MsoNormal">Litter mass loss was expressed as the percentage of mass lost from each litterbag, calculated as followed: Mass Loss = 100 x (Initial (ash free) mass – Final (ash free) mass)/Initial (ash free) mass. <span>For details on litter decomposition measurements, we refer to Joly et al. (2017; 2023).</span></p> <p class="MsoNormal">b.      Nitrogen resorption efficiency: Difference in N content between green and senescent leaves divided by N content of green leaves [%] (nutrient_resorption_efficiency)</p> <p class="MsoNormal"><span>In each plot, fresh leaf and needle samples were collected from the south-exposed sun crown of all dominant tree species during the growing season (June to August) of 2012 and 2013. Twigs with leaves and needles were cut down from six trees per species in the monocultures and from three trees per species in the mixtures. Depending on the local conditions, tree loppers, tree climbers, or ruffles were used for this purpose. The selected material was placed in paper bags and was either oven-dried or air-dried, depending on the facilities available. Furthermore, collection of leaves from the litter traps, as representative of senescent leaves, has been conducted at periods of maximum litterfall during 2012 and 2013. For this purpose, five litter traps per plot were established and the collected litter was separated into the different species it originated from (see "Litter production" below). All samples were ground and analysed for nitrogen and calcium content by means of Near Infra Red Spectroscopy (NIRS) as described in detail by Pollastrini et al. (2016a)</span>. <span>For the calibration of the NIRS spectra for the Ca analysis, a subset of samples was analysed with an atom absorption spectrometer (AAS, iCE 3000 series, ThermoScientific, China). Nitrogen resorption efficiency was calculated as follows, taking into account the N content of green and senescent leaves:</span></p> <p class="MsoNormal"><span>NRE(%) = 100 x ((N green leaves - N senescent leaves)/(N green leaves))</span></p> <p class="MsoNormal"><span>Furthermore, the estimated NRE was corrected in order to take into account the leaf mass loss occurring during senescence. Thus, NRE was corrected based on the Ca foliar concentration, since Ca is rather immobile and is not resorbed during senescence (Van Heerwaarden et al., 2003). To validate the correction of NRE based on Ca concentrations, the Mass Loss Correction Factors (MLCF) suggested by Vergutz et al. (2012)</span> have also been used.</p> <p class="MsoNormal">c.       Soil C/N ratio: Soil C/N ratio in forest floor and 0–10 cm mineral soil layer combined (soil_cn_ff_10)</p> <p class="MsoNormal"><span>Soil sampling was carried out between May 2012 and October 2012 in all the regions. Nine forest floor samples were collected using a 25 x 25 cm wooden frame, and the mineral soil (0-10 cm layer) was sampled, after forest floor removal, using a cylindrical metal corer. Total soil carbon and nitrogen concentrations were measured with

A novel comparative research platform designed to determine the functional significance of tree species diversity in European forests

One of the current advances in functional biodiversity research is the move away from short-lived test systems towards the exploration of diversity-ecosystem functioning relationships in structurally more complex ecosystems. In forests, assumptions about the functional significance of tree species diversity have only recently produced a new generation of research on ecosystem processes and services. Novel experimental designs have now replaced traditional forestry trials, but these comparatively young experimental plots suffer from specific difficulties that are mainly related to the tree size and longevity. Tree species diversity experiments therefore need to be complemented with observational studies in existing forests. Here we present the design and implementation of a new network of forest plots along tree species diversity gradients in six major European forest types: the FunDivEUROPE Exploratory Platform. Based on a review of the deficiencies of existing observational approaches and of unresolved research questions and hypotheses, we discuss the fundamental criteria that shaped the design of our platform. Key features include the extent of the species diversity gradient with mixtures up to five species, strict avoidance of a dilution gradient, special attention to community evenness and minimal covariation with other environmental factors. The new European research platform permits the most comprehensive assessment of tree species diversity effects on forest ecosystem functioning to date since it offers a common set of research plots to groups of researchers from very different disciplines and uses the same methodological approach in contrasting forest types along an extensive environmental gradient