DOI: 10.5061/dryad.q83bk3jqg
title: Recent tree diversity increase in NE Iberian forests following intense management release: A task for animal-dispersed and drought tolerant species

description: Under increasing human-related threats to forests, many studies suggest
 that increasing tree species diversity may boost forest resilience by
 enhancing the range of species’ responses to disturbances. However, it
 remains unclear whether passive or active forest management strategies
 should be applied to increase tree diversity. This issue would benefit
 from investigating which management and environmental factors, together
 with species’ functional traits influence temporal changes in tree species
 diversity. We explored the influence of the bioclimatic region, land-use
 history, forest cover, protection, management, forest structure and
 changes in temperature and precipitation, to explain tree species
 diversity changes in NE Iberian forests, by comparing 3141 plots from the
 Spanish National Forest Inventory sampled between 1989 and 2016. Moreover,
 we assessed which species’ functional traits (dispersal habit, drought and
 shade tolerance) were most relevant for diversity changes. After 27 years,
 tree species richness and diversity moderately increased in the tree and
 regeneration layers. This trend occurred mostly in long-established,
 non-recently managed forests and in those with a lower initial basal area.
 Increasing temperature had negative effects for diversity increase in the
 tree layer but positive for the regeneration compartment, while decreasing
 precipitation showed the opposite effects. Tree species with higher
 drought tolerance, and especially those animal-dispersed ones arriving
 from the regional pool, mostly contributed to the local diversity
 increase. This pattern occurred in all forest types, although the
 taxonomic array of species varied. Synthesis and
 applications: The main drivers influencing the passive increase
 in tree species diversity suggest a primary role of diminishing forest
 exploitation in this recovery process, fine-tuned by climatic changes.
 This ecological scenario has particularly favored animal-dispersed tree
 species with higher drought tolerance, which mostly led the diversity
 increase. A higher presence of such highly mobile and drought-tolerant
 species can be crucial to increase functional diversity and, ultimately,
 increase forest resilience under future scenarios of greater aridity. In
 light of these results, management strategies should continue fostering
 the restoration of diversity in once intensively exploited forests while
 ensuring the maintenance of the already gained tree species diversity.
description: <strong>Forest inventory data:</strong> Data of 3141
 replicated plots from the second (NFI2) and fourth (NFI4) Spanish National
 Forest Inventories for Catalonia was used. The NFI2 took place during
 1989–1990 (hereafter 1989), while the IFN4 was conducted during 2013-2016
 (hereafter 2016). Data on species composition and abundance of the tree
 (individuals ≥7.5 cm in dbh) and regeneration (individuals &lt; 7.5 cm
 in dbh) layers was extracted. Detailed information on experimental design
 is available in our paper which will be published in the Journal of
 Applied Ecology.  <strong>Diversity change:
 </strong>For each plot and inventory, we calculated diversity
 metrics using Hill numbers of order q=0 (i.e., transformation of species
 richness), q=1 (i.e., transformation of the Shannon diversity index), q=2
 (i.e., transformation of the Simpson diversity index), and the Pielou’s
 Evenness Index. Then, we calculated the absolute change for each diversity
 metric between NFI2 and NFI4 for the tree and regeneration
 layers.  <strong>Factors influencing diversity
 changes: </strong> Bioclimatic
 region: each plot was classified according to the Bioclimatic
 Classification for Continental Spain, defined by the thermicity index,
 resulting in three broad bioclimatic regions: (1) mesomediterranean, (2)
 supramediterranean, and (3) montane.  Land
 use history and forest cover: each plot was classified into two types of
 land-use history trajectories: (1) “long existing forests” as those that
 were already present in 1956, and (2) “recent forests” as those that were
 located in pasturelands and croplands in 1956 and established after rural
 abandonment. The proportion of forest cover surrounding each NFI plot was
 calculated to account for forest connectivity in the landscape. For such,
 we used land cover maps derived from Landsat imagery corresponding to the
 years 1987 and 2017, which reasonably matched our study period
 (1989-2016) Forest protection:
 we identified those plots included in the Catalan system of protected
 areas. Past and recent forest
 management: management prior to the NFI2 was obtained from evidence
 observed and recorded during the NFI2 field survey in 1989–1990, while
 recent management between 1986 and 2016 was obtained from the disturbance
 map developed by Senf & Seidl (2021) using satellite data and
 manually interpreted plots. 
 Forest structure: total basal area (m<sup>2</sup>
 ha<sup>-1</sup>) of the NFI2 plots was obtained as a measure
 of the initial forest structure. 
 Temperature and precipitation changes: we used aggregated data
 of the years 1988–1992 as representative of the NFI2 period, and the years
 2011–2015 as to characterize the NFI4 period, obtained from the Climate
 Engine database at 9.6 km daily resolution (AgERA5).
 <strong>Functional trait data:
 </strong>All species were clustered according to seed dispersal
 type: animal-dispersed (dispersed by birds and mammals)
 <em>vs</em>. other dispersal methods, and to shade and drought
 tolerance. Clustering resulted in four distinct groups of species,
 distinguishing: (C1) animal-dispersed species with lower drought and
 higher shade tolerance (e.g., <em>Fagus sylvatica</em>,
 <em>Prunus avium</em>, <em>Ilex aquifolium</em>),
 (C2) animal-dispersed with higher drought and lower shade tolerance (e.g.,
 <em>Crataegus monogyna</em>, <em>Arbutus
 unedo</em>, <em>Quercus</em> spp.), (C3) species with
 other dispersal methods with lower drought and higher shade tolerance
 (e.g., <em>Abies alba</em>, <em>Fraxinus
 excelsior</em>, <em>Acer campestre</em>), and (C4)
 species with other dispersal methods and higher drought and lower shade
 tolerance (e.g., <em>P. nigra</em>, <em>P.
 halepensis</em>, <em>Acer monspesulanum</em>).
 <strong>Provenance of new recruited species:</strong>
 By comparing both NFIs we identified those plots that gained species
 during the study period and we built a new metric based on the
 presence/absence information of each species in each plot and NFI period.
 This metric allowed us to address whether a new species that appeared in
 the tree or the regeneration layer in NFI4 inventory was already present
 in the NFI2 and thus recruited from inside the plot (therefore local
 pool); or the new recruited species was not present in the NFI2 plots and
 arrived from outside the plot (therefore regional pool).
 <strong>Please note that all detailed information on data
 acquisition and experimental design is available in our paper which is
 published in the Journal of Applied Ecology. </strong>
description: # Recent tree diversity increase in NE Iberian forests following intense
 management release: a task for animal-dispersed and drought tolerant
 species ## Description of
 the data and file structure If you are willing to use this dataset, please
 contact author at for any further details.
 Datasets included: 1\) Diversity
 change (repository_data_diversity_change.xlsx): \- Time: 1989-2016 
 (Second (NFI2) and Fourth (NFI4) Spanish National Forest inventories). \-
 For each plot_id the change in Hill Numbers of order q0, q1 and q2 are
 shown for the tree and regeneration layers. \- For each plot_id the
 management and environmental factors influencing diversity changes are
 shown. \- Variable descriptions:  * plot_id:  ID for each replicated NFI
 plot * coords_utm_x_ETRS89; coords_utm_y_ETRS89: x, y coordinates for each
 plot * bioclimatic_region: Bioclimatic classification for each plot
 resulting in three broad bioclimatic regions: (1) mesomediterranean, (2)
 supramediterranean, and (3) montane.  * protection: plots included in the
 Catalan system of protected areas (YES:protected/ NO:unprotected). *
 Forest_100m_87; Forest_500m_87; Forest_1k_87: proportion of forest cover
 surrounding each NFI plot for 100m (Forest_100m_87), 500m (Forest_500m_87)
 and 1000m (Forest_1k_87). * past_management: managed plots prior to the
 NFI2 (1:managed/ 0:unmanaged). * recent_management: managed plots
 between1986 and 2016 (1:managed/ 0:unmanaged). * Forest_56: plot was
 forest in the year 1956 (YES/NO). * basal_NFI2: total basal area (m2 ha-1)
 of the NFI2 . * tree_q0_change: tree layer species richness change. *
 tree_q1_change: tree layer Shannon index transformation change. *
 tree_q2_change: tree layer Simpson index transformation change. *
 tree_ev_change: tree layer Evennes index change. * reg_q0_change:
 regeneration layer species richness change. * reg_q1_change: regeneration
 layer Shannon index transformation change. * reg_q2_change: regeneration
 layer Simpson index transformation change. * reg_ev_change:
 regeneration layer Evennes index change. * temp_change/ prec_change:
 temperature and precipitation change. 2\) Regeneration layer species gains
 (repository_data_reg_gains.xlsx): \- Time: 1989-2016  (Second (NFI2) and
 Fourth (NFI4) Spanish National Forest inventories). \- Each row represents
 a recruitment event for the regeneration layer. \- For each recruitment
 event, the identity of the recruited species, the plot_id, the funtional
 trait data, the provenance and the cluster classification is shown.  \-
 Variable descriptions:  * species_id: latin name of each recruited
 species. * plot_id:  ID for each NFI plot where a recruitment event
 occured. * animalDisp: seed dispersal trait (seeds are dispersed by
 animals or not). * ShadeTol: shade tolerance metric. NA values correspond
 to the species for which the shade tolerance data was not available, and
 thus were removed from the analysis. * DrghTol: drought tolerance metric.
 NA values correspond to the species for which the drought tolerance data
 was not available, and thus were removed from the analysis. * provenance:
 recruitment from the local (1) or the regional (0) pool of species. *
 cluster category: cluster classification for each species in relation to
 their functional trait characteristics. NA values correspond to the
 species for which the shade and drought tolerance data were not available,
 and thus were removed from the analysis. 3\) Tree layer species gains
 (repository_data_tree_gains.xlsx): \- Time: 1989-2016  (Second (NFI2) and
 Fourth (NFI4) Spanish National Forest inventories). \- Each row represents
 a recruitment event for the tree layer. \- For each recruitment event, the
 identity of the recruited species, the plot_id, the funtional trait data,
 the provenance and the cluster classification is shown.  \- Variable
 descriptions:  * species_id: latin name of each recruited species. *
 plot_id:  ID for each NFI plot where a recruitment event occured. *
 animalDisp: seed dispersal trait (seeds are dispersed by animals or not).
 * ShadeTol: shade tolerance metric. NA values correspond to the species
 for which the shade tolerance data was not available, and thus were
 removed from the analysis. * DrghTol: drought tolerance metric. NA values
 correspond to the species for which the drought tolerance data was not
 available, and thus were removed from the analysis. * provenance:
 recruitment from the local (1) or the regional (0) pool of species. *
 cluster category: cluster classification for each species in relation to
 their functional trait characteristics. NA values correspond to the
 species for which the shade and drought tolerance data were not available,
 and thus were removed from the analysis. ## Sharing/Access information
 Data was derived from the following sources: * For National Forest
 Inventroy data: 
 * For land use history data:
 
 * For recent management data see Senf & Seidl (2021)
 () * For
 forest protection data:
 * For
 bioclimatic region data: *
 For climatic data: * For functional
 trait data see Kattge et al., (2011)
 ()