Fig 4 -

Predicted median size distributions of early-successional and old-forest patches based on the REAL scenario: (a) stands younger than 20 years, (b) stands aged 80 years or older, (c) stands aged 100 years or older. Dashed lines denote edge proportions of the old-forest patches from the total area. Note the different scales on the panels.</p

Program code of the simulation model in R.

For political and administrative governance of land-use decisions, high-resolution and reliable spatial models are required over large areas and for various time horizons. We present a process-centered simulation model ‘NextStand’ (a forest landscape model, FLM) and its R-script, which predicts regional forest characteristics at a forest stand resolution. The model uses whole area stand data and is optimized for realistic iterative timber harvesting decisions, based on stand compositions (developing over time) and locations. We used the model for simulating spatial predictions of the Estonian forests in North Europe (2.3 Mha, about 2 M stands); the decisions were parameterized by land ownership, protection regimes, and rules of clear-cut harvesting. We illustrate the model application as a potential broad-scale Decision Support Tool by predicting how the forest age composition, placement of clear-cut areas, and connectivity of old stands will develop until the year 2050 under future scenarios. The country-scale outputs had a generally low within-scenario variance, which enabled to estimate some main land-use effects and uncertainties at small computing efforts. In forestry terms, we show that a continuation of recent intensive forest management trends will produce a decline of the national timber supplies in Estonia, which greatly varies among ownership types. In a conservation perspective, the current level of 13% forest area strictly protected can maintain an overall area of old forests by 2050, but their isolation is a problem for biodiversity conservation. The behavior of low-intensity forest management units (owners) and strict governance of clear-cut harvesting rules emerged as key questions for regional forest sustainability. Our study confirms that high-resolution modeling of future spatial composition of forest land is feasible when one can (i) delineate predictable spatial units of transformation (including management) and (ii) capture their variability of temporal change with simple ecological and socioeconomic (including human decision-making) variables.</div

Fig 2 -

Broad forest age-class composition at the start of the simulation (1.1.2022) and at the end (31.12.2050) for all simulation scenarios in Estonia: (a) all forests, (b) production forests, (c) restricted-management forests, and (d) strictly protected forests. The scenarios followed either of two starting points (a, b); see Table 1 for details. For the scenarios with several simulation runs (DEFa, MODa, REAL), the values depicted are medians.</p

Fig 3 -

Forest age composition (10-year classes) by management regime at the start (2022) and end of simulation (2050) for the DEFb scenario: (a) intensively managed stands, (b) non-intensively managed stands, (c) state-owned stands, (d) strictly protected stands.</p

The simulation scenario comparison at a glance.

For political and administrative governance of land-use decisions, high-resolution and reliable spatial models are required over large areas and for various time horizons. We present a process-centered simulation model ‘NextStand’ (a forest landscape model, FLM) and its R-script, which predicts regional forest characteristics at a forest stand resolution. The model uses whole area stand data and is optimized for realistic iterative timber harvesting decisions, based on stand compositions (developing over time) and locations. We used the model for simulating spatial predictions of the Estonian forests in North Europe (2.3 Mha, about 2 M stands); the decisions were parameterized by land ownership, protection regimes, and rules of clear-cut harvesting. We illustrate the model application as a potential broad-scale Decision Support Tool by predicting how the forest age composition, placement of clear-cut areas, and connectivity of old stands will develop until the year 2050 under future scenarios. The country-scale outputs had a generally low within-scenario variance, which enabled to estimate some main land-use effects and uncertainties at small computing efforts. In forestry terms, we show that a continuation of recent intensive forest management trends will produce a decline of the national timber supplies in Estonia, which greatly varies among ownership types. In a conservation perspective, the current level of 13% forest area strictly protected can maintain an overall area of old forests by 2050, but their isolation is a problem for biodiversity conservation. The behavior of low-intensity forest management units (owners) and strict governance of clear-cut harvesting rules emerged as key questions for regional forest sustainability. Our study confirms that high-resolution modeling of future spatial composition of forest land is feasible when one can (i) delineate predictable spatial units of transformation (including management) and (ii) capture their variability of temporal change with simple ecological and socioeconomic (including human decision-making) variables.</div

wingspot_data

This file includes data about age, reproductive performance, survival, and plumage signal traits of common gulls

Oviposition latency data for geometrid moths (Geometridae)

Oviposition latency data measured in the lab from field collected Geometridae females for Estonian and Ugandan species

Appendix B. Overview of the predictive power of models with random and fixed effects included in the models.

Overview of the predictive power of models with random and fixed effects included in the models

Supplement 1. Data about seed dispersal distances and related traits for 576 plant species used in the analyses, and references for data sources.

<h2>File List</h2><div> <p><a href="DispersalDistanceData.csv">DispersalDistanceData.csv</a> (Md5: 9f51b823e51b7570a42a832bed1fbf07)</p> </div><h2>Description</h2><div> <p>DispersalDistanceData.csv – Data about seed dispersal distances and related traits for 576 plant species. For some species dispersal distance data is available for different dispersal syndromes and thus 600 data points are included. Missing values are denoted by empty cells. 601 rows (with headers), 22 columns, separator "," , decimal ".".</p> <p>Columns are as follows:</p> <blockquote> <p><b>Species</b>: accepted binomial name (without authorship) of a species, validated using Taxonstand (Cayuela et al. 2012) library </p> <p><b>Original</b>: binomial name of a species in a referenced study if different from accepted binomial name</p> <p><b>Genus</b>: taxonomic genus of a species</p> <p><b>Family</b>: taxonomic family of a species, obtained using Taxonstand (Cayuela et al. 2012) library</p> <p><b>Order</b>: taxonomic order of a species, following APG III for angiosperms (The Angiosperm Phylogeny Group 2009) and Christenhuzs and others (2011) for gymnosperms</p> <p><b>Dispersal_syndrome</b>: species’ dispersal syndrome corresponding to the dispersal distance data, derived from the referenced study, categories include ‘animal’, ‘ant’, ‘ballistic’, ‘wind (none)’, ‘wind (special)’</p> <p><b>Growth_form</b>: growth form data derived from the referenced study or databases LEDA (Kleyer et al. 2008) and PLANTS (USDA and NRCS 2011), categories include ‘herb’, ‘shrub’, ‘tree’</p> <p><b>Seed_weight_(mg)</b>: seed mass (mg) data derived from the referenced study or Seed Information Database (Royal Botanic Gardens Kew 2008)</p> <p><b>Seed_release_height_(m)</b>: seed releasing height (m) data derived from the referenced study or LEDA (Kleyer et al. 2008) database</p> <p><b>Seed_terminal_velocity_(m/s)</b>: seed terminal velocity (m/s) data derived from the referenced study or LEDA (Kleyer et al. 2008) database</p> <p><b>Maximum_recorded_dispersal_distance_(m)</b>: species’ maximum dispersal distance (m) found in the literature corresponding to a specific dispersal syndrome</p> <p><b>99th_percentile_dispersal_distance_(m)</b>: 99th percentile of a species’ dispersal distance distribution (m) corresponding to a specific dispersal syndrome</p> <p><b>90th_percentile_dispersal_distance_(m)</b>: 90th percentile of a species’ dispersal distance distribution (m) corresponding to a specific dispersal syndrome</p> <p><b>Mean_dispersal_distance_(m)</b>: species’ mean dispersal distance (m) corresponding to a specific dispersal syndrome<b> </b></p> <p><b>Mode_dispersal_distance_(m)</b>: mode of a species’ dispersal distance distribution (m) corresponding to a specific dispersal syndrome</p> <p><b>Median_dispersal_distance_(m)</b>: median of a species’ dispersal distance distribution (m) corresponding to a specific dispersal syndrome</p> <p><b>Maximum_dispersal_distance_(m)</b>: equals to maximum_recorded_dispersal_distance_(m) if given, otherwise equals to 99th_percentile_dispersal_distance_(m) or 90th_percentile_dispersal_distance_(m)</p> <p><b>Calculated_maximum_dispersal_distance_(m)</b>: when no maximum dispersal distance data was available, we estimated maximum dispersal distance using the formula log10(maximum) = 0.795 + 0.984 * log10(mean); if mean dispersal distance was not available, we used the mode or median of a species’ dispersal distance distribution </p> <p><b>Maximum_dispersal_distance_analysis_(m)</b>: data used in the analyses, equals to Maximum_dispersal_distance_(m) or (if maximum was not available) to Calculated_maximum_dispersal_distance_(m)</p> <p><b>Data_type</b>: denotes whether data source was an observational (‘field’) or modeling (‘model’) study</p> <p><b>Region</b>: region of the case study, categories include ‘temperate’ or ‘tropics’</p> <p><b>Reference</b>: reference for a data source</p> </blockquote> <p> </p> </div

Appendix A. Overview of the results for linear models to explain maximum dispersal distances using different combinations of plant traits, and histograms of maximum dispersal distance data for different dispersal syndromes and growth forms.

Overview of the results for linear models to explain maximum dispersal distances using different combinations of plant traits, and histograms of maximum dispersal distance data for different dispersal syndromes and growth forms