Received: 30 June 2021  | Accepted: 19 October 2021 DOI: 10.1111/1365-2664.14079 R E S E A R C H A R T I C L E Diversity for Restoration (D4R): Guiding the selection of tree species and seed sources for climate- resilient restoration of tropical forest landscapes Tobias Fremout1,2  | Evert Thomas2  | Hermann Taedoumg3,4  | Siebe Briers1 | Claudia Elena Gutiérrez- Miranda5 | Carolina Alcázar-C aicedo6  | Antonia Lindau7 | Hubert Mounmemi Kpoumie8  | Barbara Vinceti9  | Chris Kettle9,10  | Marius Ekué4  | Rachel Atkinson2  | Riina Jalonen11  | Hannes Gaisberger9,12  | Stephen Elliott13  | Esther Brechbühler10 | Viviana Ceccarelli2  | Smitha Krishnan14  | Harald Vacik7  | Gabriela Wiederkehr- Guerra9  | Beatriz Salgado- Negret15  | Mailyn Adriana González6 | Wilson Ramírez6 | Luis Gonzalo Moscoso-H iguita16 | Álvaro Vásquez17 | Jessica Cerrón2 | Colin Maycock18  | Bart Muys1 1Division of Forest, Nature and Landscape, KU Leuven, Leuven, Belgium; 2Alliance Bioversity International— CIAT, Lima, Peru; 3Department of Plant Biology, Faculty of Science, University of Yaoundé, Yaoundé, Cameroon; 4Alliance Bioversity International— CIAT, Yaoundé, Cameroon; 5Facultad de Ciencias Forestales, Universidad Nacional Agraria La Molina, Lima, Peru; 6Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogotá, Colombia; 7University of Natural Resources and Life Sciences (BOKU), Vienna, Austria; 8University of Yaoundé, Yaoundé, Cameroon; 9Alliance Bioversity International— CIAT, Maccarese, Italy; 10Department of Environmental System Science, ETH Zurich, Zurich, Switzerland; 11Alliance Bioversity International—C IAT, Serdang, Malaysia; 12Department of Geoinformatics, Paris Lodron University of Salzburg, Salzburg, Austria; 13Environmental Science Research Centre and Forest Restoration Research Unit, Biology Department, Science Faculty, Chiang Mai University, Chiang Mai, Thailand; 14Alliance Bioversity International— CIAT, Bengaluru, India; 15Departamento de Biología, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia; 16Forestpa SAS, Medellín, Colombia; 17Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Medellín, Colombia and 18Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia Correspondence Tobias Fremout Abstract Email: tobias.fremout@gmail.com 1. At the start of the UN Decade of Ecosystem Restoration (2021– 2030), the res- Funding information toration of degraded ecosystems is more than ever a global priority. Tree plant- Forest Ecosystem Restoration Initiative ing will make up a large share of the ambitious restoration commitments made (FERI); Austrian Development Agency; Copernicus (European Union); Vlaamse by countries around the world, but careful planning is needed to select species Interuniversitaire Raad; government of and seed sources that are suitably adapted to present and future restoration site Antioquia, Colombia; Bundesministerium für Wirtschaftliche Zusammenarbeit conditions and that meet the restoration objectives. und Entwicklung, Grant/Award Number: 2. Here we present a scalable and freely available online tool, Diversity for 8121944; Empresas Públicas de Medellín; Ecopetrol; CGIAR fund donors Restoration (D4R), to identify suitable tree species and seed sources for climate- resilient tropical forest landscape restoration. Handling Editor: Emilia Hannula 3. The D4R tool integrates (a) species habitat suitability maps under current and future climatic conditions; (b) analysis of functional trait data, local ecological This is an open access article under the terms of the Creati ve Commo ns Attrib utio n- NonCo mmerc ial- NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non- commercial and no modifications or adaptations are made. © 2021 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. J Appl Ecol. 2021;00:1–16. wileyonlinelibrary.com/journal/jpe  | 1 2  |  Jo urnal of Applied Ecology FREMOUT ET al. knowledge and other species characteristics to score how well species match the restoration site conditions and restoration objectives; (c) optimization of species combinations and abundances considering functional trait diversity or phylogenetic diversity, to foster complementarity between species and to en- sure ecosystem multifunctionality and stability; and (d) development of seed zone maps to guide sourcing of planting material adapted to present and pre- dicted future environmental conditions. We outline the various elements behind the tool and discuss how it fits within the broader restoration planning process, including a review of other existing tools. 4. Synthesis and applications. The Diversity for Restoration tool enables non- expert users to combine species traits, environmental data and climate change models to select tree species and seed sources that best match restoration site condi- tions and restoration objectives. Originally developed for the tropical dry for- ests of Colombia, the tool has now been expanded to the tropical dry forests of northwestern Peru– southern Ecuador and the countries of Burkina Faso and Cameroon, and further expansion is underway. Acknowledging that restoration has a wide range of meanings and goals, our tool is intended to support decision making of anyone interested in tree planting and seed sourcing in tropical forest landscapes, regardless of the purpose or restoration approach. K E Y W O R D S climate change, forest landscape restoration, functional and phylogenetic diversity, functional traits, habitat suitability models, seed sourcing, seed zones, species selection 1  |  INTRODUC TION Thomas et al., 2017). Given that trees are long- lived and play a central role in the functioning of forest landscapes, this selec- In times of unprecedented human pressure on the Earth's planetary tion has important long-l asting ecological and economic conse- boundaries, ecosystem restoration is seen as a fundamental strategy quences. Species selection needs to be tailored to project- specific to overcoming global environmental and socio- economic challenges restoration objectives while maximizing persistence under current (Aronson & Alexander, 2013; Suding et al., 2015). More recently, an and future conditions at the restoration site, also considering local increased emphasis on the interconnectedness of ecosystem health stress factors such as eroded soils or the risk of fire (Brancalion and human health, underlined brutally by the COVID- 19 pandemic, et al., 2020; Reubens et al., 2011; Thomas et al., 2017). In addition, is adding yet another impetus to restoration (Breed et al., 2020; unless every generation is to be planted anew as in some commer- Keesing & Ostfeld, 2021). Many ambitious restoration pledges cial plantations, the planting material of any given species should have been made, such as Initiative 20 × 20 in Latin- America and be genetically diverse enough to form viable, productive popu- AFR100 in Africa, both contributing to the Bonn Challenge which lations capable of regenerating and adapting to climate change aims at initiating the restoration of 350 million hectares of de- (Lowe et al., 2011; Thomas et al., 2014). Hence, the selection of graded lands by 2030. Initiatives like the UN Decade of Ecosystem the most appropriate tree species and seed sources requires inte- Restoration (2021– 2030) and The One Trillion Tree initiative of the gration of different knowledge domains and techniques, such as World Economic Forum build further on these global commitments habitat suitability modelling, functional trait analysis, traditional (FAO, 2020). However, turning political commitments into success- and expert knowledge and assessments of adaptive genetic vari- fully restored landscapes will require careful planning (Brancalion ation (e.g. through provenance trials or genetic marker studies). et al., 2020; Holl & Brancalion, 2020). As it is often difficult for restoration practitioners to integrate A considerable part of global restoration commitments will be such knowledge in their decision making, especially in the tropics achieved through tree planting (Brancalion et al., 2020). An im- where local species richness is high and resources are limited, the portant aspect of planning restoration efforts involving tree plant- knowledge-p ractice gap remains an important constraint to the ing (or direct seeding, we refer to both as ‘tree planting’) is the implementation of diverse and climate- resilient restoration plant- selection of tree species and seed sources that match both resto- ings (Jalonen et al., 2018; Reubens et al., 2011). Consequently, spe- ration objectives and local site requirements (Atkinson et al., 2021; cies selection and seed sourcing decisions are commonly driven FREMOUT ET al. Journal of Applied Ecolo gy   |  3 by availability of planting material, often resulting in the selection including a review of currently available tools, and conclude with of a few well- known, often exotic tree species, rather than those some practical considerations and the way forward. species that best match the restoration site conditions and objec- tives, whereby climate change is typically not taken into account (Atkinson et al., 2021; Jalonen et al., 2018; Valette et al., 2020). 2  |  METHODS INTEGR ATED IN THE This situation constrains the wider use of native tree species di- DECISION SUPPORT TOOL versity in restoration, which would enhance biodiversity and cli- mate change mitigation benefits from restoration efforts. Several 2.1  |  Habitat suitability modelling decision support tools have been developed to guide tree species selection (e.g. Reubens et al., 2011; Van Der Wolf et al., 2017) The tool starts from a regional species pool (with the number of and seed sourcing (e.g. Rossetto et al., 2019; Shryock et al., 2018) species currently varying between 74 for Burkina Faso and 224 for or both (plantevalg.dk), but no tools currently exist that combine Cameroon), consisting of mainly native species but also including a both while also taking into account climate change. limited number of socio- economically important exotic species. To Here we present a scalable online decision support tool: ‘Diversity filter out those species not suited to the present or predicted future for Restoration’ (D4R; www.diver sityf orres torati on.org) that en- environmental conditions at a restoration site, the tool uses habi- ables restoration practitioners to make case-s pecific decisions on tat suitability models, also called species distribution models. These the most appropriate tree species and seed sources. Depending models correlate species presence locations with the environmental on user- defined inputs including restoration site location, local site conditions at these locations to estimate the spatial distribution of conditions (e.g. steep slopes, compacted soils), restoration objec- suitable habitat of species. While such correlative habitat suitabil- tives (e.g. bird conservation, timber production) and climate change ity models have certain limitations, many of them can be overcome scenarios, the tool recommends tree species combinations and seed by applying appropriate modelling techniques and interpretation sourcing areas best aligned with these inputs. Recommended spe- (Araújo & Peterson, 2012). The alternative— use of mechanistic cies combinations are accompanied by species- specific propagation models based on species physiology—t o estimate the impact of cli- information and basic monitoring suggestions (e.g. which variables mate change on species distributions, is impractical when dealing to measure and how frequent, depending on the restoration ob- with tropical forests, which typically have very high species rich- jectives). As forest landscape restoration is interpreted variously ness. Modelling was carried out using an ensemble approach, that by different stakeholders and scientists (Mansourian, 2018), our is, combining the predictions of different algorithms, implemented tool supports decision making of anyone interested in tree plant- in the ‘BiodiversityR’ package for r (Kindt, 2018), which were com- ing in tropical forest landscapes for any purpose regardless of the bined in single consensus distribution maps for each of the species. restoration approach. Use of the word ‘restoration’ in the following Inputs and outputs of the modelling are illustrated in Figure 2, fur- should be interpreted as such. The tool can be readily used by non- ther methodological details are given in Fremout et al. (2020). expert users, as long as they have some understanding about the Calibrated habitat suitability models were projected to future restoration site conditions and restoration objectives. Typical users climatic conditions for the 2050s and 2070s, as predicted by dif- may include restoration project managers, NGOs, local govern- ferent general circulation models (GCMs, also called global climate ments, cooperatives or other institutions carrying out tree planting models) under the representative concentration pathways RCP4.5 initiatives, scientists supporting restoration planning, among others. and RCP8.5, the latter being the worst-c ase scenario and the former Starting from a prototype version developed for the tropical dry a more optimistic scenario. The time horizon and RCP considered by forests (TDFs) of Colombia (Thomas et al., 2017), the tool has now the tool are determined by the user. Selected GCMs differ between been improved and expanded to the TDFs of northwestern Peru and regions, ranging from the AfriClim ensemble (Platts et al., 2014) for southern Ecuador and the countries of Burkina Faso and Cameroon, Burkina Faso to six GCMs in Cameroon. and further scaling to other regions is underway. Figure 1 summa- rizes the tool mechanics, integrating four main elements: (a) habitat suitability modelling to assess the suitability of species to be grown 2.2  |  Species scoring using species trait data at the restoration site under current and future climatic conditions; (b) analysis of functional trait data, local ecological knowledge and After indicating the location of a restoration plot on a map, users other relevant species characteristics to score how well species of the tool are asked to indicate the prevalent local site conditions match the restoration site conditions and restoration objectives; (Table 1), consisting mostly of anthropogenic and water- and soil- (c) optimization of functional diversity or phylogenetic diversity to related stress conditions, and to select the priority restoration ob- foster complementarity between species; and (d) development of jectives (Table 2), the latter of which are grouped in four categories: seed zone maps to guide the sourcing of planting material adapted (a) biodiversity conservation, (b) regulating ecosystem services, (c) to present and expected future environmental conditions. In the fol- agroforestry and commercial uses and (d) traditional uses (Table 2). lowing, we present the various elements behind the tool, illustrate Users have the option to weigh different restoration objective how the tool fits within the broader restoration planning process, categories. 4  |  Jo urnal of Applied Ecology FREMOUT ET al. F I G U R E 1  Schematic overview of the different components of the Diversity for Restoration tool. Tables 1 and 2 and Sections 2.1– 2.4 refer to the corresponding tables and sections of the present paper To score how well candidate species match restoration site between 0.4 and 0.6 as ‘intermediate’, and those higher than 0.6 conditions and restoration objectives, the tool uses various spe- as ‘high’). cies characteristics data, including functional traits, local ecolog- To estimate how well species match the site conditions and res- ical knowledge and expert knowledge, among others, to which toration objectives in a quantitative way, the tool scores species we will refer as ‘traits’ for simplicity. They include mostly func- using a trait- based scoring approach (Figure 3). Based on literature tional traits in the classical sense, that is, morpho- physiological– review and expert judgement, we assigned weights to traits accord- phenological characteristics that indirectly affect the fitness of ing to the expected magnitude of their influence on species' adap- individuals (Violle et al., 2007), but also include (a) local uses (e.g. tation to site conditions or contribution to restoration objectives, firewood), (b) conservation priorities (e.g. IUCN Red List status) ranging from 1 to 5. For example, the trait ‘fodder and forage’ was and (c) information about species' adaptation to site conditions or given a higher weight than ‘rooting depth’ for the restoration ob- ability to contribute to restoration objectives without mention of jective ‘silvopastoral systems’ (Figure 3). In addition, we assigned specific functional traits (e.g. ‘recommended for riverine protec- each trait level with an aptness score, ranging from 0 to 1, with 0.5 tion’). Traits include both categorical (e.g. pollination mode) and corresponding to a ‘neutral’ score. For example, evergreen species continuous variables (e.g. wood density), but given the consider- were given a score of 1 for the objective ‘silvopastoral systems’, be- able intraspecific variation for most of these continuous traits, the cause they provide shade (and possibly fodder) year- round, while latter were also converted into categorical variables (e.g. wood semi- deciduous and deciduous species were given scores of 0.5 and densities lower than 0.4 g/cm3 were classified as ‘low’, those 0 respectively. To avoid overestimating species aptness in the case FREMOUT ET al. Journal of Applied Ecolo gy   |  5 F I G U R E 2  Illustration of habitat suitability modelling for Vitellaria paradoxa (shea tree) in Burkina Faso. Annual precipitation and soil available water capacity are shown here as examples of predictor variables, but note that the models consider a wide range of climate and soil variables. Only presence and absence locations within Burkina Faso are shown, but note that both were selected from a wider geographic extent of missing data, these were given a score of 0. While specific traits 2.3  |  Optimization of functional or can be linked to multiple site conditions and/or restoration objec- phylogenetic diversity tives, the weights assigned to the traits and the scores assigned to the trait levels are specific to particular site conditions or restoration After filtering the regional species pool using habitat suitability objectives. For each of the site conditions and restoration objectives models (Section 2.1) and scoring the retained species using the trait- selected by the user, the tool calculates species aptness scores as based scoring approach (Section 2.2), the tool calculates the recom- the weighted average of the trait-s pecific scores described above. mended relative species abundances (i.e. relative planting densities) The overall match of species to the combination of site conditions by jointly optimizing species aptness scores and functional or phylo- and restoration objectives selected by the user is estimated by aver- genetic diversity. The use of diverse species assemblages has several aging the corresponding aptness scores, giving equal weights to the advantages. First, it has the potential to improve specific ecosystem scores linked to the selected restoration site conditions and resto- functions through complementarity effects, that is, niche differenti- ration objectives respectively. ation and facilitation, and selection effects, that is, high- performing We included around 85 traits in the scoring (exact number de- species are more likely to occur in and dominate more diverse com- pending on the location) and established the relationships between munities (Loreau & Hector, 2001). As a single species is unlikely to these traits and species' contributions to restoration objectives and have high levels of all ecosystem functions, diverse species assem- ability to persist under given site conditions through literature review blages are essential to ensure ecosystem multifunctionality (van der and expert judgement (Tables S1.1 and S1.2, Supporting Information Plas et al., 2016). Furthermore, when species fulfilling similar func- 1). Trait data were sourced from a variety of sources, including scien- tions respond differently to environmental disturbances, the de- tific articles, books and databases such as TRY (Kattge et al., 2020) cline in function of one species may be compensated for by another and the Agroforestree database (Orwa et al., 2009). In the TDFs of species (Mori et al., 2013). In this way, diverse species assemblages northwestern Peru and southern Ecuador, this was complemented by can contribute to the recovery of more stable ecosystem functions. local ecological knowledge on species' uses, conservation status and In addition, more functionally diverse communities are less likely resistance to stress conditions sourced through interviews in local to leave ecological niches unfilled, thus reducing opportunities for communities, which prove to be a more than valuable complement invasive species to establish. Both functional and phylogenetic di- to scientific knowledge (Fremout, Gutiérrez- Miranda, et al., 2021). versity are good predictors of biodiversity— ecosystem function 6  |  Jo urnal of Applied Ecology FREMOUT ET al. TA B L E 1  Site conditions included Type of site condition Site condition in the tool. There is no minimum or Water related Extreme drought maximum number of conditions that Flooding risk needs to be selected; users can skip this Next to a perennial river or waterbody question if none of the listed conditions Irrigated or next to irrigated farmland are prevalent. These conditions are based Soil related Compacted soils on the most important stress conditions Shallow or rocky soils in the regions where the tool is currently Saline soils functional, but additional conditions can Sandy soils and will likely be included in other regions Heavy clay soils Ferralitic soils Eroded soils Degraded soils due to mining or pollution Others Fire Fragmentation Grazing pressure Steep slopes relationships (e.g. Cadotte et al., 2009; Flynn et al., 2011), ecosys- Vander Mijnsbrugge et al., 2010). However, the scale of local adapta- tem multifunctionality (e.g. Gross et al., 2017; Huang et al., 2019), tion in trees is likely much broader (Boshier et al., 2015) than prevail- ecosystem stability (e.g. Cadotte et al., 2012; Hallett et al., 2017) ing seed sourcing practices, which tend to involve seed collection and invasion resistance of the ecosystem being restored (e.g. Funk at very close distances to the planting site (Jalonen et al., 2018). et al., 2008; Qin et al., 2020). The choice between optimizing func- Furthermore, remaining local seed sources are often fragmented, tional or phylogenetic diversity is not trivial. Therefore, we opted reducing their genetic diversity (Vranckx et al., 2012) and increasing to optimize functional diversity by default, while also giving expert the risks of inbreeding and concomitantly poor growth and mortality users the option to maximize phylogenetic diversity as one of the of seedlings (Broadhurst et al., 2006). In the light of ongoing and ac- biodiversity-r elated objectives. celerating climate change, it may also be prudent to supplement local The tool optimizes functional or phylogenetic diversity by choos- provenances with ‘climate- matched’ provenances, that is, where cur- ing relative species abundances (the maximum number of species rent climatic conditions are similar to those anticipated in the future being user- defined) by maximizing functional or phylogenetic dis- at the planting site, also called ‘predictive provenancing’ (e.g. Crowe tance while making sure the average aptness score of the species & Parker, 2008; Gray & Hamann, 2011). assemblage converges on a specific value (Appendix S2.2), using Seed zones, also called seed transfer zones or seed provenance the ‘Select’ package for r (Laughlin et al., 2018). Functional dis- zones, are a useful tool to guide seed sourcing decisions. They are tance between species is calculated with the ‘fd’ package (Laliberté geographic areas in which planting material can be moved freely et al., 2014) as the Gower distance between a set of traits readily while minimizing the risk of reducing population fitness and dis- available for most species (leaf phenology, maximum height, rooting rupting population genetic patterns (Miller et al., 2011). To facil- depth, seed mass, specific leaf area, wood density). Phylogenetic dis- itate climate-r esilient seed sourcing, the D4R tool uses dynamic tance is calculated with the ‘ape’ package (Paradis & Schliep, 2018), seed zones (Kramer & Havens, 2009; Vitt et al., 2010), whose using phylogenetic trees constructed with the ‘V.PhyloMaker’ pack- boundaries can change under climate change. Since genetic dis- age (Jin & Qian, 2019). Recognizing that there are no silver bullet tance between trees within and across populations is explained solutions to species selection, the tool generates three different op- by geographic distance, environmental distance or both (Fremout, tions of recommended species combinations, the first one striking Thomas, Bocanegra-G onzález, et al., 2021; Jiang et al., 2019; Sexton a balance between species aptness scores and functional or phylo- et al., 2014), environmentally homogeneous seed zones were con- genetic diversity, and the two other options putting more focus on structed, while also avoiding large geographic distances between diversity and species aptness respectively. Methodological details locations within the same seed zone, by clustering climate and soil are provided in Appendix S2.2. variables along with longitude and latitude. The optimal number of seed zones is ideally determined by the results of provenance trials (e.g. Crow et al., 2018; Kramer et al., 2015). In the absence of these, 2.4  |  Seed zone maps population genetic data (e.g. Durka et al., 2017; Fremout, Thomas, Taedoumg, et al., 2021) or expert knowledge can be used, as we did Tree planting requires consideration of the provenance(s) of the for the TDFs of Colombia and the other regions where the tool is planting material. A common recommendation is to source locally, functional respectively. To facilitate pragmatic implementation of to ensure adaptation to local environmental conditions and to these seed zones, considering that the logistic capacity of restoration avoid disruption of population genetic patterns (McKay et al., 2005; practitioners in tropical countries is often limited, we constructed a FREMOUT ET al. Journal of Applied Ecolo gy   |  7 TA B L E 2  Restoration objectives included in the tool. These objectives are Type of restoration objective Objective based on the most common restoration objectives and local uses in the regions Biodiversity Bats where the tool is currently functional, but conservation Birds additional restoration objectives can and Endemic woody species will likely be included in other regions Nurse plants Pollinating insects and ants Spectacled bear** Terrestrial mammals Threatened woody species White- tailed deer* White- winged guan** Regulating ecosystem Carbon sequestration services Erosion control Riverine protection: ephemeral streams Riverine protection: perennial streams Soil fertility improvement Agroforestry and Alley cropping commercial uses Biodiesel Charcoal Commercial timber Fibre for paper production Live fences and hedgerows Non- wood products with economic potential Shade tree agroforestry Silvopastoral systems and forage production Windbreaks Traditional uses Cosmetics Cultural uses Dye Fibre Firewood Food Handicrafts Honey Medicinal plants Ornamental species Poison and insect repellent Timber for local use Tools *Only included in Peru– Ecuador and Colombia.; **Only included in Peru–E cuador. single set of seed zones for each target country or ecosystem, appli- recommends sourcing part of the planting material in each of the cable across the tree species considered. Analogous to the habitat future seed zones as predicted by different GCMs. This approach co- suitability maps, we projected the seed zone maps to future climatic incides with the risk- minimizing ‘portfolio approach’ of seed sourcing conditions for each of the selected GCMs (see Section 2.1) under proposed by Crowe and Parker (2008), directly incorporating the un- emission scenarios RCP4.5 and RCP8.5 for the 2050s and 2070s, as certainty of future climate predictions (Figure 5). illustrated in Figure 4. Further methodological details can be found in Fremout, Thomas, Taedoumg, et al. (2021). Using the seed zones outlined above, the tool recommends mix- 3  |  THE ROLE OF THE TOOL IN ing planting material from the seed zone in which the planting site is RESTOR ATION PL ANNING AND DECISION currently located (i.e. local provenancing) with material from areas MAKING currently located in the seed zone anticipated at the planting site under future climatic conditions (i.e. predictive provenancing). While Past forest restoration initiatives have often failed due to various GCMs usually coincide in predicting temperature increases under reasons, such as species- site mismatches, inappropriate silvicul- climate change, the direction of predicted precipitation changes is tural techniques, planting material with a low inter- and intraspe- not always consistent. As a result, GCMs do not always coincide with cific diversity, lack of post- planting maintenance and monitoring, each other in future seed zone projections, in which case the tool lack of benefits for local communities, land tenure security issues, 8  |  Jo urnal of Applied Ecology FREMOUT ET al. F I G U R E 3  Illustration of the trait- based species matching to the restoration objectives ‘erosion control’ (left) and ‘silvopastoral systems’ (right). Trait weights are indicated by shades of blue and aptness scores by green– yellow– red (see legend in the middle), with scores of 0.5 corresponding to a ‘neutral’ score. A hypothetical species aptness score is given for both restoration objectives, calculated using the trait levels indicated with an ‘x’. The calculation is further detailed in Appendix S2.1 (Supporting Information 2) among others (Godefroid et al., 2011; Höhl et al., 2020; Kodikara is also the only tool that includes such a wide range of local site con- et al., 2017; Le et al., 2014). While these experiences provide learn- ditions and restoration objectives. ing opportunities to improve restoration practices, failing restora- As illustrated in Table 3, while the D4R tool supports decisions tion initiatives are likely to diminish the interest and support of local across multiple aspects of the restoration process, several other communities, governments, donors and other stakeholders (Höhl building blocks are crucial for successful restoration. For example, et al., 2020), and time is running short to mitigate the ongoing bio- many countries still have a long way to go to deliver the enormous diversity and climate crisis. Carefully planning restoration efforts is quantities of site-a dapted and genetically diverse seeds that will be therefore crucial, and potential problems should be avoided as much needed to meet ambitious restoration targets (Atkinson et al., 2021; as possible (Brancalion et al., 2020; Thomas et al., 2017). The D4R Jalonen et al., 2018). Without existing tree seed networks that can tool does not tackle all these problems, but supports planning spe- deliver such seeds for a diversity of native species, the use of the cies and seed choices in restoration initiatives once restoration sites D4R tool, and diverse and climate- resilient restoration in general, have been identified, objectives have been agreed upon, and active becomes more challenging (Wiederkehr- Guerra & Gotor, 2020), as planting is among the planned interventions. As such, it complements restoration projects then have to set up such networks themselves. a wide array of existing tools to support decision making in different Another important bottleneck for successful long-t erm restoration stages of the restoration process, recently reviewed by Chazdon and consists of the mismatch between limited and short-t erm funding Guariguata (2018). Table 3 provides an updated overview of avail- available and the long- term funding needed for maintaining trees, able tools, focusing on those that are scalable and ready- to- use. The monitoring and adaptive management (Höhl et al., 2020; Holl & D4R tool is unique in that it is— to the best of our knowledge— the Brancalion, 2020). Importantly, the necessary building blocks are only spatially explicit tool that provides recommendations on both not limited to those listed in Table 3, but also include an enabling the selection of tree species and seed sources while also taking into environment with financial sustainability, protection of land rights account climate change. Adding to this comprehensiveness, the tool and tenure, etc. (Perring et al., 2018). FREMOUT ET al. Journal of Applied Ecolo gy   |  9 F I G U R E 4  Seed zones (n = 15) for the tropical dry forests of northwestern Peru– southern Ecuador under present climatic conditions (panel a) and future climatic conditions for the 2050s under the representative concentration pathways RCP4.5 and RCP8.5 (panels b and c). The seed zones are based on the clustering of climate and soil variables (Fremout, Thomas, Bocanegra-G onzález, et al., 2021). Note that it is possible that specific seed zones disappear under climate change, as is almost the case for the yellow seed zone along the Andean foothills under the RCP8.5 scenario. The future seed zones shown here are those predicted by the HadGEM2- ES model, one of the five selected general circulation models (GCMs), but note that the tool combines the predictions of five GCMs F I G U R E 5  Illustration of the climate- resilient seed sourcing strategy proposed in the tool. The squares represent maps with seed zones indicated in different colours, with case 1 and case 2 depicting two different hypothetical climate change scenarios as predicted by two general circulation models (GCMs), and the pie charts indicating the relative proportion of seeds to be sourced from the different seed zones. The recommended approach involves sourcing 50% of the seeds from the present seed zone, and 50% from the future seed zone(s), the latter of which consist of seed zones 1 and 2 for case 1, and of seed zones 2 and 3 for case 2. This is illustrated here with two GCMs for simplicity, but note that more GCMs were used in the tool 10  |  Jo urnal of Applied Ecology FREMOUT ET al. TA B L E 3  Selected tools and methods to guide decision making in different (chronological) phases of the forest restoration process. For each of the tools/methods, we indicate between brackets the scale (predominantly national/subnational level or local level) and whether it is spatially explicit or not. Tools specifically focused on engaging stakeholders and seeking financing for restoration initiatives are not included (please refer to Chazdon and Guariguata (2018) for this), nor are platforms bundling information of specific restoration projects Type of tool according to phases across the forest restoration process Tool/methodology with short description Tools for identifying readiness and • Restoration Diagnostic (WRI, 2015): a methodology for developing strategies for successful restoration, bottlenecks for restoration based on an evaluation of success factors and identification of policies, incentives or practices to address the missing factors (national or subnational level; not spatially explicit) • Restoration Opportunities Assessment Methodology— ROAM (IUCN & WRI, 2014): a holistic set of methods including methods to evaluate readiness for restoration, identify priority areas and restoration intervention types, quantify costs and benefits and analyse finance and investment options (national or subnational level; spatially explicit) • Atkinson et al. (2021): a five- component indicator system to evaluate national seed supply systems, evaluating readiness and bottlenecks for the supply of large quantities of diverse, locally adapted seeds for climate- resilient restoration (national level; not spatially explicit) Tools for spatial prioritization of • Restoration Opportunities Assessment Methodology (ROAM; IUCN & WRI, 2014): see above areas to restore • Restoration Opportunities Optimization Tool (ROOT; Beatty et al., 2018): a software- based tool that uses information about potential restoration impacts together with spatial prioritization maps to identify priority areas for ecosystem service provision (national or subnational level; spatially explicit) • WePlan- Forests (wepla n- fores ts.org): a web-b ased tool that considers trade-o ffs between carbon sequestration, species- specific extinction reduction benefits, opportunity and establishment costs and five restoration area targets (Strassburg et al., 2019) (national or subnational level; spatially explicit) Tools for assessing ecosystem • Forest Landscape Assessment Tool (FLAT; Ciecko et al., 2016): a methodology to assess forest ecological degradation status baseline conditions and to determine and prioritize restoration needs (local level; spatially explicit) • ENVI Forest health tool (L3Harris Geospatial, 2020): software-b ased forest health assessment using multispectral remote sensing data in ENVI software (typically local level; spatially explicit) • Collect Earth (openfo ris.org/tools/ colle ct- earth.html): a software-b ased tool that enables data collection through visual interpretation of Google Earth imagery, which can be used for assessing ecosystem conditions or monitoring restoration progress (local level; spatially explicit) Tools for deciding on the type of • Restoration Opportunities Assessment Methodology (ROAM; IUCN & WRI, 2014): see above (national or restoration intervention (e.g. subnational level; recommendations for the types of restoration interventions are not spatially explicit) assisted natural regeneration • Crouzeilles et al. (2020): spatial modelling methodology to predict regeneration success in the Atlantic or active planting) Forest in Brazil (subnational level; spatially explicit) • Quanto é Plantar Floresta (quant oeflo resta.escol has.org): web- based tool that estimates the costs and economic returns of different restoration intervention types, for example direct seeding, plantations with 50% eucalypt, agroforestry (subnational level; not spatially explicit) • Greener Land (green er.land): web- based tool that gives recommendations on restoration interventions depending on the site conditions (local level; not spatially explicit) Tools to guide species selection • Diversity for Restoration (diver sityf orres torat ion.org): this paper (local level; spatially explicit) (for initiatives involving active • Useful Tree Species for Eastern Africa and Africa Tree Finder: a web-b ased tool and Android application, planting) respectively, both based on Vegetationmap4Africa (veget ationm ap4a frica.org; Kindt et al., 2015), linking potential natural vegetation (PNV) types with native species and their uses (local level; spatially explicit) • Agroforestry Species Switchboard (apps.worlda grofo restr y.org/produ cts/switc hboard; Kindt et al., 2016): a website bundling links to a wide range of online information sources and databases for thousands of species used in agroforestry and restoration (local level; not spatially explicit) • Multi-c riteria Tree Selection (MCTS) tool (Reubens et al., 2011): An Excel- based multi- criteria decision support tool to select species for land rehabilitation in Ethiopia (local level; not spatially explicit) • plant evalg.dk: a web- based tool for selecting species and seed sources in Denmark (local level; spatially explicit) • Shade tree ICT tool (shadet reead vice.org; Van Der Wolf et al., 2017): a web- based tool to select tree species in cacao and coffee agroforestry systems based on local ecological knowledge (local level; not spatially explicit) • i-T ree species (speci es.itree tools.org/): a web-b ased tool to help urban foresters select the most appropriate tree species based on potential environmental services and geographic area (local level; not spatially explicit) • Tree Species Selector (green ingca nadia nland scape.ca/tree- speci es- selector): a web- based tool to select tree species in urban forestry, with a focus on restoring degraded soils (local level; not spatially explicit) • Select (Laughlin et al., 2018): an r package that can be used to generate species assemblages for restoration, simultaneously converging on average trait values and maximizing functional diversity (local level; not spatially explicit) FREMOUT ET al. Journal of Applied Ecolo gy   |  11 TA B L E 3  (Continued) Type of tool according to phases across the forest restoration process Tool/methodology with short description Tools for supporting seed sourcing • Diversity for Restoration (diver sityfo rres torat ion.org): this paper (local level; spatially explicit) (for initiatives involving active • Seedlot selection tool (seedlo tsele ction tool.org/sst): a web- based tool to help forest managers to match planting) seedlots with planting site based on current or future climatic conditions in the United States (local level; spatially explicit) • Climate Smart Restoration Tool (clima teres torat ionto ol.org/csrt): a web- based tool for mapping current and future seed transfer limits for plant species using climate data in the United States (local level; spatially explicit) • plantevalg.dk: see above (local level; spatially explicit) • Climate Distance Mapper (usgs- werc-s hiny tools.shiny apps.io/Climat e_Dista nce_Mapper; Shryock et al., 2018): a web- based tool to support the selection of seed sources by mapping the multivariate climate distances to the seed sources in the Desert Southwest of the United States (local level; spatially explicit) • Restore and Renew (resto re- and- renew.org.au; Rossetto et al., 2019): a web- based tool for delimiting seed sourcing areas and identifying similar climates under present and future conditions in the southeast of Australia (local level; spatially explicit) • Capfitogen (capfi togen.net; Parra- Quijano et al., 2012): a software- based tool that provides seed zones based on ecogeographical clustering (local level; spatially explicit) • SeedIT (seedit.io): a smartphone application to track, manage and diversify seed collections (local level; spatially explicit only in the sense that it allows recording coordinates) Tools to guide monitoring and • FAO Forest Restoration Monitoring Tool (FAO, 2012): a survey- like template for monitoring restoration adaptive management projects, focused on dryland forests (local level; not spatially explicit) • SER 5- Star Recovery System tool (Gann et al., 2016): a visual methodological tool to record and communicate ecological recovery in restoration projects, using 5 levels of progress (local level; not spatially explicit) • Collect Earth (openf oris.org/tools/c olle ct- earth): see above (local level; spatially explicit); also other environmental monitoring tools available at openf oris.org • Regreening Africa App (regre ening africa.org): a smartphone application to collect data on tree planting/ protecting and tree management by farmers (local level; spatially explicit only in the sense that it allows recording coordinates) • Restor (restor.eco): a web-b ased open data platform to access and share ecological spatial data and to monitor restoration initiatives (local level; spatially explicit) • Sustainability Index for Landscape Restoration (Zamora-C ristales et al., 2020): a methodological framework for monitoring the biophysical and socio-e conomic impacts of landscape restoration through the construction of an index (local level; to be applied across a landscape but results not spatially explicit) provide a starting point based on the best information available and 4  |  PR AC TIC AL CONSIDER ATIONS AND should be discussed with relevant stakeholders including local com- PROSPEC TS munities, assessed in light of what is logistically possible, and ad- justed where necessary. The D4R tool is aligned with the forest landscape restoration (FLR) Once an appropriate combination of species and seed zones has approach, defined as a ‘planned process that aims to regain eco- been identified, restoration planners need to identify at least one logical integrity and enhance human wellbeing in deforested or de- seed source or seed provider in each of the seed zones (more is bet- graded landscapes’ (WWF & IUCN, 2000). Similar to the flexible and ter to increase adaptive potential, but logistics and costs may make pragmatic FLR approach, the tool is meant to support tree planting this unrealistic). To mitigate the issue of seed availability, the tool efforts for various purposes, ranging from biodiversity conservation proposes different options of tree species for a particular set of site to timber production and agroforestry. While the tool does not pro- conditions and restoration objectives (Section 2.3). Furthermore, vide landscape- scale recommendations, it is meant to be run sepa- wherever possible, contact details are provided of people or institu- rately for different land units within the landscape mosaic, as local tions who can provide seeds of selected species from particular seed site conditions and restoration objectives usually differ in different sources. This is currently already implemented for the TDFs of Peru parts of the landscape (Figure 6). Users are free to select the number (Cerrón et al., 2019), and planned for other regions. of species according to their objectives while considering practical Choosing tree species and seed sources are not the only practical and financial limitations, but we do recommend to always use mul- decisions to make when planting trees: other decisions need to be made tiple species for the reasons mentioned in Section 2.3. Another im- on the planting strategy (e.g. planting in nuclei, along contour lines, etc.) portant consideration is that the species recommendations provided and the spatial configuration of the selected species. Similarly, when by the tool should not be seen as a cook book recipe; they merely trees die, a choice needs to be made between replanting and letting 12  |  Jo urnal of Applied Ecology FREMOUT ET al. F I G U R E 6  Illustration of the use of the Diversity for Restoration tool for different land units, with differing site conditions and restoration objectives further community assembly occur naturally, a decision which depends the tool in other countries and ecosystems is underway in western on budgetary flexibility and how dependent the desired ecosystem Ethiopia, northern Thailand, the Sabah state of Malaysia and the services are on the presence of specific species, among other aspects. Western Ghats in India, which will also allow the addition of context- While the tool currently does not include these aspects, we are plan- specific restoration objectives (e.g. conservation of sun bear, orang- ning to include some guidance on them in the future. utan and hornbills in Sabah, Malaysia). While these are all forest D4R is a dynamic and scalable tool, both in terms of the inclu- ecosystems, the tool can be extended to other ecosystems as well. sion of additional restoration objectives and the expansion to other Furthermore, we plan to integrate an economic simulation module to countries. Among the new restoration objectives being rolled out provide users with additional information about which of the possi- are those related to nutrition and food security, and shade tree se- ble species combinations simultaneously maximize the benefit- cost lection in cacao and coffee agroforestry systems. Application of ratio, considering a series of threats (e.g. extreme drought, fire, pests FREMOUT ET al. Journal of Applied Ecolo gy   |  13 and diseases) that may affect a restoration site over a given period DATA AVAIL ABILIT Y S TATEMENT of time. The R scripts behind the tool are available at https://github.com/ Ultimately, the success of the D4R tool (www.diver sityf orrest orat tobias frem out/D4R, data to test the scripts are available via Figshare ion.org) will be measured through its uptake by restoration planners. (https://doi.org/10.6084/m9.figsh are.16764628) (Fremout, Thomas, We hope that the explanation of the mechanics behind the tool can Taedoumg, et al., 2021). contribute to this, as the recommendations of ‘black box’ approaches are less likely to be accepted by users. While the expected usefulness ORCID of the tool has been positively evaluated by both scientists and resto- Tobias Fremout https://orcid.org/0000-0002-0812-3027 ration practitioners (Wiederkehr- Guerra & Gotor, 2020), its application Evert Thomas https://orcid.org/0000-0002-7838-6228 on the ground is only starting (Aping, 2019). One of the main priorities Hermann Taedoumg https://orcid.org/0000-0002-8137-2396 now is therefore to test the tool through the network that has been Carolina Alcázar- Caicedo https://orcid. established in the different regions where the tool is functional. org/0000-0002-9366-8098 Long- term monitoring of restoration plantings based on the recom- Hubert Mounmemi Kpoumie https://orcid. mendations of the D4R tool will be important to improve the quality of org/0000-0003-1920-6116 these recommendations. For example, it remains unclear how well our Barbara Vinceti https://orcid.org/0000-0001-8908-2994 habitat distribution models can predict the realized long-t erm commu- Chris Kettle https://orcid.org/0000-0002-9476-0136 nity composition, as they do not consider biotic interactions and there- Marius Ekué https://orcid.org/0000-0002-5829-6321 fore have their limitations in predicting species co-o ccurrence. While Rachel Atkinson https://orcid.org/0000-0001-8977-5707 the tool allows to maximize functional diversity (Section 2.3), which Riina Jalonen https://orcid.org/0000-0003-1669-9138 is expected to promote niche complementarity and reduce competi- Hannes Gaisberger https://orcid.org/0000-0002-6023-1236 tion between species (Wagg et al., 2017), such long- term monitoring is Stephen Elliott https://orcid.org/0000-0002-5846-3353 needed to better understand how we can predict community assembly Viviana Ceccarelli https://orcid.org/0000-0003-2160-9483 based on habitat suitability models and functional traits. Smitha Krishnan https://orcid.org/0000-0002-2851-6813 Harald Vacik https://orcid.org/0000-0002-5668-6967 ACKNOWLEDG EMENTS Gabriela Wiederkehr-G uerra https://orcid. We thank Camilo Rodríguez, Laura Mendoza and Jaime Tarapues for org/0000-0002-2742-6285 their support in website development and R programming, and Diego Beatriz Salgado- Negret https://orcid.org/0000-0002-3103-9878 Cobos and Andrea Rios from Puntoaparte Editores for the design of Colin Maycock https://orcid.org/0000-0002-4368-2545 Figure 6. We are grateful to all data contributors and collaborators Bart Muys https://orcid.org/0000-0001-9421-527X of the Diversity for Restoration tool, listed at https://www.diver sityf orrest orati on.org/contr ibuto rs- collab orat ors.php. This work received R E FE R E N C E S financial support from the Flemish Interuniversity Council, the German Aping, P. J. (2019). Testing the effectiveness of different restoration interven- Federal Ministry for Economic Cooperation and Development (BMZ), tions of the tropical dry forest in the hidroitunango compensation zone commissioned and administered through the Deutsche Gesellschaft Antioquia, Colombia (MSc thesis). Department of Biology, Faculty of Mathematics and Natural Sciences, University of Hamburg. für Internationale Zusammenarbeit (GIZ) Fund for International Araújo, M. B., & Peterson, A. T. (2012). Uses and misuses of biocli- Agricultural Research (FIA), grant number 8121944, the CGIAR Fund matic envelope modeling. Ecology, 93(7), 1527– 1539. https://doi. Donors (www.cgiar.org/funders), the Austrian Development Agency, org/10.1890/11-1 930.1 the Forest Ecosystem Restoration Initiative (FERI) of the Convention of Aronson, J., & Alexander, S. (2013). Ecosystem restoration is now a global priority: Time to roll up our sleeves. Restoration Ecology, 21(3), 293– Biological Diversity, the Colombian companies Ecopetrol and Empresas 296. https://doi.org/10.1111/rec.12011 Públicas de Medellín, the government of the Colombian department of Atkinson, R. J., Thomas, E., Roscioli, F., Cornelius, J. P., Zamora- Cristales, Antioquia, and the Copernicus project of the European Union. Chiang R., Franco Chuaire, M., Alcázar, C., Mesén, F., Lopez, H., Ipinza, R., Mai University supported Stephen Elliott's work on the manuscript. Donoso, P. J., Gallo, L., Nieto, V., Ugarte, J., Sáenz- Romero, C., Fremout, T., Jalonen, R., Gaisberger, H., Vinceti, B., … Kettle, C. (2021). Seeding resilient restoration: An indicator system for the analysis of tree seed CONFLIC T OF INTERE S T systems. Diversity, 13, 367. https://doi.org/10.3390/d1308 0370 No conflict of interest has been declared by the authors. Beatty, C. R., Raes, L., Vogl, A. L., Hawthorne, P. L., Moraes, M., Saborio, J. L., & Meza-P rado, K. (2018). Landscapes, at your service: Applications of the Restoration Opportunities Optimization Tool (ROOT). IUCN. AUTHORS' CONTRIBUTIONS https://doi.org/10.2305/iucn.ch.2018.17.en T.F., E.T. and B.M. conceived the ideas and designed the methodology; Boshier, D., Broadhurst, L., Cornelius, J., Gallo, L., Koskela, J., Loo, J., T.F., E.T., H.T., S.B., C.E.G.- M., C.A.-C ., A.L. and H.M.K. collected and Petrokofsky, G., & St Clair, B. (2015). Is local best? Examining the compiled trait data; T.F., E.T., H.G., B.V., E.B., V.C. and A.V. compiled evidence for local adaptation in trees and its scale. Environmental Evidence, 4, 20. https://doi.org/10.1186/s13750 - 015- 0046- 3 occurrence data; T.F., E.T., S.B., E.B., V.C. and A.V. contributed to data Brancalion, P. H. S. S., Holl, K. D., & Cruz, S. (2020). Guidance for suc- analysis; T.F. led the writing of the manuscript. All authors contributed cessful tree planting initiatives. Journal of Applied Ecology, 57(12), critically to the drafts and gave final approval for publication. 2349– 2361. https://doi.org/10.1111/1365- 2664.13725 14  |  Jo urnal of Applied Ecology FREMOUT ET al. Breed, M. F., Cross, A. T., Wallace, K., Bradby, K., Flies, E., Goodwin, N., Gonzalo Moscoso- Higuita, L., López- Lavalle, L. A. B., de Carvalho, Jones, M., Orlando, L., Skelly, C., Weinstein, P., & Aronson, J. (2020). D., & Muys, B. (2021). Dynamic seed zones to guide climate-s mart Ecosystem restoration: A public health intervention. EcoHealth. seed sourcing for tropical dry forest restoration in Colombia. Forest https://doi.org/10.1007/s1039 3- 020- 01480- 1 Ecology and Management, 490, 119127. https://doi.org/10.1016/j. Broadhurst, L. M., North, T., & Young, A. G. (2006). Should we be more foreco.2021.119127 critical of remnant seed sources being used for revegetation? Fremout, T., Thomas, E., Gaisberger, H., Van Meerbeek, K., Muenchow, Ecological Management and Restoration, 7(3), 211– 217. https://doi. J., Briers, S., Gutierrez- Miranda, C. E., Marcelo-P eña, J. L., Kindt, org/10.1111/j.1442- 8903.2006.00311.x R., Atkinson, R., Cabrera, O., Espinosa, C. I., Aguirre-M endoza, Z., Cadotte, M. W., Cavender- Bares, J., Tilman, D., & Oakley, T. H. (2009). & Muys, B. (2020). Mapping tree species vulnerability to multi- Using phylogenetic, functional and trait diversity to understand ple threats as a guide to restoration and conservation of tropical patterns of plant community productivity. PLoS ONE, 4(5), e5695. dry forests. Global Change Biology, 26(6), 3552–3 568. https://doi. https://doi.org/10.1371/journ al.pone.0005695 org/10.1111/gcb.15028 Cadotte, M. W., Dinnage, R., & Tilman, D. (2012). Phylogenetic diversity Fremout, T., Thomas, E., Taedoumg, H., Briers, S., Gutiérrez-M iranda, C. promotes ecosystem stability. Ecology, 93(8), S223–S 233. https:// E., Alcázar- Caicedo, C., Lindau, A., Mounmemi Kpoumie, H., Vinceti, doi.org/10.1890/11-0 426.1 B., Kettle, C., Ekué, M., Atkinson, R., Jalonen, R., Gaisberger, H., Cerrón, J., Fremout, T., Atkinson, R., Thomas, E., & Cornelius, J. (2019). Elliott, S., Brechbühler, E., Ceccarelli, V., Krishnan, S., Vacik, H., … Experiencias de restauración y fuentes semilleras en el bosque seco trop- Muys, B. (2021). Test data to run the R scripts behind the Diversity ical del norte del Perú. Bioversity International, World Agroforestry. for Restoration Tool. figshare, https://doi.org/10.6084/m9.figsh https://doi.org/10.13140/ RG.2.2.13126.63040 are.16764628 Chazdon, R. L., & Guariguata, M. R. (2018). Decision support tools for for- Funk, J. L., Cleland, E. E., Suding, K. N., & Zavaleta, E. S. (2008). est landscape restoration: Current status and future outlook (No. 183). Restoration through reassembly: Plant traits and invasion resis- CIFORhttps://doi.org/10.17528/ cifor/ 006792 tance. Trends in Ecology & Evolution, 23(12), 695– 703. https://doi. Ciecko, L., Kimmett, D., Saunders, J., Katz, R., Wolf, K. L., Bazinet, O., org/10.1016/j.tree.2008.07.013 Richardson, J., Brinkley, W., & Blahna, D. J. (2016). Forest Landscape Gann, G. D., McDonald, T., Walder, B., Aronson, J., Nelson, C. R., Jonson, Assessment Tool (FLAT): Rapid assessment for land management. J., Hallett, J. G., Eisenberg, C., Guariguata, M. R., Liu, J., Hua, F., General Technical Report, US Forest Service Echeverría, C., Gonzales, E., Shaw, N., Decleer, K., & Dixon, K. Crouzeilles, R., Beyer, H. L., Monteiro, L. M., Feltran- Barbieri, R., Pessôa, W. (2016). International standards for the practice of ecological A. C. M., Barros, F. S. M., Lindenmayer, D. B., Lino, E. D. S. M., Grelle, restoration—I ncluding principles and key concepts. Restoration C. E. V., Chazdon, R. L., Matsumoto, M., Rosa, M., Latawiec, A. E., Ecology, 27, S1–S 46. & Strassburg, B. B. N. (2020). Achieving cost- effective landscape- Godefroid, S., Piazza, C., Rossi, G., Buord, S., Stevens, A. D., Aguraiuja, R., scale forest restoration through targeted natural regeneration. Cowell, C., Weekley, C. W., Vogg, G., Iriondo, J. M., Johnson, I., Dixon, Conservation Letters, 13(3), e12709. https://doi.org/10.1111/ B., Gordon, D., Magnanon, S., Valentin, B., Bjureke, K., Koopman, conl.12709 R., Vicens, M., Virevaire, M., & Vanderborght, T. (2011). How suc- Crow, T. M., Albeke, S. E., Buerkle, C. A., & Hufford, K. M. (2018). cessful are plant species reintroductions? Biological Conservation, Provisional methods to guide species-s pecific seed transfer 144(2), 672–6 82. https://doi.org/10.1016/j.biocon.2010.10.003 in ecological restoration. Ecosphere, 9(1), e02059. https://doi. Gray, L. K., & Hamann, A. (2011). Strategies for reforestation under un- org/10.1002/ecs2.2059 certain future climates: Guidelines for Alberta, Canada. PLoS ONE, Crowe, K. A., & Parker, W. H. (2008). Using portfolio theory to guide 6(8), e22977. https://doi.org/10.1371/journ al.pone.0022977 reforestation and restoration under climate change scenarios. Gross, N., Bagousse- Pinguet, Y. L., Liancourt, P., Berdugo, M., Gotelli, N. Climatic Change, 89(3–4 ), 355– 370. https://doi.org/10.1007/s1058 J., & Maestre, F. T. (2017). Functional trait diversity maximizes eco- 4-0 07-9 373-x system multifunctionality. Nature Ecology & Evolution, 1(5), 0132. Durka, W., Michalski, S. G., Berendzen, K. W., Bossdorf, O., Bucharova, https://doi.org/10.1038/s41559 - 017- 0132 A., Hermann, J.- M., Hölzel, N., & Kollmann, J. (2017). Genetic differ- Hallett, L. M., Stein, C., & Suding, K. N. (2017). Functional diversity in- entiation within multiple common grassland plants supports seed creases ecological stability in a grazed grassland. Oecologia, 183(3), transfer zones for ecological restoration. Journal of Applied Ecology, 831– 840. https://doi.org/10.1007/s0044 2- 016- 3802- 3 54(1), 116– 126. https://doi.org/10.1111/1365- 2664.12636 Höhl, M., Ahimbisibwe, V., Stanturf, J. A., Elsasser, P., Kleine, M., & Bolte, FAO. (2012). Forest restoration monitoring tool. Draft version for field test. A. (2020). Forest landscape restoration—W hat generates failure Rome, Italy. Retrieved from http://www.fao.org/susta inable -f ores and success? Forests, 11, 938. https://doi.org/10.3390/F11090 938 t-m anag ement/t oolb ox/tools/t ool-d etail /en/c/23327 6/ Holl, K. D., & Brancalion, P. H. S. (2020). Tree planting is not a simple FAO. (2020). Restoring the Earth—T he next decade (Vol. 71). FAO. https:// solution. Science, 368(6491), 580– 581. https://doi.org/10.1126/ doi.org/10.4060/cb1600en scien ce.aba8232 Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I., & Naeem, S. (2011). Huang, X., Su, J., Li, S., Liu, W., & Lang, X. (2019). Functional diversity Functional and phylogenetic diversity as predictors of biodiversity drives ecosystem multifunctionality in a Pinus yunnanensis nat- - ecosystem function relationships. Ecology, 92(8), 1573– 1581. ural secondary forest. Scientific Reports, 9, 6979. https://doi. https://doi.org/10.1890/10- 1245.1 org/10.1038/s4159 8-0 19-4 3475- 1 Fremout, T., Gutiérrez- Miranda, C. E., Briers, S., Marcelo- Peña, J. L., IUCN & WRI. (2014). A guide to the Restoration Opportunities Assessment Cueva- Ortiz, E., Linares-P alomino, R., La Torre- Cuadros, M. D. L. Methodology (ROAM): Assessing forest landscape restoration opportu- Á., Chang-R uíz, J. C., Villegas- Gómez, T. L., Acosta-F lota, A. H., nities at the national or sub-n ational level. Working Paper (Road- test Plouvier, D., Atkinson, R., Charcape-R avelo, M., Aguirre- Mendoza, edition). IUCN. Z., Muys, B., & Thomas, E. (2021). The value of local ecological Jalonen, R., Valette, M., Boshier, D., Duminil, J., & Thomas, E. (2018). knowledge to guide tree species selection in tropical dry for- Forest and landscape restoration severely constrained by a lack est restoration. Restoration Ecology, 29(4), e13347. https://doi. of attention to the quantity and quality of tree seed: Insights org/10.1111/rec.13347 from a global survey. Conservation Letters, 11, e12424. https://doi. Fremout, T., Thomas, E., Bocanegra-G onzález, K. T., Aguirre-M orales, org/10.1111/conl.12424 C. A., Morillo-P az, A. T., Atkinson, R., Kettle, C., González- M., R., Jiang, S., Luo, M.- X., Gao, R.- H., Zhang, W., Yang, Y.-Z ., Li, Y.- J., & Liao, Alcázar-C aicedo, C., González, M. A., Gil- Tobón, C., Gutiérrez, J. P., P.- C. (2019). Isolation-b y- environment as a driver of genetic FREMOUT ET al. Journal of Applied Ecolo gy   |  15 differentiation among populations of the only broad- leaved ever- of restoration. Restoration Ecology, 13(3), 432– 440. https://doi. green shrub Ammopiptanthus mongolicus in Asian temperate des- org/10.1111/j.1526- 100X.2005.00058.x erts. Scientific Reports, 9(1), 12008. https://doi.org/10.1038/s4159 Miller, S. A., Bartow, A., Gisler, M., Ward, K., Young, A. S., & Kaye, T. 8- 019- 48472 -y N. (2011). Can an ecoregion serve as a seed transfer zone? Jin, Y., & Qian, H. (2019). V.PhyloMaker: An R package that can generate Evidence from a common garden study with five native spe- very large phylogenies for vascular plants. Ecography, 42(8), 1353– cies. Restoration Ecology, 19(201), 268– 276. https://doi. 1359. https://doi.org/10.1111/ecog.04434 org/10.1111/j.1526-1 00X.2010.00702.x Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Mori, A. S., Furukawa, T., & Sasaki, T. (2013). Response diversity de- Tautenhahn, S., Werner, G. D. A., Aakala, T., Abedi, M., Acosta, A. T. termines the resilience of ecosystems to environmental change. R., Adamidis, G. C., Adamson, K., Aiba, M., Albert, C. H., Alcántara, Biological Reviews, 88(2), 349–3 64. https://doi.org/10.1111/ J. M., Alcázar C, C., Aleixo, I., Ali, H., … Wirth, C. (2020). TRY plant brv.12004 trait database—E nhanced coverage and open access. Global Change Orwa, C., Mutua, A., Kindt, R., Jamnadass, R., & Anthony, S. (2009). Biology, 26(1), 119– 188. https://doi.org/10.1111/gcb.14904 Agroforestree Database: A tree reference and selection guide version Keesing, F., & Ostfeld, R. S. (2021). Impacts of biodiversity and biodiver- 4.0. https://doi.org/10.1007/978-9 4- 007- 5628- 1_11 sity loss on zoonotic diseases. Proceedings of the National Academy Paradis, E., & Schliep, K. (2018). ape 5.0: An environment for modern of Sciences of the United States of America, 118(17), 1–4 . https://doi. phylogenetics and evolutionary analyses in R. Bioinformatics, 35, org/10.1073/pnas.20235 40118 526–5 28. https://doi.org/10.1093/bioinf ormat ics/bty633 Kindt, R. (2018). Ensemble species distribution modelling with trans- Parra- Quijano, M., Iriondo, J. M., & Torres, E. (2012). Ecogeographical formed suitability values. Environmental Modelling & Software, 100, land characterization maps as a tool for assessing plant adaptation 136–1 45. https://doi.org/10.1016/j.envsof t.2017.11.009 and their implications in agrobiodiversity studies. Genetic Resources Kindt, R., Blanchet, F. G., Legendre, P., Minchin, P. R., O'Hara, R. B., and Crop Evolution, 59(2), 205– 217. https://doi.org/10.1007/s1072 Simpson, G. V., Solymos, P., Stevens, M. H. H., & Wagner, H. (2016). 2-0 11- 9676- 7 Agroforestry species switchboard. Retrieved from http://apps.world Perring, M. P., Erickson, T. E., & Brancalion, P. H. S. (2018). Rocketing agrofo rest ry.org/produ cts/switch board restoration: Enabling the upscaling of ecological restoration in the Kindt, R., Graudal, L., Jamnadass, R., Lillesø, J.- P.-B ., Orwa, C., & van Anthropocene. Restoration Ecology, 26(6), 1017–1 023. https://doi. Breugel, P. (2015). Usefult tree species for Eastern Africa: A species org/10.1111/rec.12871 selection tool based on the VECEA map. Version 2.0. Retrieved from Platts, P. J., Omeny, P. A., & Marchant, R. (2014). AFRICLIM: High- https://veget ation map4af rica.org/ resolution climate projections for ecological applications in Africa. Kodikara, K. A. S., Mukherjee, N., Jayatissa, L. P., Dahdouh- Guebas, F., & African Journal of Ecology, 53, 103–1 08. https://doi.org/10.1111/ Koedam, N. (2017). Have mangrove restoration projects worked? aje.12180 An in-d epth study in Sri Lanka. Restoration Ecology, 25(5), 705– 716. Qin, T. J., Zhou, J., Sun, Y., Müller- Schärer, H., Luo, F. L., Dong, B. C., Li, https://doi.org/10.1111/rec.12492 H. L., & Yu, F. H. (2020). Phylogenetic diversity is a better predictor Kramer, A. T., & Havens, K. (2009). Plant conservation genetics in a of wetland community resistance to Alternanthera philoxeroides in- changing world. Trends in Plant Science, 14(11), 599–6 07. https:// vasion than species richness. Plant Biology, 22(4), 591–5 99. https:// doi.org/10.1016/j.tplant s.2009.08.005 doi.org/10.1111/plb.13101 Kramer, A. T., Larkin, D. J., & Fant, J. B. (2015). Assessing potential seed Reubens, B., Moeremans, C., Poesen, J., Nyssen, J., Tewoldeberhan, transfer zones for five forb species from the Great Basin Floristic S., Franzel, S., Deckers, J., Orwa, C., & Muys, B. (2011). Tree spe- Region, USA. Natural Areas Journal, 35(1), 174– 188. https://doi. cies selection for land rehabilitation in Ethiopia: From fragmented org/10.3375/043.035.0119 knowledge to an integrated multi- criteria decision approach. L3Harris Geospatial. (2020). Forest health tool. Retrieved from https:// Agroforestry Systems, 82(3), 303– 330. https://doi.org/10.1007/ www.l3har risge ospat ial.com/docs/Fores tHeal thTool.html s1045 7- 011- 9381- 8 Laliberté, E., Legendre, P., & Shipley, B. (2014). FD: Measuring functional Rossetto, M., Bragg, J., Kilian, A., McPherson, H., van der Merwe, M., diversity from multiple traits, and other tools for functional ecology. R & Wilson, P. D. (2019). Restore and renew: A genomics-e ra frame- package version 1.0-1 2. work for species provenance delimitation. Restoration Ecology, Laughlin, D. C., Chalmandrier, L., Joshi, C., Renton, M., Dwyer, J. M., & 27(3), 538– 548. https://doi.org/10.1111/rec.12898 Funk, J. L. (2018). Generating species assemblages for restoration Sexton, J. P., Hangartner, S. B., & Hoffmann, A. A. (2014). Genetic iso- and experimentation: A new method that can simultaneously con- lation by environment or distance: Which pattern of gene flow is verge on average trait values and maximize functional diversity. most common? Evolution, 68(1), 1– 15. https://doi.org/10.1111/ Methods in Ecology and Evolution, 9(7), 1764– 1771. https://doi. evo.12258 org/10.1111/2041-2 10X.13023 Shryock, D. F., DeFalco, L. A., & Esque, T. C. (2018). Spatial decision- Le, H. D., Smith, C., & Herbohn, J. (2014). What drives the success of support tools to guide restoration and seed-s ourcing in the Desert reforestation projects in tropical developing countries? The case Southwest. Ecosphere, 9(10), e02453. https://doi.org/10.1002/ of the Philippines. Global Environmental Change, 24(1), 334–3 48. ecs2.2453 https://doi.org/10.1016/j.gloen vcha.2013.09.010 Strassburg, B. B. N., Beyer, H. L., Crouzeilles, R., Iribarrem, A., Barros, F., Loreau, M., & Hector, A. (2001). Partitioning selection and comple- de Siqueira, M. F., Sánchez- Tapia, A., Balmford, A., Sansevero, J. B. mentarity in biodiversity experiments. Nature, 412(6842), 72–7 6. B., Brancalion, P. H. S., Broadbent, E. N., Chazdon, R. L., Filho, A. https://doi.org/10.1038/35083573 O., Gardner, T. A., Gordon, A., Latawiec, A., Loyola, R., Metzger, J. Lowe, A. J., Hoffmann, A. A., Sgro, C. M., Sgrò, C. M., Lowe, A. J., Hoffmann, P., Mills, M., … Uriarte, M. (2019). Strategic approaches to restoring A. A., & Sgro, C. M. (2011). Building evolutionary resilience for con- ecosystems can triple conservation gains and halve costs. Nature serving biodiversity under climate change. Evolutionary Applications, Ecology & Evolution, 3(January), 62–7 0. https://doi.org/10.1038/ 4(2), 326– 337. https://doi.org/10.1111/j.1752- 4571.2010.00157.x s41559 -0 18- 0743-8 Mansourian, S. (2018). In the eye of the beholder: Reconciling inter- Suding, K., Higgs, E., Palmer, M., Callicott, J. B., Anderson, C. B., Baker, pretations of forest landscape restoration. Land Degradation and M., Gutrich, J. J., Hondula, K. L., Lafevor, M. C., Larson, B. M. H., Development, 29(9), 2888– 2898. https://doi.org/10.1002/ldr.3014 Randall, A., Ruhl, J. B., & Schwartz, K. Z. S. (2015). Committing to McKay, J. K., Christian, C. E., Harrison, S., & Rice, K. J. (2005). ‘How local is ecological restoration. Science, 348(6235), 638– 640. https://doi. local?’—A review of practical and conceptual issues in the genetics org/10.1126/scien ce.aaa4216 16  |  Jo urnal of Applied Ecology FREMOUT ET al. Thomas, E., Alcazar, C., Moscoso, H. L. G., Vasquez, A., Osorio, L. F., through habitat fragmentation. Conservation Biology, 26(2), 228– Salgado-N egret, B., Gonzalez, M., Parra, M., Bozzano, M., Loo, 237. https://doi.org/10.1111/j.1523-1 739.2011.01778.x J., Jalonen, R., & Ramirez, W. (2017). The importance of species Wagg, C., Ebeling, A., Roscher, C., Ravenek, J., Bachmann, D., Eisenhauer, selection and seed sourcing in forest restoration for enhancing N., Mommer, L., Buchmann, N., Hillebrand, H., Schmid, B., & Weisser, adaptive potential to climate change: Colombian tropical dry for- W. W. (2017). Functional trait dissimilarity drives both species com- est as a model. In L. Rodríguez & I. Anderson (Eds.), CBD Technical plementarity and competitive disparity. Functional Ecology, 31(12), series N° 89: The lima declaration on biodiversity and climate change: 2320–2 329. https://doi.org/10.1111/1365- 2435.12945 Contributions from science to policy for sustainable development (pp. Wiederkehr-G uerra, G., & Gotor, E. (2020). Evaluation report: An as- 122–1 32). Convention on Biological Diversity. sessment of the Diversity For Restoration (D4R) tool. Retrieved from Thomas, E., Jalonen, R., Loo, J., Boshier, D., Gallo, L., Cavers, S., https://hdl.handle.net/10568/1 11078 Bordács, S., Smith, P., & Bozzano, M. (2014). Genetic consider- WRI. (2015). The restoration diagnostic: A method for developing forest ations in ecosystem restoration using native tree species. Forest landscape restoration strategies by rapidly assessing the status of key Ecology and Management, 333, 66– 75. https://doi.org/10.1016/j. success factors. WRI. foreco.2014.07.015 WWF and IUCN. (2000). Minutes of the forests reborn workshop. Segovia, Valette, M., Vinceti, B., Gregorio, N., Bailey, A., Thomas, E., & Jalonen, Spain. R. (2020). Beyond fixes that fail: Identifying sustainable improve- Zamora-C ristales, R., Herrador, D., Cuellar, N., Díaz, O., Kandel, S., ments to tree seed supply and farmer participation in forest and Quezada, J., de Larios, S., Molina, G., Rivera, M., Ramírez, W. M., landscape restoration projects. Ecology and Society, 25(4), 30. Jimenez, A., Flores, E., Chuaire, M. F., Lomeli, L. G., & Vergara, W. https://doi.org/10.5751/ES-1 2032 -2 50430 (2020). Sustainability index for landscape restoration. A tool for mon- van der Plas, F., Manning, P., Allan, E., Scherer- Lorenzen, M., Verheyen, itoring the biophysical and socioeconomic impacts of landscape resto- K., Wirth, C., Zavala, M. A., Hector, A., Ampoorter, E., Baeten, L., ration. World Resources Institute. Barbaro, L., Bauhus, J., Benavides, R., Benneter, A., Berthold, F., Bonal, D., Bouriaud, O., Bruelheide, H., Bussotti, F., … Fischer, M. (2016). Jack- of- all-t rades effects drive biodiversity- ecosystem SUPPORTING INFORMATION multifunctionality relationships in European forests. Nature Additional supporting information may be found in the online ver- Communications, 7, 11109. https://doi.org/10.1038/ncomm s11109 sion of the article at the publisher’s website. Van Der Wolf, J., Gram, G., Bukomeko, H., Mukasa, D., Giller, O., Kirabo, E., Angebault, C., Vaast, P., Asaré, R., & Jassogne, L. (2017). The shade tree advice tool an ICT solution to advise coffee and cocoa farmers on shade tree selection. CCAFS Info Note. https://doi. How to cite this article: Fremout, T., Thomas, E., Taedoumg, org/10.13140/ RG.2.2.25488.40960 H., Briers, S., Gutiérrez- Miranda, C. E., Alcázar- Caicedo, C., Vander Mijnsbrugge, K., Bischoff, A., & Smith, B. (2010). A question of or- Lindau, A., Mounmemi Kpoumie, H., Vinceti, B., Kettle, C., igin: Where and how to collect seed for ecological restoration. Basic and Applied Ecology, 11(4), 300– 311. https://doi.org/10.1016/j. Ekué, M., Atkinson, R., Jalonen, R., Gaisberger, H., Elliott, S., baae.2009.09.002 Brechbühler, E., Ceccarelli, V., Krishnan, S., Vacik, H., … Muys, Violle, C., Navas, M.- L., Vile, D., Kazakou, E., Fortunel, C., Hummel, I., & B. (2021). Diversity for Restoration (D4R): Guiding the Garnier, E. (2007). Let the concept of trait be functional! Oikos, 116(5), selection of tree species and seed sources for climate- 882–8 92. https://doi.org/10.1111/j.0030-1 299.2007.15559.x resilient restoration of tropical forest landscapes. Journal of Vitt, P., Havens, K., Kramer, A. T., Sollenberger, D., & Yates, E. (2010). Assisted migration of plants: Changes in latitudes, changes Applied Ecology, 00, 1– 16. https://doi. in attitudes. Biological Conservation, 143, 18– 27. https://doi. org/10.1111/1365-2 664.14079 org/10.1016/j.biocon.2009.08.015 Vranckx, G., Jacquemyn, H., Muys, B., & Honnay, O. (2012). Meta- analysis of susceptibility of woody plants to loss of genetic diversity