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The methodological framework for simulating the dispersal of bryophytes under changing climate conditions is presented in Fig. 4. A grid of pixel-specific environmental conditions and dispersal kernels, combining information on species dispersal traits, local wind conditions, as well as landscape features affecting dispersal by wind, is generated and used as input in simulations of species dispersal in the landscape under changing climate conditions.
Fig. 4: Overview of workflow implemented in the present study to integrate mechanistic dispersal kernels and correlative climatic suitability models in simulations of future wind-dispersed species distributions under climate change.

Species distribution data (left) are combined with climatic variables to produce climatic suitability models that are calibrated under present and projected under future climatic conditions (Part 1) and used to build mechanistic dispersal models (Part 2). The latter combine species intrinsic features (spore settling velocity Vt and release height Z0) and extrinsic environmental features (mean horizontal wind speed Ū and canopy height h) to generate maps of spatially explicit dispersal kernels. Climatic suitability and dispersal kernel maps, updated at regular intervals, are finally combined to parameterize simulations of dynamic range shifts under changing climatic conditions (Part 3).

Full size image

Data sampling
The European bryophyte flora includes 1817 native or naturalized species41. Because information on bryophyte species distribution is scarce and very heterogeneous, challenging the application of climatic suitability models42, we selected 10 species based upon their representativeness for each of the four main biogeographic elements (i.e., groups of species sharing similar distribution patterns), namely the Arctic-Alpine, Atlantic, Mediterranean, and wide-temperate elements (Supplementary Table 2). For each of these species, we downloaded data from the Global Biodiversity Information Facility (https://www.gbif.org). We excluded data collected before 1960, which represented, on average, 41 ± 12% of the data available, for two reasons. First, old records often lack sufficiently precise location information. Second, we wanted to avoid a potential mismatch between old observations and current climate conditions used for modeling. To complete these data and generate a dataset across the entire range of each species in Europe, we specifically performed a thorough literature review to document their occurrence from more than 600 sources. Only points that were separated by at least 0.1° from each other were subsequently retained for modeling (“ecospat.occ.desaggregation” function in Ecospat 3.143) to avoid sampling bias and reduce the risk of spatial autocorrelation. Altogether, the number of observations available for each species ranged between 55 and 34,035 (database available from Figshare, https://doi.org/10.6084/m9.figshare.8289650).
Average spore diameter was recorded for each species from Zanatta et al.44 and references therein. Species unknown to produce sporophytes were assigned a spore size of 150 µm to take dispersal through larger asexual propagules into account. Spore settling velocities Vt and release height (0.03, 1 and 10 m, which roughly correspond to habitat preferences for ground-dwelling, saxicolous, and epiphytic species, respectively) were determined for each species (Supplementary Table 2) following Zanatta et al.44.
Nineteen bioclimatic variables, averaged over the period from 1970 to 2000, were retrieved from WorldClim 1.4 at a resolution of 30 arc-seconds45. Although snow is an important driver of species distributions in Arctic regions46, the lack of sufficiently detailed information on snow precipitation across Europe prevented us from implementing this variable.
Given the spatial grain of our study, the hypothesis that some species will persist in small microhabitats, where temperatures can be cooler and humidity higher than in the surrounding environment, cannot be rejected. Data at finer scales for both present and future conditions would therefore be desirable47. Recently developed methods to generate fine-grained climatic data taking into account microclimatic effects modulated by microtopographic variation in the terrain, vegetation cover and ground properties using energy balance equations cannot, however, yet be implemented across large spatial scales48.
For future climate conditions, a wide range of GCMs have been described and their variation represents the largest source of uncertainty in future range prediction studies49. No criterion exists to evaluate GCMs, whose performance may vary among regions and variables50. Due to computational constrains associated with our migration simulations (see below), we followed Didersky et al.51. and selected two GCMs that reflected the highest and lowest levels of predicted changes due to climate change for two angiosperm species in Europe50, namely MPI-ESM-LR52 and HadGem2-ES53. For each GCM, we analyzed two climate change scenarios. These scenarios are expressed by the representative concentration pathways (RCPs), using values comparing the level of radiative forcing between the preindustrial era and 2100. The moderate scenario RCP4.5 assumes 650 ppm CO2 and 1.0–2.6 °C increase by 2100, and refers to AR4 guideline scenario B1 of IPCC AR4 guidelines. The pessimistic scenario RCP8.5 assumes 1350 ppm CO2 and 2.6–4.8 °C increase by 2100, and refers to A1F1 scenario of IPCC AR4 guidelines54. Climatic data for each GCM and each RCP were averaged for each of the four time periods considered, i.e., 2010–2020, 2020–2030, 2030–2040 and 2040–2050.
Monthly average and daily maximum wind speeds measured at 10 m as well as predicted wind speeds for the same ten-year time periods between 2010 and 2050, were computed from EURO-CORDEX (https://euro-cordex.net). Canopy height data were obtained from the global scale mapping of canopy height and biomass at a 1-km spatial resolution55. Wind speed and canopy height were sampled for each pixel and each time-slice to generate kernel maps through time (see below).
Deriving climatic suitability maps
The correlation among the 19 bioclimatic variables was computed from 50,000 random points. To avoid multicollinearity, five bioclimatic variables with a Pearson correlation value of R 10 km from a potential source could be colonized by LDD. The maximum LDD distance was set to unlimited based on phylogeographic evidence39. Following Robledo-Arnuncio et al.31, we employed the results of previous Approximate Bayesian Computation methods for LDD inference from genetic structure data in bryophytes39,77 to define the range of LDD probability values, set to 0, 10−4, 10−3, 10−2 and 10−1.
Migclim simulations
We modeled the dispersal of a species under a climate change scenario over a period of 40 years, from 2010 to 2050. Starting with an initial distribution for the year 2010, the climatic suitability of cells was updated every 10 years to reflect the projected changes in climatic conditions under the considered climate change scenario. Since our simulations run over 40 years, we need four different climatic suitability maps. The wind layers were updated at the same 10 years intervals as the climatic data to produce series of spatially and temporally explicit kernel maps. We assume that our species disperse once a year, and hence, our simulations performed a total of 40 dispersal steps between 2010 and 2050. For each 10 years climatic period, pixels were identified as potentially suitable based on the binarized climatic suitability model projections. While climatic suitability thus drove colonization probability, a recent study raised the intriguing idea that spread rates at the migration front increase as climatic suitability decreases as a response to the need to seek for more suitable habitats78. In bryophytes, however, such a mechanism would be unlikely as inadequate resources and investment in environmental stress defence typically result in shifts from sexual to asexual reproduction79.
For each species, we ran a sensitivity analysis by testing the impact of variation of the free parameters described above: two values of horizontal windspeed Ū (monthly average and daily maximum), three values of spore release height Z0 (0.03, 1 and 10 m), and four values of LDD probabilities (see above). For each parameter combination, 30 MigClim replicates were performed.
We computed the ratio between the predicted loss of suitable area (fraction of initially suitable cells that became unsuitable by 2050) and the simulated effective colonization rate (fraction of newly suitable cells by 2050 that were effectively colonized) using two extreme values of the LDD probability range, that is, 0 and 0.1.
To determine the time-lag of the colonization of newly suitable habitats, the analyses were run for 500 years, keeping the environmental parameters at their 2050 values.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

Drosophila rearing
The starting colony of D. suzukii was collected from wild Rubus sp. and Fragaria sp. fruits in various sites in Switzerland in 201523. The flies from the initial collection are described molecularly by Fraimout et al.43. The starting colony of D. melanogaster and D. simulans were obtained from laboratory colonies of INRA (Sophia-Antipolis, France) in 2015 and 2019, respectively. The general rearing of flies was done in plastic tubes (5 cm diameter, 10 cm height) containing approximately 10 g of artificial diet (Formula 4-24 medium, Carolina Biological SupplyCo., Burlington, NC), 40 ml of methyl-4-hydroxylbenzoate solution (1.43 g/L) to inhibit fungal growth, and a few grains of commercial instant dry yeast. The tubes were kept in growth chambers at 22 ± 2 °C, 60% ± 10% RH, and a 16 h photoperiod (hereafter called general rearing conditions). To collect eggs and resulting larvae on different nutritive media (i.e., fresh and decomposing fruits or artificial diet) for the below-described parasitoid rearing and experiments with parasitoids, some adult flies were kept in gauze cages (BugDorm-4F4545) at general rearing conditions. They were fed with sugar water provided on dental cotton rolls and dried instant yeast, additional water was provided on cellulose paper. The nutritive media were exposed to adult flies when needed.
Parasitoid rearing
The starting colonies of G. cf. brasiliensis were obtained during surveys in Asia from 2015–2017 and names to describe their origin are based on the collection sites described by Girod et al.19: Dali, Fumin, Kunming, Shiping, and Kunming—Xining temple (Xining in this study) in the Yunnan Province of China, as well as Hasuike (Nagano) and Tokyo—Naganuma park (actually on the territory of Hachioji but named Tokyo in this study) in Japan. The parasitoids were reared in the quarantine laboratory at CABI-Switzerland (Delémont, Switzerland) separated by origin in gauze cages (BugDorm-4F4545) to prevent them from interbreeding. The general rearing was done on D. suzukii larvae feeding on blueberries as described by Girod et al.19, with the difference that fruits were only exposed for 24 h to D. suzukii for oviposition. The environmental parameters of the quarantine chamber were the above-described general rearing conditions. Up to 50 adult wasps were kept in transparent plastic containers (9 cm diameter, 5 cm height) inside each gauze cage. An Eppendorf tube with a wet cellulose paper was added as a water source and the container was closed with a foam plug on which a drop of honey was placed as food source. Six fresh blueberries, which were placed 24 h before in the D. suzukii rearing cages to collect eggs, were added every 2–3 days to each container with adults to allow for parasitism of young fly larvae. After the exposure to the wasps, infested fruits were removed from the containers and kept in clear plastic tubes (5 cm diameter, 10 cm height) with a filter paper at the bottom to absorb leaking fruit juice. Every 2–3 days, the presence of newly hatched wasps was checked among rearing tubes and adult wasps were transferred to the oviposition containers.
Molecular characterization
The molecular characterization was performed on (1) individuals originating from the field (nine locations from five provinces in China and three locations from three prefectures of the Honshu island in Japan), (2) the derived laboratory strains and (3) individuals used for the experiments (Table S2). Two molecular markers were used, the mitochondrial coding gene Cytochrome Oxidase subunit 1 (COI) and the nuclear region Internal Transcripted Spacer 2 (ITS2). Both were previously used to characterize Ganaspis individuals from Eastern Asia22,23 and elsewhere.
The DNA was extracted in a total of 30 µl using either the prepGEM Insect kit (Zygem) (3 h at 75 °C and 5 min at 95 °C), or the QuickExtract DNA Extraction Solution (n°QE09050, Lucigen) (15 min at 65 °C and 2 min at 98 °C). For both molecular markers (COI and ITS2), each individual PCR was realized in a total of 25 µl, including 12.5 µl of the Multiplex PCR Master Mix (Qiagen), 0.125 µl of each primer (100 µM), and 1 µl DNA. For COI, the primers LCO (5′-GGTCAACAAATCATAAAGATATTGG-3′) and HCO (5′-TAAACTTCAGGGTGACCAAAAAATCA-3′)44 were used for more than 400 individuals. PCR conditions consisted of (1) 15 min at 95 °C, (2) 35 cycles of 30 s at 94 °C, 90 s at 50 °C and 60 s at 72 °C, (3) 10 min at 72 °C. For ITS2, the primers ITS2-F (5′-TGTGAACTGCAGGACACATG-3′) and ITS2-R (5′-AATGCTTAAATTTAGGGGTA-3′)45 were used for a subset of representative individuals. PCR conditions consisted of (1) 15 min at 95 °C; (2) 40 cycles of 30 s at 94 °C, 90 s at 53 °C, and 60 s at 72 °C; and (3) 10 min at 72 °C. In both cases, the PCR was checked using a QIAxcel DNA Fast Analysis Kit on a QIAxcel Advanced System (Qiagen). Positive PCR products were then sequenced with the Sanger method in one direction with the HCO primer for COI and both directions for ITS2. Sequences were trimmed, assembled and aligned using ClustalW for COI and Muscle for ITS2 (Geneious, version 10.2.3). For COI, only haplotypes observed twice within the panel of high-quality sequences (length  > 520 bp and no undetermined nucleotide) were considered. These data were then enriched with 83 additional GenBank accessions, including in particular sequences from Nomano et al.22 and Giorgini et al.23. The whole dataset (our own haplotypes and GenBank accessions) was then analyzed on a common part of 519 bp included between the two marks, ATTGGDTCAA and TTAGCAGGTG (5′ → 3′ on the positive strand). Three criteria were then applied to summarize and clean the data including: (1) the conservation of repres entative, necessary and sufficient sequences from the three main sources22,23 (and this study); (2) the exclusion of sequence with undetermined nucleotide(s); (3) the exclusion of each sequence with a unique amino-acid sequence. A final dataset of 62 sequences (haplotypes from this study and GenBank accessions) remained after this process. Based on this dataset, three complementary approaches were used to investigate the molecular clustering: (1) a Neighbour Joining approach using the Tamura 3 parameters distance (the best evolutionary model according to the software MEGA10.1.746), using 500 replicates for bootstrapping; (2) a Maximum Likelihood approach using the evolutionary model HKY85 + I (the best model according to the software PhyML3.047); and (3) the constitution of a network using the Median Joining method (ε set to zero, PopArt48). The Kimura 2 parameters distance (often used in the frame of barcoding’s studies) was also used to investigate the pairwise distances within and between clusters (see Discussion). For ITS2, the identified haplotypes were directly compared to those available on GenBank and mapped into the COI Neighbor-Joining tree.
Crossing experiments
Ganaspis brasiliensis is arrhenotokous, unmated females produce only male progeny while mated females are able to produce both males (unfertilized eggs) and females (fertilized eggs). Thus, the proportion of female progeny can be used as an indicator of reproductive isolation. With regard to already acquired knowledge on Asian Ganaspis cf. brasiliensis19,22,25,30, we more precisely investigated here the reproductive (in)compatibilities between the two main molecular clusters (G1 and G3-4—see Results and Discussion) and, within the cluster G1, between two geographically distant populations (one Chinese and one Japanese). Thus, crossing experiments with individuals from three locations were done here: Tokyo, Hasuike and Kunming. For the latter, only individuals that were a posteriori affiliated to G1 through the molecular characterization described above were taken into account. For individuals from each location, parasitized Drosophila pupae from the general parasitoid rearing (see above) were identified under a microscope (parasitoid pupae can be seen through the translucent Drosophila pupal case) and kept individually in plastic vials containing moisturized plastic foams. Within 24 h after emergence, 1–2 males were placed with each virgin female during 24 h for mating. Females were then transferred to a plastic vial containing 10–30 first instar D. suzukii larvae feeding in fresh blueberries and drops of honey for the parasitoid’s nutrition. After 3 days, females were collected and kept in 95% ethanol for potential molecular analysis. The vials containing the potentially parasitized D. suzukii larvae in blueberries were kept until adult emergence under the general rearing conditions described above. Upon emergence of the F1 generation, adults were sexed based on antennal length (males have longer antennae than females24) and the percentage of female progeny was calculated for each parental female. To test the fertility of F1 females, they were allowed mating with males from the same origin for 24 h. Then, the above described oviposition procedure was repeated, and upon emergence, the F2 progeny was sexed and percentage of females was calculated. The number of parental females for each crossing varied from 9–24 (Table 1), depending on emergence during the experimental period.
Affinity towards the targeted host and its nutritive media
To study the specificity of G. cf. brasiliensis from the above mentioned seven different origins in Asia, three combinations of hosts and nutritive media were tested under no-choice conditions: (1) D. suzukii larvae feeding on blueberries, (2) D. suzukii larvae feeding on artificial diet, and (3) D. melanogaster larvae feeding on artificial diet. The blue formula of the above-mentioned artificial diet was used to facilitate counting of Drosophila eggs. Additionally, the diet was blended with about 25 g of fresh blueberries, as described by Girod et al.25. The artificial diet and fresh blueberries were exposed to the respective Drosophila species for 1–3 h, until 10–30 eggs were counted under a microscope, and incubated for 24 h at room temperature to allow eggs to hatch. Mated and naïve (i.e., never exposed to hosts for oviposition) 3–4 d old G. cf. brasiliensis females were then released individually into plastic tubes (2.7 cm diameter, 5.2 cm height) containing one of the three media. The tubes were closed with a moist foam lid containing a drop of honey to nourish the parasitoids. Females were removed from the tubes after 48 h and placed in 95% ethanol for genetic identification based on CO1, as described above. The tubes containing potentially parasitized Drosophila larvae were kept at the general rearing conditions and observed for fly and parasitoid emergence on a regular basis for 40 d. For each tube, the number of Drosophila flies and parasitoids were recorded. For each parasitoid origin, 20 replicates per host species-nutritive media combination were tested, for a total of 420 individual females.
Influence of the nutritive media on the parasitism of non-target species
A second no-choice test was done to investigate whether G. cf. brasiliensis’ host specificity is dependent on the nutritive medium of the host. To this end, four host species-nutritive medium combinations were tested: D. melanogaster or D. simulans larvae feeding on either blueberries or artificial diet. Because both Drosophila species do not have a serrated ovipositor and can therefore not oviposit through the skin of fresh fruits, slightly decomposed blueberries were cut in half and exposed to these species until 10–30 eggs were counted on each half. As in the first no-choice test, the artificial diet used in this experiment was the blue formula blended with about 25 g blueberries. The experiment was then conducted as described above for the first no-choice test, with the difference that 10 replicates for each host species-nutritive medium combination were used for parasitoids originating from Tokyo, Xining, and Hasuike only. This brought the total number of females for this experiment to 120.
Preference for the targeted host and its habitats
To investigate differences in preferences for the targeted host and its habitats among the different genetic groups of G. cf. brasiliensis, a three- and a four-choice bioassay were done. The bioassays took place in a cylindrical transparent plastic container (10 cm diameter, 5 cm height) with two holes of 2.5 cm diameter in the lid: one was covered with netting for ventilation and the other closed with a foam plug on which a drop of honey was placed to nourish the parasitoid. Inside each container, one 4–5 days old mated parasitoid female was placed, a plastic vial with wet cellulose paper as a water source, and small dishes (2.5 cm diameter, 1 cm height) containing the choices for oviposition in a random order. To avoid the influence of light and colors on the wasp’s directional choice, the choice arenas were placed inside a white plastic box (100 × 50 cm), leaving only one light source from above. After 24 h in the choice arena at the general rearing conditions, female parasitoids were kept in 95% ethanol to allow for further DNA analysis confirming the genetic group they belonged to. The dishes containing the different hosts and nutritive media were placed separately in rearing tubes (5 cm diameter, 10 cm height) containing a moist filter paper at the bottom and covered with a moist foam lid to avoid drying of the media. Three weeks after the beginning of the choice test, all adult Drosophila were removed from the rearing tubes and were counted. Until the eighth week after the choice test, emerging parasitoids were collected once a week, sexed, and counted.
The three-choice bioassay was designed to determine if also when given the choice, G1 G. cf. brasiliensis are specific to fruits as the host’s nutritive medium, rather than to the host species, while G3-4 parasitoids are not specific to either. Therefore, the three host-species-nutritive medium combinations were (1) D. suzukii or (2) D. melanogaster larvae feeding on fresh blueberry, and (3) D. melanogaster larvae feeding on artificial diet. All media were prepared as described above for the no-choice experiments. In total, 68 female wasps were tested in the three-choice bioassay, 20 originating from Hasuike, 24 from Tokyo, and 24 from Xining.
To determine if the habitat specificity of G1 and generality of G3-4 G. cf. brasiliensis also hold true when comparing fresh to decomposing fruits, a four-choice bioassay was designed. The host species-nutritive media combinations were (1) D. suzukii or (2) D. melanogaster larvae feeding on either (3) fresh or (4) decomposing blueberry. Infestation of fresh blueberries with fly larvae was done as described above. To decompose fruits, blueberries were exposed to room temperature in a plastic container for 7–10 days until growth of molt was visible. They were then exposed to D. suzukii and D. melanogaster for the collection of eggs as described for fresh fruits. In total, 27 and 22 females originating from Tokyo (G1) and Hasuike (G3-4) were tested, respectively, in the four-choice bioassay. For all choice tests, only results from females that produced at least one offspring were analyzed.
Statistical analysis
Apparent parasitism (AP) was calculated as the proportion of parasitoid offspring among the total number of insects that emerged from the nutritive medium (i.e. Drosophila sp. and parasitoids). The proportion of ovipositing females (POF) was calculated as the number of female parasitoids which produced at least one offspring (or which showed an oviposition response, in the case of the behavioral experiments) divided by the number of females tested. All data were analyzed using logistic regression followed by post-hoc comparisons of means with Tukey adjustments. Differences in proportions of females in the crossing experiment as well as AP and POF in the no-choice experiments was analyzed using quasibinomial distributions to account for overdispersion of the residuals (glm function of the ‘stats’ package in R49). For the no-choice experiment with parasitoids from different origins, AP was analyzed with the explanatory variables parasitoid origin, nutritive medium, and their interaction; and the POF developing on D. melanogaster feeding on artificial diet was analyzed with the parasitoid’s genetic group (G1 or G3-4) as explanatory variable. AP in the no-choice experiment with non-target species, the explanatory variables were parasitoid origin, host species, nutritive medium, and all possible interactions.
Mixed effects logistic regressions (glmer function of the ‘lme4’ package in R50) were used to analyze AP in the choice tests. Analyses were done for each parasitoid origin separately because of convergence problems with more than one fixed effect. Therefore, nutritive medium was the sole fixed-effect explanatory variable for all analyses concerning the choice tests. In all cases, individual females were included as a random effect to account for correlation of parasitism between the media by the same female and an additional observation-level random effect was introduced to solve the problem of residual overdispersion. More

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Forest land surrounding Ethiopia’s churches are important islands of biodiversity. The government has pledged to restore 15 million hectares of degraded and deforested land by 2030.Credit: Kieran Dodds/Panos

The Great Green Wall of Africa, a plan to restore a 7,000-kilometre-long stretch of degraded land from Senegal in West Africa to Djibouti in the east, is a bold and ambitious idea intended to help combat drought and desertification, which currently affect around 45% of Africa’s land area. Proposed 13 years ago by two of the continent’s elder statesmen, Nigeria’s then president Olusegun Obasanjo and Senegal’s former president Abdoulaye Wade, it is even more important now, given the threat from climate change and the reliance of the continent’s people on agriculture for their livelihoods.
But, so far, the project has struggled to reach key goals. Less than one-fifth of the designated land area has been restored or rehabilitated. The African Union’s top decision makers don’t see the green wall as a priority, and inter-national donors seem reluctant to commit further funding. Researchers, governments and international agencies must work together better to rehabilitate this crucial scheme.
The project’s focus has widened from its founders’ vision because there are more ways to restore degraded land than by reforestation, such as creating communal gardens and nature reserves. But the addition of these and other measures has made the green wall more complex. It has required different ministries in individual countries to work together. That is always difficult, but it becomes even more so when two further variables are added: the African Union and the international donor community. These and other observations are confirmed in an independent assessment of the project, commissioned by the project’s partners and published in September by the United Nations Convention to Combat Desertification (UNCCD).
The assessment report tries to look on the bright side. It says that 11 countries along the green wall have re-habilitated nearly 4 million hectares of land and created 350,000 jobs in the process. It also confirms that a broader group of 21 African countries is committed to restoring and rehabilitating 100 million hectares of land by 2030, creating 10 million green jobs. But it doesn’t sugar-coat the fact that governments and donors will need to find between US$3.6 billion and $4.3 billion every year for the next decade if the 100-million-hectare target is to be achieved. That will be a tall order — the report calls it a “quantum leap” — considering that the project raised around $2 billion in its first decade. But it is not impossible — and there are several key ways in which researchers can contribute.
The UNCCD report provides headline information on each country’s progress — such as the numbers of plants and seedlings produced; the area of land reforested; and the numbers of people trained and jobs created. Most of these data were provided by each country. The next step should be for independent researchers — for example, members of IPBES (the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services) — to assess these data and publish their own reviews, to help all sides have more confidence in the data and in the monitoring process.
Funding is always a challenge in such projects. But although it might seem feasible that the 55 member states of the African Union and their inter-national partners could raise the required amounts, nations have already committed funding to inter-national initiatives with similar goals to those of the green wall. African countries, for example, are signatories to the Aichi Bio-diversity Targets, which include a goal to reduce habitat loss and degradation. Countries have also signed up to the UN Sustainable Development Goals, which include a target of combating desertification and restoring degraded land and soil. And they are also members of the UNCCD, which has pledged to reach what it is calling “land degradation neutrality” by 2030.
The UNCCD report suggests a single trust fund could be the answer. That would work if countries and international agencies agree to pool their resources and create harmonized reporting requirements. Researchers could help here by developing a method for measuring whether countries are succeeding in meeting their green-wall goals, as well as providing a common accounting framework.
The need to restore and rehabilitate land is urgent. People in the affected countries are among the world’s poorest. The overwhelming majority earn their living from agriculture or livestock production. Climate change is projected to lift average temperatures by 3–6 °C by the end of the century, compared with a late-twentieth-century baseline. More extremes of weather are expected, and these, in turn, will reduce crop yields.
The green-wall project needs international agencies to cooperate better, it needs researchers to help, and it needs the present generation of the continent’s leaders to step up and take on a more visible role in championing it, just as its two founding presidents did. More

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