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Identification of two Zn/Co responsive proteins in diatomsZn and Co growth rate experiments in which Zn or Co (omitting the other) were added to the growth media were conducted and harvested for proteomic analysis. Growth rates of the marine diatom species Thalassiosira pseudonana CCMP1335, Phaeodactylum tricornutum CCMP632, Pseudo-nitzschia delicatissima UNC1205 and Chaetoceros sp. RS19 (Chaetoceros RS19 herein) were conducted in a consistent media composition to allow for intercomparison among species (see “Methods”). The onset of growth limitation by Zn and Co was evident by decreased growth rates under low [Zn2+] and [Co2+], and the ability to use Co to restore Zn-limited growth was species-specific and consistent with prior results for the diatoms T. pseudonana, P. tricornutum and P. delicatissima (Fig. 1a, b)9 and for other eukaryotic algae2,8,10. Growth rates of Chaetoceros RS19 were not stimulated by increasing [Co2+] up to 23.5 pM in the absence of added Zn. This inability to substitute Co for Zn in Chaetoceros RS19 was clearly distinct from that of other diatoms, but was consistent with previous observations in Chaetoceros calcitrans10, implying a genus-wide attribute.Fig. 1: Growth responses of diatoms to varying [Zn2+] and [Co2+] and initial detection of ZCRPs in T. pseudonana.Growth rates of four diatoms over a range of a [Zn2+] and b [Co2+]. Data are presented as mean values of biological duplicate cultures. Data is available in Supplementary Table 1. Global proteomic analyses comparing the proteomes of pooled biological duplicate cultures (n = 2) of T. pseudonana in c high vs. low added Zn and d high vs. low added Co. Each point is an identified protein with the mean of technical triplicate abundance scores in one treatment plotted against the mean of abundance scores in another treatment. The solid line denotes 1:1 abundance. Error bars in c are the standard deviation of technical triplicate measurements.Full size imageThe proteome as a function of Zn2+ and Co2+ was explored in the marine diatom T. pseudonana harvested during log phase growth. Global proteomic analysis comparing low (1.1 pM) versus high (10.2 pM) added [Zn2+] and low (2.3 pM) versus high (23.4 pM) added [Co2+] revealed two uncharacterized diatom proteins that greatly increased in abundance at low [Zn2+] or [Co2+] (Fig. 1c, d). These proteins were annotated as a CobW/HypB/UreG, nucleotide binding domain and a bacterial extra-cellular solute binding domain, respectively, within the manually curated JGI Thaps3 T. pseudonana genome17 and were identified in T. pseudonana cultures with high confidence (≥9 exclusive unique peptides, 100% protein probability; Supplementary Fig. 1). BLAST sequence alignments showed these proteins to be homologous with CobW-like proteins (with 31.69% identity relative to Pseudomonas denitrificans CobW) and with the bacterial nickel transport protein NikA (with 30.5% identity relative to E. coli NikA), respectively. Based on their clear response to Zn and Co in the proteomes of multiple diatom species (Fig. 2a–d), the lack of definitive annotations in diatoms, and their genetic distance from bacterial homologs, these proteins are referred to as ZCRP-A and ZCRP-B (Zn/Co Responsive Protein A and B) in this study. Abundance patterns of these proteins were also investigated in P. tricornutum, P. delicatissima and Chaetoceros RS19. ZCRP-A spectral abundance counts were significantly (Kendall correlation, p  10 times. j Topology predictions from five sub-methods (OCTOPUS, Philius, PolyPhobius, SCAMPI, and SPOCTOPUS), consensus prediction (TOPCONS), and predicted ΔG values for P. tricornutum ZCRP-B generated using the TOPCONS webserver (https://topcons.cbr.su.se/)27,28. k Extent of Co uptake after 24 h for wild-type (WT), ZCRPA-knockout (KO), and ZCRPA-overexpression (OE) lines of P. tricornutum normalized to fluorescence units (fsu). Data are presented as mean values ± the standard deviation of biological triplicate cultures (n = 3). Individual data points are overlaid as white circles. The extent of Co uptake was found to be significantly larger in the ZCRPA-OE line compared to the wild-type via one-way ANOVA (f(3) = 23.16, p = 0.000268) and post hoc Dunnett test (p = 0.00048).Full size imageTo date, connections between COG0523 proteins and utilization of Zn and Co have been explored primarily in prokaryotic organisms. For example, the COG0523 protein CobW has a role in vitamin B12 biosynthesis and thus Co use19,21. In contrast, a subgroup of other COG0523 proteins (YjiA, YeiR, ZigA, and ZagA) have been implicated in Zn2+ metabolism8,13,14,15,16, and a client protein to the metallochaperone ZagA in Bacillus subtilis has been identified22.Compared to bacteria, less is known about the function of COG0523 proteins in marine phytoplankton, though COG0523 protein family members are known to occur in all kingdoms8,23. A recent study described the presence of COG0523 domain proteins upregulated under low Zn in the coccolithophore Emiliania huxleyi, but without further functional characterization24, implying a potential Zn-related function of a COG0523 protein in a marine alga distinct from the marine diatoms included in this study.Although various proteins belonging to the COG0523 subgroup share similar conserved domains, they possess different metal binding abilities and thus likely have different functions among the diverse organisms in which they are found. For example, recent work has established that CobW preferentially binds Co2+ as the cognate metal and acts as a Co2+ chaperone ultimately supplying vitamin B12 in bacteria, whereas the closely related putative metal chaperones YeiR and YjiA (homologs of CobW) bind Zn2+19. We can infer from homology and the response to low Zn and low Co in the present study that Zn2+ and Co2+ are likely both cognate metals for diatom ZCRP-A. Further metal binding and affinity assays can confirm and characterize metal binding in this protein.Frustule morphologyPhenotypic plasticity in P. tricornutum is well documented. Two basic cell morphotypes, fusiform and triradiate, are found in natural liquid environments. It is thought that by adopting the triradiate form, a cell increases its surface area and thus the area of membrane available for enzymatic activity or molecular diffusion of dissolved inorganic carbon (DIC) into the cell. The triradiate form is known to be more common under DIC limiting conditions, which supports this hypothesis25. Distinct morphological differences resulted from the knockout (KO) of the ZCRP-A gene. In P. tricornutum, ZCRP-A knockout cells consistently adopted a triradiate shape while wild-type cells were fusiform (Fig. 4i). Normally, triradiate cells of P. tricornutum spontaneously revert to fusiform across generations26, thus it is notable that ZCRP-A knockout cells have consistently maintained their triradiate shape for 4+ years in culture irrespective of media [Zn2+]. As Zn2+ is the predominant metal cofactor used in diatom CAs, the adoption of the triradiate form in knockout P. tricornutum cells may be a response to a disruption of the carbon concentrating mechanism caused by a reduction in Zn acquisition capability due to ZCRP-A knockout. This is consistent with the observed relative increase in Mn2+-utilizing CA (ι-CA) in the knockout line compared to the wild-type (Supplementary Fig. 5).ZCRP-B sequence analysis and cellular localizationUnlike COG0523 proteins, the relationship of ZCRP-B abundance to environmental Zn and Co concentrations does not appear to have been previously described. Topology predictions of P. tricornutum ZCRP-B using TOPCONS27,28 revealed a single predicted transmembrane domain near the N-terminus, with the majority of the protein predicted to be oriented outside the membrane (Fig. 4j). Overexpression and fluorescent tagging of ZCRP-B confirmed localization to the cell membrane (Fig. 4e–h; Supplementary Fig. 3b). A single predicted transmembrane domain contrasts with the Zrt/Irt-like divalent metal transporters (ZIPs) in eukaryotic algae, which have 7+ transmembrane domains and are key Zn transporters in many organisms29,30. It is therefore most likely that ZCRP-B is not a transporter itself, but one part of a multi-protein membrane complex and potentially interacts with the ZIP system. A sequence database similarity search (BLASTp, NCBI) found the ZCRP-B protein to be homologous with NikA, a protein subunit of the bacterial ATP-binding cassette (ABC) type Ni transport system protein Nik (30.5% identity with E. coli NikA, E = 7e−49, Supplementary Fig. 6). This transporter is well characterized in bacteria and is comprised of five subunits NikA-E. NikB and NikC are two pore-forming integral inner membrane proteins, NikD and NikE are two inner membrane-associated proteins with ATPase activity, and NikA is the periplasmic component that functions as the initial metal receptor31. No proteins with homology to NikB nor NikC were detected in the P. tricornutum proteomes generated in this study. Two uncharacterized P. tricornutum proteins were homologous with NikD (28.8% identity, E = 1e−14) and NikE (34.9% identity, E = 1.33e−8), though neither had abundance trends similar to ZCRP-B, implying that their function and regulation are independent of ZCRP-B.The sequence of a functionally similar bacterial ABC transport complex, CntABCDF (cobalt nickel transporter, also known as Opp1) from Staphylococcus aureus was also compared to NikA and ZCRP-B (Supplementary Fig. 6). CntA shares 25.6% identity with ZCRP-B (E = 3e−28), and similar to NikA, is an extra-cytoplasmic solute-binding protein that transports Ni, Zn and Co. CntA functions as a Ni/Co acquisition system in Zn-limited S. aureus32. Although the Nik and Cnt systems serve Ni and Co transport in bacteria, ZCRP-B responds to Zn and Co in marine diatoms, which have a significant Zn demand. This may imply a recruitment and repurposing of this bacterial Ni transporter component as part of the Zn acquisition systems during the evolution of marine diatoms.ZCRP-B as a putative high-affinity ligandSequence similarity to the extracellular transport components NikA and CntA (Supplementary Fig. 6), localization to the plasma membrane (Fig. 4b; Supplementary Fig. 3b), and increased abundance under low Zn and Co conditions (Fig. 2b) of P. tricornutum ZCRP-B suggests a metal-binding role as part of a high-affinity transport complex. The induction of ZCRP-B expression at low [Zn2+] (Fig. 2a–c) fits the description of a high-affinity Zn uptake system observed in marine algae that is known to be induced at low free [Zn2+]33,34, suggesting that this protein is involved in an adaptive response to extremely scarce Zn availability. Furthermore, ZCRP-B could contribute to the pool of high-affinity organic ligands that complex dissolved Zn, either by dissociation from living cells or upon cell death by viral lysis and grazing, in the upper water column12,35.The identification of a membrane-associated Zn-Co responsive protein-containing putative metal-binding sites allows us to reconsider the mechanisms of cellular metal uptake in diatoms. Prior physiological experiments observed Zn uptake in marine diatoms to approach the limits of diffusion33, and predicted kinetic control with fast cell surface metal binding and uptake relative to dissociation and release back to the seawater environment36. To enable this transport capability, it was postulated that transporters might be so abundant that the membrane becomes crowded37. Here, the observation of a putative Zn-binding, membrane-associated protein with only 1 predicted transmembrane domain instead implies a separation of the Zn concentrating function at the cell surface relative to its transport into the cell. In this scenario when Zn is scarce, biosynthesis of ZCRP-B increases and is tethered to the cell surface to compete Zn away from natural dissolved Zn ligands35 and/or chelate Zn atoms that make it through the diffusive boundary layer to the membrane. In this manner, ZCRP-B would increase the surface Zn concentration in the vicinity of Zn transporters, and multiple ZCRP-B proteins could supply nearby surface ZIP transporters or be endocytosed, avoiding the predicted membrane crowding of transporters problem. Aristilde and colleagues have previously demonstrated that weak natural Zn-binding ligands containing cysteine do indeed enhance cellular Zn uptake within the diatom Thalassiosira weissflogii, with heightened effects in Zn-limited compared to Zn-replete cells38. They proposed the formation of a transient tertiary complex between the Zn-bound ligand and Zn transporters (ZIPs and heavy metal P-type ATPases) at the cell surface, which could be mediated by a surface-tethered Zn binding ligand such as ZCRP-B. Future studies could examine the mechanism of Zn exchange between ZCRP-B and Zn/Co transporters such as the ZIPs in eukaryotic algae, which were also detected at lower Zn and Co abundances in P. tricornutum but with relatively lower spectral counts (Supplementary Fig. 7a, b), consistent with this model. Furthermore, the proposed mechanism of ZCRP-B binding is similar to that of the high-affinity Fe3+ binding protein ISIP2a, previously characterized in marine algae as an iron starvation-induced protein39. ISIP2a has been characterized as a phytotransferrin involved in endocytosis-mediated high-affinity Fe uptake in P. tricornutum that acts to concentrate Fe at the cell surface and is an extracellular protein anchored to the membrane with one transmembrane domain39. As the protein sequences of P. tricornutum ZCRP-B and ISIP2a share no significant similarity, it is possible that the uptake mechanism of ZCRP-B is similar to that of ISIP2a, but specific to high-affinity Zn and Co uptake rather than Fe. This suggests a common strategy of using extracellular membrane-anchored metal acquisition proteins in marine algae faced with metal limitation.Co uptake in wild-type and mutant diatom strainsAs ZCRP-A and ZCRP-B abundance is related to media [Co2+] (Fig. 2a–d), we investigated differences in the extent of Co uptake after 24 h among Zn/Co-limited wild-type, ZCRP-A knockout, ZCRP-A overexpression, and ZCRP-B overexpression lines of P. tricornutum via addition of the radiotracer 57Co (see methods). The extent of Co uptake among genetically modified P. tricornutum lines was observed to be significantly different via one-way ANOVA (f(3) = 23.16, p = 0.000268). A Dunnet post hoc test revealed that uptake was significantly greater (2.6× larger) in the ZCRP-A overexpression line compared to wild-type (p = 0.00048, Fig. 4k). We interpret this result as the overexpression of ZCRP-A creating a larger intracellular binding capacity for Co, thus protecting it from intracellular sensor or regulatory systems and/or efflux pumps. In contrast, no significant difference in Co uptake rates was observed when comparing ZCRP-A knockout, ZCRP-B overexpression, and wild-type lines, suggesting that P. tricornutum ZCRP-A knockout cells are capable of compensating for knockout to maintain Co metabolism, perhaps through the use of low-affinity transporters33. This is consistent with these uptake experiments being conducted using seawater media with a relatively abundant concentration of Zn (background of 7.7 pM Co and 4.0 nM Zn in the absence of EDTA), thus the use of low-affinity transporters was likely sufficient to acquire Zn and Co for growth, and neither ZCRP-A knockout nor ZCRP-B overexpression would be expected to add any metabolic benefit (Fig. 4k). Moreover, if ZCRP-B is only one part of a multi-protein acquisition and transport complex as hypothesized, overexpression of the single protein may not result in enhanced functionality.Abundance patterns of CAs in two diatomsCarbonic anhydrase enzymes constitute a major reservoir of Zn and Co within marine diatoms7. Within the stroma, intracellular chloroplastic CAs are essential in supplying CO2 to RUBISCO as they convert HCO3−, the predominant species of inorganic carbon in the pyrenoid, into CO240,41. Seven subclasses of CAs have been identified in marine diatoms to date and are designated as alpha, beta, gamma, delta, zeta, theta, and iota (α, β, γ, δ, ζ, θ, and ι). While Zn2+ is the cofactor most commonly used in algal CAs, utilization of both cadmium (Cd2+) and cobalt (Co2+) in place of Zn2+ at the active site of ζ-CA (CDCA) and a δ-CA, respectively, has been previously documented2,5,42. Overall, Zn-utilizing CAs increased in abundance with increasing Zn, consistent with the need for rapid HCO3− conversion at faster growth rates (Fig. 5; Supplementary Fig. 7). Specifically, spectral abundance counts of two β-type CAs, PtCA1 and PtCA2, became abundant in high [Co2+] (23.4 pM) and [Zn2+] ( > 1.1 pM) and were inversely related to ZCRP-A abundance (Supplementary Fig. 7). Both PtCA1 and PtCA2 are known to localize to the chloroplast pyrenoid41,43. Moreover, the increasing abundance trends of the Zn-utilizing α-CAs (CA-II and CA-VI) and the θ-CA Pt43233, which localize to the periplastidial compartment, chloroplast endoplasmic reticulum, and thylakoid lumen, respectively, at higher and Zn/Co provide further evidence for this strategy of increasing CA use under Zn-replete and higher growth rate conditions (Fig. 5; Supplementary Fig. 7)43,44.Fig. 5: Comparison of α-CA, ι-CA, and ZCRP abundances.Spectral counting abundance scores of a alpha CA, iota CA, and b ZCRP-A and ZCRP-B detected in Zn and Co treatments of P. tricornutum measured by global proteomic analysis. Data are plotted as means ± the standard deviation of technical triplicate measurements of pooled biological duplicate cultures (n = 2). Protein names are shown with their corresponding JGI protein ID.Full size imageIn contrast, abundance trends of the recently discovered ι-CA were inversely related to Zn2+ (Fig. 5). Originally identified in T. pseudonana, ι-CA was found to localize to the inner chloroplast membrane surrounding the stroma and is unusual in that it prefers Mn2+ to Zn2+ as a cofactor45. In the present study, spectral counts of P. tricornutum ι-CA decreased as metal concentrations increased, similar to that observed for ZCRP-A and ZCRP-B (Fig. 5). This ι-CA response was consistent with a Zn sparing strategy under low [Zn2+] and [Co2+] used to prioritize the use of Zn2+ for other metalloenzyme functions.Due to the inverse relationship between the abundances of ZCRP-A and chloroplastic Zn2+-requiring CAs in P. tricornutum (that is, all CAs detected with the exception of ι-CA) and the various types of CAs in T. pseudonana (Supplementary Fig. 7), it seems unlikely ZCRP-A directly interacts with CAs. These results are instead consistent with the hypothesis that ZCRP-A functions as a Zn2+ allocation and prioritization mechanism during Zn limitation. The role of Zn2+ in key transcriptional and translational proteins such as RNA polymerase and ribosomal proteins is well known, and major reservoirs of Zn are associated with these transcription and translation systems in the fast-growing copiotrophic bacterium Pseudoalteromonas6. The availability of Zn in ribosomes and the ER is therefore likely also a cellular priority in diatoms, and could benefit from utilizing the putative chaperone and trafficking capability of ZCRP-A when Zn is scarce. We, therefore, posit that ZCRP-A may serve as a Zn2+ trafficking or storage protein that contributes to the prioritization and movement of Zn2+ to the ER or CER, while the Mn-utilizing Mn ι-CA compensates for the lowered Zn availability in the chloroplast. The increased biosynthesis of ZCRP-A may be an important function to shift Zn homeostasis, competing for intracellular Zn and trafficking it towards the ER or CER.Distribution of putative ZCRP homologs among oceanic taxaPutative ZCRP homologs among eukaryotic oceanic taxa were identified by BLAST searching the P. tricornutum ZCRP-A and ZCRP-B protein sequences against all available transcriptomes in the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP) database, which includes over 650 assembled and annotated transcriptomes of oceanic microbial eukaryotes46. Phylogenetic analysis revealed the presence of putative ZCRP-A and ZCRP-B homologs in a wide variety of organisms belonging to the Chromista kingdom that could be further categorized into Bacillariophyceae, Dinophyceae, and Prymnesiophyceae classes (Supplementary Figs. 8 and  9). Notably, the Chaetoceros RS-19 ZCRP-A homolog did not phylogenetically cluster with the other diatoms (Bacillariophyceae), but instead appears to be more closely related to E. coli YjiA (Supplementary Fig. 8). Furthermore, the lack of the conserved G2/Switch I region in the Chaetoceros RS-19 homolog (Fig. 3) is anomalous in comparison to other putative homologs identified within the MMETSP database. Overall, ZCRPs are not exclusive to oceanic diatoms, but rather are widely distributed amongst oceanic taxa.Metaproteomic detection of ZCRP-A and ZCRP-BTo investigate the use of ZCRP-A and ZCRP-B in the natural environment, we searched metaproteomic data collected during the KM1128 METZYME (Metals and Enzymes in the Pacific) research expedition on the R/V Kilo Moana October 1–25, 2011 from Oʻahu, Hawaiʻi, to Apia, Samoa (Fig. 6a). dZn followed a nutrient-like distribution as described previously, with an average surface (40 m) dZn concentration of 1.21 nM and average deep water (3000 m) concentration of 10.37 nM47 (Fig. 6b). dCo was highly depleted in the upper photic zone as the result of biological uptake48,49 (Fig. 6c). Eukaryotic homologs of ZCRP-A and ZCRP-B were detected at multiple stations at surface ( More

To estimate the potential increase in biodiversity decline and the national level of conservation investment needed to counteract it in post-conflict Colombia, we used a model developed by Waldron et al.19. This quantitative model predicts national biodiversity status change, the biodiversity decline score (BDS), based on investment in conservation actions in relation to human development pressures. The model uses seven predictors related to the economy of each country, its biodiversity status or dynamics, and its conservation spending19.ScenariosWe used the Waldron et al.19 model to predict (1) the expected increase in biodiversity decline immediately after the peace agreement (the post-conflict period), (2) the conservation funding needed to prevent this additional decline and (3) the investment necessary to avoid biodiversity decline. We used four scenarios to examine our questions.The baseline scenario was the War BDS scenario, which estimated the BDS of the last 12 years of the conflict, before the peace agreement in 2016. Predictor variables related to human pressures were from 4–5 years before to appropriately represent the lag in the modelled effect19. We used the most recent available value of ‘strict-sense’ conservation investment19. The following three scenarios examined post-conflict options and were compared with this War BDS scenario.The Peace BDS scenario predicted the BDS for a 12-year period post-conflict. The predictor variables related to human pressures were from the 11-year period immediately after the peace agreement. We assumed the same conservation spending as for the War BDS. The Lower BDS scenario estimated the necessary investment to achieve the War BDS. This represented a situation where the biodiversity loss during the conflict did not change post-conflict. For this scenario, we held the human pressure variables the same as in the Peace BDS scenario. The Prevented BDS scenario was exactly the same as the Lower BDS scenario, but we set a target of no biodiversity decline (BDS = 0).We used the War and Peace BDS estimates to calculate the expected additional biodiversity decline post-conflict. Then, we used the model with data from the Lower BDS scenario to calculate the investment needed to prevent any additional biodiversity decline post-conflict. Finally, we used data from the Prevented BDS scenario to estimate the conservation investment necessary to halt biodiversity decline in the post-conflict period.Data for predictor variablesWe modified the predictors related to agriculture and economic growth to examine anticipated changes in human pressures. This revision allowed us to consider the expected agricultural expansion, in the form of percentage of agricultural land and growth, and economic growth, as the gross domestic product (GDP) and GDP growth. We also modified the function so that we could use it to estimate funding needs given a target BDS.For the War BDS scenario, data on GDP, GDP growth, agricultural land area and agricultural land area growth were either available or easily computed. The data for GDP and the percentage of agricultural land from 2001–2012 were obtained from The World Bank28. The agricultural land growth was calculated as the difference between the percentage of agricultural land of consecutive years, and GDP growth was calculated from the GDP per capita data from The World Bank28.For the Peace, Lower and Prevented BDS scenarios, we made projections about the predictors. For the GDP we used projections for 2017–2019, and for the GDP growth projections for 2019–2022 (ref. 33), and then selected an annual increase in the GDP growth of 0.3 percentage points for the remaining 5 years, corresponding to the most conservative estimate found in ref. 34. We then used our estimates of GDP growth for the whole time period to calculate the GDP per capita for the last 10 years, and used population projection to compute the GDP for the next 10 years.To estimate the agricultural land and growth for the Peace, Lower and Prevented BDS scenarios, we used projections on deforestation. We developed our model to reflect the immediate consequences in agricultural expansion and deforestation post-conflict. Thus, we estimated the percentage agricultural land area using projected values of deforestation35. We support this approach based on two observations. First, at least 90% of deforested land was transformed to agriculture during past years36. Second, forest transformation to agriculture has been more aggressive since the peace agreement7,10,11. Thus, the processes that fuel agricultural conversion are stronger. For each year we added the deforested area to the previous agricultural land area. We then calculated the yearly percentage agricultural land area and computed the agricultural growth as the percentage difference between the agricultural land area of consecutive years. We took the minimum and maximum values of deforestation projections to create best- and worst-case scenarios.We acknowledge that our use of the Waldron et al.19 model has limitations because we did not update all the predictors. Specifically, two ‘inertia’ terms that account for the effect of biodiversity decline occurring immediately before the time period of interest19. The coefficients associated with these terms have a positive effect on the BDS, which means that a more intense decline in the past will increase the predicted biodiversity decline. Given the increase in human pressures, the actual inertia terms are probably larger than the ones we used. Thus, the Peace BDS and the actual increase in biodiversity decline post-FARC may be larger.The ModelTo create a broad proxy for the expected cost of potential conservation interventions across Colombia, we estimated the OCC for agriculture at the 1 km2 scale. We estimated the OCC by building a spatially explicit probability model of forest conversion to agriculture and then paired it with the net present value of the expected return of different agricultural activities.We calculated the OCC following the methodology proposed by Naidoo and Adamowicz24. Their approach models the expected net present value of potential net rents resulting from agricultural uses of a forested parcel, while accounting for the probability of conversion to agriculture. Provided that each agricultural use k has its own annual expected return per area of land Rk, and that each parcel i has a probability of conversion Pik from forest to agricultural use k, the expected value for a given discount rate δ is$${{{mathrm{OCC}}}} = mathop {sum}limits_{i = 1}^{{I}} {mathop {sum}limits_{k = 1}^{{K}} {{{P}}_{i,k}} } frac{{{{R}}_k}}{{delta }}$$
(1)
Thus, the OCC of an area composed of several parcels is equal to the sum of the expected returns of the probable agricultural uses, weighted according to their probability of conversion, in each of the parcels, summed across all of the parcels.We calculated the OCC for forested areas in three steps. First, we built a probability model to obtain the general risk of forest conversion (Pdef). Next, we built a second model that, given that a parcel had been transformed, predicted the probability of forest conversion to different types of agricultural activities (({{P}}_{{{{mathrm{ag}}}}_k})). We used both models to compute the total probability of conversion to each type of agricultural activity k in a parcel i (({{P}}_{ik} = {{P}}_{{{{mathrm{def}}}}_i} times {{P}}_{{{{mathrm{ag}}}}_{i,k}})). We then estimated the net present value of the expected return of each agricultural activity (Rk/δ) using literature and commercial prices and the costs of agricultural products.Types of agricultural land use modelledOur OCC model needed to represent relevant agricultural activities. Below, we justify our selection of three types of agricultural land uses: cattle ranching, coca crops and other crops.Cattle ranching is expected to be a major driver of post-conflict deforestation11. This activity has accounted for 50% of deforestation, in the form of forest conversion to pasture, in past years36, and has considerably expanded post-conflict7.Illegal coca crops are expected to be, and have been observed to be, an important driver of post-conflict deforestation12. This activity is at risk of increase where the withdrawal of FARC and the absence of state presence left a ‘power vacuum’ that facilitated other illegal groups gaining control of such crops in the territory7,11,12. Indeed, evidence shows that deforestation associated with coca cultivation increased as the conflict became less intense37.Other crops were grouped into a single category with cattle ranching due to their small percentage contribution to forest conversion in our time frame (3%) compared with cattle ranching and coca crops (47 and 50%, respectively). We proxy for the extent of all other crops by using data on the distribution of three relevant agricultural products in the post-conflict period: cacao, oil palm and coffee. The cacao crop has high potential in most of the key post-conflict areas in Colombia, so it could have a major role in the peace transition38. Oil palm is important owing to its steep increase in cultivation during the last few years12, to the point that Colombia is now the largest producer in South America39. The relevance of coffee resides in its impact on the rural population, given that coffee crops are the only source of income for approximately 563,000 families and generates over 726,000 rural jobs40.Landscape features dataWe selected ten factors relevant to deforestation in Colombia to model the probability of forest conversion: proximity to roads, presence of FARC (binary: presence or no presence), population density, slope23, elevation, proximity to deforested areas, to rivers, to mining areas and to oil wells, and belonging to national and regional PAs10. National PAs restrict economic activities and are managed by the System of National Natural Parks, while regional PAs allow multiple-use activities and are managed by regional environmental authorities8,41. We did not include indigenous reserves or Afro-Colombian lands.We used deforested areas from 1990 to 2000 from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM)42, the water bodies map from the Department of Environment and Sustainable Development43 and maps from the Instituto Geográfico Agustín Codazzi (IGAC)44 to calculate the distance to already deforested areas, rivers, roads, mining areas and oil wells. The elevation map was obtained from NASA’s (National Aeronautics and Space Administration’s) Land Topography digital images45, and we calculated the slope using the elevation map. We computed population density as the mean value of the 32 mainland administrative departments from 2000 to 2012 using data from the Departamento Administrativo Nacional de Estadística46 (DANE; see Supplementary Table 3 for dataset details). We obtained a map showing the presence of FARC from the Fundación Paz y Reconciliación (PARES)47. All spatial data calculations were performed using software QGIS (https://www.qgis.org/en/site/, version 3.12.2) and R (https://www.r-project.org/, version 3.6.2).Forest conversion and agricultural use modelWe used a two-stage modelling process. First, we modelled the probability of an area being deforested by any driver (not exclusively due to agricultural expansion), using the total deforested area in the country in a 12-year period to parametrize our model (forest conversion model). Second, we modelled the probability that the deforestation was due to a particular agricultural activity (agricultural use model). To parametrize this second model, we used patches of land that were indeed transformed to an agricultural use in this same 12-year period. We combined these two models to obtain the probability that a patch of land was deforested to a particular agricultural activity.We used a binomial logistic regression model to build our forest conversion model, which estimates the probability of forest conversion (Pdef). We used the land cover change from 2000 to 2012 across the country, available from IDEAM42, and reclassified each pixel cell as forested or transformed. We used the bayesglm function from the R arm package48.For our agricultural use model, we built a second binomial logistic regression model to estimate ({{P}}_{{{{mathrm{ag}}}}_k}), the probability of conversion to each type of agricultural activity (cattle and other crops or coca crops) for a parcel that had been transformed. We employed data on forested areas in 2000 that had been converted by 2012. The coca crops cover map was obtained from the Sistema Integrado de Control de Cultivos Ilícitos (BIESIMCI)49. For the cattle ranching map, we used forested areas converted to pasture. Our other crop data contained temporary and permanent crops obtained from a land cover map43.It should be noted that in logistic regression models, the probability of conversion does not change in a linear fashion, but the ratio of probabilities (odds) does. For the agricultural model, the odds describe the probability of conversion to coca crops over the joint probability of conversion to cattle and other crops. This implies that the variation between the probabilities, not the probability itself, changes constantly.To check for spatial autocorrelation, we plotted spatial correlograms of the models’ residuals with Moran’s I. Because spatial patterns were present, we subsampled for pixel cells at a minimum distance of 20 km between points, which reduced the spatial effects adequately for our purposes, although it was most effective for the forest conversion model (Extended Data Fig. 1). We checked for collinearity in the predictor variables using variance inflation factor scores and removed the variables with a value >3 (distance to mines and oil wells; Supplementary Tables 4 and 5). We performed tenfold cross-validation to test the prediction accuracy of the models. This process splits the data into ten subsets and repeatedly fits the model with the data of nine of the subsets to compare its predictions with the remaining subset. We calculated the percentage of correct predictions (overall accuracy) each time and computed the mean as the final forecasting accuracy indicator.Estimation of annual net rentWe estimated the net present values of the expected return of each agricultural activity to estimate the OCC of forested areas in Colombia. For cattle, we used annual net rent from a beef company50. The total annual net rent for other crops was calculated as the weighted average of the net rents for oil palm, cacao and coffee proportional to their land area in 2016 and 2017 (refs. 51,52,53). For coca crops, we used the average net profit for farmers who sell coca leaves54. We selected three discount rate values: 5, 10 and 20% (Supplementary Tables 6 and 7).Predicting forest conversion and OCCTo predict the probability of forest conversion, we updated our spatial information on roads, deforested areas from 2007 to 2017 (ref. 42), FARC presence as the presence of FARC dissidents and deserters in 2017 (ref. 47), and population density as the mean population density by department from 2017 to 2023 (ref. 55). Together with the annual net rent for each agricultural activity, we used the probabilities of conversion of the two models to compute the OCC, or expected land value, of each forested pixel cell for the three discount rates using Eq. (1).We recognize that the simplified national context of social violence when predicting the probability of forest conversion can limit the application of our results. Our models included FARC presence, and we used the presence of dissidents and deserters in this forecasting stage. However, this ignores other criminal groups that might influence the risk of forest conversion, particularly to coca crops, due to the ‘power vacuum’ left by the withdrawal of FARC and lack of state presence11. Because we overlooked the potential impact of other criminal groups, the probability of forest conversion, particularly to coca crops, could have been underestimated. This would imply an underestimation of the OCC in the areas with presence of these other criminal groups.We used the rural cadastral values56 to validate our OCC results by comparing our predicted mean land values by administrative department in the country. Although rural cadastral values might not reflect the value of illegal coca crops, they were, to the best of our knowledge, the best available data for our purposes.The STAR metricThe STAR metric is a measurement of the potential benefit to threatened and near-threatened species of actions aimed at reducing threats and restoring habitat20. The metric can be disaggregated spatially using the area of habitat for each species, showing the proportional potential contributions of conservation actions in particular regions. We focused on the STAR threat-abatement score (START) only. The START score can be further disaggregated by threat according to the contribution of each threat to the species’ risk of extinction, which allows analysis of potential abatement of species extinction risk by particular activities at particular locations. We took advantage of this trait and used the START metric in a specialized way, focusing on the threats posed by agriculture only on all the species with an area of habitat in Colombia. This resulted in 475 species considered (246 amphibians, 172 birds and 57 mammals), of which 169 are vulnerable, 124 near-threatened, 130 endangered and 52 critically endangered. Agriculture accounted for 52% of the total START. This focus on agriculture includes annual and perennial non-timber crops, wood and pulp plantations, and livestock farming and ranching, so we treated land converted to cattle and crops in the same way even though each land-use type has different impacts on species.The use of the STAR metric has some limitations associated with the spatial distribution of the threat due to agriculture. First, the STAR metric is based on documented ongoing and expected future threats to the species according to the International Union for Conservation of Nature Red List. The majority of documented threats are ongoing, thus the majority of species threatened by agriculture are already being negatively impacted. This causes uncertainty in the assumption that avoiding further agricultural conversion will reduce species extinction risk, as additional activities to mitigate the impact of current agricultural activities on the species may also be required. Nevertheless, species assessed as threatened by agriculture are known to be vulnerable to this pressure, meaning that they would almost certainly suffer negative impacts under future agricultural expansion.Second, there is uncertainty in the potential spatial distribution of agricultural expansion. Therefore, the STAR metric as we used it helped us identify sites with urgent potential benefits of avoiding agriculture. This could under-represent territories of great biodiversity value that are not currently impacted by agriculture, like the Amazon region.Prioritization mapsWe wanted to achieve a coarse methodology that could help decision-makers direct national conservation funding to the territories with the most potential benefits of halting forest conversion to agriculture. To pair the STAR scores with our modelled OCC, we divided the total range of STAR scores and OCC into high, medium and low values. Given the distribution of STAR scores, we divided the total range in the logarithmic scale. We classified each forested pixel cell into one of nine combinations of STAR scores and OCC. This analysis was later translated to the municipality resolution by calculating the mean STAR score and mean OCC of all forested pixel cells in each municipality, and applying the same classification system used at the pixel resolution. The distributions of aggregated STAR scores and OCC at the municipality resolution follow a similar pattern to the distribution by pixel cell, with small differences due to the grouping of the values in means (Extended Data Fig. 2b,c).Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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