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FennelThe main effect of fertilization (F) significantly impacted all measured parameters of fennel. Intercropping (I) pattern affected all parameters except plant height and 1000-seed weight. Significant I × F interactions occurred for umbel number, seed yield, essential oil content (EO), EO yield, oil content, and oil yield (Table 2).Table 2 Analysis of variance for the effect of cropping pattern and fertilization on evaluated traits in fennel.Full size tablePlant heightThe tallest fennel plants (125.8 cm) occurred in the sole cropping (Fs), while the shortest plants (98.6 cm) occurred in 1F:2FG. Across intercropping patterns, the Fs treatment had 28%, 14%, 11% taller fennel plants than 1F:2FG, 2F:2FG, and 2F:4FG, respectively (Fig. 1A). Compared to the unfertilized control, HA and BFS increased fennel plant height by 10% and 13%, respectively (Fig. 1B).Figure 1Means comparison for the main effects of cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on plant height (A), and fertilization [C (control), HA (humic acid), BFS (biofertilizers)] on plant height (B) and 1000-seed weight (C) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image1000-seed weightCompared to the unfertilized control (3.9 g per 1000 seeds), BFS and HA increased the 1000-seed weight of fennel by 24.1% and 14.5% (4.9 and 4.5 g per 1000 seeds), respectively (Fig. 1C).Umbel numberThe fennel sole cropping fertilized with HA produced the most umbels of fennel (51.5), while 2F:4FG without fertilization produced the least (32). Averaged across fertilizer types within each intercropping system, 1F:2FG, 2F:2FG, and 2F:4FG had 21.1%, 16.3%, and 26.7% fewer umbels than fennel sole cropping, respectively. Across intercropping systems, HA and BFS increased the umbel number by 17.8% and 16.5% compared with the unfertilized control, respectively (Fig. 2A).Figure 2Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on umbel number (A) and seed yield (B) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageSeed yieldThe different intercropping patterns had lower fennel seed yields than fennel sole cropping. Sole cropping fertilized with BFS and HA produced the highest fennel seed yields (2233 and 2209 kg ha–1, respectively), followed by unfertilized sole cropping (1960 kg ha–1). The lowest seed yields occurred in the unfertilized controls in 1F:2FG (933 kg ha–1) and 2F:4FG (1033 kg ha–1). Averaged across fertilization treatments, fennel seed yield in 1F:2FG, 2F:2FG, and 2F:4FG decreased by 41.7, 26.8, and 36.3%, respectively, compared to fennel sole cropping (Fs). Averaged across intercropping patterns, HA and BFS increased fennel seed yield by 33.3% and 39.5% compared with the unfertilized control, respectively (Fig. 2B).Essential oil content and yieldThe different intercropping patterns produced higher EO contents of fennel than fennel sole cropping. The highest absolute EO content of fennel (4.22%) occurred in 2F:2FG fertilized with BFS, although this did not statistically differ from the 2F:2FG fertilized with HA (4.04%) or 2F:4FG fertilized with HA or BFS (3.8% and 4.00%, respectively) (Fig. 3A). The lowest EO contents occurred in the unfertilized control (2.38%), HA (2.55%), and BFS (2.57%) in the Fs system. Averaged across fertilization treatments, the EO content of fennel in 1F:2FG, 2F:2FG, and 2F:4FG increased by 36%, 52%, and 44% compared to fennel sole cropping, respectively. Within each intercropping pattern, and with the exception of Fs, the HA and BFS treatments had higher EO contents of fennel, none of which significantly differed, increasing by 25% and 29%, respectively (Fig. 3A).Figure 3Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on essential oil content (A), essential oil yield (B), oil content (C), and oil yield (D) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageMaximum EO yields of fennel occurred with HA or BFS applied in 2F:2FG (65.2 and 66.6 kg ha–1) and 2F:4FG (60.7 and 65.5 kg ha–1), respectively, while the lowest EO yields occurred in the unfertilized control in 1F:2FG (27.2 kg ha–1), 2F:2FG (33.2 kg ha–1), and 2F:4FG (32.1 kg ha–1). Averaged across intercropping patterns, the EO yield of fennel increased by 66.1% and 74.7% with HA and BFS, respectively (Fig. 3B).Fennel essential oil compositionGC–FID and GC–MS analyses identified 14 components in the fennel EO (representing 97.4–99.9% of the total composition) (Table 3), with the main constituents being trans-anethole (78.3–84.85%), estragole (3.02–7.17%), fenchone (4.14–7.52%), and limonene (3.15–4.88%). The highest percentage of (E)-anethole, estragole, and fenchone occurred in 2F:2FG with BFS. The highest limonene content occurred in 2F:4 FG with HA. The relative contents of trans-anethole, fenchone, and limonene increased by 3.9%, 16.6%, and 8.4% compared with fennel sole cropping. Notably, the contents of most compounds increased with HA and BFS. Compared to the unfertilized control, trans-anethole, fenchone, and limonene contents increased by 2.9%, 21.5%, and 7.9% with BFS and 2.3%, 22.4%, and 11.9% with HA, respectively (Table 3).Table 3 Proportion of fennel essential oil constituents under different cropping patterns and fertilization.Full size tableFennel oil content and yieldAmong the studied treatments, the highest fennel oil content occurred with HA or BFS application in 1F:2FG (16.3% and 16.6%) and 2F:2FG (16.3% and 17.4%), respectively. The lowest fennel oil contents occurred in the unfertilized control, HA, and BFS treatments (12.5%, 12.8%, and 12.9%, respectively) under fennel sole cropping, and the unfertilized control in 2F:4FG (12.6%). Averaged across fertilizer treatments, fennel oil content in 1F:2FG, 2F:2FG, and 2F:4FG increased by 22.8%, 26.0%, and 12.6% compared with fennel sole cropping, respectively. Across intercropping patterns, HA and BFS increased fennel oil content by 13.5% and 16.5%, respectively (Fig. 3C).The maximum oil yield of fennel (318.6 kg ha–1) occurred in 2F:2FG fertilized with BFS, while the lowest oil yield (129.3 kg ha–1) occurred in 1F:2FG without fertilization. Across intercropping patterns, HA and BFS increased fennel oil yield by 50.8% and 62.6%, respectively (Fig. 3D).Oil compoundsGC–FID and GC–MS analyses identified nine constituents that represented 94.3–97.9% of the total fennel oil composition. The main oil constituents were oleic acid (39.2–48.3%), linoleic acid (17.1–24.8%), stearic acid (10.9–15.4%), lauric acid (10.1–14.00%), and arachidic acid (2.2–3.4%). The highest oleic and linoleic acid contents occurred in 2F:4FG and 2F:2FG fertilized with BFS, respectively. Across fertilizer treatments, oleic and linoleic acid contents increased by 6% and 21%, respectively, under different intercropping patterns compared with fennel sole cropping. Across systems, HA and BFS enhanced oleic acid content by 1.8% and 8% and linoleic acid by 7.9% and 8.2%, respectively, compared with the unfertilized control. The highest percentage of stearic and lauric acids occurred in the unfertilized control of fennel sole cropping. Conversely, the lowest stearic and lauric acid contents occurred in 2F:2FG and 2F:4FG fertilized with BFS, 16.1% and 14.2% higher than fennel sole cropping, respectively. Finally, HA and BFS decreased stearic acid content by an average of 5.4% and 7.2%, respectively (Table 4).Table 4 Proportion of fennel oil constituents under different cropping patterns and fertilization.Full size tablePhenolic compoundsThe main phenolic compounds of fennel were chlorogenic acid (10.4–15.3 ppm), quercetin (7.0–17.2 ppm), and cinnamic acid (4.1–8.9 ppm). The highest chlorogenic acid and quercetin contents occurred in 2F:2FG fertilized with BFS and HA, respectively, while the lowest contents occurred in the fennel sole cropping system without fertilizer. Averaged across the three intercropping patterns, the chlorogenic acid and quercetin contents were 18.5% and 80.1% higher than the fennel sole cropping system. The chlorogenic acid and quercetin contents increased by 13% and 17% with BFS and 22% and 15% with HA, respectively (Table 5).Table 5 Concentration of phenolic compounds in fennel under different cropping patterns and fertilization.Full size tableFenugreekThe main effects of intercropping (I) pattern (C) and fertilizer (F) were significant for all parameters analyzed in fenugreek. Significant I × F interactions occurred for plant height, pod number per plant, seed yield, oil content, and oil yield of fenugreek (Table 6).Table 6 Analysis of variance for the effects of cropping patterns and fertilization on evaluated traits in fenugreek.Full size tablePlant heightThe 2F:2FG intercropping system fertilized with BFS produced the tallest fenugreek plants (63 cm), followed by 1F:2FG with BFS (53.3 cm) and 2F:4FG with BFS (56 cm), and 2F:2FG with HA (55 cm). The unfertilized control produced the shortest fenugreek plants (42 cm) in the sole cropping. Most fertilizer treatments across different intercropping patterns produced taller fenugreek plants than their sole cropping counterparts. Across fertilizer treatments, 1F:2FG, 2F:2FG, and 2F:4FG produced 16.2%, 26.8%, and 14.6% taller fenugreek plants than sole cropping, respectively. Across cropping patterns, BFS and HA increased fenugreek plant height by 5.7% and 15.2% compared with the unfertilized control, respectively (Fig. 4A).Figure 4Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on plant height (A) and pod number per plant (B) of fenugreek. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imagePod number per plantThe fenugreek sole cropping with BFS and HA and 2F:4FG with BFS produced the most pods per plant (21.3, 20.3, and 20, respectively), while the unfertilized controls in 1F:2FG, 2F:2FG, and 2F:4FG produced the least (11.6, 12, and 13.3, respectively). Across fertilization treatments, 1F:2FG, 2F:2FG, and 2F:4FG had 30.1%, 25.6%, and 14.3% fewer pods per plant, respectively, than the fenugreek sole cropping system. Across cropping systems, HA and BFS increased pod number per plant in fenugreek by 25% and 33%, respectively, relative to the corresponding sole cropping (Fig. 4B).Seed number per podAcross fertilization treatments, fenugreek sole cropping produced the most seeds per pod (7.09), followed by 2F:4FG (6.02), 2F:2FG (4.93), and 1F:2FG (4.41) (Fig. 5A). In relative terms, sole cropping produced 60.5%, 43.9%, and 17.6% more seeds per pod than 1F:2FG, 2F:2FG, and 2F:4FG (Fig. 5A). Across cropping patterns, BFS and HA increased seed number per pod by 8.1% and 17.4% compared with the unfertilized control, respectively (Fig. 5B).Figure 5Means comparison for the main effects of cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on seed number per pod (A) and 1000-seed weight (C), and fertilization [C (control), HA (humic acid), BFS (biofertilizers)] on seed number per pod (B) and 1000-seed weight (D) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image1000-seed weightAmong different cropping patterns, sole cropping and 1F:2FG produced the highest (10.45 g) and lowest (8.34 g) fenugreek seed weights, respectively. In relative terms, fenugreek sole cropping produced 25.3%, 21.8%, and 12.4% higher seed weights than 1F:2FG, 2F:2FG, and 2F:4FG, respectively (Fig. 5C). Across cropping patterns, BFS and HA increased fenugreek seed weight by 3.7% and 5.7% compared with the control, respectively (Fig. 5D).Seed yieldMeans comparisons showed that sole cropping produced higher fenugreek seed yields than intercropping patterns. Sole cropping with BFS (1240 kg ha–1) and HA (1217 kg ha–1) produced the highest seed yields followed by the unfertilized control (Fig. 6A). The unfertilized control in 1F:2FG (437 kg ha–1) and 2F:2FG (467 kg ha–1) produced the lowest fenugreek seed yields. In all cases, and within each cropping pattern, BFS and HS produced higher fenugreek seed yields than the unfertilized control. As a result, BFS and HA increased fenugreek seed yield by 25.2% and 31.5% compared with the unfertilized control, respectively (Fig. 6A).Figure 6Means comparison for the interaction effects of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on seed yield (A), oil content (B), and oil yield (C) of fenugreek. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageOil content and yieldThe 2F:2FG cropping pattern with BFS produced the highest fenugreek oil content (8.3%), while the unfertilized control in sole cropping produced the lowest (5.9%). Across fertilizer treatments, 1F:2FG, 2F:2 FG, and 2F:4 FG produced 11.7%, 18.5%, and 15.7% higher fenugreek oil contents than sole cropping, respectively. In the 2F:2FG and 2F:4FG cropping patterns, BFS produced higher oil content (%) than HA. As a result, across cropping patterns, HA and BFS increased fenugreek oil content by 12.3% and 19.4%, respectively (Fig. 6B).Sole cropping with HA and BFS and 2F:2FG with BFS produced the highest fenugreek oil yields (77.1, 80.0, and 74.4 kg ha–1, respectively), while the unfertilized controls in 1F:2FG and 2F:4FG produced the lowest (27.51 and 29.8 kg ha–1, respectively). The 1F:2FG, 2F:2FG, and 2F:4FG cropping patterns produced 45.9%, 20.7%, and 41.5% lower fenugreek oil yields than fenugreek sole cropping, respectively. Moreover, except for sole cropping, BFS produced the highest fenugreek oil yield, followed by HA and the unfertilized control (Fig. 6C).Oil compoundsGC–FID and GC–MS analyses identified seven constituents (representing 91.09–99.27% of the total composition) in fenugreek oil. The main oil constituents were linoleic acid (26.1–37.1%), linolenic acid (16.9–22.4%), oleic acid (15.1–21.2%), palmitic acid (11.2–17.1%), lauric acid (5.0–12.3%), and myristic acid (3.1–6.4%). The highest linoleic and oleic acid percentages occurred in 1F:2FG and 2F:4FG with BFS. The 1F:2FG cropping pattern with BFS also had the highest linolenic acid percentage. The fenugreek sole cropping system without fertilization (control) had the lowest content of these three compounds. The intercropping patterns had 17%, 18.2%, and 17.1% higher oleic, linoleic, and linolenic acid contents than fenugreek sole cropping. In addition, HA and BFS increased oleic acid content by 15.6% and 8.8%, linoleic acid content by 12.8% and 7%, and linolenic acid content by 7.5% and 12.9%, respectively. Fenugreek sole cropping without fertilization produced the highest lauric acid and palmitic contents, 29.33% and 22.81% higher than the intercropping patterns (Table 7).Table 7 Proportion of fenugreek oil constituents under different cropping patterns and fertilization.Full size tablePhenolic compoundsThe main phenolic compounds in fenugreek were chlorogenic acid (2.01–5.49 ppm), caffeic acid (2.42–4.93 ppm), quercetin (1.98–4.45 ppm), comaric (1.09–2.43 ppm), apigenin (1.97–2.99 ppm), and gallic acid (1.76–2.92 ppm). The 2F:2FG cropping pattern with HA produced the highest quercetin and gallic acid contents, and 2F:4FG with HA produced the highest chlorogenic and caffeic acid contents. The 2F:2FG and 2F:4FG cropping patterns with BFS produced the highest comaric and apigenin contents, respectively. In contrast, fenugreek sole cropping without fertilization produced the lowest contents of the abovementioned compounds (Table 8).Table 8 Proportion of fenugreek concentration of phenolic compounds under different cropping patterns and fertilization.Full size tableLand equivalent ratio (LER)The 2F:4FG and 2F:2FG intercropping patterns treated with BFS had the highest partial LERs for fennel (0.82) and fenugreek (0.72), respectively. In addition, 2F:2FG with BFS and 1F:2FG without fertilization produced the highest (1.42) and lowest (0.86) total LERs, respectively (Fig. 7).Figure 7Partial and total land equivalent ratio (LER) for seed yields of different fennel and fenugreek intercropping patterns [1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] and fertilization [C (Control), HA (humic acid), BFS (biofertilizers)]. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image More

In this study we analysed how tree and arborescent palm species richness was related to aboveground carbon stock, commercially relevant timber stock, and commercially relevant NTFP abundance in tropical forests, and how these relationships were influenced by environmental stratification at different spatial scales. We found that species richness showed significant relationships with all three ecosystem services stock components, but its relationships were strongly influenced by variation across forest types and biogeographical strata. This is further explained below.Across the Guiana Shield, species richness showed a positive relationship with carbon stock and timber, but not with NTFP abundance. Although relationships only differed in significance among the biogeographical subregions, they differed in direction between terra firme forests and white sand forests. Species richness was positively related to carbon stock and timber stock in terra firme forests, whereas it was negatively related to NTFP abundance in white sand forests. The positive species-carbon relationship across forests of the Guiana Shield is in line with the effects described by hypotheses such as the ‘niche complementarity’ and ‘selection effect’10 and is in line with previous findings at regional spatial scales6,21. To our knowledge, the relationship between species richness and timber stock has not been previously analysed for tropical forests. Interestingly, the observed positive species-timber relationship in terra firme forests of the Guiana Shield contrasts with the negative species-timber relationship found for subtropical forests in both the U.S.A. and Spain20, although this may be explained by the difference in ecosystems. The non-significant species-NTFP abundance relationship across the Guiana Shield and the negative relationship within white sand forests seems to contradict previous findings. Steur et al.24 found a negative species-NTFP abundance relationship for tropical forests in Suriname. However, this negative relationship was found across multiple forest types, including flooded forests that had low species richness and high NTFP abundance. These flooded forests most likely influenced the species-NTFP abundance relationship across all forest types.In contrast to the relationship between species richness and carbon stock, no mechanism has been proposed for how species richness would influence commercial timber stock and NTFP abundance. Although our results suggest that species richness had a positive relationship with timber, the relationship was not found within multiple biogeographical subregions. For NTFP abundance, species richness did not contribute to explaining variation when variation across biogeographical subregions was accounted for (i.e. was included as an explanatory variable). We here tentatively propose that both commercial relevant timber stock and NTFP abundance are driven by variation in species floristic composition, rather than by species richness. For services such as commercial timber and NTFP provisioning, only a subset of all species is relevant (in this study, 9.4% of all morphospecies for timber and 3.8% for NTFPs), and such subsets are likely not random selections. For example, for Suriname, it was found that variation in commercially relevant NTFP abundance was driven by a particularly small selection of NTFP producing species with high abundances (referred to as ‘NTFP oligarchs’)24, and for commercial relevant timber stock, it is commonly known that selections tend to include more abundant than rare species. Additionally, as the relative abundance of species tends to vary across floristic regions in Amazonia, where, for example, certain species are dominant in particular forest types and biogeographical regions31,32, it can be expected that commercial timber stock and NTFP abundance are determined by floristic composition. In support, for NTFP abundance in Suriname tropical forests, Steur et al.24 found that floristic composition was a stronger predictor of NTFP abundance than species richness.Across all of Amazonia, species richness had a positive relationship with carbon stock, but only when variation among biogeographical regions was accounted for. The positive species-carbon relationship across Amazonia partly contrasts with previous findings at continental spatial scales11,13. When variation across climatic and/or edaphic variables was accounted for, Sullivan et al.13 found no significant species-carbon relationship across South-America, while Poorter et al.33 did find a positive relationship across Meso- and South-America. Here, we propose that accounting for differences among biogeographical regions can explain the previously found contrasts at continental spatial scales. In our dataset, for individual regions, we found either a positive relationship or a non-significant, but weakly positive, relationship between carbon stock and species richness (Fig. 2). However, when the data were aggregated across all regions, this resulted in a non-significant, and weakly negative, relationship. This reflects a known statistical phenomenon referred to as a ‘Simpson’s paradox’34, in which a relationship appears in multiple distinct groups but disappears or reverses when the groups are combined. Additional post-hoc tests of leaving one region out at a time showed that this pattern was not dependent of any particular biogeographical region. This is the first time that an analysis based on empirical data provides evidence for a Simpson’s paradox in species-ecosystem service relationships.It is likely that the observed differences in carbon stock across the biogeographical regions of Amazonia are influenced by multiple factors. For example, the biogeographical regions used in our analyses were recognised according to differences in substrate history, geological age and floristic composition, which could all contribute to variation in carbon stock. The substrate history and geological age of the biogeographical regions have been related to differences in soil fertility35, while multiple spatial gradients in floristic composition identified across the Amazon coincide with a spatial gradient in wood density28. However, further analysis is needed to obtain better insight into the relative contributions of these and other variables to explain the observed variation in carbon stock across the biogeographical regions. This requires data on multiple environmental variables, including floristic composition, climatic variables such as the length of the dry period, soil conditions, and intensity of disturbance.In our analyses, terra firme forests determined the relationship of species richness with the carbon stock, timber stock, and NTFP abundance across the datasets. Although this is most likely the effect of unequal sample sizes, with terra firme forests being the dominant forest type in terms of sample size (n = 130 vs. n = 21 for the Guiana Shield dataset; n = 257 vs. n = 26 for the Amazonia dataset), we expect that the observed relationships reflect the general pattern. Terra firme forests are the most dominant forest type in terms of geographical area32 and were representatively sampled. Regardless, the analyses per forest type had added value. The significant relationship between species richness and NTFP abundance in white sand forests across the Guiana Shield would otherwise have been overlooked.Due to the known scarcity of reliable and adequate information on which timber and NTFP species are being commercially traded36,37,38,39, we used a fixed set of timber and NTFP species to apply across the Guiana Shield plots. However, in reality, timber and NTFP species can be expected to vary according to socio-economic factors, such as culture, access, and harvest costs, which may change over space and time. Therefore, estimates of timber stock and NTFP abundance can be expected to differ across spatial gradients, and thus, their possible relationships with species richness cannot be easily generalised. To circumvent this, timber stock and NTFP abundance would have to be estimated on the basis of ‘flexible’ species selections that can change according to local socio-economic contexts. To this end, detailed information on both commercially relevant timber and NTFP species is urgently needed. Yet, for our study area, we did not observe major differences in selected species, and we included broad selections of species, which should make timber stock and NTFP abundance robust against small deviations in species selection. It must be noted that our approach of quantifying commercial relevant timber stock and NTFP abundance does not consider the value of timber and NTFPs for subsistence use. In addition, NTFPs can also be derived from other growth forms, such as lianas, shrubs and herbs. Last, because NTFP production data was not available we used NTFP abundance as a proxy for NTFP stock, following similar assessments of NTFP stock 24,40. A limitation of this approach is that each NTFP species individual has an equal contribution to NTFP stock, whereas it can be expected that large individuals may have a larger contribution than smaller individuals and that production volumes can differ for different types of NTFPs, for example barks vs. seeds.Our findings illustrate the importance of considering environmental stratification and spatial scale when analysing relationships between biodiversity and ecosystem services. First, environmental stratification can help detect relationships that are otherwise obscured by environmental heterogeneity. For example, although the association between species richness and carbon stock across Amazonia was relatively weak (explaining ~ 3% of total variation vs. ~ 15% in the Guiana Shield) and was obscured by variation in carbon stock across biogeographical strata, by using environmental stratification the positive relationship remained detectable. Second, environmental heterogeneity tends to vary with spatial scale; therefore, its importance needs to be checked according to spatial scale. For example, at the regional scale of the Guiana Shield, biogeographical subregions explained a moderate amount of variation in carbon stock (~ 20%), while at the spatial scale of Amazonia, biogeographical regions explained more than half of total variation in carbon stock (~ 55%). Such an increase and ultimate importance of variation across biogeographical strata might also explain the absence of a significant relationship between species richness and carbon stock across African and/or Asian tropical forests as reported by Sullivan et al.13.In our analyses, we found evidence of a positive relationship between species richness and carbon stock across and within Amazonia. This supports the notion that win–win scenarios are possible in conservation approaches, where, for example, REDD+ can be expected to help conserve tropical forests that contain large amounts of carbon stock and high concentrations of species9. However, we conclude that species richness is not always a strong predictor of biomass-based ecosystem services. In our analyses, NTFP abundance was not driven by species richness, and we ultimately expect the same for timber stock. We expect that differences in floristic composition, linked to differences across forest types and biogeographical strata, will be more relevant than species richness in explaining variation in timber stock and NTFP abundance. This would mean that conserving timber and NTFP related ecosystem services requires the development of additional region-specific strategies that account for differences in floristic composition. For example, areas with high concentrations of timber or NTFPs could be considered in the designation of multiple use protected areas41, such as the extractive reserves in Brazil, or be included as ‘high conservation value areas’ (HCVAs) in sustainable forest management certification42. More

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Phylogenetic analysis with multiple markersThe final alignment of 5670 bp length contained 843 variable sites (14.7%). Missing data accounted for 53.5% of the alignment cells and the relative GC content was 39.5%. Our phylogeny suggests that the investigated Holarctic taxa of the niger clade sensu Ref.34 are divided into two major clades with strong statistical support (Fig. 1). The first major clade (L. niger group) consists exclusively of Palearctic species (L. niger, L. platythorax, L. japonicus, L. emarginatus, L. balearicus, L. grandis, L. cinereus, the L. alienus-complex, L. sakagamii, L. productus and L. hayashi), with the exception of an unnamed Nearctic subclade recovered as sister to the rest of the group. This unnamed subclade we describe as a new species below (L. ponderosae sp. nov.). Lasius ponderosae sp. nov. corresponds to what was previously known as the Nearctic form of “L. niger” sensu ref.17, but includes some western Nearctic populations formerly assigned to “L. alienus”17,52 as well. Monophyly of L. ponderosae sp. nov. was fully supported by Bayesian inference (pp = 1) and moderately supported by maximum likelihood (66% bootstrap support, Fig. 1). Lasius ponderosae sp. nov. is distantly related to L. niger; and L.niger is a close relative of L. japonicus and L. platythorax, as well as other Palearctic taxa. The second major clade (L. brunneus group) within the investigated Holarctic members of the L. niger clade contains both Nearctic and Palearctic species not closely related to the taxa of interest (Fig. 1).Figure 1Molecular phylogeny of 26 Holarctic ant taxa belonging to the subgenus Lasius sensu Wilson (1955) and two outgroup taxa (L. pallitarsis and L. mixtus). The phylogeny was calculated under the coalescent model and incorporates data from 9 genes (mtDNA: COI, COII, 16S, nuDNA: Defensin, H3, LR, Wg, Top1 & 28S). Names of species native to the Nearctic are shown in red and those of species native to the Palearctic in blue. Node labels show posterior probability (Bayesian inference) followed by bootstrap support (Maximum likelihood). The scale bar indicates the length of 0.01 substitutions/site.Full size imageDNA-barcodingThe native North American species L. ponderosae sp. nov. contains at least 15 COI-mitotypes (n = 28 sequenced specimens) belonging to four distinct deep lineages, with divergences of up to 5.9%. Haplotype diversity was 0.899 and nucleotide diversity was 0.012. None of the mitotypes of this species was found to be widespread or particularly abundant. In striking contrast, low genetic diversity was found in L. niger across its entire distribution (Fig. 2). No more than 7 different COI-mitotypes were detected in samples from distant localities representing most of the known range (n = 70 specimens from 12 countries), from Spain in the West to the Siberian Baikal-region in the East (Fig. 2). Their maximum pairwise divergence was only 0.6%, with a haplotype diversity of 0.682 and a nucleotide diversity below 0.001. One mitotype of L. niger is highly dominant within the native range, occurring from Western Europe to Central Siberia (mitotype h2 in Fig. 2).Figure 2Mitotype tree and distribution maps for 98 DNA-barcodes belonging to 7 mitotypes of the ant Lasius niger (blue, n = 70) and 15 mitotypes of L. ponderosae sp. nov. (red, n = 28). The red dashed line delimits the expected natural range of L. ponderosae sp. nov.53 Maps have been created using the free R-package “ggmap” v3.0.0 (https://github.com/dkahle/ggmap) in R v4.1.1. Map tiles by Stamen Design, under CC BY 3.0.Full size imageRecent Palearctic L. niger introduction to CanadaPalearctic Lasius niger was introduced to several localities in coastal Canada in recent times, where at least 11 populations were found in two metropolitan areas (Vancouver and Halifax areas, see Table S2 for details). Those populations consist of the most dominant Palearctic mitotype of L. niger (h2). However, in 3 localities in the Vancouver area, 3 specimens with a second mitotype were found (mitotype h4, Fig. 2, Table S2) in syntopy with those carrying the most common mitotype h2. This second Canadian COI-mitotype (h4) was not found among our samples from the Old World, although it only differs by a single nucleotide substitution from mitotypes found there. A review of BOLD data revealed that the Canadian barcoded specimens of L. niger were mostly collected in anthropogenic habitats such as schoolyards (Supplementary Table S2).Description of Lasius ponderosae sp. novLasius ponderosae Schär, Talavera, Rana, Espadaler, Cover, Shattuck and Vila. ZooBank LSID: urn:lsid:zoobank.org:act:22E2743A-2F1C-4870-B318-A1F2DF2B464C Etymology: ponderosae alludes to the ponderosa pine tree (Pinus ponderosa) that is at the centre of occurrence in the ponderosa pine—gambel oak communities in the western Rocky Mountains and northern Arizona.Type material: located at the Museum of Comparative Zoology, Cambridge, USA. Two paratype workers each will be deposited at the collections of University of California Davis (UCDC), the University of Utah (JTLC) and the Natural History Museum of Los Angeles County (LACM).Holotype: worker, Fig. 3a–c. Type locality: USA, Utah: Uintah Co., Uintah Mtns., 2408 m. 18.6 mi N. Jct. Rt. 40 on Rt. 191, 40.66378°N, − 109.47918°E, leg. 15.VII.2013, S. P. Cover; J. D. Rana, collection code SPC 8571. Measurements [mm]: HL: 0.899, HW: 0.823, SL: 0.821, EL: 0.239, EW: 0.189, ProW: 0.56, ML: 1.069, HTL: 0.863, CI: 92, SI: 100.Figure 3Frontal, lateral and dorsal view of the holotype worker (a–c), a paratype gyne (d–f) and a paratype male of Lasius ponderosae sp. nov. (g–i).Full size imageParatypes: 15 workers, two gynes (Fig. 3d–f), two males (Fig. 3g–i) from the same series as the holotype, morphometric data is given in the Appendix, Table S5 and Table S6. CO1 mitotype h17: Genbank Accession no. LT977508.Description of the worker caste: A member of a complex of cryptic species resembling L. niger. Intermediate in overall body size, antennal scape length and eye size and comparable to related species (Table 1). Terminal segment of maxillary palps and torulo-clypeal distance relative to head size shorter than in related Palearctic species (Table 1). Mandibles with 8 or rarely 7 or 9 regular denticles and lacking offset teeth at their basal angle. Penultimate and terminal basal mandibular teeth of subequal size, and the gap in between with subequal area than the basal tooth. Anterior margin of clypeus evenly rounded. Dorsofrontal profile of pronotum slightly angular (Fig. 4a). Propodeal dome short and flat, usually lower than mesonotum (Fig. 4a). Body with abundant and long pilosity, especially lateral propodeum, genae, hind margin and underside of head. Pilosity of tibiae and antennal scapes variable, ranging from almost no setae (“L. alienus”-like phenotype) to very hairy (“L. niger”-like phenotype). Microscopic pubescent hairs on forehead between frontal carinae long and fine. Clypeus typically with only few scattered pubescent hairs (Figs. 3, 4c). Coloration of body dark brown, occasionally yellowish- or reddish-brown or slightly bicolored with head and thorax lighter than abdomen. Femora and antennal scapes brown. Mandibles and distal parts of legs yellowish to dark brown. Specimens of all 3 castes are shown in Fig. 3a–i and morphometric data are summarized in Table 1 and raw measurements are available in Table S5 and S6.Table 1 Morphometric data of Lasius ponderosae sp. nov. and comparison to morphologically similar Palearctic species.Full size tableFigure 4Average thorax profile of Lasius ponderosae sp. nov. (a) and members of the Palearctic L. niger-complex (b). Figures were created by image averaging (L. ponderosae sp. nov n = 35; Palearctic L. niger-complex n = 30 specimens). Frontal view of head and detail of clypeus of the Holotype worker of L. ponderosae sp. nov. (c) and a non-type worker of L. niger (d).Full size imageDiagnosis: Lasius ponderosae sp. nov. workers key out to “L. niger” using Wilson’s 1955 key to the Nearctic Lasius species. However, some populations with reduced pilosity may also be identified as “L. alienus” using this key. Lasius alienus is a Eurasian species not known from North America33. The Nearctic “L. alienus” sensu Wilson (1955) includes both, L. americanus as well as populations of L. ponderosae sp. nov. with sparse setae counts on tibia and/or scapes. Lasius ponderosae sp. nov. can be distinguished from L. americanus by the presence of abundant, long setae surpassing the sides of the head in full face view (nGen  > 5 and nOcc  > 10 vs. nGen  0.8 across models and runs). The strongest predictors were: Annual Mean Temperature (mean variable importance = 0.32), Mean Temperature of Coldest Quarter (0.23), Temperature Annual Range (0.23) and Temperature Seasonality (0.24). The contribution of land cover was low (0.02). The model predicted high probabilities of occurrence of L. niger in the eastern United States and southeastern Canada, including the island of Newfoundland, and small areas of suitable habitat in southwestern Canada and the Aleutians (Fig. 6). The area with high predicted occurrence probability of L. niger in the New World includes the two sites where populations have actually established (which were not used in the modeling): Nova Scotia and Vancouver. Further areas with high occurrence probabilities are New England, Southern Ontario, the Great Lakes-region and the Northern Appalachians. Low occurrence probabilities were found for the central North American prairies as well as arctic, boreal, arid, subtropical and tropical regions (Fig. 6). Considering the highest occurrence probability range (0.8–1 on a 0–1 probability scale), the area of suitable habitats for L. niger is 4,547,537 km2 in Europe and 1,308,920 km2 in North America. For an intermediate to high occurrence probability range (0.5–1) we estimated 5,371,055 km2 in Europe and 3,054,283 km2 in North America, and for the widest probability range (0.2–1) we estimated 6,155,643 km2 of suitable areas in Europe and 6,889,745 km2 in North America (Fig. 6).Figure 6Projected occurrence probability from ecological niche modeling for the Palearctic ant Lasius niger which has been introduced to Canada, based on 19 climatic and one land use variable. The intensity of blue colour indicates the probability of occurrence on a 0–1 scale based on 180 presences (black circles) and 182 absences (white circles) in the native range in the Old World (a). The model was then projected to North America to estimate areas of suitable habitat for this introduced species (b). These maps have been created using the free R-package “ggplot2” v3.3.5 (https://ggplot2.tidyverse.org) in R v4.1.1.Full size image More

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