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Data sourcesThe data come from the Ministry of Commerce of the People’s Republic of China’s 2019 Guide to Foreign Investment and Cooperation Country, as well as the websites and research literature from the Fraser Institute and the World Bank. The datasets include 14 factors that influence the environmental risk of overseas mining investment in the Philippines are summarized in Tables 1 and 2. The specific reasons that we choose these data in the Philippines are as follows:Table 1 Evaluation factors and grading standards of environmental risk for an evaluation of overseas mining investment in the Philippines.Full size tableTable 2 Risk index data.Full size tableTable 3 Correlation function value of each evaluation index used in an evaluation of overseas mining investment in the Philippines.Full size tableThe Philippines is a multi-ethnic island nation in Southeast Asia located in the western Pacific Ocean. The country has a total land area of 299,700 km2 and a population of 101 million. The Philippines is rich in mineral resources, and the area of known mineralization accounts for 30% of the land area in the country. According to the National Bureau of Geology and Mining in the Philippines, gold, copper, nickel, and chromium reserves rank third, fourth, fifth, and sixth in the world, respectively, in terms of mineral reserves per unit area. Nonferrous metal mining in the Philippines has great potential. To date, 13 types of metal minerals have been discovered, including gold, copper, nickel, aluminium, chromium, silver, lead, and zinc, with total reserves of 7.1 billion tons. Twenty-nine types of nonmetallic minerals have also been discovered with total reserves of 51 billion tons. The Philippines is an important producer and exporter of metallic mineral resources such as copper and nickel6,33.The Philippines has been one of the countries most in favour of overseas mining investment in the region near China. Before the mid-1990s, the Philippines was a favoured country for international mining investors; however, in the late 1990s, changes to national policies and social unrest led to a decline in the mining investment environment. Since January 2003, President Arroyo has proposed a reform of the mining development strategy in the Philippines, and the mining investment environment has improved. However, combined with the political, religious and security issues in the Philippines, especially the peoples’ attitude towards foreign investment, the current mining policy environment in the Philippines is not ideal. Therefore, to comprehensively and objectively understand and analyse the mining investment environment in the Philippines, relevant documents were collated and analysed. Following the principles of importance, practicality, scientificity and systematicness in the design of the index system, the accepted classification rules and data released by authoritative agencies such as the World Bank were used for the evaluation basis1,2,3,4,5,6,7,8, which selected 14 factors that have bearing on political policy, economic, financial, sociocultural, and infrastructure risks. The classification standard and valuation of each index are provided in Table 1. According to the classification standard and valuation index objectives, the risks were divided into five levels (i.e., I–V, which reflect high, higher, general, lower, and low risks, respectively). The Philippines’ risk index data are listed in Table 2.Determination of index weights: analytical hierarchy processThis method integrates quantitative and qualitative evaluations to improve the accuracy of decision making32,33,34,35,36,37,38. The basic principles and steps of the AHP method are as follows:Step 1: The complex problem is decomposed to make it multi-element in nature.Step 2: These elements are grouped, and a hierarchical structural model is established.Step 3: A discrimination matrix is constructed, and any two factors are compared with a 1–9 scaling method to obtain the relative importance of each index at each level, which can be expressed quantitatively.Step 4: The largest eigenvalue and the corresponding eigenvector of the discrimination matrix are calculated using the mathematical method, where the eigenvectors and weight coefficient values are listed in terms of the importance of the evaluation factors.Step 5: The consistency of the discrimination matrix is tested based on the consistency index ( CI) calculated as (CI = frac{{{lambda_{max }} – n}}{n – 1}) as well as with the average random consistency index (RI). If the random consistency ratio (CR = frac{CI}{{RI}} < 0.10), then the results of the hierarchy analysis are considered to be consistent, and the resulting weight distribution values are reasonable. If this is not the case, then the weight coefficient values should be redistributed to adjust the values.Entropy weight theoryIn information theory, the importance of studying the degree of dispersion of the whole system is central to the entropy method. The specific steps for these calculations are as follows:Step 1: Data collection and sorting: The initial evaluation matrix composed of (m) evaluation indexes and (n) evaluation objects is as follows:$$ {{text{X}}_{{text{ij}}}} = left[ {begin{array}{*{20}{c}} {{x_{11}}}&{{x_{12}}}& cdots &{{x_{1{text{n}}}}} \ {{x_{21}}}&{{x_{22}}}& cdots &{{x_{2n}}} \ vdots & vdots & vdots & vdots \ {{x_{m1}}}&{{x_{m2}}}& cdots &{{x_{mn}}} end{array}} right] $$ (1) Step 2: Data standardization: All index values ( {x_{ij}}) in matrix ( {X_{ij}}) are normalized as follows:$$ {text{x}}_{ij}^{prime} = {raise0.7exhbox{${{x_{ij}}}$} !mathord{left/ {vphantom {{{x_{ij}}} {sumlimits_{i = 1}^m {{x_{ij}}} }}}right.kern-nulldelimiterspace}!lower0.7exhbox{${sumlimits_{i = 1}^m {{x_{ij}}} }$}} $$ (2) Step 3: Calculation of information entropy: The entropy of each evaluation index can be obtained from$$ {E_i} = frac{{sumlimits_{j = 1}^n {x_{ij}^{prime}ln x_{ij}^{prime}} }}{ln n} $$ (3) Step 4: Calculation weight: The weight of each evaluation index can be calculated as follows:$$ {w_i} = frac{{1 - {E_i}}}{{sumlimits_{i = 1}^m {left( {1 - {E_i}} right)} }} $$ (4) where ( {w_j}) is the index weight and ( sumlimits_{j = 1}^n {{w_j} = 1} ). The larger the entropy weight is, the greater the effect of the index on the scheme, in that it contains and transmits more decision information that has a greater influence on the final evaluation decision39,40,41,42,43,44.Combination weighting model based on game theoryThis approach differs from the traditional simple linear combination weighting method. The central idea of this approach is to “coordinate conflicts and maximize benefits” by comprehensively considering the relationship between the indexes, balancing the subjective and objective weights, and optimising the index weight values. The basic algorithm is as follows:Construction of the basic weight vector setAssuming that ( H) weight values are obtained using the ( H) weighting method, the basic weight vector set of the ( H) method is$$ {w_k} = left( {{w_{k1}},{w_{k2}}, cdots {w_{kn}}} right),k = 1,2, cdots ,H $$ (5) Any linear combination of ( H) weight vectors is$$ w = sumlimits_{k = 1}^H {{a_k}{w_k}^T} ,{a_k} > 0 $$
(6)
where ( {a_k}) is the linear combination coefficient, and ( w) is the comprehensive index weight value of the ( H) weight set.Optimal combination weightTo find the balance between the different weights, the optimal effect weight vector ( W) was obtained. In the calculation process, it is converted into an optimisation of the weight coefficient ( {a_k}) to minimise the deviation between ( w) and ( {w_k}), as follows:$$ minleft| {sumlimits_{j = 1}^H {{a_j}{W_j}^T – {W_i}^T} } right|,i = 1,2, cdots ,H;j = 1,2 cdots ,H $$
(7)
From the differential properties of the matrix, the first-order derivative condition for the optimisation of Eq. (7) becomes$$ sumlimits_{j = 1}^H {a_j} {W_i}W_j^T = {W_i}W_i^T $$
(8)
By solving Eq. (8), the combination coefficients ( left[ {{a_1},{a_2}, cdots ,{a_H}} right]) can be obtained and normalised according to ( a_k^* = {a_k}/sumlimits_{k = 1}^H {a_k} ). The final combination index weight is ( W = sumlimits_{k = 1}^H {a_k^*W_k^T} ,k = 1,2, cdots ,H) 31,32.Workflow of extension matter element theoryThe theoretical basis of extenics involves the matter element and extension set theories, and its logical cell is the matter element. As such, extenics introduces the concept of the matter element that organically combines quality and quantity. It is a triple group composed of things, features, and quantity values for things, which are depicted as R = (things, features, quantity values). The matter element concept correctly describes the relationship between quality and quantity, and it can be more appropriate to describe the change process of objective things. Different objects can have the same characteristic element and are represented by the matter element with the same characteristics. For convenience, many matter elements with the same characteristics are expressed in a simple way.Determination of the classical and joint domains$$ {R_{ij}} = left( {{N_j},{C_i},{V_{ij}}} right) = left[ {begin{array}{*{20}{c}} {N_j}&{C_1}&{{V_{1j}}} \ {}&{C_2}&{{V_{2j}}} \ {}& vdots & vdots \ {}&{C_i}&{{V_{ij}}} end{array}} right] = left[ {begin{array}{*{20}{c}} {N_j}&{C_1}&{left( {{a_{1j}},{b_{1j}}} right)} \ {}&{C_2}&{left( {{a_{2j}},{b_{2j}}} right)} \ {}& vdots & vdots \ {}&{C_i}&{({a_{ij}},{b_{ij}})} end{array}} right] $$
(9)

Equation (9) is a matter element body with the same characteristics of a matter element with the same characteristics ( {R_{ij}}), in which ( {N_j}) is the ( j) evaluation category, ( {C_i}) is the ( i) evaluation index, and ({V_{ij}} = left( {{a_{ij}},{b_{ij}}} right)left( {i = 1,2, cdots ,n;j = 1,2, cdots ,m} right)) is the range of quantity values ( {N_j}) for the index ( {C_i}), which is the classical domain of the data range taken by each category for the corresponding evaluation index.$$ {R_P} = left( {P,{C_i},{V_{iP}}} right) = left[ {begin{array}{*{20}{c}} P&{C_1}&{{V_{1P}}} \ {}&{C_2}&{{V_2}_P} \ {}& vdots & vdots \ {}&{C_n}&{{V_{nP}}} end{array}} right] = left[ {begin{array}{*{20}{c}} P&{C_1}&{left( {{a_{1P}},{b_{1P}}} right)} \ {}&{C_2}&{left( {{a_{2P}},{b_{2P}}} right)} \ {}& vdots & vdots \ {}&{C_n}&{({a_{nP}},{b_{nP}})} end{array}} right] $$
(10)
where ( P) is the whole of the category, ( {V_{iP}}) is the range of quantity values taken of ( P) for ( {C_i}), and ( {R_P}) is the ( P) joint domain.Determination of the matter element to be evaluatedFor ( q) to be evaluated and using the matter element to express the detected data or analysis results, the matter element ( {R_q}) to be evaluated can be expressed as$$ {R_q} = left( {q,{C_i},{v_i}} right) = left[ {begin{array}{*{20}{c}} q&{C_1}&{v_1} \ {}&{C_2}&{v_2} \ {}& vdots & vdots \ {}&{C_n}&{v_n} end{array}} right] $$
(11)
where ( q) is some thing and ( {v_i}) is the quantity value ( q) for ( {C_i}), which are the specific data obtained by the monitoring of the things that are to be evaluated.Determination and calculation of the degree of relationDetermination of the degree of relation for the thing to be evaluated in each category is expressed as follows:$$ {K_j}left( {v_i} right) = = left[ {begin{array}{*{20}{c}} {frac{{rho left( {{v_i},{V_{ij}}} right)}}{{rho left( {{v_i},{V_{iP}}} right) – rho left( {{v_i},{V_{ij}}} right)}}begin{array}{*{20}{c}} {}&{} end{array}rho left( {{v_i},{V_{iP}}} right) – rho left( {{v_i},{V_{ij}}} right) ne 0} \ {begin{array}{*{20}{c}} {}&{} end{array} – rho left( {{v_i},{V_{ij}}} right) – 1begin{array}{*{20}{c}} {}&{}&{} end{array}rho left( {{v_i},{V_{iP}}} right) – rho left( {{v_i},{V_{ij}}} right) = 0} end{array}} right] $$where ( rho left( {{v_i},{V_{ij}}} right) = rho left( {{v_i},left( {{a_{ij}},{b_{ij}}} right)} right) = left| {{v_i} – frac{{{a_{ij}} + {b_{ij}}}}{2}} right|-frac{{{b_{ij}} – {a_{ij}}}}{2}).The calculation of the thing ( q) to be evaluated for the degree of relation ( j) is expressed as$$ {K_j}left( q right) = sumlimits_{i = 1}^n {{a_i}{K_j}left( {v_i} right)} $$Determination of the levelDetermination of the level is expressed as follows:If ( {K_{j0}} = max left{ {K{}_jleft( q right)} right},j in left( {1,2, cdots ,m} right)), ( q) belongs to level ( {j_0}).In the extension set, the concept of a relational function is established. Any element in ( U) can be quantitatively described by the relational function value, which can belong to the positive, negative, or zero domains (i.e., belongs to the elements in the same domain). It is also possible to separate different levels from the size of the relational function valu27,28,29,30. More

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In this study, we produced a global fLNR and ({V}_{{c}_{{max }}}^{25}) map using an RF model trained primarily by remote sensing and in situ observations and examined seven ({V}_{{c}_{{max }}}^{25}) models based on 5 competing hypotheses with regard to their assumptions on fLNR. Our results suggested that the global average fLNR was 18.2 ± 6.2%, and the global distribution of fLNR was dominated by the interaction between fLNR and leaf traits (i.e., LMA and LPC), followed by regional influences from climate (i.e., VPD and PAR) and soil characteristics (i.e., soil pH and sand percentage). We used RF fLNR distribution and its relationships with environmental covariates to evaluate five empirical and two optimal ({V}_{{c}_{{max }}}^{25}) models, and found that the models showed different degrees of inefficacy in reproducing RF fLNR. Here, we discuss the mechanisms underlying the detected fLNR responses to leaf traits, climate, and soil characteristics and propose future directions to improve the simulation of fLNR and ({V}_{{c}_{{max }}}^{25}) in models.Negative correlation between fLNR and LMAOur finding that fLNR is negatively related to LMA agrees with a previous meta-analysis that found fLNR decreases by 0.54 ± 0.08% with a 1 g/m2 increase in LMA based on a univariate regression10, though another study reported that the negative relationship between fLNR and LMA was non-significant using a smaller dataset11. Using the global dataset, we found a relatively small sensitivity of fLNR to LMA (−0.19 ± 0.001% per 1 g/m2) when accounting for climate and soil (Fig. 3b).Higher LMA is the result of plants allocating more biomass and nitrogen to building cell walls, which may cause a reduction in CO2 diffusion into the mesophyll as well as relative nitrogen allocated to RuBisCO38. Leaves with greater LMA are tougher and usually have a longer leaf lifespan11,36. Therefore, the negative correlation between fLNR and LMA highlights the trade-off between photosynthesis and persistence along the leaf economic spectrum: on one end, leaves invest more nitrogen in RuBisCO to increase the photosynthetic capacity and enhance carbon uptake; on the other end leaves invest more nitrogen in structural biomass to improve leaf longevity and lengthen the carbon uptake period. The latter is especially true for evergreen species that have greater LMA and smaller fLNR than deciduous and herbaceous species10. The coordination of fLNR and LMA is also consistent with a recent analysis highlighting the role of LMA in determining the variation and predictability of LNC in ecosystem models39.In addition, we found that LPC increases fLNR in tropical evergreen forests and mixed forests, which tend to be more phosphorus limited40. Our result is consistent with previous studies reporting coupled leaf photosynthetic capacity (i.e., ({V}_{{c}_{{max }}}^{25}) or maximum photosynthetic capacity (Amax)) and LPC for tropical species41,42. This result indicates potential widespread adjustments of plants nitrogen use by phosphorus investment for photosynthesis and plant growth43 in tropical and mixed forests. In addition, we note that the productivity of some grasslands44,45 and boreal forests46,47 has also been reported to be limited by phosphorus availability, however, we did not detect a strong positive dependence of fLNR on LPC globally for these ecosystems in our study. The difference potentially suggests that the phosphorus limitation of grasslands and boreal forests is not as prevalent as that for tropical and mixed forests (though some mixed forests are in the boreal region).Climate and soil impacts on fLNRThe response of fLNR to climate is often implicitly included in ({V}_{{c}_{{max }}}^{25}) models. We found that fLNR was sensitive to annual VPD globally. Several studies have reported that plants in arid environments (i.e., high VPD) tend to have a higher Amax and LNC48,49 as plants enhance photosynthetic capacity to maintain a given assimilation rate with lower stomatal conductance and reduced water loss. Such a response to aridity has been described using the least-cost theory19,21. Our results show that other than Amax and LNC, fLNR also increases with VPD, consistent with a recent study reporting higher nutrient use efficiency for plants in semi-arid ecosystems of the African Sahel49. We note that an earlier study reporting differently that a dry site has a smaller ({V}_{{c}_{{max }}}^{25})/LNC ratio (i.e., smaller fLNR) than a wet site19, though it used annual precipitation, not VPD to define aridity.In addition, the positive relationship between PAR and fLNR for non-forests (Fig. 3c) provides a potential explanation of the light acclimation of photosynthesis, as several studies have found that leaf and ecosystem Amax can be enhanced by intermediate to long-term average PAR50,51,52. For non-forest ecosystems, our results suggest that photosynthetic light acclimation emerges as plants increase fLNR in response to increasing annual PAR. However, for forests (except EBF) the results suggest that photosynthetic light acclimation may emerge more due to the increase in LNC as we did not detect a positive response of fLNR to light (Fig. 3c).Soil characteristics have been reported to influence Amax and LNC37, but we found no studies that have examined the impact of soil characteristics on fLNR. Among the eight soil properties we examined, we found positive responses of fLNR to soil pH and soil sand percentage, followed by small influences of bulk density and silt for certain ecosystems (i.e., croplands, needle leaf forests). pH influences the ability of soil to hold on to nutrients, including Ca2+, K2+, and Mg2+, that are essential to plant growth. A higher pH means more available nutrient cations as acid soils replace nutrient cations with H+. Several studies have reported a positive effect of pH on Amax37, non-temperature standardized (V_{c_{max}})20, and LNC39. Soil sand percentage had a positive impact on fLNR, possibly because sandy soils tend to be less fertile53 and thus stimulate plants to use their nitrogen more efficiently for photosynthesis and growth. The global influence of soil on fLNR was generally smaller than leaf traits and climate, but our analysis indicated that on 11.9% of the vegetated surface, soil characteristics contributed more than 15% of the changes in fLNR (Fig. 3a).Notably, our study found that the soil nitrogen content has a limited impact on the spatial variation of fLNR (Fig. 3). The result implies that processes such as nitrogen deposition/addition are unlikely to affect plants fLNR. The soil nitrogen map we used was upscaled from ground observations of soil profiles in the World Soil Information Service (WoSIS) database. About 47.4–81.4% of the soil profiles in WoSIS are collected from the 1980s to 2020s54, when there were strong N deposition effects55. Therefore, we expect the N deposition effect has been implicitly included in our analysis. We acknowledge that some studies have suggested N deposition influenced leaf nitrogen content and photosynthesis56,57, however, the influence is limited to certain biomes, deposition load range, and time after the deposition. It is unclear whether these localized and time-dependent effects can influence the global variation of fLNR.Uncertainty in the derivation of fLNR and ({V}_{{c}_{{max}}})
fLNR was derived based on Eq. (1) (see “Methods”) that mechanistically links ({V}_{{c}_{{max }}}^{25}), LNC, and fLNR, with the assumption that specific activity of RuBisCO (α25) and mass ratio of RuBisCO to nitrogen (fNR) are relatively constant values. The average uncertainty of RF fLNR was about 4.20 ± 2.20% (Supplementary Fig. 3). The uncertainty of fLNR was propagated from several sources including RF ({V}_{{c}_{{ma}x}}^{25}), α25, fNR, and LNC (Supplementary Fig. 3). Among them, the α25 ranges between 47.34 and 60 μmol CO2/g RuBisCO/s, and fNR ranges between 6.11 and 7.16 g RuBisCO/g N4. Our uncertainty test showed that the influence of α25 and fNR uncertainties on global fLNR were only around 1.13 ± 0.39% and 0.80 ± 0.27%, respectively (see “Methods”; Supplementary Fig. 3). Physiologically, α25 is a value that reflects the change in active sites of RuBisCO and the kinetic constant of the enzyme RuBisCO (k25). The number of active sites of RuBisCO is often regarded as a fixed value (set at 6 × 1023/mol RuBisCO) for vegetation on the land surface5, but there are reports showing that k25 varies with species9, leaf ages58, and temperature59. While these dependencies are elusive due to limited observations, previous studies have reported that k25 negatively correlates with LNC60 and LMA61. The negative relationship between k25 and LMA or LNC is potentially caused by the relatively lower drawdown of CO2 from intercellular spaces to the chloroplast as increased LMA increases mesophyll resistance. In that case, the negative dependence of k25 and α25 on LNC and LMA might account for part of the negative dependence of fLNR on LMA that we found (Fig. 3b), though the negative influence of LMA on α25 was weak and within the range of uncertainty, we quantified (Supplementary Fig. 3).Compared to α25 and fNR, the uncertainties in LNC and RF ({V}_{{c}_{{max }}}^{25}) incurred larger uncertainties in fLNR. We found that LNC alone caused changes of 3.35 ± 2.16% in fLNR and RF ({V}_{{c}_{{max }}}^{25}) caused 3.13 ± 1.50% (Supplementary Fig. 3). Our study is the first attempt to upscale in situ ({V}_{{c}_{{max }}}^{25}) to the globe using remote sensing, while similar studies have done that for other leaf traits33. The observations used for training RF were densely distributed in Europe and North America, while inner Asia, Southeast Asia, Africa, and high-latitude regions are much less constrained by observations (Supplementary Fig. 6a). In addition, we did not consider temperature acclimation when standardizing in situ (V_{c_{max}}) to ({V}_{{c}_{{max }}}^{25}) (Eq. (2)), in order to facilitate the comparison with models that only estimate ({V}_{{c}_{{max }}}^{25}). However, the uncertainty related to temperature scaling should be limited as acclimated and non-acclimated temperature scaling factors for (V_{c_{max}}) are similar under 30 °C62,63.The choice of an LNC map is another source of uncertainty in the derivation of fLNR. There are several global LNC maps available other than the EB1728 map we used, namely AMM1833 and CB2031. Each product has been validated in their respective studies (Supplementary Table 3). To examine the uncertainty incurred by the choice of LNC maps, we calculated fLNR using each of the three LNC maps. The three resulting fLNR maps show similar spatial patterns (Supplementary Fig. 10), with the spatial correlation coefficients (r) between them ranging from 0.57 to 0.71 (p  More

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Intra- and interspecific differences: comparison with former studies on Pagellus species and other fish speciesTo understand the relationship between function, shape, and the environment, it is essential to include the morphological variability of otoliths, considering biological and environmental variability leads to otolith shape heterogeneity through morpho-functional adaptation to different habitats. Several authors have highlighted changes in otolith shape between species and, in many cases, among populations of the same species (e.g., herrings, salmonids, and lutjanids). The intra-specific variability of otolith morphology and shape are the basis of stock separation and assessment and is related, especially in sagittae, with environmental (e.g., water temperature, salinity, and depth) and biological factors (e.g., sex, ontogeny, and genetic variability)20.The analysis of the three Pagellus species revealed that otolith morphology and morphometry did not follow those described in a previous study1 conducted in the western Mediterranean Sea and the Atlantic Ocean in term of rectangularity, circularity, sagitta aspect ratio and sagitta length to total fish length ratio. Although the images provided in our study closely resembled those from research in other geographical areas, the morphometric measures (obtained according to the procedures and methods described in the previous literature1,20,23,32) exhibited several differences. Considering the scale of our study compared to previous studies, it is difficult to provide an entirely valid comparison; the differences in sagitta morphology and morphometry could have been triggered by biotic and abiotic parameters (e.g., temperature, salinity, genotype, habitat type, differences in food quality and quantity)13,33,34,35. Such environmental and genetic factors may be primary drivers of otolith morphometry and morphology among fishes in different habitats. Therefore, detected shape differences are at the basis of fish stock differentiation36.Our results indicate that the min–max circularity and rectangularity of P. bogaraveo from the Southern Tyrrhenian Sea differ from those calculated in a previous study1 in the western Mediterranean Sea, and the north and central-eastern Atlantic ocean. Moreover, the increase in circularity in larger specimens, confirms a greater tendency toward circular than elliptical otolith shape in southern Tyrrhenian Sea species compared to those in other Mediterranean and Atlantic areas.Despite statistical differences and correlations in this study supported the hypothesis that some changes in sagitta morphology are related to fish size differences, several aspects and studies should be performed to better understand this relation. The negative correlation between the ratio of sulcus acusticus surface to the entire sagitta, rostral morphology, and the increase in specimen’s size was related to the expansion in the length and surface of the entire sagitta and rostral area in larger specimens. These features, with no statistical relevant increment in sulcus acusticus surface and increased rostrum length, could be correlated with more pronounced peripheral sagitta growth in this species. Sagitta, in fact, after fish pelagic phase, might increases its surface in the rostral area and the margins. Since the present study did not take into account ontogenetic stages and specimens age, it is hard to relate this result with sagitta and sulcus acusticus growth. But reading this increase by an ecological point of view, it could be related to the lifecycle of the species. During the juvenile stage, in the early stage of pelagic life, the species inhabits shallow water. Adults inhabit deep-water environments, migrating down the continental slope to a depth of 800 m after the juvenile stage. These changes in habitat might be the cause of morphological variations in the sagittae, highlighting the relationship between sagitta features and environmental and biological factors.The P. acarne specimens demonstrated the highest number of morphometrical parameters that did not follow those of the same species described in a previous study (i.e., circularity, rectangularity, sagitta length to total fish length ratio, and sagitta aspect ratio)1. These morphometrical changes are reflected in otolith shape. The otoliths from specimens in our study were largely circular, with highly irregular margins and a rostrum that varied in length and width through the left and right sagitta, as indicated by the significant differences in rostrum aspect ratio values.The morphometrical results in P. erythrinus revealed differences in circularity, rectangularity, sagitta length to total fish length ratio and sagitta aspect ratio compared to a previous study1 in the western Mediterranean Sea and north-central eastern Atlantic ocean.The P. erythrinus specimens were characterized by a pentagonal otoliths shape, and increased circularity compared to the same species from other areas. The results also indicated small differences between the left and right sagitta. This small differences were previously described in other Mediterranean sub-areas, for example, otolith width values in P. erythrinus specimens collected in the Gulf of Tunisia37,38.As said above for P. bogaraveo, it is hard to relate the differences between juveniles and adults with fish growth due to the absence in present paper of ontogenetic and age analysis. The higher width than length, demonstrated by min–max width values in Tables 1 and 2, in sagittae of adults P. erythrinus specimens could be correlated with an exponential increment in fish size compared to the sagittal length. Further analyses on ontogenetic development of this species are required to better define the sagitta growth related to fish growth.The increase in sulcus acusticus surface exhibited in the adult specimens could be correlated with feeding habits; during its adult life, this species is a benthic feeder and inhabits deeper environments than juveniles28,29.Although meaningful lateral dimorphism of the sagittae was detected only in flatfish, statistical analysis revealed several small differences between the left and right sagitta in P. erythrinus and P. acarne, as previously described in other round fish species, such as Chelon ramada (Risso, 1827)40, Diplodus annularis (Linnaeus, 1758)41, Diplodus puntazzo (Walbaum, 1792)42, Clupea harengus (Linnaeus, 1758)43, and Scomberomorus niphonius (Cuvier, 1832)44.Our study confirmed slight differences between width values in left and right sagitta previously described in P. erythrinus and extend the differences to other parameters, such as circularity and rectangularity (Tables 1, 2). Concerning P. acarne, however, marginal differences between the left and right sagittae were observed for the first time.This slight differences are supported by the literature concerning genetic and environmental stressors41. Since the functional morphology of otoliths is not completely understood, it is difficult to find a direct link between these small differences and the ecology of the species. However, several eco-functional factors, such as feeding behavior, deserve attention as fundamental for a better understanding of the relationship between otolith features and species habitat. For example, P. erythrinus largely preys on strictly benthic organisms, such as polychaetes, brachyuran crabs, and benthic crustaceans. Most of these species frequently escape predators by hiding under the sandy substrate. Other Sparidae (Lythognathus mormyrus, Linnaeus, 1758) feed on benthic fauna, engulfing sediment and filtering it in the buccal cavity, demonstrated by the high percentage of detritus and benthic remains (e.g., scales, urchin spines, and benthic foraminifers) in the gut and stomach contents29. To engulf sediment, P. erythrinus performs a particular movement with the head and body, laterally shifting and pushing forward, to dig the bottom sand and reach prey. This kind of behavior, common in all benthopelagic species with the same feeding habits, could influence the sagitta growth and morphology, triggering small differences between the left and right sagitta. Further studies on this and other species with this behavior (e.g., L. mormyrus) are necessary to confirm this hypothesis.Concerning inter-specific differences in sagitta morphology among the three species, it is difficult to read the results obtained in this study eco-morphologically since an insufficient understanding of the functional morphology and physiology of otoliths prohibits a direct relationship, valid for all the species, between eco-functional features and otolith morphology. Nevertheless, as expected, the shape analysis (Fig. 1) revealed clear differences between the three congeneric species. Considering several ecological, functional, and biological features in each species, the results have demonstrated a sagitta morphology that could be in accordance with the ecology and lifestyle of these three congeneric seabreams.Relationship between otolith morphology and ecology/lifestyleThe sagittae of P. acarne exhibited a shape resembling those in other pelagic species, with a long rostrum and the entire sagitta elongated and narrower than those in other two seabream species. The species that show the most pelagic habits, with largely planktivorous feeding at a small size, adapt also to benthopelagic feeding activity in adult life. The statistically relevant similarity found in P. bogaraveo could be proof of the ecomorphological adaptation of sagittae to pelagic and demersal environments. This hypothesis may be confirmed by marked differences in shape compared to those in P. erythrinus, which is the most benthic among the three species.Pagellus erythrinus was the species with the shortest rostrum. It also has the most benthic habits, largely preying on epibenthic and infaunal species. Moreover, its ecology and life cycle differ among the three species under study since they are strictly related to the benthic environment. This lifestyle could be in accordance with the differences observed in the shape analysis results. The sagitta contours appeared more circular and wider than those in the other two species. The PCA and LDA also confirmed the most difference in shape among the three species.The species with the most marked antirostrum and sagitta shape was P. bogaraveo, which is a cross between the other two congeneric species. Pagellus bogaraveo is a demersal species, which inhabits the deep biocenosis and feeds in both benthic and mesopelagic environments. Furthermore, the ecology of this species could support the sagitta shape described in our study27,30.Otolith morphology and morphometry in congeneric Pagellus species described in this study has followed the relationship between sagittal parameters, habitat, and depth described in previous literature15. According to several authors, the percentage of species with large otoliths increases with depth, except for abyssal depth. The specimens of P. bogaraveo analyzed in this paper (especially adult individuals) had larger otoliths than the other two Pagellus species due to their demersal habits (they inhabit the continental slope to a depth of 800 m). A larger sagitta is essential in demersal environments to compensate for light reduction by providing improved acoustic communication, sound perception15,45, and a sense of equilibrium46.Sulcus shapeConsidering the sulcus acusticus, in the otolith atlas for the western Mediterranean Sea and Atlantic ocean1, studies describing and comparing otoliths10 and the diversity and variability of otoliths in teleost fishes9, the sulcus in P. bogaraveo, P. acarne and P. erythrinus was described as heterosulcoid, with an ostium shorter than the cauda and a long, narrowed rostrum, especially in adult P. bogaraveo and P. acarne individuals. Heterosulcoid otoliths were also observed in south Tyrrhenian Sea Pagellus individuals, with marked differences between juvenile and adult specimens. In a demersal species, such as P. bogaraveo, juveniles live in shallow, coastal water. Once adults, they inhabit deeper water (to a depth of 800 m). Changes in the crystalline and morphological structure of sulcus acusticus between juveniles and adults reflect this species’ need to adapt to deeper environments with less light.The results indicate that in P. bogaraveo, the sulcus acusticus does not differ in surface between juvenile and adult specimens. This feature could be correlated with earlier sulcus acusticus development in this species, compared to P. erythrinus and P. acarne, emphasizing the role of the sulcus acusticus in this demersal species48,49. This might also confirm the strict correlation between biological and environmental factors and sagitta morphology in studied seabreams species.Another morphological feature of the sulcus acusticus, which might support the ecology of the species, is the deep ostium and cauda. In adult specimens of P. bogaraveo and P. acarne, the sulcus structure deeply penetrated in the sagitta carbonate structure. Conversely, in adult P. erythrinus specimens, the sulcus did not penetrate as deeply as in the other two seabream species. This sulcal feature could correspond with the ecology and feeding behavior of P. erythrinus, which specializes in benthic strategies, including small differences between left and right sagitta and the absence of the notch and antirostrum in sagittae.Although the deeper sulcus acusticus in P. bogaraveo and P. acarne might be linked to depth distribution, as in P. bogaraveo, it may also correspond with high mobility related to feeding behavior, as in both P. bogaraveo and P. acarne. The different depths of sulcus acusticus can change the thickness of the otolithic membrane, by varying the relative motion of otoliths with the macula sacculi2. As previously demonstrated50, the different thicknesses of the otolithic membrane induce differences in mechanical resistance between the otolith and sensory epithelium.The differences in sulcus acusticus and otolith ratio between P. bogaraveo specimens and the other congeneric species, demonstrated by the results, might be also correlated to the differences in habitat, feeding habits, and soundscape.Despite the lack of information concerning the physiological ear response related to variations in macula or sulcus size, the sensory hair cells in macula sacculi are likely to be affected by changes in sulcus depth, shape, 3D structure (planar vs. curved), and surface.The significant difference in relative sulcus area may be due to typical alteration in this parameter concerning differences in the mobility patterns, food, feeding behavior, and spatial niche.Higher relative sulcus area ratios have been observed in the deepest species or those with high mobility49. In our study, the morphometry results concerning the sulcus did not follow those in the previous literature, displaying higher values in P. erythrinus and P. acarne compared to P. bogaraveo, although the latter inhabits a deeper environment than the other congeneric species.This higher relative sulcus acusticus surface and the larger, curved sulcus acusticus of P. acarne and P. erythrinus could be correlated with higher mobility in these species (especially P. acarne). In P. erythrinus, however, these features might be related to its benthic lifestyle.As demonstrated by the PCA and LDA of sulcus acusticus parameters, P. erythrinus and P. acarne, which share similar depths and habitats, revealed marked similarities, whereas P. bogaraveo, which lives in the deepest strata of the water column, displayed the most different sulcus acusticus. However, PCA and LDA indicated that the otolith shape in the entire P. erythrinus sagitta was significantly different compared to those in P. acarne and P. bogaraveo.These features could provide a reading key for sagitta and sulcus acusticus eco-morphology in the life cycle and environmental adaptation of fish.The connection between the otoliths and the macula sacculi is fundamental for transducing environmental acoustic signals and for the relative motion of fish (balance). The sulcus acusticus is the area of the otoliths in which this connection occurs.Features of the textureFurthermore, the external textural organization23 changes between juveniles and adults or when environmental changes occur. The differences in the external textural organization found in juveniles and adults support those reported in the literature concerning other species39. Figures 3b, c and 5a–h, demonstrate that our study supported this prediction. However, P. bogaraveo and P. erythrinus juveniles, compared with other species (such as gurnards)23 displayed a more uniform, mineralized, external textural organization.Figure 5SEM images of the crystalline structure of P. bogaraveo, juveniles (a, b) and adults (c, d), and P. erythrinus, juveniles (e, f) and adults (g, h) sagittae.Full size imageAccording to previous literature8, improved hearing capabilities in a species are closely related to a higher value of relative sulcus area ratio. Habitat features, such as depth, feeding strategies, mobility, trophic distribution, and ontogeny, could also influence this ratio.Hence, it may be concluded that morphological differences in sulcus acusticus shape and surface among species are important for comprehending the ecomorphological and eco-functional role of sagitta 2,48.Comparing the intra-specific differences indicated by our results with those in the literature, discussing other populations, we cannot determine whether site-differences observed in sagitta shape are related to genetic evolution and/or adaptative response to environment. To make this distinction it would require a specific experiment in which offspring from different populations are raised in a controlled environment.Furthermore, the knowledge about physiology and functional morphology is insufficient to provide a clear correlation between inter-specific differences among the three congeneric Pagellus species and their ecological and functional features. However, differences in sagitta morphology and morphometry among these three Pagellus species may be related to differences in lifestyle, ecology, and biology since they follow the ecomorphological features of sagittae and species ecology described in the literature. More