Philippa Kaur

Cohort demographicsA total of 206 female participants were enrolled in the study and passed our quality control standards. All participants were required to be between the ages of 18–26 years old (22.5 ± 2.1) and to be born and at the time living in one of four geographically distinct regions of the world: Barbados; Santiago, Chile; Pretoria, S. Africa; and Bangkok, Thailand. The regions do, however, differ by an order of magnitude in their geographic spread as the intra-distance separating the residence neighborhood of participants ranged from 34 (Barbados) to 681 km (Pretoria, S. Africa) (Fig. S2). The Chilean and the South African datasets are further divided into two contiguous sub-regions, or neighborhoods, to allow for a micro-geographic analysis. The study population is largely dominated by individuals with self-identified Thai heritage (33%), followed by Black African (16%), Afro-Caribbean (14%) and white (14%) descent, although 19% of the Chilean population did not report ethnicity.Study participants, despite the divergent geographies, mostly have similar dietary and lifestyle habits (Table S1). Over half the study population (62%) have a normal BMI, with the mean BMI in this range (22.6 ± 5.5). The diets of the different cohorts are also similar as of the total cohort, 78% consume a starch heavy diet (≥ 4 days a week) of rice, bread and pasta, followed by 66% who frequently consume (≥ 4 days a week) vegetables and fruit and 49% who frequently consume dairy products. The study population is split by level of tobacco exposure, with 51% of the population having never smoked, and 43% being exposed to second-hand smoke through living with a smoker. Over half (56%) of the study population own one or more pets.Stool microbiomeThe OTUs identified using the UPARSE pipeline17 were used to compute the alpha diversity of the microbial communities using the Chao1 (species richness) and Shannon (species evenness) indices. The mean Shannon indices reveal that the microbiota diversity is only significant between Thailand-Chile with FDR  More

Our results show that GoM BFT larval survey samples can provide the crucial mark events for eventual CKMR estimates of adult abundance. The adult parents marked by larval samples can be directly recaptured in the fishery many years later as POPs, and indirectly through their progeny in future samples of larvae, as evidenced by the two cross-cohort HSPs (XHSPs) recovered in this study, which imply that a parent survived and spawned in the GoM in consecutive years. As more cohorts are sampled in future, the growing number of XHSPs could be used to estimate average adult survival rates, in addition to helping with the estimation of adult abundance31, as is now done for southern blue tuna40.There is a modest level of sibship within our 2016 samples, and a high level (involving over half the samples) in 2017, but it turns out not to be high enough to cause serious problems for POP-based CKMR. High sibship per se does not lead to bias in CKMR by virtue of the statistical construction of the estimate, but it does increase variance, which can be summarized through a reduction in effective sample size. In a POP-based CKMR model, our effective sample size would be about 75% of nominal for the two years combined, or 66% of nominal for the targeted sampling of 2017. Since it is actually the product of adult and juvenile sample sizes which drives precision in CKMR14, one way to think about the 75% is that we will need about 33% more adult samples to achieve a given precision on abundance estimates than if we had somehow been able to collect the same number of “independent” juvenile samples (i.e. without oversampling siblings). That increase is appreciable but entirely achievable; for WBFT, it is logistically much easier to collect more feeding-ground adult samples than to collect more larvae, and at present there is no known practical way to collect large numbers of older, more dispersed, and thus more independent, juvenile western origin bluefin tuna (WBFT).This study was motivated by the concern that sibship might be a serious impediment to use of WBFT larvae for CKMR. High levels of sibship have been found in larval collections for other taxa despite a pelagic larval phase, suggesting that abiotic factors can impede random mixing of larvae after a spawning event41. Our larval samples were only a few days old (4–11) and thus had little time to disperse since fertilization; our concern beforehand was that each tow might sample the offspring of a very small number of adults (one spawning group in one night), and in 2017 that repeatedly towing the same water mass might simply be resampling the same “family”. In practice, though, the cumulative effect was limited. Samples were not dominated by progeny from just a few adults; the maximum DPG size (i.e., number of offspring from any one adult) was 5, which is under 2% of the larval sample size. There are several possible reasons for this finding. First, plankton sample tows are typically standardized to a ten-minute duration, covering on average about 0.3 nautical miles. Based on continuous plankton cameras42, each tow is likely to tow through multiple patches of zooplankton, and therefore potentially multiple patches of BFT larvae. Second, spawning aggregations of BFT may contain many adults. For example, on the spawning grounds near the Balearic Islands in the Mediterranean, purse seine fisheries target spawning fish and individual net sets routinely capture upwards of 500 mature individuals43. These numbers suggest that BFT spawner aggregations can be quite large, although the number of individuals that contribute gametes to a single spawning event may be lower. The results of this study pose intriguing scenarios for understanding BFT larval ecology and spawning behavior, which could be explored with larger sample sizes paired with data on oceanographic conditions, direct observation of spawning aggregations, and modeling to compare observed and predicted dispersal. The results of this study are based on just two years of sampling, and numerous practical and theoretical challenges remain to fully understand BFT reproduction in the GoM.Our sibship impact calculations assume use of an unmodified adult-size-based CKMR POP model, where each juvenile is compared to each adult taking into account the latter’s size (e.g.,14). That will give unbiased estimates, which we regard as essential in a CKMR model. However, for WBFT the estimates are not fully statistically efficient, in that some adults receive more statistical weight than others because they are marked more often (by having a large DPG), and thus variance might not be the lowest achievable. Modifying the model to fix that would be simple in a “cartoon” CKMR setting where all adults are identical (e.g., Fig. 1 of14), simply by first condensing each DPG to a single representative, then only using those representatives (rather than all the larvae) in POP comparisons. Each marked parent then receives the same weight, giving maximum efficiency. For the cartoon, this condensed-DPG model still gives an unbiased estimate of abundance, because each DPG has one parent of given sex, and the chance of any sampled cartoon adult of that sex being that parent is 1/N. The DPG-condensed effective sample size is simply half the number of distinct parents, which would be a little larger than the effective sample sizes for the unmodified model shown in Table 3; e.g., in 2017, 504/2 = 252 versus 209. However, no such straightforward improvement is available for an adult-size-based CKMR model such as is needed for WABFT. Using condensed DPGs directly would bias the juvenile sampling against larger more-fecund adults, whose DPGs will tend on average to be larger and thus to experience disproportionate condensation. Those adults would be marked less often by the DPG-condensed juveniles than the model assumes, violating the basic requirements for unbiased CKMR in14. A more sophisticated model might be able to combine unbiasedness with higher efficiency but, since the unmodified adult-size-based POP model that we expect to use is unbiased and only mildly inefficient (at worst 209/252 = 83% efficient, in 2017) there seems no particular need for extra complications at present. However, that may not hold true if we eventually move to a POP + XHSP model, where the impact on unmodified CKMR variance is worse (though there is still no bias, for the same reason as with POPs). Intuitively, the biggest impact that a DPG of size 5 can have in a POP model is to suddenly raise the number of POPs by 5 if its parent happens to be sampled; within a useful total of, say, 75 POPs, the influence is not that large. But if two DPGs both of size 5 in different cohorts happen to share a parent, then the total of XHSPs suddenly jumps by 25— likely a substantial proportion of total XHSPs. Supplementary Material B also includes effective sample size formulae for a simplified XHSP-only model, which demonstrate the increased impact of within-cohort sibship; for our WBFT samples, it turns out that the XHSP-effective size is slightly lower for the targeted 2017 samples (110) than for the 2016 samples (130), unlike the POP-only effective size. Dropping from a maximum theoretical effective sample size of 252 (half the number of DPGs) down to 110 would be rather inefficient and would increase the number of years of sampling required to yield a useful XHSP dataset. This motivates developing a modified POP + XHSP model that retains unbiasedness without sacrificing too much efficiency. In principle, that can be done by condensing each DPG but then conditioning its comparison probabilities on the DPG’s original size, in accordance with the framework in14. This is a topic for subsequent research, and the results will inform future sampling strategy decisions for WBFT.One potential difficulty for western BFT CKMR might occur if a substantial proportion of animals reaching maturity are the offspring of “Western” (in genetic terms) adults who persistently spawn in the western North Atlantic but outside the GoM. However, as long as the adults marked by GoM larvae are well mixed at the time of sampling with any western adults that do spawn outside of the GoM, the total POP-based population estimate of genetically-western BFT from CKMR will remain unbiased. Given evidence from tagging of widespread adult movements within the western North Atlantic2, good mixing in the sampled feeding grounds seems likely; so, even if successful non-GoM western BFT spawning really is commonplace, there should not be a problem with relying on GoM larvae for at least the POP component of CKMR14.Studies of fish early life history have long been considered to have great potential to provide novel insight into the unique population dynamics of fishes44,45,46. Sampling efforts aimed at estimating fish recruitment dynamics have spawned a diversity of larval survey programs. Examples of these long-term programs include the California Cooperative Oceanic Fisheries Investigations, International Council for the Exploration of the Sea (ICES) surveys in the North Atlantic and adjacent areas, Southeast Monitoring and Assessment Program (SEAMAP) in the GoM, Ecosystem Monitoring (EcoMon) in the Northeast U.S., and numerous others, many of which provide indices of larval abundance widely used in fisheries and ecosystem assessments. Yet, as a result of the inherent patchiness of larvae42, sampling variability, and highly variable density dependent mortality45, fisheries scientists have often struggled to determine how larval surveys relate to the adult fish populations. Inclusion of estimates of sibship among larvae collected in surveys could refine estimates of adult spawning stock biomass estimated from these surveys.The results of this study also represent products of decades of work and coordination in obtaining high-quality DNA from larval specimens. Key steps to successful genotyping of larvae include ensuring that larvae are preserved, sorted, and handled in 95% non-denatured ethanol. In addition, strict instrument cleaning protocols must be followed, and stomachs should be removed or avoided (this study used larval tails and, when possible, eyes to avoid cross contamination of prey contents, including possible congeners and other BFT individuals). Exposure to hot lamps during the sorting and dissection processes should also be minimized to ensure that DNA quality is sufficiently high for genotyping-by-sequencing. Although the tissues available for genetic analysis were limited by the needs of other experiments that required BFT tissues, otoliths, gut contents, and other information from the same larvae, we were able to successfully genotype most larvae greater than 6 mm SL and identify thousands of informative SNPs. The lower size limit of larvae could likely be decreased if whole specimens were available for genotyping, although the use of younger larvae could increase the incidence of sibship.In summary, while we observed both FSPs and HSPs in larval collections, with elevated sibship overall and with siblings being more prevalent within tows and in nearby tows, the level of sibship was sufficiently low that collections of GoM BFT larvae can still provide the critical genetic mark of parental genotypes required for CKMR. Our results demonstrate a crucial proof of concept and are the first step towards an operational CKMR modelling estimate of spawning stock abundance for western BFT. More

RESEARCH BRIEFINGS
02 November 2022

The world’s largest tropical peatland complex is in the central Congo Basin. A drying of the climate between 5,000 and 2,000 years ago triggered decomposition of peat in the Congo Basin and emission of carbon into the atmosphere. The tipping point at which drought results in carbon release might accelerate future climate change if regional droughts become more common. More

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UTrack atmospheric moisture tracking modelThe UTrack atmospheric moisture tracking model is a novel Lagrangian model that tracks parcels of moisture forward in three-dimensional space9. UTrack is the first moisture tracking model to employ ERA5 reanalysis data8. The basic principle of the model is that for each mm of evaporation, a certain number of “moisture parcels” is released and subsequently tracked through time and space. At each time step, the moisture budget of the parcels is updated based on evaporation and precipitation at the respective time and location, meaning that for each location of evaporation, a detailed image of the “footprint” of evaporation can be created. All types of evapotranspiration are included, and here is simply called evaporation.For each mm of evaporation, 100 parcels are released 50 hPa above the surface height at random spatial locations within each 0.25° grid cell of input evaporation data. The trajectories of the parcels are based on interpolated three-dimensional ERA5 wind speed and wind direction data, which also have a horizontal resolution of 0.25° and consist of 25 pressure layers in the atmospheric column. The spatial coordinates of each parcel are updated at each time step of 0.1 h. Also, at each time step, there is a certain probability that a parcel is redistributed randomly along the atmospheric column such that, on average, every parcel is redistributed every 24 h (see methods section Moisture recycling dataset: validation and uncertainties below for further details). The relative probability of the new position in the atmospheric column is scaled with the vertical moisture profile. Parcels are tracked for 30 days or until 99% of their moisture has precipitated.To allocate a certain fraction of any moisture parcel to precipitation events at the current time and location, ERA5 hourly total precipitation (P) and total precipitable water (TPW) are interpolated to the simulation time step of 0.1 h. The amount of moisture that precipitates at a certain time step equals the amount of precipitation at that time step over the total precipitable water in the atmospheric water column (P/TPW). Specifically, precipitation A in mm per time step at location x, y at time t that originated as evaporation from a particular source is described as:$${A}_{x,y,t}={P}_{x,y,t}frac{{W}_{{{{{{{{rm{parcel,t}}}}}}}}}{E}_{{{{{{{{rm{source,t}}}}}}}}}}{{{{{{mathrm{TP}}}}}}{{{{{{mathrm{W}}}}}}}_{x,y,t}}$$
(1)
with P being precipitation in mm at time step t, Wparcel,t (mm) the amount of moisture in the parcel of interest, Esource,t the fraction of moisture present in the parcel at time t that has evaporated from the source, and TPW (mm) the precipitable water in the atmospheric water column. The moisture content of parcels is updated each time step using evaporation and precipitation at its current location:$${W}_{{{{{{{{rm{parcel,t}}}}}}}}}={W}_{{{{{{{{rm{parcel,t-1}}}}}}}}}+({E}_{{{{{{{{rm{x,y,t}}}}}}}}}-{P}_{{{{{{{{rm{x,y,t}}}}}}}}})frac{{W}_{{{{{{{{rm{parcel,t-1}}}}}}}}}}{{{{{{mathrm{TP}}}}}}{{{{{{mathrm{W}}}}}}}_{{{{{{{{rm{x,y,t}}}}}}}}}}$$
(2)
The moisture (fraction) that has evaporated from the source is updated as follows:$${E}_{{{{{{{{rm{source,t}}}}}}}}}=frac{{E}_{{{{{{{{rm{source,t-1}}}}}}}}}{W}_{{{{{{{{rm{parcel,t-1}}}}}}}}}{A}_{x,y,t}}{{W}_{{{{{{{{rm{parcel,t}}}}}}}}}}$$
(3)
The moisture flow mij from evaporation in cell i to precipitation in cell j is aggregated on a monthly basis (mm/month), where [x, y] ∈ j becomes:$${m}_{ij}=mathop{sum }limits_{t=0}^{{{{{{{{rm{month}}}}}}}}}{A}_{j,t}frac{{E}_{i,t}}{{W}_{i,t}}$$
(4)
with Wi,t being the tracked amount of moisture from the source cell i at time t. These simulations were performed for all evaporation on Earth during 2008–2017. The results were then aggregated on a mean-monthly basis to produce monthly means, and stored at 0.5 degree resolution. This dataset can be downloaded from ref. 53. For details on how to process the data, we refer to the accompanying paper by ref. 3.Moisture recycling dataset: validation and uncertaintiesAs with all moisture recycling simulations, the ones used in this study rely on a number of assumptions that may affect the moisture recycling rates. All offline moisture recycling models use atmospheric model output to simulate the path of evaporation through the atmosphere to the location where it precipitates. Therefore, there are two sources of uncertainty that affect the moisture recycling estimates: (1) the quality of the atmospheric forcing data and (2) the assumptions in the moisture tracking model.Tuinenburg and Staal (2020)9 explored these sources of uncertainty for a number of locations globally. The effects of a decrease in the quality of the atmospheric forcing data were most important in the vertical resolution of the atmospheric data: the forcing data should have enough vertical levels to resolve any vertical shear in atmospheric moisture transport. If the forcing data has a low vertical resolution, the moisture tracking model is forced with the mean atmospheric flow over a number of layers. In many regions, there are surface moisture flows that are in a different direction than the moisture flow aloft, resulting in a very small vertically integrated transport, which would distort the simulation of atmospheric moisture transport. Compared to the vertical resolution of the forcing data, the horizontal and temporal resolutions were less important in order to keep errors as small as possible. Because of the importance of this high vertical resolution, it was recommended9 to use the ERA5 dataset8 as its forcing dataset, as this currently is the atmospheric reanalysis dataset with the highest vertical resolution.In addition, the change of ERA-interim to ERA5 resulted in a much better land-surface scheme with monthly varying vegetation and better bare soil evaporation. Also, many more observations are assimilated, which results in a better precipitation product compared to ERA-interim. Following this, the tracking of atmospheric moisture using ERA5 allows for a better quality of the atmospheric moisture cycle than before. But, of course, also the already high horizontal resolution of 0.5∘ × 0. 5∘ has the limitation that very localized moisture recycling features like orography and locally varying land use cannot be resolved. Out of these reasons, the uncertainty in the evaporation estimates is a lot larger than that in the precipitation estimates, because of the lack of global evaporation measurements and the difficulty in measuring evaporation in general54,55.There are also uncertainties due to the assumptions in the moisture tracking model that can be split into a category of simulation assumptions and physical assumptions. The simulation assumptions include model formulation (Eulerian vs. Lagrangian model set-ups), time step lengths, number of parcels released, and types of interpolation. Of these simulation assumptions, the most important aspects were the model formulation, with Lagrangian models better able to resolve complex terrain and atmospheric flows. For the other model assumptions (see methods section UTrack atmospheric moisture tracking model), it was chosen to simulate with the highest level of precision before any more information (e.g., more parcels) would no longer affect evaporation footprints and moisture recycling statistics (see ref. 9 for further details). Even though the ERA5 dataset is known to have some precipitation biases in the tropics, the results of UTrack (forced by ERA5) have recently been validated across the tropics by independent measurements of deuterium excess, a measure of a stable isotope that depends on terrestrial precipitation recycling56. UTrack estimates and isotope-based estimates of terrestrial moisture recycling corresponded, especially in tropical rainforests (Kendall’s (overline{tau }=0.52)56), which are found to be moisture recycling hubs on a global scale.Network constructionMotivated by the network-like structure of the data, we here employ a network perspective to study moisture flows. Hence, nodes in such a network are grid cells on a regular spherical grid and edges represent the moisture transported. However, interpreting the dataset directly as a weighted network is neither computationally feasible nor does a weighted network allow for identifying motifs, the building blocks of complex networks17. We, therefore, aim for an approach utilizing an unweighted network.As shown in Fig. S1, moisture recycling strengths are heterogeneously distributed over multiple powers of magnitude. Thus, it is not appropriate to just withdraw the moisture transport volume and include all moisture transport connections within the dataset as equal and unweighted links. Instead, we attempt to highlight the strongest moisture pathways and, thus, the backbone of the Earth’s moisture recycling network. To, on the one hand, include as much moisture volume as possible but also keep the absolute volume of moisture transport represented per edge as similar as possible, we decided to include edges in a data-adaptive way: we step-wise include links starting from the strongest and stop this procedure as the total moisture transport volume exceeds the variable threshold ρ. The resulting edges then represent the backbone of the global moisture recycling network. In the main text, we have shown the results for a network where all edges together represent ρ = 25% of the total moisture transport. Here and in the SI figures, we add a sensitivity analysis for ρ = 20% and ρ = 30% and find that the results are stable for this broader range of total moisture volume thresholds.Network measures and motifsThe topology of an unweighted network is typically encoded in an adjacency matrix A with elements aij indicating if there exists an edge from node i to node j (aij = 1) or not (aij = 0). The degree k of a node i describes the number of adjacent edges pointing towards or away from node i. Hence, the in-degree is defined by25$${k}_{{{{{{mathrm{in}}}}}}}^{i}=mathop{sum }limits_{i=1}^{N}{a}_{ji}$$
(5)
and out-degree is defined by25$${k}_{{{{{{mathrm{out}}}}}}}^{i}=mathop{sum }limits_{i=1}^{N}{a}_{ij}.$$
(6)
To further analyze the topology of a network and, in particular, the local connectivity patterns, we study the presence of three motifs—the feed-forward loop, the neighboring loop, and the zero loop.The feed-forward loop (FFL) consists of three nodes, A, B, and C, where nodes A and C are directly connected via a detour over node B (intermediary node). Therefore, we have two different pathways that focus on node C. Hence, this motif can be referred to as a directed lens, due to its focused flow from two nodes on one singular and its purely directed linkage. This network motif has been studied in the context of tipping elements and has been proven to facilitate tipping cascades by lowering critical thresholds19. The zero loop (ZL) is made up of a bidirectional connection of two nodes. In contrast to the FFL, where node A does not receive feedback from node C, here, both nodes are dependent on each other without a preferred direction of network flow. This facilitates tipping to a much lesser degree than the FFL motif19. The neighboring loop (NBr) is an extension of the ZL. In this case, there is an additional node connected to one of the nodes of a zero loop. Hence, there is a two-step directionality in the motif, but in contrast to the FFL, this motif is characterized by reciprocity.We count the number of motifs a certain node is involved in the network. The number of FFLs is counted as the number that a certain node is a so-called “target” node. The target node is the node, on which the triangular structure of the motif is converging to, i.e., the node that has been referred to as node C above. The ZL is a symmetric motif for the two involved nodes. Therefore, the number of ZLs of a certain node in the network is counted directly as the number of bidirectional interactions of the inspected node. Lastly, the number of NBrs of a certain node is the number of being in the center of a neighboring loop. With this procedure, each node is characterized by its number of FFLs, ZLs, and NBrs (cf. ref. 19).Motif strength and their spatially aggregated differenceTo assess the presence of motifs and, in particular, their relative frequency, we first determine the numbers of FFLs, ZLs, and NBrs per node. Subsequently, we normalize these counts by the respective maximum to obtain the motif strength, which is shown for each network motif in Fig. S5. In Fig. S5a–c, we display the motifs for the global network, and in Fig. S5d–f for the land-to-land network.To specifically characterize the focus regions by means of the network topology, we evaluate which motifs dominate in which region. Consequently, we compute the difference of the motif strengths shown in Fig. S5 and reveal the patterns shown in Fig. 2. For spatially aggregated motif strength differences (Fig. 2c, d), we then compute the average of the respective values inside the highlighted boxes.Sensitivity to link threshold ρ
The network analysis featured in the main text uses those moisture recycling edges that together represent ρ = 25% of all atmospheric moisture recycling on Earth. As we aimed to focus on the strongest moisture flows, we chose a threshold of ρ = 25% aggregating the strongest moisture transport pathways. This allows us to reveal the regions of strongest moisture connections, which are located in and close to the tropics, as we expected. Overall, the aim of this thresholding procedure is to utilize a network approach with unweighted edges but also take into account the large spread of moisture recycling strengths. To test the robustness of the results to the threshold value, we here show the same figures as above in the main text but with different thresholds ρ. Note that the error bars in Fig. 2 are based on the analysis featured in this part (the resulting differences using thresholds of ρ = 20% and ρ = 30%).Figures S6 and S7 show the in- and out-degree of the all-to-all and land-to-land network using a threshold of ρ = 20% (Fig. S6) and ρ = 30% (Fig. S7). Note that the color bar has been adjusted as the number of links differs substantially between the networks. The main difference between Figs. S6 and S7 is the greater emphasis on moisture recycling in the mid-latitudes in Fig. S7. This is a direct consequence of considering more, and thus also some weaker, links. Acknowledging this difference, we stress that especially the land-to-land patterns (Figs. S6c, d, S7c, d) are consistent. In particular, the four focus regions, as defined in the main text, stand out as the main global land-to-land moisture recycling hubs. To support this visual analysis of the in- and out-degree pattern, we furthermore compute the motif strengths for both network configurations for quantitative validation of the results.In line with the main text, we compare the FFL and ZL strength (see Fig. 2a–d). Not only the spatial patterns in our sensitivity analysis agree remarkably well with the results in the main text above, but also the focus regions remain basically the same (cf. Fig. S8 for ρ = 20% and Fig. S9 for ρ = 30% with Fig. 2). The only slight change is the shift towards a directed lens (spatially aggregated FFL and ZL strength difference) for the Amazon basin in the all-to-all network for increasing ρ (cf. Fig. S8c vs Fig. S9c vs Fig. 2c). We attribute the overproportional increase of the number of FFLs to those that include at least one oceanic grid cell to this noticeable shift. This underscores our characterization of the Amazon basin as a directed lens.The spatially aggregated FFL and NBr difference (Figs. S10, S11) is structurally the same as above, where we computed the FFL and ZL difference (see Figs. S8, S9). The spatial patterns and the aggregated values are robust against shifts of ρ. However, for the Amazon basin (AB), the number of FFLs increases overproportionally in the all-to-all network when we include more links in our analysis. In other words, the spatially aggregated FFL-strength for AB increases for higher thresholds ρ (cf. Figs. S10c, S11c and Fig. 2g).Sensitivity to the size of the focus regionsAnother aspect affecting the results is the spatial extent chosen as a focus region (i.e., the rectangles in Fig. 2). Varying the size of these rectangles affects the spatially aggregated measures. For all focus regions besides the Amazon Basin (AB), the values are not significantly affected by changing the rectangle size, as the values close to the focus regions are either coherently negative, as for the Congo Rainforest (CR) and the Indonesian Archipelago (IA), or close to zero (South Asia: SA). The AB is characterized by positive values (tendency to lensing), whereas the more southern parts along the Andes are marked by more negative (corridor/washing machine) values.Hence, we assess the stability of the results by using the spatial region covered by the Amazon rainforest (the extent of the Amazon rainforest is based on ref. 6) and compare them to the ones obtained by using the rectangle. The results featured in Fig. S12 indicate that only considering the rainforest-covered parts of the AB leads to similar or even more positive (lensing) values, confirming our conclusions that the Amazon rainforest region functions differently from the other focus regions.Notes on mapsThis paper makes use of perceptually uniform color maps developed by ref. 57. The underlying world maps have been created by cartopy58. More

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02 November 2022

Predicting the risk of extinction from climate change requires an understanding of the interactions between species. An analysis of how changes in rainfall affect competition between plant species offers a way of tackling this challenge. More

Birds and housing62 males and 8 females of house sparrows were caught with mist nets in September and October 2019 in several sites in Kraków, Poland. Before releasing them to the outdoor aviary located on the campus of the Jagiellonian University, Kraków, Poland, each bird was weighed and banded with a metal band. The aviary measured 3.5 m in width, 10.0 m in length, 2.5 m in height, and was outfitted with trees, bushes, perches, wooden shelters, a water source, and food dishes. Initially, birds were maintained with water and a mixture of seeds: wheat, barley, millet, and sunflower seeds, provided ad libitum. Additionally, they had access to sand with shells and sepia.Experimental designAfter a few weeks of acclimation to captivity, the aviary was divided into two separate parts (3.5 × 5 m): aviary no. 1 (A1) and aviary no. 2 (A2). At the same time male individuals were assigned to two crossed experimental treatments, ensuring that in each aviary birds originated from all sampled populations. The experiment comprised of two different treatments conducted simultaneously—one designed to simulate a deficiency in an environmental factor influencing colouration (the quality of available food), the other—to introduce physiological stress and facilitate trade-offs in the allocation of resources limited by the first treatment (an immune response induced by a bacteria-derived compound, S1).The dietary manipulation was achieved by feeding one group of birds with a low-quality protein food (diet reduced in exogenous amino acids, namely phenylalanine and tyrosine content, which are precursors essential for melanin synthesis; PT-reduced diet), and the other one with a wholesome diet (control diet). At the same time, two levels of immune challenge were achieved within each dietary group, by injecting half of the birds with either lipopolysaccharide (LPS) from the cell wall of Escherichia coli, or a 0.9% saline vehicle (as a control). Four females were placed in each group of males to alleviate interspecific conflicts occurring in all-male sparrow flocks, but they did not take part in the experiments. After three weeks of experiment, birds housed in A1 were moved to A2, whereas birds from A2 were moved to A1.Immune challengeBefore receiving injections, birds were first weighed and then transferred from the outdoor aviary to the laboratory. 31 house sparrows (from both dietary groups) were injected intraperitoneally with 0.026 mg LPS (serotype O55:B5, Sigma-Aldrich) diluted in 0.1 mL of 0.9% saline vehicle, so that each bird received a dose of ca. 1 mg/kg body mass, which had previously been shown to induce sickness behaviour in another passerine, the white-crowned sparrow, Zonotrichia leucophrys55. 31 control males were injected with the same volume (0.1 mL) of 0.9% saline vehicle. All individuals were injected twice throughout the experiment with an interval of three weeks between the injections. Birds were always injected at the same time in the morning and early afternoon (between 9:00 am and 12:30 pm).Diet manipulationDuring the six weeks of the experiment (S1), birds received synthetic diet ad libitum, which constituted of a mixture of protein (WPC80, free amino acids and whey protein isolate BiPRO GMP 9000 (Agropur Inc., Appleton, USA)), fats, carbohydrates, and fiber30. The ingredients were thoroughly mixed to produce small pellets (6 mm in diameter) that the sparrows consumed readily. The experimental diet had phenylalanine and tyrosine at 42% (N = 32) of their level in the control diet (N = 30)30. The food pellets were prepared by ZooLab (zoolab.pl/en/home, Sędziszów, Poland). Each bird was weighed before and after the experiment to monitor potential effects of diet on body mass of each animal. Following the experiment, during the next three consecutive days, the amount of food consumed by passerines within every 24 h (starting from 10 am each day to 10 am next day) was noted for both compartments of the aviary. Because of different numbers of individuals per aviary, an overall weight of food consumed in A1 and A2 was calculated per individual, respectively.Feathers samplingMoult of the black bib feathers was stimulated at the end of the moulting period occurring in natural conditions in early November. At day 1 of the dietary/immunological experiment (S1) a small area of the bib (around 25 mm2) was plucked from each male sparrow held in A1. At day 2 the same procedure was performed on individuals from A2. The time difference is orders of magnitude smaller than the timescale of feather growth and hence it would not affect the results in any way.Because the feather growth rate may differ during melanogenesis, with consequences for final colouration (if feathers grow at a faster rate, pigments may be deposited over a larger surface and therefore result in less intense colouration56, we measured the rate of feather development during the course of the experiment. After three weeks of the experiment, three feathers from the upper, central, and lower region of the previously plucked bib were plucked once again. The mass of the collected feathers was determined to the nearest 0.01 mg (XP26 Micro Balance, Mettler-Toledo, Greinfensee, Switzerland). The experiment was completed after six weeks after fully regrown and developed feathers from the bib and PC2 were sampled the second time (S1). Three feathers from the central part of previously plucked bib region were collected to perform transmission electron microscopy (TEM) imaging, whereas the feathers obtained from the rest of the regrown bib area were subjected to electron paramagnetic resonance (EPR) spectroscopy and feather microstructure analyses (greater spatial density of melanized barbs or barbules may affect colouration17.Feathers measurementsReflectance measurementsAn USB4000 spectrophotometer (range 300–700 nm) with the PX-2 Pulsed Xenon Lamp (Ocean Optics, Dunedin, FL, USA) and a bifurcated probe with 7 × 400 μm optical fibres, equipped with a permanently attached 3 mm long black collar, was used to quantify the brightness of the bib feathers collected at the end of the experiment. The measurements were taken with 90 ms integration time and the probe held at 90° to a feather’s surface. Calibration measurements of a Spectralon white standard (Ocean Optics. Largo, FL, USA) were taken every 15 min during measurements. The order in which the samples were measured was randomized in terms of belonging to the experimental group. From each sample (N = 62), seven feathers were chosen and stacked in one pile on a piece of black paper. Ten reflectance measurements were taken on each pile, avoiding distal, brighter parts of the feathers. The obtained spectra were averaged and smoothed in the package ‘pavo’57. Brightness was calculated as a sum of the reflectance values over all wavelengths of a spectrum, and its lower values were interpreted as those indicative of a more melanin-rich feathers (i.e., absorbing more light).Feather developmentEach feather (3 per individual; N = 62 individuals) was laid on a white card and covered by a microscope slide to flatten the naturally curved feathers. Digital photographs were taken using camera (Canon EOS 7D) and imported to ImageJ v1.52a Software (National Institutes of Health, USA). The lengths of fully developed and undeveloped (still in sheath) parts of each feather were measured. To estimate the degree of a feather’s development, the length of the developed part of the vane was divided by its total length (quill with rachis plus the developed vane, Fig. 4A).Figure 4House sparrow feathers sampled from bib after three weeks of the experiment. Feathers during development (A), a TEM cross-sections of feather sampled from bib after the experiment (B).Full size imageFeather densityBarb density measurements were performed on the sampled regrown black bib feathers (N = 2–3 for each individual; N = 62 individuals), but because of their sparser structure we calculated the number of non-down (i.e., rigid) barbs on both sides of the vane, and divided this number by two (to obtain an average single-sided number of barbs) and then by the length of the rachis.Melanosome density (TEM)Feathers sampled from the bib of male sparrows (N = 62) were fixed for transmission electron microscopy (TEM) analysis in a mixture of 0.25 M sodium hydroxide and 0.1% Tween for 20 to 30 min on a bench-top shaker. Next, the feathers were treated with formic acid and ethanol in the ratio of 2:3 for 2.5 h and dehydrated twice for 20 min in 100% ethanol. Samples were embedded in a mixture of the PolyBed 812 resin (20 ml), DDSA (9 ml), NMA (12 ml) and DMP-30 (0.82 ml). Resin infiltration was gradual from 15% resin content in ethanol through 50%, 70% to 100% without alcohol. Each step lasted for 24 h. Then, the feathers were placed in silicone embedding moulds (Agar Scientific) and transferred to an oven. The polymerization proceeded at the temperature of 60 °C for 16 h. The epoxy resin blocks were then trimmed to get rid of excess resin. The surface of each block was prepared by its trimming, starting from the end of the feather, to approximately 5 mm using a glass knife. Next, ultrathin sections (70 nm) were cut with a diamond knife (DIATOME A. G., Berno, Switzerland) on a microtome (UC7, Leica, Wetzlar, Germany) and collected on single slot grids coated with a formvar film. The sections were then contrasted in uranyl acetate and lead citrate for 3 min. They were viewed and photographed with a transmission electron microscope (TEM) JEOL 2100HT (Jeol Ltd, Tokyo, Japan) for the purpose of investigating the number and density of the embedded pigment granules. For each individual three photographs of the cross-sections from a similar feather region were selected. Melanosome density was measured as the number of melanin granules observed in the barb cross-section divided by its area. Images were analysed using Adobe Photoshop (cross-sections area) and ImageJ (number of melanosomes, Fig. 4B).Melanin content: electron paramagnetic resonance (EPR) spectroscopyQuality and quantity of melanin pigments58 in individual feather samples obtained from the bib of house sparrows (N = 57) were characterized using a Varian E3 spectrometer (Varian, Sunnyvale, LA, USA) equipped with a rectangular resonance (TE 102) cavity. Five milligrams of feathers per individual were placed inside the Wilmad finger quartz dewar WG-816-Q (Rototec-Spintec GmbH, Griesheim, Germany). Prior to inserting the vessel into the resonance cavity of the EPR spectrometer, feathers were pressed down the quartz finger to a height of approximately 0.5 cm to ensure comparable volumes of each sample. Measurements were performed at room temperature, at X-band (9.26–9.27 GHz frequency), using the following parameters: magnetic field range 3240–3340 Gs, microwave power 1 mW, modulation frequency 100 kHz, modulation amplitude and time constant—5 Gs and 0.3 s for quantitative analysis, 1 Gs and 0.1 s for qualitative analysis. An EPR signal was recorded as its first derivative, averaged from three consecutive scans, lasting 160 s each (giving a total of 480 s of scan time per EPR spectrum). Then, the following parameters were measured: peak-to-peak amplitude, area under the microwave absorption curve (the integral intensity of the recorded signal) and linewidth of the EPR absorption curve (ΔH;59).Statistical analysesStatistical analysis was performed in R (version 4.0.2,60) using a two-way ANOVA test, with bird’s diet (control vs. PT-reduced) and applied immune challenges (LPS vs. saline-injections) as the independent variables. The following parameters were used as the dependent variables: feathers reflectance (brightness), feather growth rate, feather density (number of barbs per mm), and melanisation level (expressed as the EPR spectrum amplitude measured in arbitrary units [a.u.]). The density of melanosomes was analysed by fitting a linear mixed-effects model. In this model, melanosome density was used as the dependent variable, with diet, immunological challenge, and slice ID as independent variables, and individual ID as a random-effect term. Additionally, to assess the reliability of measurements, the intraclass correlation coefficient (i.e., technical repeatability) was calculated. The models’ residuals were checked for normality and homoscedasticity. Mean food consumption per individual was analysed by the Friedman test. Body mass before and after the experiment was analysed by fitting a linear mixed-effect model. Body mass was used as the dependent variable, whereas diet, immunological challenge, and time as the independent variables, and individual ID as a random-effect term. The model included the following interaction terms: time × diet, time × injection, and diet × injection, and was reduced by removing the non-significant interactions. Results are reported with appropriate statistical tests and estimates (accompanied by standard errors) signifying relevant factor contrasts (relative to the reference group, which in all analyses was diet: control; injection: LPS, body mass: before experiment).
Ethical noteAll applicable national and institutional guidelines for the care and use of animals were followed. The research was performed under permit no. 25/2019 (with a supplementary permit no. 78/2020) from the 2nd Local Institutional Animal Care and Use Committee in Kraków. More

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