More stories

  • in

    Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan

    Database constructionCollection of metagenomic dataBetween March 2012 and May 2016, seawater samples were collected from five different locations around Sendai Bay, Ofunato Bay, and A-line. Samples (N = 142) were collected from the surface and the subsurface chlorophyll-a maximum (SCM) layers (Fig. 1). DNA was extracted from microorganisms trapped in filters (pore sizes, 0.2, 0.8, 5, 20, and 100 μm). Shotgun metagenomic sequence data (N = 454) were acquired (Supplementary Information 1), comprising 3.57 × 109 reads and 3.56 × 1011 bases in total (Supplementary Information 2). 16S rRNA gene sequences in the 0.2-μm fraction were subjected to PCR using universal primers to obtain 111 amplicon metagenomic sequences (6.92 × 106 reads, 1.69 × 109 bases) (Supplementary Information 2).Figure 1Location, changes in water temperature, and changes in chlorophyll-a (Chl-a) concentrations at the sampling points. (a) Sampling points along the Pacific coast of northeastern Japan (Sendai Bay, Ofunato Bay, and A-line). (b) Sampling points C5 and C12 in Sendai Bay. The map was generated using Ocean Data View (https://odv.awi.de) with data imported from the NOAA server (accessed on 22 February 2021). (c) Changes in water temperature and chlorophyll concentrations at Sendai Bay and A-line sampling points. Red circles indicate the depth of the sampled water. X-axis: dates from 2012 to 2014. Y-axis: water depth from the surface.Full size imageTime-series analysis of microbial species compositionWe performed microbial taxonomic assignments through analyses of amplicon and shotgun metagenomic sequence data using the SILVA and NCBI NT databases. For C5 and C12 samples from Sendai Bay and A4 and A21 samples from A-line, sufficient time-series data were available to plot changes in microbiota over time (Fig. 2). By clicking the displayed taxon at the website of Ocean Monitoring Database, the microbiota composition of lower taxa is revealed. For example, Fig. 2 shows that the cyanobacteria community increased in abundance during the summer.Figure 2Time-series analysis of microbial communities along the Pacific coast of northeastern Japan. Each sampling point shows the number of ribosomal sequences normalized to 1000 (excluding no hits). Clicking on the graph at the website of Ocean Monitoring Database exhibits the next taxonomic levels. This figure shows an example of the change in Cyanobacteria communities over time from April 2012 to May 2014. SUF: surface layer (1 m), SCM: the subsurface chlorophyll-a maximum layer.Full size imageWe acquired substantial 16S rRNA gene amplicon sequencing data at the C5 fixed point at Sendai Bay between 2012 and 2014. We generated a 3D graph to simultaneously display the date, water depth, and species composition at this site (Fig. 3). By clicking on the taxon shown in the graph at the website of Ocean Monitoring Database, the composition of the microbiota within a lower taxon is displayed. The 3D display is suitable for presenting a bird’s-eye view of the metagenomic data, which is extremely useful for visualizing and understanding the relationships among microbial communities among sampling points. This innovative function was incorporated into the metagenomic database. Figure 3 shows a contour map of chlorophyll concentrations on the x-axis and the proportion of microbial communities on the z-axis. The proportion of flavobacteria may increase following an increase in chlorophyll concentration during a spring bloom. For example, Buchan et al. reported that the proportion of flavobacteria increase late in a spring bloom16; our results show similar patterns (Fig. 3).Figure 3Three-dimensional (3D) display of microbial communities. 3D display of bacterial communities identified using 16S rRNA gene amplicon analysis of the Sendai Bay C5 samples from 2012 to 2014. The x-axis indicates the date, the y-axis indicates the water depth, and the z-axis indicates the percentage abundance of bacterial genera. The contour plot on the xy plane indicates the chlorophyll concentration. The composition of Flavobacteriaceae is shown as an example.Full size imageDigital DNA chip (DDC) databaseA DDC analysis (DDCA) system is useful for visualizing the characteristics of shotgun metagenomic data as a microarray17 of, for example, filter size, water sampling point, water sampling time, temperature, salinity, and nitrate and phosphate concentrations (Fig. 4a). By mapping sequence data against the probe sets described above, which are associated with environmental factors, we predicted that sequence data would be more enriched and inclusive of environmental information. Figure 4b displays the DDCA shotgun metagenomic data of the 0.2–0.8-μm fraction of the C5 sample collected from Sendai Bay on December 1, 2013. The sample contains a bacterial-fraction DNA marker with filter sizes of 0.2–0.8 µm and a specific DNA marker for December in Sendai Bay. Even if there is only NGS data and no environmental information, just by looking at the digital DNA chip, we can assume this sample is extracted from 0.2 to 0.8 µm fraction and is from Sendai Bay (Fig. 4b).Figure 4Visualization of metagenomics data using digital DNA chips. (a) Overview of in silico probes associated with the environmental factors on a digital DNA chip (See Supplementary Information 8 for details). (b) Digital DNA chip of shotgun metagenomics data of a 0.2–0.8-μm fraction of December 1, 2013, Sendai Bay C5. There are 748 probes, and spots that are positive for digital hybridization are shown in red. Negative spots are black. The hybridization positive probes are an indicator of environmental information of the sequence data.Full size imageDevelopment of a shotgun metagenomic databaseWe assembled the shotgun metagenomic sequence data using Megahit version 1.0.218. There were 57.95 M contigs, with an N50 of 995 bp, a maximum length of 307,212 bp, and a total of 12.39 Gbp (Supplementary Information 3). We calculated the abundance pattern of each contig. Those contigs whose appearance pattern matched with a Pearson correlation coefficient of ≥ 0.95 were clustered into a MAG. We next added the annotation of assembled contigs to the results of the BLAST search of the NCBI NT database and using classification by clustering with Pfam (CCP). This novel annotation method is described below. We developed the database showing the abundance pattern of homologous contigs against a queried sequence by BLAST for each sampling point and filter size (Fig. 5). For example, we found novel PolD families using this database.Figure 5Search for homologous contigs to a query sequence and display of temporal variation patterns. Using nucleotide and amino acid sequences as queries, contigs homologous to the query sequence are identified using BLAST, and the temporal variation patterns and taxonomy information of the hit contigs are displayed.Full size imageDevelopment of a new annotation method for metagenome contigsWe annotated the assembled contigs using BLAST to analyze the NCBI NT database. However, we were unable to annotate  > 50% of the contigs (Fig. 6). Therefore, we developed a novel method, i.e., CCP, to annotate contigs according to their species names. Analysis using a single contig generally does not provide sufficient information for assigning an annotation. However, CCP assigns the appropriate annotation to the sequence because it aggregates the Pfam information of all contigs in a MAG. CCP annotates a MAG by comparing the similarity to the reference genome. Comparing the nucleotide sequences of a MAG directly using blastn to analyze the NCBI NT database shows relatively low homology to de novo virus sequences.Figure 6Comparison between BLAST and CCP annotation results at the super-kingdom level. Comparison of classification results using BLAST to annotate contigs and classification by clustering with Pfam (CCP); the percentage of unknowns was 57% for BLAST and 8% for CCP.Full size imageHowever, a Pfam domain search using HMMER, which employs a different principle19 than BLAST, often detects more informative sequences, even those of viruses. For example, phylogenetic trees constructed according to the type and number of Pfam domains of individual bacterial genomes and those of higher eukaryotes such as humans closely approximate those generated using existing phylogenetic trees20. Thus, genomes with a similar Pfam domain may represent phylogenetically closely related species. We, therefore, searched the Pfam domains for reference genomes of viruses, bacteria, archaea, and eukaryotes included in RefSeq (as of August 31, 2015). We next calculated the number of domains for each species and constructed a CCP database. The types and numbers of Pfam domains contained in the contig obtained from the metagenome were summarized in MAG units. We compared the results using the CCP database and annotated the known genomes with the closest correlation coefficient (Fig. 7).Figure 7Overview of CCP. Flowchart of the search of the Pfam domain against known genomes of viruses, bacteria, archaea, and eukaryotes included in RefSeq to create a Pfam hit database. The Pfam domains were searched in metagenome-assembled genome (MAG) units and the known genomes whose type and number of Pfam domains are closest to the MAG.Full size imageBy annotating the top 10,000 contigs with the highest abundance in our database using CCP,  > 90% of the contigs were explained (Fig. 6). In contrast, the BLAST species search (the existing method) returned  15%, indicating that CCP is a robust method, particularly when applied to the identification of virus annotation. We next compared the agreement between CCP and BLAST annotations using contigs annotated using both CCP and BLAST (Supplementary Information 4). The virus-level agreement between CCP and BLAST was 89.6%, and the kingdom-level agreement was 76.9%. It was difficult to determine whether the contig represented a virus using BLAST; however, CCP showed higher accuracy.Shotgun metagenomic analysisPeriodicity of metagenomic dataFor the bacterial fraction (0.2–0.8 µm) of the shotgun metagenomic data from Sendai Bay, we generated a multidimensional scaling (MDS) plot according to the pattern of the abundance of the assembled contigs (Fig. 8). The MDS plot shows similarities among the samples collected during the same month during different years. Furthermore, the plot reveals that the shotgun metagenomic data exhibit an annual seasonal cycle like the 18S rRNA amplicon data10. However, we did not observe the same annual cycle among all contigs. We, therefore, extracted contigs included in the top 20 highly abundant MAGs from the bacterial fractions of Sendai Bay samples collected from March 2012 to April 2014 and plotted the fluctuation patterns. Only one such contig showed a complete 2-year cycle (Fig. 9a), and four contigs showed an incomplete 2-year cycle with peaks in March 2012 and 2013 but not in 2014 (Fig. 9b). Furthermore, 13 MAGs showed a transient pattern (Fig. 9c), and two MAGs showed peaks with irregular patterns (Fig. 9d). These results suggest that marine microbial communities generally undergo an annual cycle.Figure 8Multidimensional scaling (MDS) plot as a function of the abundance of contigs. MDS plots of bacterial fractions (0.2–0.8 µm) of shotgun metagenomic data from 2012 to 2015 acquired from Sendai Bay according to the pattern of abundance of assembled contigs.Full size imageFigure 9Variation patterns of contigs in the top 20 most abundant metagenome-assembled genomes (MAGs).The top 20 MAGs in the bacterial fractions of Sendai Bay C5 and C12 from March 13, 2012, to April 2, 2014, were classified as follows: (a) complete 1-year cycle for 2.5 years, (b) Incomplete 1-year cycle for 2.5 years, (c) transient peaks, and (d) irregular peaks. A peak within 1 month of ≥ 25% relative to the previous year’s peak was considered cyclical.Full size imageIdentification of repeat sequences in the metagenomesDuring the collection and analysis of the DNA sequencing data, we identified a number of repeat sequences in the metagenomes as follows: (TAG)n, (TGA)n, (GAA)n, and (ACA)n microsatellites. We then determined the frequencies and highest numbers of (TAG)n repeats as a function of filter size. We found that the (TAG)n repeats included up to 7.5% of the 5–20-μm fraction (Supplementary Information 5a,b). To investigate whether this was a characteristic feature of the northeastern coastal region of Japan, we analyzed the shotgun metagenomic sequence data of Tara Oceans21,22. As shown in Supplementary Information 5c,d, Tara Oceans data contained up to 1.9% of TAG repeats.To determine whether these (TAG)n repeats represented artifacts of the NGS method, we performed Southern blot and dot-blot hybridization analyses of the DNA samples extracted from seawater (Fig. 10). The dot-blot hybridization experiment analyzed 13 different samples with various content rates (Fig. 10a). We detected signals from the eight samples containing the (TAG)n that were repeated in  > 0.9% of the labeled d54-mer with the (TAG)18 repeat. In contrast, six samples with a low content ( > 0.2%) were negative (Fig. 10b). To determine whether these repeated sequences originated from a single locus or multiple loci, we performed Southern blot analysis (Fig. 10c, Supplementary Information 6) using two samples with high contents of (TAG)n repeats. A (TAG)n representing a single locus is detectable as a discrete band versus the diffuse bands exhibited by two samples with a high content of (TAG)n repeats. The data (Fig. 10c) suggest that the (TAG)n repeats were derived from multiple loci of distinct genomes. Samples with low numbers of (TAG)n repeats were negative.Figure 10Detection of TAG repeats using Southern blot and dot-blot hybridization analyses. (a) Contents of the TAG repeats of the samples according to next-generation sequencing analysis. (b) Dot-blot analyses. The sample numbers and their amounts, (right side) correspond to the signals of each dot in the left panels. The intact pTV119N plasmid without an insert indicates pTV(0). The calculated contents of TAG repeats (%) are indicated in parentheses. (c) Southern blot analysis of EcoRI-digested samples subjected to 0.8% agarose gel electrophoresis. The plasmid pTV (TAG) (0.35 ng and 1 ng) served as a positive control. E. coli genomic DNA served as a negative control. The calculated contents of TAG repeats (%) are indicated on the bottom of each graph. The length (nt) of the TAG-repeated fragment excised from pTV (TAG) is shown on the right.Full size imageThese results reveal for the first time that such repeat sequences are abundant in the genomes of marine microorganisms. However, their species of origin and functional roles were not identified here. The repeat sequences found in Escherichia coli23, subsequently called CRISPR, led to fundamental discoveries that are essential in the field of genetic engineering24. Thus, understanding the biological significance of trinucleotide repeats in marine microorganisms is of particular importance and may reveal a new research frontier. More

  • in

    Water, energy and climate benefits of urban greening throughout Europe under different climatic scenarios

    Calculation of the indicatorsFigure 1 shows the distribution of the urban greening benefit indicators computed at European scale under the current scenario, while Fig. 2 shows the cumulative distribution of impervious urban areas by increasing value of each indicator, under the current and future scenarios. It should be stressed that, while the indicators of Eqs. (1–4) are computed for every grid cell, the curves of Fig. 2 reflect also the spatial distribution of impervious urban surfaces, and hence they give more prominence to the values of the indicators in the most densely urbanized areas of the continent.Figure 1Maps of benefits per m2 across Europe for ΔTs (a), ΔT (b), RR/P (c) and CB (d), in the present scenario.Full size imageFigure 2Cumulative curves of urban surfaces versus the indicator ΔTs (a), ΔT (b), RR/P (c) and CB (d). The black line represents present conditions, while lines in color stand each for one climatic scenario. The y-axis is the cumulative surface area of the present European urban areas.Full size imageThe reduction of surface temperature ΔTs (Fig. 1a) is highest in the warmer and not excessively dry climates of Central and Southern Europe, reflecting the patterns of actual evapotranspiration. Most European urban areas would achieve temperature reductions of about 3–3.5 °C (Fig. 2a), slightly increasing with the severity of climate heating under the various scenarios, causing a reduction of sensible heat to the atmosphere, a driver of urban heat island effects, between 20 and 40% (see Appendix 1, Supplementary Material for further details). The highest temperature reduction at the roof surface, ΔT, is mostly perceived in the South of Europe (Fig. 1b), consistent with the pattern of potential evapotranspiration, similarly to the production of dry biomass CB (Fig. 1d). The reduction of temperature at the roof is predicted between 15 and 17 °C for most of Europe under the current scenario, and may increase of about 2 °C under the most severe climate scenario (Fig. 2b). Runoff reduction is significantly higher in areas with moderate precipitation, particularly in the plains, compared to rainier areas such as the Atlantic edge of the continent and high mountain ranges (Fig. 1c).The maximum storage volume, Vmax , calculated by Eq. 6, would allow to reuse 92% of the annual runoff, while Vmin and Vavg would allow to store 77% and 86% of the runoff, respectively, as resulting from a daily balance of the storage volume calculated over the 14 year time series. As the storage volume normalized to the annual runoff Rc is 0.24, 0.36 and 0.51 for Vmin, Vavg and Vmax, respectively (Figure 4b), choosing a storage volume equal to Vmin appears to be the most cost-effective solution. Vmin is mapped as shown in Fig. 3a for the case of constant demand, under the current scenario, while in Fig. 3b the volumes are plotted versus the cumulated areas.Figure 3Storage volume Vmin required to store the runoff in the case of constant demand.Full size imageFigure 4Runoff that could be harvested, and normalized storage volume Vmin versus the annual average runoff (Rc) for the case of constant withdrawal, calculated throughout the 14 year time series.Full size imagePhysical and environmental implicationsThese potential effects of green surfaces at European scale correspond to potential benefits. The total benefits extrapolated for the EU are summarized in Table 1. Results are referred to the impervious surfaces corresponding to building roofs, that are assumed to amount to a total of 26,450 km2 as per Bódis et al.40 . This represents 35% of the European impervious surface. Although it is highly unlikely that the majority of the roofs may support a uniform soil cover of 30 cm, they could still bear patches of that thickness over a part of their surface. Moreover, additional surfaces such as sealed ground could be greened. Overall, having in mind these considerations, we pragmatically regard this 35% of impervious urban areas as a maximum extent that could be greened in Europe. All benefits calculated below would obviously scale proportionally for any reduction of the percentage of area subjected to greening. The quantification of Table 1 is explained below.Table 1 Climatic descriptors and quantification of annual benefits at the European scale in the present and future climatic scenarios, assuming to green all roof surfaces, or 35% of the European impervious surfaces.Full size tableThe reduction of land surface temperatures, ΔTs, reduces the thermal irradiation and convective heat flux from urban surfaces (see Appendix 1 of Supplementary Material), which are the drivers of the heat island effect44. As a first order approximation, the reduction of air temperature at 2 m from the surfaces can be expected to be about a half of ΔTs45 as an average value in summer. The reduction of air temperature would generate economic benefits, like the life cycle extension of electronic material and cars, benefits in the health and transport sectors, reduction of social stress and morbidity, and reduction of damages to trees and animals46,47,48.The reduction of the surface temperature ΔT potentially reduces the cooling demand in summer (Eq. 5) by 92 TWh year−1. This energy saving corresponds to 29.9 Mtons of CO2 for the present scenario, considering emissions of 0.325 kg CO2 equivalent kWh−1 for European electricity39. Our estimate is arguably an upper limit of cooling energy savings. In many cases, underroof spaces of buildings are not cooled and effectively work already as an insulation, hence the reduction in the heat transferred from the roofs to underlying inhabited spaces may be lower than we estimate.The yearly produced biomass CB is a benefit in itself whenever the biomass may be used (e.g. crops from urban agriculture). However, more importantly, it may be appraised in terms of carbon and carbon dioxide sequestration. The carbon dioxide sequestered from the atmosphere through biomass growth is 25.9 Mtons year−1 in the present scenario. This must be summed to the reduction of carbon emission following the expected decrease in cooling energy use for a total of 55.8 Mtons, or about 1.2% of the 4500 Mtons CO2 produced in the EU every year37.It should be stressed that carbon dioxide sequestration by the biomass in green roofs is effective only if residues are not significantly degraded. This may be achieved by removing the biomass periodically before it undergoes respiration and mineralization. One could alternatively employ woody plants with a higher carbon accumulation capacity instead of herbaceous vegetation. Although our calculations are referred to a herbaceous annual crop, the results in terms of dry biomass would not be radically different had we considered a tree or shrub crop, as the dry matter potentially produced per unit surface is relatively independent of the plant49. On the other hand, trees and shrubs may be expected to have higher evapotranspiration, thus enhancing the benefits quantified here for a herbaceous crop.If greening is implemented on about 35% of the impervious urban areas, we expect a reduction of runoff in the order of 17.5% compared to the total. Considering that pollutant loads associated to runoff are estimated in the order of about 30 million population equivalents (PE) in terms of biochemical oxygen demand (BOD), about 18 million PE in terms of total nitrogen and about 6 million PE in terms of total phosphorus 6,35, this can be a sizable contribution to the treatment of pollution from European urban areas. Besides the reduction of runoff volume, greened surfaces may also help reduce the frequency of combined sewer overflows because they buffer runoff and release it more slowly than impervious surfaces. This effect is arguably more important for smaller storm events, and tends to disappear as events cause the saturation of green roof storage.It should be stressed that the above analysis considers a soil thickness of 30 cm on greened surfaces. Using the meta-models proposed in30 for the thickness of 10 cm we obtain a ratio between the indicators for thickness of 10 and 30 cm ranging between 80–97% for the reduction of surface temperatures, 55–57% for roof temperatures, 47–57% for biomass, and 84–86% for runoff. Soil thickness affects in particular the roof temperature, due to the associated thermal insulation effect, and the biomass, because a thicker soil can store a larger amount of water and allows a higher evapotranspiration for vegetation growth, while not impeding root growth. A comparison of different climate scenarios sheds light on the sensitivity of our results to the input climatic predictors (P and ET0). From Table 1, it can be calculated that the range (difference between the maximum and minimum value) of precipitation and potential evapotranspiration, as a percentage of the average value, is 20.3%, and 21.4% respectively. The corresponding ranges are 7% of the average for the cooling reduction, 3.7% for the reduced carbon dioxide emission, and 34% for the runoff reduction. The curves in Fig. 2 visualize the relatively small sensitivity of results to the climatic scenario.Economic implicationsMost of the benefits of green roofs are collective. Only a few (e.g. energy saving in summer, and gardening) have an apparent private nature. The costs of greening roofs, on the contrary, are primarily borne by the private owners50. It has been observed that, in the absence of specific incentives, green roof implementation can be economically convenient only for specific commercial and multifamily buildings25. Therefore, private investments should be encouraged through appropriate fiscal and funding policies if the objective is to facilitate a mainstream uptake of this solution. In this section, an indicative cost-benefit analysis is carried out in order to shed light on the possible financing needs at stake, and considering to green the impervious surfaces covered by roofs.The two main benefits that can be easily monetized are the avoided cost of cooling in summer (based on energy prices) and the reduction of carbon dioxide emissions (based on greenhouse gas emissions market prices). By summing the results of Eq. (5) for all gridcells in Europe where the greened surface is assumed to be 35% of the impervious urban area in the gridcell, cooling savings can reach 18.4 billion Є each year for the current scenario. For comparison, the current expenditure for residential cooling in summer can be assumed to be 78 billion Є year−1, based on an electricity use of 391 TWh51. Therefore, the cooling energy saving is 23.5% (18.4 billion Є/78 billion Є), in agreement with the results of Manso et al.15 for the value of 15% estimated for the hot-summer Mediterranean climate.At the present carbon market price of 22.5 Є tons−1 (Ruf and Mazzoni43), the annual benefit related to the estimated reduction of greenhouse gas emissions corresponds to about 1.26 billion Є. It should be stressed how this is apparently an upper limit of this benefit, because not all greened surfaces may correspond to roofs of cooled building volumes, and because the biomass is likely to undergo at least a partial mineralization if not timely removed from the green surfaces. The benefit associated to the reduction of the heat island effect can also be quantified to some extent on the basis of existing literature studies, although their estimation is very complex and would require ad hoc studies. For example, for the city of Phoenix, this benefit was quantified in 80 € for 1 °C decrease per working resident, considering costs of electronic devices, maintenance of cars and performance of cooling47. In another analysis for the Melbourne area, the annual cost was quantified in 18 € per inhabitant, including health, transport, social distress, electric grid faults and damages to animal and trees48. In Malaysia, the annual cost of hazes, related to the urban heat island, was quantified in 12 € per habitant in 1997, including cost of illness, productivity loss, flight cancellation, tourism reduction, decline in fish landings, fire-fighting, cloud seeding and masks46. Therefore, costs can vary significantly among different contexts. Assuming conservatively a yearly benefit of 20 € for each of the ca. 559.5 million European urban inhabitants living in urban areas (75% of the total52), the Net Present Value (NPV) of this benefit over 40 years would be 221 billion € using a discount rate of 4%.The cost of greening the roofs or other impervious surfaces is more difficult to quantify as it depends on several design details and site-specific conditions. For example, in Finland the cost ranges between 70 and 80 Є m−2, in Germany between 13 and 41 Є m−2, while in Switzerland around 20 Є m−253. Assuming an average unit cost of 50 Є m−2, the costs to turn 26,450 km2 of impervious urban areas in Europe into green surfaces amounts to 1323 billion Є. This corresponds to an annual cost (discount rate 4%, 40 years life) of 63 billion euro. This means a cost of 6.3 € m−3 of annual runoff saved (assuming an average annual runoff saving of 10 km3), which is reasonably in line with an estimate of 9.2 € m−3 for the U.S. context, where the annual runoff volume reduction was 12%54 compared to our estimate of 17.5%.Assuming a lifespan of 40 years55 and a discount rate of 4%50, the NPV of the cost saving of summer cooling over 40 years (18.4 billion Є year−1 in Table 1), that is the main private benefit of a green roof installed in a private building, is 364 billion Є (using a discount rate of 4%). The benefits of CO2 reduction, monetized in an emission trading system, would lead to a NPV of 24.85 ≈ 25 billion Є over 40 years (55.8 Mtons year−1). The NPV of the heat island benefit over 40 years would be 221 billion €. Deducting the sum of these benefits (totalling 610 billion €) from the estimated investment of 1323 billion €, yields a net gap of 713 billion Є, corresponding to an annual cost of about 60 € for each of the 559.5 million European citizens living in urban areas. This estimated annual cost is apparently affected by the uncertainty on green roof costs: it could reduce to 4 Є/year per urban citizen if the cost of the green roof is 25 Є m−2, and 129 Є/year per urban citizen if the cost is 80 Є m−2. An annual cost of 60 Є/year per urban citizen may be in many cases compensated by the additional benefits not quantified here. For example, the average increase of property value (rental prices) was estimated to be 8%15. Other benefits can be associated e.g. to leisure and recreation, socialization, amenity of the urban environment, and the creation of habitat or ecological connections in urban areas, besides the abovementioned positive effects in terms of water pollution and floods. Table 2 summarizes the economic results. Table 2 Summary of benefits and costs of urban greening considered in this study for the European context.Full size tableThe harvesting of runoff is a potential additional benefit, but it also entails costs. These can be quantified as a first approximation considering a cost of the storage volume Cs = 50 € m−3, a lifetime of the storage of 100 years, a discount rate of 4% and annual operation and maintenance costs of 3% of the investment. For a unit greened surface, the runoff potentially harvested equals P-RR and can be computed from Eq. 3, while the required storage volume to harvest it is given by Eq. 6. The cost of harvesting one m3 of runoff (marginal harvesting costs) follows from the abovementioned costing parameters. Figure 5 depicts the cumulate value of runoff as a function of the marginal harvesting cost. It can be seen that about 75% of the runoff can be harvested with marginal costs below 0.7 € m−3, a value compatible with urban water prices usually applied in Europe. Cs may be lower than 50 € m−3 , but often it may also be higher. Hence our calculation can be only regarded as a first indication and is accurate not more than within one order of magnitude. The quality of water from green surface runoff harvesting is arguably adequate for non-potable domestic use, but depends on the type of green roof and vegetation13. Figure 5Cumulate runoff versus the cost of storage per unit of runoff, for a storage cost of 50 € m−3. Different climatic scenarios are shown.Full size image More

  • in

    Nitrogen and phosphorus fertilization consistently favor pathogenic over mutualistic fungi in grassland soils

    1.Carpenter, S. R. et al. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 8, 559–568 (1998).Article 

    Google Scholar 
    2.Galloway, J. N. et al. Transformation of the nitrogen cycle: Recent trends, questions, and potential solutions. Science 320, 889–892 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.LeBauer, DavidS. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Fay, P. A. et al. Grassland productivity limited by multiple nutrients. Nat. Plants 1, 1–5 (2015).Article 
    CAS 

    Google Scholar 
    5.Yue, K. et al. Stimulation of terrestrial ecosystem carbon storage by nitrogen addition: a meta-analysis. Sci. Rep. 6, 1–10 (2016).Article 
    CAS 

    Google Scholar 
    6.Avolio, M. L. et al. Changes in plant community composition, not diversity, during a decade of nitrogen and phosphorus additions drive above-ground productivity in a tallgrass prairie. J. Ecol. 102, 1649–1660 (2014).CAS 
    Article 

    Google Scholar 
    7.Isbell, F. et al. Nutrient enrichment, biodiversity loss, and consequent declines in ecosystem productivity. Proc. Natl Acad. Sci. USA 110, 11911–11916 (2013).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Van der Putten, W. H., Bradford, M. A., Pernilla Brinkman, E., van de Voorde, T. F. J. & Veen, G. F. Where, when and how plant–soil feedback matters in a changing world. Funct. Ecol. 30, 1109–1121 (2016).Article 

    Google Scholar 
    9.Revillini, D., Gehring, C. A. & Johnson, N. C. The role of locally adapted mycorrhizas and rhizobacteria in plant–soil feedback systems. Funct. Ecol. 30, 1086–1098 (2016).Article 

    Google Scholar 
    10.Peay, K. G., Kennedy, P. G. & Talbot, J. M. Dimensions of biodiversity in the Earth mycobiome. Nat. Rev. Microbiol. 14, 434–447 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Semchenko, M. et al. Fungal diversity regulates plant-soil feedbacks in temperate grassland. Sci. Adv. 4, eaau4578 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Větrovsky, T. et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat. Commun. 10, 1–9 (2019).Article 
    CAS 

    Google Scholar 
    13.Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis. (Academic Press, 2008).14.Johnson, N. C. Resource stoichiometry elucidates the structure and function of arbuscular mycorrhizas across scales. New Phytol. 185, 631–647 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    15.Velásquez, A. C., Castroverde, C. D. M. & He, S. Y. Plant–pathogen warfare under changing climate conditions. Curr. Biol. 28, R619–R634 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    16.Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Reynolds, H. L., Packer, A., Bever, J. D. & Clay, K. Grassroots ecology: plant-microbe-soil interactions as drivers of plant community structure and dynamics. Ecology 84, 2281–2291 (2003).Article 

    Google Scholar 
    18.Veresoglou, S. D., Barto, E. K., Menexes, G. & Rillig, M. C. Fertilization affects severity of disease caused by fungal plant pathogens. Plant Pathol. 62, 961–969 (2013).Article 

    Google Scholar 
    19.Walters, D. R. & Bingham, I. J. Influence of nutrition on disease development caused by fungal pathogens: implications for plant disease control. Ann. Appl. Biol. 151, 307–324 (2007).CAS 
    Article 

    Google Scholar 
    20.Knorr, M., Frey, S. D. & Curtis, P. S. Nitrogen additions and litter decomposition: a meta-analysis. Ecology 86, 3252–3257 (2005).Article 

    Google Scholar 
    21.Chai, Y. et al. Patterns of taxonomic, phylogenetic diversity during a long-term succession of forest on the Loess Plateau, China: insights into assembly process. Sci. Rep. 6, 27087 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Crowther, T. W. et al. Sensitivity of global soil carbon stocks to combined nutrient enrichment. Ecol. Lett. 22, 936–945 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Fogg, K. The effect of added nitrogen on the rate of decomposition of organic matter. Biol. Rev. 63, 433–462 (1988).Article 

    Google Scholar 
    24.Bonner, M. T. et al. Why does nitrogen addition to forest soils inhibit decomposition? Soil Biol. Biochem. 137, 107570 (2019).CAS 
    Article 

    Google Scholar 
    25.Zak, D. R. et al. Anthropogenic N deposition, fungal gene expression, and an increasing soil carbon sink in the Northern Hemisphere. Ecology 100, 1–8 (2019).Article 

    Google Scholar 
    26.Hobbie, S. E. et al. Response of decomposing litter and its microbial community to multiple forms of nitrogen enrichment. Ecol. Monogr. 82, 389–405 (2012).Article 

    Google Scholar 
    27.Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

    Google Scholar 
    29.Borer, E. T. et al. Finding generality in ecology: a model for globally distributed experiments. Methods Ecol. Evol. 5, 65–73 (2014).Article 

    Google Scholar 
    30.Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6, 343–351 (2012).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    31.MacKinnon, D. P., Krull, J. L. & Lockwood, C. M. Equivalence of the mediation, confounding and suppression effect. Prev. Sci. 1, 173–181 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Kiers, E. T. et al. Reciprocal rewards stabilize cooperation in the mycorrhizal symbiosis. Science 333, 880–882 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Lekberg, Y. et al. Relative importance of competition and plant–soil feedback, their synergy, context dependency and implications for coexistence. Ecol. Lett. 21, 1268–1281 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Hart, M. M. & Reader, R. J. Taxonomic basis for variation in the colonization strategy of arbuscular mycorrhizal fungi. New Phytol. 153, 335–344 (2002).Article 

    Google Scholar 
    35.Johnson, N. C. Can fertilization of soil select less mutualistic mycorrhizae? Ecol. Appl. 3, 749–757 (1993).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Weber, S. E. et al. Responses of arbuscular mycorrhizal fungi to multiple coinciding global change drivers. Fungal Ecol. 40, 62–71 (2019).Article 

    Google Scholar 
    37.Han, Y., Feng, J., Han, M. & Zhu, B. Responses of arbuscular mycorrhizal fungi to nitrogen addition: a meta-analysis. Glob. Change Biol. 26, 7229–7241 (2020).ADS 
    Article 

    Google Scholar 
    38.Treseder, K. K. et al. Arbuscular mycorrhizal fungi as mediators of ecosystem responses to nitrogen deposition: A trait- ­based predictive framework. J. Ecol. 106, 480–489 (2018).39.Sikes, B. A., Cottenie, K. & Klironomos, J. N. Plant and fungal identity determines pathogen protection of plant roots by arbuscular mycorrhizas. J. Ecol. 97, 1274–1280 (2009).Article 

    Google Scholar 
    40.Maherali, H. & Klironomos, J. N. Influence of phylogeny on fungal community assembly and ecosystem functioning. Science 316, 1746–1748 (2007).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    41.Johnson, N. C., Graham, J. H. & Smith, F. A. Functioning of mycorrhizal associations along the mutualism – parasitism continuum. New Phytol. 135, 575–585 (1997).42.Balser, T. C., Treseder, K. K. & Ekenler, M. Using lipid analysis and hyphal length to quantify AM and saprotrophic fungal abundance along a soil chronosequence. Soil Biol. Biochem. 37, 601–604 (2005).CAS 
    Article 

    Google Scholar 
    43.Cappelli, S. L., Pichon, N. A., Kempel, A. & Allan, E. Sick plants in grassland communities: a growth‐defense trade‐off is the main driver of fungal pathogen abundance. Ecol. Lett. 23, 1349–1359 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Grman, E. Plant species differ in their ability to reduce allocation to non-beneficial arbuscular mycorrhizal fungi. Ecology 93, 711–718 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Kardol, P., Martijn, Bezemer, T. & van der Putten, W. H. Temporal variation in plant-soil feedback controls succession. Ecol. Lett. 9, 1080–1088 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Firn, J. et al. Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs. Nat. Ecol. Evol. 3, 400–406 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Cleland, E. E. et al. Belowground biomass response to nutrient enrichment depends on light limitation across globally distributed grasslands. Ecosystems 22, 1466–1477 (2019).CAS 
    Article 

    Google Scholar 
    48.Adler, P. B. et al. Functional traits explain variation in plant life history strategies. Proc. Natl Acad. Sci. USA 111, 10019–10019 (2014).Article 
    CAS 

    Google Scholar 
    49.Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant-soil feedbacks: a meta-analytical review. Ecol. Lett. 11, 980–992 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1052–1053 (2014).Article 
    CAS 

    Google Scholar 
    51.Davison, J. et al. Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science 349, 970–973 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Morriën, E. et al. Soil networks become more connected and take up more carbon as nature restoration progresses. Nat. Commun. 8, 14349 (2017).53.Karimi, B. et al. Microbial diversity and ecological networks as indicators of environmental quality. Environ. Chem. Lett. 15, 265–281 (2017).CAS 
    Article 

    Google Scholar 
    54.Carr, A., Diener, C., Baliga, N. S. & Gibbons, S. M. Use and abuse of correlation analyses in microbial ecology. ISME J. https://doi.org/10.1038/s41396-019-0459-z (2019).55.Prober, S. M. et al. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol. Lett. 18, 85–95 (2015).PubMed 
    Article 

    Google Scholar 
    56.Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    57.Corradi, N. et al. Gene copy number polymorphisms in an arbuscular mycorrhizal fungal population. Appl. Environ. Microbiol. 73, 366–369 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    58.Tedersoo, L. et al. Response to Comment on “Global diversity and geography of soil fungi. Science 349, 936 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    59.Malcolm, G. M., Kuldau, G. A., Gugino, B. K. & Jiménez-Gasco, M. D. M. Hidden host plant associations of soilborne fungal pathogens: an ecological perspective. Phytopathology 103, 538–544 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    60.Taylor, D. L. et al. Accurate estimation of fungal diversity and abundance through improved lineage-specific primers optimized for illumina amplicon sequencing. Appl. Environ. Microbiol. 82, 7217–7226 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Zou, K., Thebault, E., Lacroix, G. & Barot, S. Interactions between the green and brown food web determine ecosystem functioning. Funct. Ecol. 30, 1454–1465 (2016).Article 

    Google Scholar 
    62.Chen, W. et al. Fertility‐related interplay between fungal guilds underlies plant richness–productivity relationships in natural grasslands. New Phytol. 226, 1129–1143 (2020).PubMed 
    Article 

    Google Scholar 
    63.Busby, P. E., Peay, K. G. & Newcombe, G. Common foliar fungi of Populus trichocarpa modify Melampsora rust disease severity. New Phytol. 209, 1681–1692 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    64.Li, X., Ding, C., Zhang, T. & Wang, X. Fungal pathogen accumulation at the expense of plant-beneficial fungi as a consequence of consecutive peanut monoculturing. Soil Biol. Biochem. 72, 11–18 (2014).Article 
    CAS 

    Google Scholar 
    65.Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

    Google Scholar 
    66.Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, 1–11 (2012).Article 
    CAS 

    Google Scholar 
    67.Kurtz, Z., Mueller, C., Miraldi, E. & Bonneau, R. SpiecEasi: Sparse Inverse Covariance For Ecological Statistical Inference. R package version 1.0.6 (2019).68.Oksanen, J. et al. vegan: Community Ecology Package. R package (2019).69.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag New York, 2016). More

  • in

    American martens use vigilance and short-term avoidance to navigate a landscape of fear from fishers at artificial scavenging sites

    1.Case, T. J. & Gilpin, M. E. Interference competition and niche theory. Proc. Natl. Acad. Sci. 71, 3073–3077 (1974).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Linnell, J. D. & Strand, O. Interference interactions, co-existence and conservation of mammalian carnivores. Divers. Distrib. 6, 169–176 (2000).Article 

    Google Scholar 
    3.Prugh, L. R. & Sivy, K. J. Enemies with benefits: Integrating positive and negative interactions among terrestrial carnivores. Ecol. Lett. 23, 902–918 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Polis, G. A., Myers, C. A. & Holt, R. D. The ecology and evolution of intraguild predation: Potential competitors that eat each other. Annu. Rev. Ecol. Syst. 20, 297–330 (1989).Article 

    Google Scholar 
    5.Belant, J. L., Griffith, B., Zhang, Y., Follmann, E. H. & Adams, L. G. Population-level resource selection by sympatric brown and American black bears in Alaska. Polar Biol. 33, 31–40 (2010).Article 

    Google Scholar 
    6.Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

    Google Scholar 
    7.Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: Reestablishing the “landscape of fear” in Yellowstone National Park, USA. Can. J. Zool. 79, 1401–1409 (2001).Article 

    Google Scholar 
    8.Moll, R. J. et al. The many faces of fear: A synthesis of the methodological variation in characterizing predation risk. J. Anim. Ecol. 86, 749–765 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Kohl, M. T. et al. Diel predator activity drives a dynamic landscape of fear. Ecol. Monogr. 88, 638–652 (2018).Article 

    Google Scholar 
    10.Kuijper, D. P. J. et al. Landscape of fear in Europe: Wolves affect spatial patterns of ungulate browsing in Białowieża Primeval Forest, Poland. Ecography 36, 1263–1275 (2013).Article 

    Google Scholar 
    11.Smith, J. A., Donadio, E., Pauli, J. N., Sheriff, M. J. & Middleton, A. D. Integrating temporal refugia into landscapes of fear: Prey exploit predator downtimes to forage in risky places. Oecologia 189, 883–890 (2019).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Flagel, D. G., Belovsky, G. E. & Beyer, D. E. Natural and experimental tests of trophic cascades: Gray wolves and white-tailed deer in a Great Lakes forest. Oecologia 180, 1183–1194 (2016).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: Spatial patterns of risk perception and response. Trends Ecol. Evol. 34, 355–368 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Prugh, L. R. et al. Designing studies of predation risk for improved inference in carnivore-ungulate systems. Biol. Conserv. 232, 194–207 (2019).Article 

    Google Scholar 
    15.Fisher, J. T., Anholt, B., Bradbury, S., Wheatley, M. & Volpe, J. P. Spatial segregation of sympatric marten and fishers: The influence of landscapes and species-scapes. Ecography 36, 240–248 (2013).Article 

    Google Scholar 
    16.Manlick, P. J., Woodford, J. E., Zuckerberg, B. & Pauli, J. N. Niche compression intensifies competition between reintroduced American martens (Martes americana) and fishers (Pekania pennanti). J. Mammal. 98, 690–702 (2017).Article 

    Google Scholar 
    17.Powell, R. A., Buskirk, S. W., & Zielinski, W. J. Fisher and marten. In Wild Mammals of North America: Biology, Management, and Conservation (eds. Feldhamer, G. A et al.), 635–649 (JHU Press, 2003).18.Krohn, W. B., Elowe, K. D. & Boone, R. B. Relations among fishers, snow, and martens: Development and evaluation of two hypotheses. For. Chron. 71, 97–105 (1995).Article 

    Google Scholar 
    19.Williams, B. W., Gilbert, J. H., & Zollner, P. A. Historical Perspective on the Reintroduction of the Fisher and American Marten in Wisconsin and Michigan, vol. 5. (US Department of Agriculture, Forest Service, Northern Research Station, 2007).20.McCann, N. P., Zollner, P. A. & Gilbert, J. H. Survival of adult martens in northern Wisconsin. J. Wildl. Manag. 74, 1502–1507 (2010).Article 

    Google Scholar 
    21.Kupferman, C. A. An Expanding Meso-Carnivore: Fisher (Pekania pennanti) Occupancy and Coexistence with Native Mustelids in Southeast Alaska (University of Idaho, 2019).
    Google Scholar 
    22.Hall, L. K. et al. Vigilance of kit foxes at water sources: A test of competing hypotheses for a solitary carnivore subject to predation. Behav. Proc. 94, 76–82 (2013).Article 

    Google Scholar 
    23.Chitwood, M. C., Lashley, M. A., Higdon, S. D., DePerno, C. S. & Moorman, C. E. Raccoon vigilance and activity patterns when sympatric with coyotes. Diversity 12, 341 (2020).Article 

    Google Scholar 
    24.Vanak, A. T., Thaker, M. & Gompper, M. E. Experimental examination of behavioural interactions between free-ranging wild and domestic canids. Behav. Ecol. Sociobiol. 64, 279–287 (2009).Article 

    Google Scholar 
    25.Croose, E., Bled, F., Fowler, N. L., Beyer, D. E. Jr. & Belant, J. L. American marten and fisher do not segregate in space and time during winter in a mixed-forest system. Ecol. Evol. 9, 4906–4916 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Gilbert, J. H., Zollner, P. A., Green, A. K., Wright, J. L. & Karasov, W. H. Seasonal field metabolic rates of American martens in Wisconsin. Am. Midl. Nat. 162, 327–334 (2009).Article 

    Google Scholar 
    27.Hughes, N. K., Price, C. J. & Banks, P. B. Predators are attracted to the olfactory signals of prey. PLoS ONE 5, e13114 (2010).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    28.Bytheway, J. P., Carthey, A. J. & Banks, P. B. Risk vs. reward: How predators and prey respond to aging olfactory cues. Behav. Ecol. Sociobiol. 67, 715–725 (2013).Article 

    Google Scholar 
    29.Haynes, G. Utilization and skeletal disturbances of North American prey carcasses. Arctic 35, 266–281 (1982).Article 

    Google Scholar 
    30.Kaufmann, J. H. On the definitions and functions of dominance and territoriality. Biol. Rev. 58, 1–20 (1983).Article 

    Google Scholar 
    31.Zielinski, W. J., Tucker, J. M. & Rennie, K. M. Niche overlap of competing carnivores across climatic gradients and the conservation implications of climate change at geographic range margins. Biol. Conserv. 209, 533–545 (2017).Article 

    Google Scholar 
    32.Jensen, P. G. & Humphries, M. M. Abiotic conditions mediate intraguild interactions between mammalian carnivores. J. Anim. Ecol. 88, 1305–1318 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Manlick, P. J., Windels, S. K., Woodford, J. E. & Pauli, J. N. Can landscape heterogeneity promote carnivore coexistence in human-dominated landscapes?. Landsc. Ecol. 35, 2013–2027 (2020).Article 

    Google Scholar 
    34.Krohn, W., Hoving, C., Harrison, D., Phillips, D., & Frost, H. Martes foot-loading and snowfall patterns in eastern North America. In Martens and Fishers (Martes) in Human-Altered Environments (eds. Harrison, D. J. et al.) 115–131 (Springer, 2005).35.Hiller, T. L., Etter, D. R., Belant, J. L. & Tyre, A. J. Factors affecting harvests of fishers and American martens in northern Michigan. J. Wildl. Manag. 75, 1399–1405 (2011).Article 

    Google Scholar 
    36.Childress, M. J. & Lung, M. A. Predation risk, gender and the group size effect: Does elk vigilance depend upon the behaviour of conspecifics?. Anim. Behav. 66, 38–398 (2003).Article 

    Google Scholar 
    37.Gehr, B. et al. Stay home, stay safe—Site familiarity reduces predation risk in a large herbivore in two contrasting study sites. J. Anim. Ecol. 89, 1329–1339 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Bull, E. L. & Heater, T. W. Survival, causes of mortality, and reproduction in the American marten in northeastern Oregon. Northwest. Nat. 82, 1–6 (2001).Article 

    Google Scholar 
    39.White, K. S., Golden, H. N., Hundertmark, K. J. & Lee, G. R. Predation by wolves, Canis lupus, on wolverines, Gulo gulo, and an American marten, Martes americana, Alaska. Can. Field Nat. 116, 132–134 (2002).
    Google Scholar 
    40.Erb, J., Sampson, B., & Coy, P. Survival and causes of mortality for fisher and marten in Minnesota. Minnesota Department of Natural Resources Summary of Wildlife Research Findings, 2009, 24–31 (2009).41.Wengert, G. M., Gabriel, M. W., Foley, J. E., Kun, T. & Sacks, B. N. Molecular techniques for identifying intraguild predators of fishers and other North American small carnivores. Wildl. Soc. Bull. 37, 659–663 (2013).
    Google Scholar 
    42.Stricker, H. K. et al. Use of modified snares to estimate bobcat abundance. Wildl. Soc. Bull. 36, 257–263 (2012).Article 

    Google Scholar 
    43.Kautz, T. M. et al. Predator densities and white-tailed deer fawn survival. J. Wildl. Manag. 83, 1261–1270 (2019).Article 

    Google Scholar 
    44.Caravaggi, A. et al. A review of camera trapping for conservation behaviour research. Remote Sens. Ecol. Conserv. 3, 109–122 (2017).Article 

    Google Scholar 
    45.Berger, K. M. & Gese, E. M. Does interference competition with wolves limit the distribution and abundance of coyotes?. J. Anim. Ecol. 76, 1075–1085 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Merkle, J. A., Stahler, D. R. & Smith, D. W. Interference competition between gray wolves and coyotes in Yellowstone National Park. Can. J. Zool. 87, 56–63 (2009).Article 

    Google Scholar 
    47.Crimmins, S. M. & Van Deelen, T. R. Limited evidence for mesocarnivore release following wolf recovery in Wisconsin, USA. Wildl. Biol. 2019, 1–7 (2019).Article 

    Google Scholar 
    48.Petroelje, T. R., Belant, J. L., Beyer, D. E., & Kautz, T. M. Interference competition between wolves and coyotes during variable prey abundance. Ecol. Evol 11, 1413–1431 (2021).
    49.Switalski, T. A. Coyote foraging ecology and vigilance in response to gray wolf reintroduction in Yellowstone National Park. Can. J. Zool. 81, 985–993 (2003).Article 

    Google Scholar 
    50.Hilborn, A. et al. Cheetahs modify their prey handling behavior depending on risks from top predators. Behav. Ecol. Sociobiol. 72, article 74 (2018).Article 

    Google Scholar 
    51.Elgar, M. A. Predator vigilance and group size in mammals and birds: A critical review of the empirical evidence. Biol. Rev. 64, 13–33 (1989).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Bøving, P. S. & Post, E. Vigilance and foraging behaviour of female caribou in relation to predation risk. Rangifer 17, 55–63 (1997).Article 

    Google Scholar 
    53.Hunter, L. T. B. & Skinner, J. D. Vigilance behaviour in African ungulates: The role of predation pressure. Behaviour 135, 195–211 (1998).Article 

    Google Scholar 
    54.Liley, S. & Creel, S. What best explains vigilance in elk: Characteristics of prey, predators, or the environment?. Behav. Ecol. 19, 245–254 (2008).Article 

    Google Scholar 
    55.Makin, D. F., Chamaillé-Jammes, S. & Shrader, A. M. Herbivores employ a suite of antipredator behaviours to minimize risk from ambush and cursorial predators. Anim. Behav. 127, 225–231 (2017).Article 

    Google Scholar 
    56.Wikenros, C., Ståhlberg, S. & Sand, H. Feeding under high risk of intraguild predation: Vigilance patterns of two medium-sized generalist predators. J. Mammal. 95, 862–870 (2014).Article 

    Google Scholar 
    57.Welch, R. J., le Roux, A., Petelle, M. B. & Périquet, S. The influence of environmental and social factors on high-and low-cost vigilance in bat-eared foxes. Behav. Ecol. Sociobiol. 72, article 29 (2018).Article 

    Google Scholar 
    58.Yang, L. et al. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS J. Photogramm. Remote. Sens. 146, 108–123 (2018).ADS 
    Article 

    Google Scholar 
    59.Lovallo, M. J. & Anderson, E. M. Bobcat (Lynx rufus) home range size and habitat use in northwest Wisconsin. Am. Midl. Nat. 135, 241–252 (1996).Article 

    Google Scholar 
    60.Burton, A. C. et al. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article 

    Google Scholar 
    61.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    62.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed Aug 2020.63.National Operational Hydrologic Remote Sensing Center. Snow Data Assimilation System (SNODAS) Data Products at NSIDC, Version 1. Boulder, Colorado USA. https://doi.org/10.7265/N5TB14TC (NSIDC: National Snow and Ice Data Center, 2004).64.Hutchings, M. R. & White, P. C. Mustelid scent-marking in managed ecosystems: Implications for population management. Mammal Rev. 30, 157–169 (2000).Article 

    Google Scholar 
    65.Mumm, C. A., & Knörnschild, M. Mustelid Communication. In Encyclopedia of Animal Cognition and Behavior (ed. Choe, J.), 1–11 (Springer International, 2018).66.Sullivan, T. P., Nordstrom, L. O. & Sullivan, D. S. Use of predator odors as repellents to reduce feeding damage by herbivores. J. Chem. Ecol. 11, 903–919 (1985).CAS 
    PubMed 
    Article 

    Google Scholar 
    67.Rowcliffe, J. M., Kays, R., Kranstauber, B., Carbone, C. & Jansen, P. A. Quantifying levels of animal activity using camera trap data. Methods Ecol. Evol. 5, 1170–1179 (2014).Article 

    Google Scholar  More

  • in

    Landscape condition influences energetics, reproduction, and stress biomarkers in grizzly bears

    1.Coristine, L. E. & Kerr, J. T. Habitat loss, climate change, and emerging conservation challenges in Canada. Can. J. Zool. 89, 435–451 (2011).Article 

    Google Scholar 
    2.Proctor, M. F. et al. Population fragmentation and inter-ecosystem movements of grizzly bears in Western Canada and the Northern United States. Wildl. Monogr. 180, 1–46 (2012).Article 

    Google Scholar 
    3.Festa-Bianchet, M. Status of the grizzly bear (Ursus arctos) in Alberta: Update 2010. Wildlife Status Report No. 37. (Alberta Sustainable Resource Development, Fish and Wildlife Division, Alberta Conservation Association, Edmonton, Alberta, Canada, 2010).4.Berland, A., Nelson, T., Stenhouse, G., Graham, K. & Cranston, J. The impact of landscape disturbance on grizzly bear habitat use in Foothills Model Forest, Alberta, Canada. For. Ecol. Manag. 256, 1875–1883 (2008).Article 

    Google Scholar 
    5.Nielsen, S. E., Cranston, J. & Stenhouse, G. B. Identification of priority areas for grizzly bear conservation and recovery in Alberta, Canada. J. Conserv. Plan. 5, 38–60 (2009).
    Google Scholar 
    6.Boulanger, J. & Stenhouse, G. B. The impact of roads on the demography of grizzly bears in Alberta. PLoS ONE 9, e115535 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    7.Acevedo-Whitehouse, K. & Duffus, A. L. J. Effects of environmental change on wildlife health. Philos. Trans. R. Soc. B Biol. Sci. 364, 3429–3438 (2009).Article 

    Google Scholar 
    8.Stephen, C. Toward a new definition of animal health: Lessons from the Cohen Commission and the SPS agreement. Optim. Online 43, 1–8 (2013).
    Google Scholar 
    9.Stephen, C. Toward a modernized definition of wildlife health. J. Wildl. Dis. 50, 427–430 (2014).PubMed 
    Article 

    Google Scholar 
    10.Wittrock, J., Duncan, C. & Stephen, C. A determinants of health conceptual model for fish and wildlife health. J. Wildl. Dis. 55, 285–297 (2019).PubMed 
    Article 

    Google Scholar 
    11.Stephen, C. The Pan-Canadian approach to wildlife health. Can. Vet. J. 60, 145–146 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    12.Ricklefs, R. E. & Wikelski, M. The physiology/life- history nexus. Trends Ecol. Evol. 17, 462–468 (2002).Article 

    Google Scholar 
    13.Dammhahn, M., Dingemanse, N. J., Niemelä, P. T. & Réale, D. Pace-of-life syndromes: A framework for the adaptive integration of behaviour, physiology and life history. Behav. Ecol. Sociobiol. 72, 62 (2018).Article 

    Google Scholar 
    14.Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).Article 

    Google Scholar 
    15.Lovegrove, B. G. The influence of climate on the basal metabolic rate of small mammals: A slow-fast metabolic continuum. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 173, 87–112 (2003).CAS 
    Article 

    Google Scholar 
    16.Garshelis, D., Gibeau, M. & Herrero, S. Grizzly bear demographics in and around Banff National Park and Kananaskis Country, Alberta. J. Wildl. Manag. 69, 277–297 (2005).Article 

    Google Scholar 
    17.Ferguson, S. H. & Mcloughlin, P. D. Effect of Energy Availability, Seasonality, and Geographic Range on Brown Bear Life History. Ecography (Cop.) 23, 193–200 (2000).Article 

    Google Scholar 
    18.Brewis, I. A. & Brennan, P. Proteomics Technologies for the Global Identification and Quantification of Proteins. Advances in Protein Chemistry and Structural Biology Vol. 80 (Elsevier, 2010).
    Google Scholar 
    19.Cox, J. & Mann, M. Is proteomics the new genomics?. Cell 130, 395–398 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    20.Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 25, 117–124 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    21.Vidova, V. & Spacil, Z. A review on mass spectrometry-based quantitative proteomics: Targeted and data independent acquisition. Anal. Chim. Acta 964, 7–23 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    22.Hoofnagle, A. N. et al. Multiple-reaction monitoring-mass spectrometric assays can accurately measure the relative protein abundance in complex mixtures. Clin. Chem. 58, 777–781 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Addona, T. A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27, 633–641 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Percy, A. J., Chambers, A. G., Yang, J., Hardie, D. B. & Borchers, C. H. Advances in multiplexed MRM-based protein biomarker quantitation toward clinical utility. Biochim. Biophys. Acta 1844, 917–926 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    25.Michaud, S. A. et al. Molecular phenotyping of laboratory mouse strains using 500 multiple reaction monitoring mass spectrometry plasma assays. Commun. Biol. 1, 1–9 (2018).CAS 
    Article 

    Google Scholar 
    26.Burke, H. B. Predicting clinical outcomes using molecular biomarkers. Biomark. Cancer 8, BIC.S33380 (2016).Article 

    Google Scholar 
    27.Zhang, A., Sun, H., Wang, P. & Wang, X. Salivary proteomics in biomedical research. Clin. Chim. Acta 415, 261–265 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Wilson, A. E. et al. Development and validation of protein biomarkers of health in grizzly bears. Conserv. Physiol. 8, coaa056 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Zmijewski, M. A. & Slominski, A. T. Neuroendocrinology of the skin: An overview and selective analysis. Dermatoendocrinol. 3, 3–10 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Slominski, A. T., Zmijewski, M. A., Plonka, P. M., Szaflarski, J. P. & Paus, R. How UV light touches the brain and endocrine system through skin, and why. Endocrinology 159, 1992–2007 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Slominski, A. T. et al. Sensing the environment: Regulation of local and global homeostasis by the skin neuroendocrine system. Adv. Anat. Embryol. Cell Biol. 212, 1–98 (2012).Article 

    Google Scholar 
    32.Esmaili, S., Hemmati, M. & Karamian, M. Physiological role of adiponectin in different tissues: A review. Arch. Physiol. Biochem. 126, 67–73 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    33.Ishaq, S., Kaur, H. & Bhatia, S. Clusterin: It’s implication in health and diseases. Ann. Appl. Bio-Sciences 4, R30–R34 (2017).Article 

    Google Scholar 
    34.Bali, S. & Utaal, M. S. Serum lipids and lipoproteins: A brief review of the composition, transport and physiological functions. Int. J. Sci. Rep. 5, 309 (2019).Article 

    Google Scholar 
    35.Linder, M. C. Ceruloplasmin and other copper binding components of blood plasma and their functions: An update. Metallomics 8, 887–905 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Dietzel, E., Floehr, J. & Jahnen-dechent, W. The biological role of fetuin-B in female reproduction. Ann. Reprod. Med. Treat 1(1), 1003 (2016).
    Google Scholar 
    37.Helliwell, R. J. A., Adams, L. F. & Mitchell, M. D. Prostaglandin synthases: Recent developments and a novel hypothesis. Prostaglandins Leukot. Essent. Fat. Acids 70, 101–113 (2004).CAS 
    Article 

    Google Scholar 
    38.Meyer, E. J., Nenke, M. A., Rankin, W., Lewis, J. G. & Torpy, D. J. Corticosteroid-binding globulin: A review of basic and clinical advances. Horm. Metab. Res. 48, 359–371 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    39.Hoter, A., El-Sabban, M. E. & Naim, H. Y. The HSP90 family: Structure, regulation, function, and implications in health and disease. Int. J. Mol. Sci. 19, 2560 (2018).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    40.Bruschi, M. et al. Annexin a1 and autoimmunity: From basic science to clinical applications. Int. J. Mol. Sci. 19, 1–13 (2018).Article 
    CAS 

    Google Scholar 
    41.Bogdan, A. R., Miyazawa, M., Hashimoto, K. & Tsuji, Y. Regulators of iron homeostasis: New players in metabolism, cell death, and disease. Trends Biochem. Sci. 41, 274–286 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Dieplinger, H. & Dieplinger, B. Afamin—A pleiotropic glycoprotein involved in various disease states. Clin. Chim. Acta 446, 105–110 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    43.Ricklin, D., Reis, E. S., Mastellos, D. C., Gros, P. & Lambris, J. D. Complement component C3—The “Swiss Army Knife” of innate immunity and host defense. Immunol. Rev. 274, 33–58 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Bteich, M. An overview of albumin and alpha-1-acid glycoprotein main characteristics: Highlighting the roles of amino acids in binding kinetics and molecular interactions. Heliyon 5, e02879 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Tóthová, C. & Nagy, O. Transthyretin in the evaluation of health and disease in human and veterinary medicine. In Pathophysiology—Altered Physiological States (ed. Gaze, D. C.) (IntechOpen, 2017). https://doi.org/10.5772/57353.Chapter 

    Google Scholar 
    46.Willis, E. L., Kersey, D. C., Durrant, B. S. & Kouba, A. J. The acute phase protein ceruloplasmin as a non-invasive marker of pseudopregnancy, pregnancy, and pregnancy loss in the giant panda. PLoS One 6, e21159 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Floehr, J. et al. Association of high fetuin-B concentrations in serum with fertilization rate in IVF: A cross-sectional pilot study. Hum. Reprod. 31, 630–637 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Khalkhali-Ellis, Z. Maspin: The new frontier. Clin. Cancer Res. 12, 7279–7283 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Chim, S. S. C. et al. Detection of the placental epigenetic signature of the maspin gene in maternal plasma. Proc. Natl. Acad. Sci. U.S.A. 102, 14753–14758 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Carillon, J., Rouanet, J. M., Cristol, J. P. & Brion, R. Superoxide dismutase administration, a potential therapy against oxidative stress related diseases: Several routes of supplementation and proposal of an original mechanism of action. Pharm. Res. 30, 2718–2728 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    51.Demers, N. & Bayne, C. Immediate increase of plasma protein complement C3 in response to an acute stressor. Fish Shellfish Immunol. 107, 411–413 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    52.Bourbonnais, M. L., Nelson, T. A., Cattet, M. R. L., Darimont, C. T. & Stenhouse, G. B. Spatial analysis of factors influencing long-term stress in the grizzly bear (Ursus arctos) population of alberta, canada. PLoS One 8, e83768 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    53.Zedrosser, A., Bellemain, E., Taberlet, P. & Swenson, J. E. Genetic estimates of annual reproductive success in male brown bears: The effects of body size, age, internal relatedness and population density. J. Anim. Ecol. 76, 368–375 (2007).PubMed 
    Article 

    Google Scholar 
    54.Pop, M. I., Iosif, R., Miu, I. V., Rozylowicz, L. & Popescu, V. D. Combining resource selection functions and home-range data to identify habitat conservation priorities for brown bears. Anim. Conserv. 21, 352–362 (2018).Article 

    Google Scholar 
    55.Pagano, A. M. et al. High-energy, high-fat lifestyle challenges an Arctic apex predator, the polar bear. Science 359, 568–572 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    56.Wasser, S. K. et al. Scat detection dogs in wildlife research and management: Application to grizzly and black bears in the Yellowhead Ecosystem, Alberta, Canada. Can. J. Zool. 82, 475–492 (2004).Article 

    Google Scholar 
    57.Cristescu, B., Stenhouse, G. B., Symbaluk, M., Nielsen, S. E. & Boyce, M. S. Wildlife habitat selection on landscapes with industrial disturbance. Environ. Conserv. 43, 327–336 (2016).Article 

    Google Scholar 
    58.Naves, J., Wiegand, T., Revilla, E. & Delibes, M. Endangered species constrained by natural and human factors: The case of brown bears in northern Spain. Conserv. Biol. 17, 1276–1289 (2003).Article 

    Google Scholar 
    59.Munro, R. H. M., Nielsen, S. E., Price, M. H., Stenhouse, G. B. & Boyce, M. S. Seasonal and diel patterns of grizzly bear diet and activity in west-central Alberta. J. Mammal. 87, 1112–1121 (2006).Article 

    Google Scholar 
    60.Nielsen, S. E., Boyce, M. S. & Stenhouse, G. B. Grizzly bears and forestry: I. Selection of clearcuts by grizzly bears in west-central Alberta, Canada. For. Ecol. Manag. 199, 51–65 (2004).Article 

    Google Scholar 
    61.Larsen, T. A., Nielsen, S. E., Cranston, J. & Stenhouse, G. B. Do remnant retention patches and forest edges increase grizzly bear food supply?. For. Ecol. Manag. 433, 741–761 (2019).Article 

    Google Scholar 
    62.Nielsen, S. E., Stenhouse, G. B. & Boyce, M. S. A habitat-based framework for grizzly bear conservation in Alberta. Biol. Conserv. 130, 217–229 (2006).Article 

    Google Scholar 
    63.Wilson, A. E. et al. Population-level monitoring of stress in grizzly bears between 2004 and 2014. Ecosphere 11, e03181 (2020).Article 

    Google Scholar 
    64.Graham, K. & Stenhouse, G. B. Home range, movements, and denning chronology of the grizzly bear (Ursus arctos) in west-central Alberta. Can. Field-Nat. 128, 223–234 (2014).Article 

    Google Scholar 
    65.Blanchard, B. M. & Knight, R. R. Movements of yellowstone grizzly bears. Biol. Conserv. 58, 41–67 (1991).Article 

    Google Scholar 
    66.McLoughlin, P. D., Case, R. L., Gau, R. J., Ferguson, S. H. & Messier, F. Annual and seasonal movement patterns of barren-ground grizzly bears in the central northwest territories. Ursus 11, 79–86 (1999).
    Google Scholar 
    67.Kadowaki, T. et al. Adiponectin and adiponectin receptors in insulin resistance, diabetes, and the metabolic syndrome. J. Clin. Investig. 116, 1784–1792 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    68.Rivet, D. R., Nelson, O. L., Vella, C. A., Jansen, H. T. & Robbins, C. T. Systemic effects of a high saturated fat diet in grizzly bears (Ursus arctos horribilis). Can. J. Zool. 95, 797–807 (2017).CAS 
    Article 

    Google Scholar 
    69.Rigano, K. S. et al. Life in the fat lane: Seasonal regulation of insulin sensitivity, food intake, and adipose biology in brown bears. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 187, 649–676 (2017).CAS 
    Article 

    Google Scholar 
    70.Lee, Y. S. et al. Adipocytokine orosomucoid integrates inflammatory and metabolic signals to preserve energy homeostasis by resolving immoderate inflammation. J. Biol. Chem. 285, 22174–22185 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Ráez-bravo, A. et al. Acute phase proteins increase with sarcoptic mange status and severity in Iberian ibex (Capra pyrenaica, Schinz 1838). Parasitol. Res. 114, 4005–4010. https://doi.org/10.1007/s00436-015-4628-3 (2015).Article 
    PubMed 

    Google Scholar 
    72.Agra, R. M. et al. Orosomucoid as prognosis factor associated with inflammation in acute or nutritional status in chronic heart failure. Int. J. Cardiol. 228, 488–494 (2017).PubMed 
    Article 

    Google Scholar 
    73.Mugahid, D. A. et al. Proteomic and transcriptomic changes in hibernating grizzly bears reveal metabolic and signaling pathways that protect against muscle atrophy. Sci. Rep. 9, 1–16 (2019).Article 
    CAS 

    Google Scholar 
    74.Vella, C. A. et al. Regulation of metabolism during hibernation in brown bears (Ursus arctos): Involvement of cortisol, PGC-1α and AMPK in adipose tissue and skeletal muscle. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 240, 110591 (2020).CAS 
    Article 

    Google Scholar 
    75.Jansen, H. T. et al. Hibernation induces widespread transcriptional remodeling in metabolic tissues of the grizzly bear. Commun. Biol. 2, 336 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    76.Phoebus, I., Segelbacher, G. & Stenhouse, G. B. Do large carnivores use riparian zones? Ecological implications for forest management. For. Ecol. Manag. 402, 157–165 (2017).Article 

    Google Scholar 
    77.Nielsen, S. E., McDermid, G., Stenhouse, G. B. & Boyce, M. S. Dynamic wildlife habitat models: Seasonal foods and mortality risk predict occupancy-abundance and habitat selection in grizzly bears. Biol. Conserv. 143, 1623–1634 (2010).Article 

    Google Scholar 
    78.Bielli, P. & Calabrese, L. Cellular and molecular life sciences structure to function relationships in ceruloplasmin: A ‘moonlighting’ protein. Cell. Mol. Life Sci. 59, 1413–1427 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    79.Pagano, A. M. et al. Energetic costs of locomotion in bears: Is plantigrade locomotion energetically economical?. J. Exp. Biol. 221, jeb175372 (2018).PubMed 
    Article 

    Google Scholar 
    80.Kurki, S., Nikula, A., Helle, P. & Linden, H. Landscape fragmentation and forest composition effects on grouse breeding success in boreal forests. Ecology 81, 1985–1997 (2000).
    Google Scholar 
    81.Graham, K., Boulanger, J., Duval, J. & Stenhouse, G. Spatial and temporal use of roads by grizzly bears in west-central Alberta. Ursus 21, 43–56 (2010).Article 

    Google Scholar 
    82.McLellan, B. N. & Shackleton, D. M. Grizzly bears and resource-extraction industries: Effects of roads on behaviour, habitat use and demography. J. Appl. Ecol. 25, 451–460 (1988).Article 

    Google Scholar 
    83.Massey, A. J. et al. Relationship between hair and salivary cortisol and pregnancy in women undergoing IVF. Psychoneuroendocrinology 74, 397–405 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    84.Benn, B. & Herrero, S. Grizzly bear mortality and human access in Banff and Yoho National Parks, 1971–98. Ursus 13, 213–221 (2002).
    Google Scholar 
    85.Nielsen, S. E. et al. Modelling the spatial distribution of human-caused grizzly bear mortalities in the Central Rockies ecosystem of Canada. Biol. Conserv. 120, 101–113 (2004).Article 

    Google Scholar 
    86.Pagano, A. M., Peacock, E. & Mckinney, M. A. Remote biopsy darting and marking of polar bears. Mar. Mammal Sci. 30, 169–183 (2014).Article 

    Google Scholar 
    87.Berland, A., Nelson, T., Stenhouse, G., Graham, K. & Cranston, J. The impact of landscape disturbance on grizzly bear habitat use in the Foothills Model Forest, Alberta, Canada. For. Ecol. Manag. 256, 1875–1883 (2008).Article 

    Google Scholar 
    88.Stenhouse, G. et al. Grizzly bear associations along the eastern slopes of Alberta. Ursus 16, 31–40 (2005).Article 

    Google Scholar 
    89.Nielsen, S. E., Munro, R. H. M., Bainbridge, E. L., Stenhouse, G. B. & Boyce, M. S. Grizzly bears and forestry: II. Distribution of grizzly bear foods in clearcuts of west-central Alberta, Canada. For. Ecol. Manag. 199, 67–82 (2004).Article 

    Google Scholar 
    90.Cattet, M., Boulanger, J., Stenhouse, G., Powell, R. A. & Reynolds-Hogland, M. J. An evaluation of long-term capture effects in ursids: Implications for wildlife welfare and research. J. Mammal. 89, 973–990 (2008).Article 

    Google Scholar 
    91.McDermid, G. J. Remote Sensing for Large-Area, Multi-Jurisdictional Habitat Mapping. PhD Thesis. University of Waterloo: Canada. 258p (2005).92.Smulders, M. et al. Quantifying spatial-temporal patterns in wildlife ranges using STAMP: A grizzly bear example. Appl. Geogr. 35, 124–131 (2012).Article 

    Google Scholar 
    93.Sorensen, A. A., Stenhouse, G. B., Bourbonnais, M. L. & Nelson, T. A. Effects of habitat quality and anthropogenic disturbance on grizzly bear (Ursus arctos horribilis) home-range fidelity. Can. J. Zool. 93, 857–865 (2015).Article 

    Google Scholar 
    94.Franklin, S. E., Peddle, D. R., Dechka, J. A. & Stenhouse, G. B. Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bar habitat mapping. Int. J. Remote Sens. 23, 4633–4652 (2002).ADS 
    Article 

    Google Scholar 
    95.Gessler, P. E., Moore, I. D., McKenzie, N. J. & Ryan, P. J. Soil-landscape modelling and spatial prediction of soil attributes. Int. J. Geogr. Inf. Syst. 9, 421–432 (1995).Article 

    Google Scholar 
    96.Wilson, J. P. & Gallant, J. C. Terrain Analysis: Principles and Applications (Wiley, 2000).
    Google Scholar 
    97.Riley, S. J., DeGloria, S. D. & Elliot, R. A Terrain ruggedness index that quantifies topographic heterogeneity. Int. J. Sci. 5, 23–27 (1999).
    Google Scholar 
    98.Stoneberg, R. P. & Jonkel, C. J. Age determination of black bears by cementum layers. J. Wildl. Manag. 30, 411–414 (1966).Article 

    Google Scholar 
    99.Matson, G. M., Van Daele, L., Goodwin, E., Aumiller, A., Reynolds, H.V. & Hristienko, H. A Laboratory Manual for Cementum Age Determination of Alaskan Brown Bear First Premolar Teeth. 1–52 (Matson’s Laboratory, Milltown, MT, 1993).100.Nielsen, S. E. et al. Environmental, biological and anthropogenic effects on grizzly bear body size: Temporal and spatial considerations. BMC Ecol. 13, 1 (2013).CAS 
    Article 

    Google Scholar 
    101.Bourbonnais, M. L. et al. Environmental factors and habitat use influence body condition of individuals in a species at risk, the grizzly bear. Conserv. Physiol. 2, 1–14 (2014).Article 
    CAS 

    Google Scholar 
    102.Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    103.Cattet, M. et al. The quantification of reproductive hormones in the hair of captive adult brown bears and their application as indicators of sex and reproductive state. Conserv. Physiol. 5, 1–21 (2017).Article 
    CAS 

    Google Scholar 
    104.Cattet, M. et al. Can concentrations of steroid hormones in brown bear hair reveal age class?. Conserv. Physiol. 6, 1–20 (2018).Article 
    CAS 

    Google Scholar 
    105.Carlson, R. et al. Development and application of an antibody-based protein microarray to assess stress in grizzly bears (Ursus arctos). Conserv. Physiol. 4, 1–17 (2016).Article 
    CAS 

    Google Scholar 
    106.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical information-Theoretic Approach (Springer, 2002).MATH 

    Google Scholar 
    107.Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    108.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J 9, 378–400 (2017).Article 

    Google Scholar 
    109.R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, Austria, 2020). More

  • in

    Divergence of a genomic island leads to the evolution of melanization in a halophyte root fungus

    1.Hoekstra H. Genetics, development and evolution of adaptive pigmentation in vertebrates. Heredity. 2006;97:222–234.CAS 
    PubMed 
    Article 

    Google Scholar 
    2.McNamara ME, Rossi V, Slater TS, Rogers CS, Ducrest AL, Dubey S, et al. Decoding the evolution of melanin in vertebrates. Trends Ecol Evol. 2021; https://doi.org/10.1016/j.tree.2020.12.012.3.Roulin A. Melanin-based colour polymorphism responding to climate change. Glob Chang Biol. 2014;20:3344–3350.PubMed 
    Article 

    Google Scholar 
    4.Laurent S, Pfeifer SP, Settles ML, Hunter SS, Hardwick KM, Ormond L, et al. The population genomics of rapid adaptation: disentangling signatures of selection and demography in white sands lizards. Mol Ecol. 2016;25:306–323.CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Naranjo-Ortiz MA, Gabaldón T. Fungal evolution: major ecological adaptations and evolutionary transitions. Biol Rev Camb Philos Soc. 2019;94:1443–1476.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Cordero RJ, Casadevall A. Functions of fungal melanin beyond virulence. Fungal Biol Rev. 2017;31:99–112.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Treseder KK, Lennon JT. Fungal traits that drive ecosystem dynamics on land. Microbiol Mol Biol Rev. 2015;79:243–262.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Kejžar A, Gobec S, Plemenitaš A, Lenassi M. Melanin is crucial for growth of the black yeast Hortaea werneckii in its natural hypersaline environment. Fungal Biol. 2013;117:368–379.PubMed 
    Article 
    CAS 

    Google Scholar 
    9.Singaravelan N, Grishkan I, Beharav A, Wakamatsu K, Ito S, Nevo E. Adaptive melanin response of the soil fungus Aspergillus niger to UV radiation stress at “Evolution Canyon”, Mount Carmel, Israel. PLoS ONE. 2008;3:e2993.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    10.Krishnan P, Meile L, Plissonneau C, Ma X, Hartmann FE, Croll D, et al. Transposable element insertions shape gene regulation and melanin production in a fungal pathogen of wheat. BMC Biol. 2018;16:78.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    11.Pereira D, Croll D, Brunner PC, McDonald BA. Natural selection drives population divergence for local adaptation in a wheat pathogen. Fungal Genet Biol. 2020;141:103398.CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Desjardins CA, Giamberardino C, Sykes SM, Yu CH, Tenor JL, Chen Y, et al. Population genomics and the evolution of virulence in the fungal pathogen Cryptococcus neoformans. Genome Res. 2017;27:1207–1219.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Robertson KL, Mostaghim A, Cuomo CA, Soto CM, Lebedev N, Bailey RF, et al. Adaptation of the black yeast Wangiella dermatitidis to ionizing radiation: molecular and cellular mechanisms. PLoS ONE. 2012;7:e48674.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Knapp DG, Németh JB, Barry K, Hainaut M, Henrissat B, Johnson J, et al. Comparative genomics provides insights into the lifestyle and reveals functional heterogeneity of dark septate endophytic fungi. Sci Rep. 2018;8:6321.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Fernandez CW, Koide RT. The function of melanin in the ectomycorrhizal fungus Cenococcum geophilum under water stress. Fungal Ecol. 2013;6:479–486.Article 

    Google Scholar 
    16.Redman RS, Sheehan KB, Stout RG, Rodriguez RJ, Henson JM. Thermotolerance generated by plant/fungal symbiosis. Science. 2002;298:1581.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Peay KG, Kennedy PG, Talbot JM. Dimensions of biodiversity in the earth mycobiome. Nat Rev Microbiol. 2016;14:434–447.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Rodriguez RJ, White JF, Arnold AE, Redman RS. Fungal endophytes: diversity and functional roles. N Phytol. 2009;182:314–330.CAS 
    Article 

    Google Scholar 
    19.Yuan ZL, Su ZZ, Zhang CL. Understanding the biodiversity and functions of root fungal endophytes: the ascomycete Harpophora oryzae as a model case. In: Irina S Druzhinina IS, Kubicek CP editors). The mycota Vol. IV: environmental and microbial relationships. 3rd ed. Springer; 2016, pp 205–214.20.Berthelot C, Leyval C, Foulon J, Chalot M, Blaudez D. Plant growth promotion, metabolite production and metal tolerance of dark septate endophytes isolated from metal-polluted poplar phytomanagement sites. FEMS Microbiol Ecol. 2016;92:fiw144.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    21.Hill PW, Broughton R, Bougoure J, Havelange W, Newsham KK, Grant H, et al. Angiosperm symbioses with non-mycorrhizal fungal partners enhance N acquisition from ancient organic matter in a warming maritime Antarctic. Ecol Lett. 2019;22:2111–2119.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Mateu M, Baldwin A, Maul J, Yarwood S. Dark septate endophyte improves salt tolerance of native and invasive lineages of Phragmites australis. ISME J. 2020;14:1943–1954.Article 
    CAS 

    Google Scholar 
    23.Porras-Alfaro A, Herrera J, Sinsabaugh RL, Odenbach KJ, Lowrey T, Natvig DO. Novel root fungal consortium associated with a dominant desert grass. Appl Environ Microbiol. 2008;74:2805–2813.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Qin Y, Pan XY, Kubicek CP, Druzhinina IS, Chenthamara K, Labbé J, et al. Diverse plant-associated pleosporalean fungi from saline areas: ecological tolerance and nitrogen-status dependent effects on plant growth. Front Microbiol. 2017;8:158.PubMed 
    PubMed Central 

    Google Scholar 
    25.Gostinčar C, Grube M, de Hoog S, Zalar P, Gunde-Cimerman N. Extremotolerance in fungi: evolution on the edge. FEMS Microbiol Ecol. 2010;71:2–11.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    26.Yuan ZL, Druzhinina IS, Labbé J, Redman R, Qin Y, Rodriguez R, et al. Specialized microbiome of a halophyte and its role in helping non-host plants to withstand salinity. Sci Rep. 2016;6:32467.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Yuan ZL, Druzhinina IS, Wang X, Zhang X, Peng L, Labbé J. Insight into a highly polymorphic endophyte isolated from the roots of the halophytic seepweed suaeda salsa: Laburnicola rhizohalophila sp. nov. (Didymosphaeriaceae, Pleosporales). Fungal Biol. 2020;124:327–337.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Ellison CE, Hall C, Kowbel D, Welch J, Brem RB, Glass NL, et al. Population genomics and local adaptation in wild isolates of a model microbial eukaryote. Proc Natl Acad Sci USA. 2011;108:2831–2836.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 2010;26:589–595.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    30.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–2079.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    31.Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinform. 2013;43:11.10.1–11.10.33.Article 

    Google Scholar 
    32.Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    33.Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19:1655–1664.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006;23:254–267.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Wilken M, Steenkamp E, Wingfield M, De Beer ZW, Wingfield B. Which MAT gene? Pezizomycotina (Ascomycota) mating-type gene nomenclature reconsidered. Fungal Biol Rev. 2017;31:199–211.Article 

    Google Scholar 
    37.Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–3402.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Wright S. The genetical structure of populations. Ann Eugen. 1951;15:323–354.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Nei M (ed). Molecular evolutionary genetics. Columbia University Press; 1987.40.Carlson CS, Thomas DJ, Eberle MA, Swanson JE, Livingston RJ, Rieder MJ, et al. Genomic regions exhibiting positive selection identified from dense genotype data. Genome Res. 2005;15:1553–1565.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Pembleton LW, Cogan NOI, Forster JW. StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations. Mol Ecol Resour. 2013;13:946–952.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Axelsson E, Ratnakumar A, Arendt ML, Maqbool K, Webster MT, Perloski M, et al. The genomic signature of dog domestication reveals adaptation to a starch-rich diet. Nature. 2013;495:360–364.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16:284–287.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Zhao S, Gibbons JG. A population genomic characterization of copy number variation in the opportunistic fungal pathogen Aspergillus fumigatus. PLoS ONE. 2018;13:e0201611.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    45.Klambauer G, Schwarzbauer K, Mayr A, Clevert DA, Mitterecker A, Bodenhofer U, et al. cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate. Nucleic Acids Res. 2012;40:e69.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989;123:585–595.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Nei M, Li WH. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA. 1979;76:5269–5273.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Hutter S, Vilella AJ, Rozas J. Genome-wide DNA polymorphism analyses using VariScan. BMC Bioinform. 2006;7:409.Article 
    CAS 

    Google Scholar 
    49.Wagner DN, Baris TZ, Dayan DI, Du X, Oleksiak MF, Crawford DL. Fine-scale genetic structure due to adaptive divergence among microhabitats. Heredity. 2017;118:594–604.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Rech GE, Sanz-Martín JM, Anisimova M, Sukno SA, Thon MR. Natural selection on coding and noncoding DNA sequences is associated with virulence genes in a plant pathogenic fungus. Genome Biol Evol. 2014;6:2368–2379.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Sterken R, Kiekens R, Coppens E, Vercauteren I, Zabeau M, Inzé D, et al. A population genomics study of the Arabidopsis core cell cycle genes shows the signature of natural selection. Plant Cell. 2009;21:2987–2998.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Yu F, Keinan A, Chen H, Ferland RJ, Hill RS, Mignault AA, et al. Detecting natural selection by empirical comparison to random regions of the genome. Hum Mol Genet. 2009;18:4853–4867.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Nielsen R, Williamson S, Kim Y, Hubisz MJ, Clark AG, Bustamante C. Genomic scans for selective sweeps using SNP data. Genome Res. 2005;15:1566–1575.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Pavlidis P, Živkovic D, Stamatakis A, Alachiotis N. SweeD: likelihood-based detection of selective sweeps in thousands of genomes. Mol Biol Evol. 2013;30:2224–2234.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–265.CAS 
    PubMed 
    Article 

    Google Scholar 
    56.Zhan F, He Y, Zu Y, Li T, Zhao Z. Characterization of melanin isolated from a dark septate endophyte (DSE), Exophiala pisciphila. World J Microbiol Biotechnol. 2011;27:2483–2489.CAS 
    Article 

    Google Scholar 
    57.Taylor JW, Hann-Soden C, Branco S, Sylvain I, Ellison CE. Clonal reproduction in fungi. Proc Natl Acad Sci USA. 2015;112:8901–8908.CAS 
    PubMed 
    Article 

    Google Scholar 
    58.McGuire IC, Davis JE, Double ML, MacDonald WL, Rauscher JT, McCawley S, et al. Heterokaryon formation and parasexual recombination between vegetatively incompatible lineages in a population of the chestnut blight fungus, Cryphonectria parasitica. Mol Ecol. 2005;14:3657–3669.CAS 
    PubMed 
    Article 

    Google Scholar 
    59.Szulkin M, Gagnaire PA, Bierne N, Charmantier A. Population genomic footprints of fine-scale differentiation between habitats in Mediterranean blue tits. Mol Ecol. 2016;25:542–558.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Hamilton JA, De la Torre AR, Aitken SN. Fine-scale environmental variation contributes to introgression in a three-species spruce hybrid complex. Tree Genet Genomes. 2015;11:817.Article 

    Google Scholar 
    61.Yeaman S. Genomic rearrangements and the evolution of clusters of locally adaptive loci. Proc Natl Acad Sci USA. 2013;110:E1743–E1751.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Kroken S, Glass NL, Taylor JW, Yoder OC, Turgeon BG. Phylogenomic analysis of type I polyketide synthase genes in pathogenic and saprobic ascomycetes. Proc Natl Acad Sci USA. 2003;100:15670–15675.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Woo PCY, Tam EW, Chong KT, Cai JJ, Tung ET, Ngan AH, et al. High diversity of polyketide synthase genes and the melanin biosynthesis gene cluster in Penicillium marneffei. FEBS J. 2010;277:3750–3758.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Kameyama K, Montague PM, Hearing VJ. Expression of melanocyte stimulating hormone receptors correlates with mammalian pigmentation, and can be modulated by interferons. J Cell Physiol. 1988;137:35–44.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Upadhyay S, Xu X, Lowry D, Jackson JC, Roberson RW, Lin X. Subcellular compartmentalization and trafficking of the biosynthetic machinery for fungal melanin. Cell Rep. 2016;14:2511–2518.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Coleman JJ, Mylonakis E. Efflux in fungi: la pièce de résistance. PLoS Pathog. 2009;5:e1000486.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    67.Cosgrove DJ. Microbial expansins. Annu Rev Microbiol. 2017;71:479–497.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

  • in

    Tracking the invasive hornet Vespa velutina in complex environments by means of a harmonic radar

    Study areasThe technique of harmonic radar tracking has been applied in nine different localities of Liguria (Italy), in the framework of the control activities developed to contain the spread of V. velutina in this region19,21,30. Four of these study areas (Ameglia, Arcola, Riccò del Golfo in La Spezia district and Finale Ligure in Savona district) were new invasive outbreaks characterised by a low nest density of V. velutina and low predation pressure on honey bee colonies. The other five study areas of Imperia district (Camporosso, Dolceacqua, Ospedaletti, and the two villages of Calvo and Latte in the municipality of Ventimiglia) were located inside the colonised range of the species21, and were characterised by a high nest density and an intensive predation pressure on honey bee colonies (Supplementary Table S1).Harmonic radar trackingThe harmonic radar and the tags that have been used for tracking the flight of V. velutina were designed and developed ad-hoc for following insects in complex environments; their technical and innovative characteristics have been previously described by the authors18. At the beginning of a new tracking session, worker hornets are trapped, usually in apiaries while preying on honey bees, and the transponders are attached on their thorax using an orthodontic glue, without anesthetising the insects. Subsequently, hornets are released from the tagging location and are immediately able to resume their activity, such as flying and preying on honey bees (Fig. 6). The whole tagging procedure requires less than one minute per hornet. Tag weight (15 mg) is approximately 4–7% of the weight of V. velutina workers (mean worker’s weight changes over the season between 189 and 386 mg)26. Moreover, the tag is 3–4 times lighter than the weight of prey’s pellet generally transported to the nest by this species. This information, together with multiple observations of tagged hornets in apiaries and the results achieved by other authors with a radio-tracking experiment (in which it was found that hornets equipped with a tag of weight lesser than 80% of their body weight are considered good flyers)22, suggest that the tags used in this study do not affect the behaviour and the flying abilities of V. velutina.Figure 6Tagged hornets performing their usual predatory behaviour. Tagged individuals of V. velutina hovering in front of honey bee colonies for preying on forager bees (a,b). A tagged hornet that is disjointing a honey bee for gathering the thorax (most energetic part of its prey), that will be brought back to the nest for feeding the brood (c). Two tagged hornets in proximity of the entrance hole of the nest (d).Full size imageThe harmonic radar records independently all the tracks of flying hornets that are inside its detection range. The real-time analysis of the recorded tracks allows understanding the main flying directions. If the nest of V. velutina is located outside of the maximum detection range of the radar (about 500 m in flat terrain)18 or behind physical obstacles, the harmonic radar is moved according to the flying directions of the hornets. The presence of a diffused road network, as in many of our study areas, facilitated the movement of the radar from one position to another. This operation is repeated until the position of the nest is determined. The area where the nest is located is generally highlighted by the presence of several tracks that converge or begin from the same site. The visual inspection of the area permits the exact detection of the position of the nest. In several cases, tagged hornets were visually observed on the surface of the nests (Fig. 6d).The total number of tagged hornets was recorded for each tracking session, together with the radar operation time, the number of radar movements per session, the number of detected nests per session and the minimum distance between the nests and the apiaries where hornets were hunting honey bees (Supplementary Table S2). Hornets were trapped with standard entomological procedures for trapping insects, and experiments were conducted ethically since no hornets were killed, injured, or kept captive after being tagged.Tracking lengths and environmental characteristicsThe main parameter selected for estimating the performance of the harmonic radar in tracking V. velutina in different natural and complex environments is the length of the tracks of tagged insects. To obtain this parameter, fixes (hornets detected by the harmonic radar at each radar’s rotation) were extracted for each tracking session and uploaded on a GIS software32. Afterwards, consecutive fixes of the same track were connected with the shortest line, so to obtain hornet tracks and calculate their length. The advanced radar analyses used for processing the received signals18 allow discriminating the true fixes (position of the hornet) from clutter (reflected signals received from objects in the landscape). However, the presence of obstacles may generate gaps in the received signals (e.g. when a hornet is temporarily flying behind an obstacle such as a house), but these gaps were rare and never occurred for long periods of time. In these cases, if fixes were not clearly recognizable to a track of the same hornet, these were excluded from the analysis. The exclusion of the tracks was performed also in the rare cases during which the presence of multiple tagged hornets did not allow a clear identification of the tracks.The length of the tracks in each fix position (n = 2580) was modelled with a GLMM (see “Data analysis”) to evaluate the effect of environmental features (land cover, elevation above sea level, slope gradient, road density). The land cover layer was obtained through a photo interpretation of satellite images (in a buffer area of 100 m around the minimum convex polygon that encompass all the tracks in each locality) and classification in three macro-levels: open terrains (landscapes predominantly characterised by open areas, such as fields), urban areas (matrices formed by buildings/roads) and woodlands (matrices formed by forests). Elevation above sea level and slope degree were obtained by a digital elevation model (resolution of 20 m).Visual tracking of flying hornetsThe length of the tracks recorded by the harmonic radar was compared with the length of the tracks recorded when adopting a customary technique for tracking insects, such as the visual tracking and triangulation of flying directions20,25. In six of the nine localities where the harmonic radar tracking has been applied (Fig. 4), an operator was waiting near a honey bee colony till one V. velutina worker caught a honey bee. Subsequently, after the hornet disjoined the most energetic parts of its prey (the thorax)33, the operator visually tracked the flight of the hornet when flying back to its nest, using a binocular and by recording with a GPS the position where the hornet disappeared from view. In some cases (n = 4), common flying routes were identified, and we were able to resume the visual tracking with other hornets from the previous disappearance position. Finally, GPS positions were uploaded on a GIS software to calculate the length of the tracks with this technique.In this study, the visual tracking technique has not been implemented systematically for nest detection, therefore the two approaches are compared only by evaluating the recorded length of the tracks. The effectiveness in locating nests, the required time and the associated costs are discussed in the framework of previous studies for tracking V. velutina, taking into account advantages and limits of the different techniques20,22,25.Estimation of V. velutina ground flying speedHarmonic radar tracking allows estimating the ground flying speed of V. velutina, by analysing the distance between each recorded position at consecutive radar rotations. Giving that the time of each radar rotation is fixed (3 s), it is possible to estimate the hornet’s speed between each detection8.The ground flying speed of V. velutina has been estimated in the three localities of La Spezia district, due to the availability of a subsample of clear tracks with consecutive detections per each rotation of the radar and good weather conditions. Furthermore, based on their direction, tracks were classified in homing tracks (H), which belong to hornets flying from the apiary to the nest, and foraging tracks (F), which belong to hornets flying towards the apiary for hunting honey bees. Data on wind speed and direction were obtained from weather stations close to the study areas.Data analysisData analyses were performed with the software R34. Environmental characteristics of the localities were analysed with a Principal Component Analysis (PCA; package factoextra), to understand affinities between study areas and correlations between the considered variables. The length of the tracks between localities recorded with the harmonic radar was compared with the Kruskal–Wallis and the Dunn tests with Bonferroni correction, while the flying speed between foraging and homing hornets was compared with the Wilcoxon rank-sum test (two-tailed).Generalized linear mixed models (GLMM; package lme4) with gamma distribution and log link function were used to assess (1) the influence of environmental variables on the length of the tracks and (2) compare tracking methods between study areas. In the first case, a random slope model has been implemented, by defining the locality and the slope degree as random effects (uncorrelated). In the second case, a standard random intercept model has been implemented, by selecting the locality as random effect. In both cases, continuous variables were standardized, and multi-collinearity of environmental variables was taken into account by calculating the Variance Inflation Factor (VIF). This was 1.5 for elevation and slope degree, and 1.0 for road density. More

  • in

    Trophic niches of native and nonnative fishes along a river-reservoir continuum

    1.Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182. https://doi.org/10.1017/S1464793105006950 (2006).Article 
    PubMed 

    Google Scholar 
    2.Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: recent progress and future challenges. J. N. Am. Benthol. Soc. 29, 344–359. https://doi.org/10.1899/08-171.1 (2010).Article 

    Google Scholar 
    3.Reid, A. J. et al. Emerging threats and persistent challenges for freshwater biodiversity. Biol. Rev. 94, 849–873. https://doi.org/10.1111/brv.12480 (2019).Article 
    PubMed 

    Google Scholar 
    4.Cucherousset, J. & Olden, J. D. Ecological impacts of nonnative freshwater fishes. Fisheries 36, 215–230. https://doi.org/10.1080/03632415.2011.574578 (2011).Article 

    Google Scholar 
    5.Vander Zanden, M. J., Casselman, J. M. & Rasmussen, J. B. Stable isotope evidence for the food web consequences of species invasions in lakes. Nature 401, 464–467. https://doi.org/10.1038/46762 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Britton, J. R., Davies, G. D. & Harrod, C. Trophic interactions and consequent impacts of the invasive fish Psuedorasbora parva in a native aquatic food web: a field investigation in the UK. Biol. Invasions 12, 1533–1542. https://doi.org/10.1007/s10530-009-9566-5 (2010).Article 

    Google Scholar 
    7.Cox, J. G. & Lima, S. L. Naiveté and an aquatic-terrestrial dichotomy in the effects of introduced predators. Trends Ecol. Evol. 21, 674–680. https://doi.org/10.1016/j.tree.2006.07.011 (2006).Article 
    PubMed 

    Google Scholar 
    8.Marks, J. C., Haden, G. A., O’Neil, M. & Pace, C. Effects of flow restoration and exotic species removal on recovery of native fish: Lessons from a dam decommissioning. Restor. Ecol. 18, 934–943. https://doi.org/10.1111/j.1526-100X.2009.00574.x (2010).Article 

    Google Scholar 
    9.Walsworth, T. E., Budy, P. & Thiede, G. P. Longer food chains and crowded niche space: effects of multiple invaders on desert stream food web structure. Ecol. Freshw. Fish 22, 439–452. https://doi.org/10.1111/eff.12038 (2013).Article 

    Google Scholar 
    10.Rogosch, J. S. & Olden, J. D. Invaders induce coordinated isotopic niche shifts in native fish species. Can. J. Fish. Aquat. Sci. 77, 1348–1358. https://doi.org/10.1139/cjfas-2019-0346 (2020).Article 

    Google Scholar 
    11.Connell, J. H. The influence of interspecific competition and other factors on the distribution of the barnacle Chthamalus stellatus. Ecology 42, 710–723. https://doi.org/10.2307/1933500 (1961).Article 

    Google Scholar 
    12.Zaret, T. M. & Rand, A. S. Competition in tropical stream fishes: Support for the competitive exclusion principle. Ecology 52, 336–342. https://doi.org/10.2307/1934593 (1971).Article 

    Google Scholar 
    13.Britton, J. R., Ruiz-Navarro, A., Verreycken, H. & Amat-Trigo, F. Trophic consequences of introduced species: comparative impacts of increased interspecific versus intraspecific competitive interactions. Funct. Ecol. 32, 486–495. https://doi.org/10.1111/1365-2435.12978 (2018).Article 
    PubMed 

    Google Scholar 
    14.Connell, J. H. On the prevalence and relative importance of interspecific competition: evidence from field experiments. Am. Nat. 122, 661–696. https://doi.org/10.1086/284165 (1983).Article 

    Google Scholar 
    15.David, P. et al. Impacts of invasive species on food webs: a review of empirical data. Adv. Ecol. Res. 56, 1–60. https://doi.org/10.1016/bs.aecr.2016.10.001 (2017).Article 

    Google Scholar 
    16.Vannote, R. L., Wayne Minshall, G., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137. https://doi.org/10.1139/f80-017 (1980).Article 

    Google Scholar 
    17.Ibañez, C. et al. Convergence of temperate and tropical stream fish assemblages. Ecography 32, 658–670. https://doi.org/10.1111/j.1600-0587.2008.05591.x (2009).Article 

    Google Scholar 
    18.Winemiller, K. O. et al. Stable isotope analysis reveals food web structure and watershed impacts along the fluvial gradient of a Mesoamerican coastal river. River Res. Appl. 27, 791–803. https://doi.org/10.1002/rra.1396 (2011).Article 

    Google Scholar 
    19.Ward, J. V. & Stanford, J. A. The serial discontinuity concept: extending the model to floodplain rivers. River Res. Appl. 10, 159–168. https://doi.org/10.1002/rrr.3450100211 (1983).Article 

    Google Scholar 
    20.Sabo, J. L. et al. Pulsed flows, tributary inputs and food-web structure in a highly regulated river. J. Appl. Ecol. 55, 1884–1895. https://doi.org/10.1111/1365-2664.13109 (2018).Article 

    Google Scholar 
    21.Sabater, S. Alterations of the global water cycle and their effects on river structure, function and services. Freshw. Rev. 1, 75–89. https://doi.org/10.1608/FRH-1.1.5 (2008).Article 

    Google Scholar 
    22.Arrantes, C. C., Fitzgerald, D. B., Hoeinghaus, D. J. & Winemiller, K. O. Impacts of hydroelectric dams on fishes and fisheries in tropical rivers through the lens of functional traits. Curr. Opin. Environ. Sustain. 37, 28–40. https://doi.org/10.1016/j.cosust.2019.04.009 (2019).Article 

    Google Scholar 
    23.Cross, W. F. et al. Ecosystem ecology meets adaptive management: food web response to a controlled flood on the Colorado River, Glen Canyon. Ecol. Appl. 21, 2016–2033. https://doi.org/10.1890/10-1719.1 (2011).Article 
    PubMed 

    Google Scholar 
    24.Cross, W. F. et al. Food web dynamics in a large river discontinuum. Ecol. Monogr. 83, 311–337. https://doi.org/10.1890/12-1727.1 (2013).Article 

    Google Scholar 
    25.Wellard Kelley, H. A. et al. Macroinvertebrate diets reflect tributary inputs and turbidity-driven changes in food availability in the Colorado River downstream of Glen Canyon Dam. Freshw. Sci. 32, 397–410. https://doi.org/10.1899/12-088.1 (2013).Article 

    Google Scholar 
    26.Thornton, K. W., Kimmel, B. L. & Payne, F. E. Reservoir Limnology: Ecological Perspectives (John Wiley and Sons, 1990).
    Google Scholar 
    27.Havel, J. E., Lee, C. E. & Vander Zanden, J. M. Do reservoirs facilitate invasions into landscapes?. Bioscience 55, 518–525. https://doi.org/10.1641/0006-3568(2005)055[0518:DRFIIL]2.0.CO;2 (2005).Article 

    Google Scholar 
    28.Southwood, T. R. E. Habitat, the templet for ecological strategies?. J. Anim. Ecol. 46, 337–365. https://doi.org/10.2307/3817 (1977).Article 

    Google Scholar 
    29.Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. A. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460. https://doi.org/10.1016/j.tree.2008.03.011 (2008).Article 
    PubMed 

    Google Scholar 
    30.Mercado-Silva, N., Helmus, M. R. & Vander Zanden, M. J. The effects of impoundment and non-native species on a river food web in Mexico’s central plateau. River Res. Appl. 25, 1090–1108. https://doi.org/10.1002/rra.1205 (2009).Article 

    Google Scholar 
    31.Villéger, S., Blanchet, S., Beauchard, O., Oberdorff, T. & Brosse, S. Homogenization patterns of the world’s freshwater fish faunas. Proc. Natl. Acad. Sci. U. S. A. 108, 18003–18008. https://doi.org/10.1073/pnas.1107614108 (2011).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Delong, M. D., Thorp, J. H., Thoms, M. C. & McIntosh, L. M. Trophic niche dimensions of fish communities as a function of historical hydrological conditions in a Plains river. River Syst. 19, 177–187. https://doi.org/10.1127/1868-5749/2011/019-0036 (2011).Article 

    Google Scholar 
    33.Pilger, T. J., Gido, K. B. & Propst, D. L. Diet and trophic niche overlap of native and nonnative fishes in the Gila River, USA: implications for native fish conservation. Ecol. Freshw. Fish 19, 300–321. https://doi.org/10.1111/j.1600-0633.2010.00415.x (2010).Article 

    Google Scholar 
    34.Mor, J. R. et al. Dam regulation and riverine food-web structure in a Mediterranean river. Sci. Total Environ. 625, 301–310. https://doi.org/10.1016/j.scitotenv.2017.12.296 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Tyus, H. M. & Saunders, J. F. III. Nonnative fish control and endangered fish recovery: lessons from the Colorado River. Fisheries 25, 17–24. https://doi.org/10.1577/1548-8446(2000)025%3c0017:NFCAEF%3e2.0.CO;2 (2000).Article 

    Google Scholar 
    36.Strayer, D. L. Alien species in fresh waters: ecological effects, interactions with other stressors, and prospects for the future. Freshw. Biol. 55, 152–174. https://doi.org/10.1111/j.1365-2427.2009.02380.x (2010).Article 

    Google Scholar 
    37.Marks, J. C., Williamson, C. & Hendrickson, D. A. Coupling stable isotope studies with food web manipulations to predict the effects of exotic fish: lessons from Cuatro Ciénegas, Mexico. Aquat. Conserv. 21, 317–323. https://doi.org/10.1002/aqc.1199 (2011).Article 

    Google Scholar 
    38.Cooke, S. J., Paukert, C. & Hogan, Z. Endangered river fish: factors hindering conservation and restoration. Endanger. Species Res. 17, 179–191. https://doi.org/10.3354/esr00426 (2012).Article 

    Google Scholar 
    39.Pennock, C. A., Farrington, M. A. & Gido, K. B. Feeding ecology of early life stage Razorback Sucker relative to other sucker species in the San Juan River. Trans. Am. Fish. Soc. 148, 938–951. https://doi.org/10.1002/tafs.10188 (2019).Article 

    Google Scholar 
    40.Cucherousset, J., Bouletreau, S., Martino, A., Roussel, J. M. & Santoul, F. Using stable isotope analyses to determine the ecological effects of non-native fishes. Fish. Mgmt. Ecol. 19, 111–119. https://doi.org/10.1111/j.1365-2400.2011.00824.x (2012).Article 

    Google Scholar 
    41.Finlay, J. C. Stable-carbon-isotope ratios of river biota: Implications for energy flow in lotic food webs. Ecology 82, 1052–1064. https://doi.org/10.1890/0012-9658(2001)082[1052:SCIROR]2.0.CO;2 (2001).Article 

    Google Scholar 
    42.France, R. L. Differentiation between littoral and pelagic food webs in lakes using stable carbon isotopes. Limnol. Oceanogr. 40, 1310–1313. https://doi.org/10.4319/lo.1995.40.7.1310 (1995).ADS 
    Article 

    Google Scholar 
    43.Fry, B. Stable Isotope Ecology (Springer-Verlag, 2006).Book 

    Google Scholar 
    44.Vander Zanden, M. J., Cabana, G. & Rasmussen, J. B. Comparing trophic position of freshwater fish calculated using stable nitrogen isotope ratios (δ15N) and literature dietary data. Can. J. Fish. Aquat. Sci. 54, 1142–1158. https://doi.org/10.1139/f97-016 (1997).Article 

    Google Scholar 
    45.Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718. https://doi.org/10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2 (2002).Article 

    Google Scholar 
    46.Layman, C. A., Arrington, D. A., Montaña, C. G. & Post, D. M. Can stable isotope ratios provide for community-wide measures of trophic structure?. Ecology 88, 42–48. https://doi.org/10.1890/0012-9658(2007)88[42:CSIRPF]2.0.CO;2 (2007).Article 
    PubMed 

    Google Scholar 
    47.Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER: stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602. https://doi.org/10.1111/j.1365-2656.2011.01806.x (2011).Article 
    PubMed 

    Google Scholar 
    48.Swanson, H. K. et al. A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap. Ecology 96, 318–324. https://doi.org/10.1890/14-0235.1 (2015).Article 
    PubMed 

    Google Scholar 
    49.Minckley, W. L. & Deacon, J. E. Battle Against Extinction: Native Fish Management in the American West (The University of Arizona Press, 1991).
    Google Scholar 
    50.Albrecht, B. A. et al. Use of inflow areas in two Colorado River basin reservoirs by the endangered Razorback Sucker (Xyrauchen texanus). West. N. Am. Nat. 77, 500–514. https://doi.org/10.3398/064.077.0410 (2018).Article 

    Google Scholar 
    51.Pennock, C. A. et al. Reservoir fish assemblage structure across an aquatic ecotone: Can river-reservoir interfaces provide conservation and management opportunities?. Fish. Manag. Ecol. 28, 1–13. https://doi.org/10.1111/fme.12444 (2021).Article 

    Google Scholar 
    52.Gido, K. B. & Propst, D. L. Habitat use and association of native and nonnative fishes in the San Juan River, New Mexico and Utah. Copeia 1999, 321–332. https://doi.org/10.2307/1447478 (1999).Article 

    Google Scholar 
    53.Gido, K. B., Franssen, N. R. & Propst, D. L. Spatial variation in δ15N and δ13C isotopes in the San Juan River, New Mexico and Utah: implications for the conservation of native fishes. Environ. Biol. Fish. 75, 197–207. https://doi.org/10.1007/s10641-006-0009-1 (2006).Article 

    Google Scholar 
    54.Ryden, D. W. & Ahlm, L. A. Observations on the distribution and movements of Colorado Squawfish, Ptychocheilus lucius, in the San Juan River, New Mexico, Colorado, and Utah. Southwest. Nat. 41, 161–168 (1996).
    Google Scholar 
    55.Cathcart, C. N. et al. Waterfall formation at a desert river-reservoir delta isolates endangered fishes. River Res. Appl. 34, 948–956. https://doi.org/10.1002/rra.3341 (2018).Article 

    Google Scholar 
    56.Thomsen, M. S. et al. Impacts of marine invaders on biodiversity depend on trophic position and functional similarity. Mar. Ecol. Prog. Ser. 495, 39–47. https://doi.org/10.3354/meps10566 (2014).ADS 
    Article 

    Google Scholar 
    57.McIntyre, P. B. & Flecker, A. S. Rapid turnover of tissue nitrogen of primary consumers in tropical freshwaters. Oecologia 148, 12–21. https://doi.org/10.1007/s00442-005-0354-3 (2006).ADS 
    Article 
    PubMed 

    Google Scholar 
    58.Franssen, N. R., Gilbert, E. I., James, A. P. & Davis, J. E. Isotopic tissue turnover and discrimination factors following a laboratory diet switch in Colorado Pikeminnow (Ptychocheilus lucius). Can. J. Fish. Aq. Sci. 74, 265–272. https://doi.org/10.1139/cjfas-2015-0531 (2017).CAS 
    Article 

    Google Scholar 
    59.Busst, G. M. A. & Britton, J. R. Tissue-specific turnover rates of the nitrogen stable isotope as functions of time and growth in a cyprinid fish. Hydrobiologia 805, 49–60. https://doi.org/10.1007/s10750-017-3276-2 (2018).CAS 
    Article 

    Google Scholar 
    60.Arrington, D. A. & Winemiller, K. O. Preservation effects on stable isotope analysis of fish muscle. Trans. Am. Fish. Soc. 131, 337–342. https://doi.org/10.1577/1548-8659(2002)131%3c0337:PEOSIA%3e2.0.CO;2 (2002).CAS 
    Article 

    Google Scholar 
    61.Hubert, W. A., Pope, K. L. & Dettmers, J. M. Passive capture techniques. In Fisheries Techniques 3rd edn (eds Zale, A. V. et al.) 223–265 (American Fisheries Society, 2012).
    Google Scholar 
    62.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

    Google Scholar 
    63.Fox, J., & Weisberg, S. An {R} Companion to Applied Regression, 2nd edn. (Sage 2011). http://socserv.socci.mcmaster.ca/jfox/Books/Companion64.Lefcheck, S. piecewiseSEM: Piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol. Evo. 7, 573–579. https://doi.org/10.1111/2041-210X.12512 (2016).Article 

    Google Scholar 
    65.Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213. https://doi.org/10.1098/rsif.2017.0213 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Lysy, M., Stasko, A. D., Swanson, H. K. nicheROVER: (Niche) (R)egion and Niche (Over)lap metrics for multidimensional ecological niches. R package version 1.0 (2014). https://CRAN.R-project.org/package=nicheROVER67.R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2019). Available: https://www.R-project.org/68.Franssen, N. R., Davis, J. E., Ryden, D. W. & Gido, K. B. Fish community responses to mechanical removal of nonnative fishes in a large southwestern river. Fisheries 8, 352–363. https://doi.org/10.1080/03632415.2014.924409 (2014).Article 

    Google Scholar 
    69.Kelly, D. J. & Jellyman, D. J. Changes in trophic linkages to shortfin eels (Anguilla australis) since the collapse of submerged macrophytes in Lake Ellesmere, New Zealand. Hydrobiologia 579, 161–173. https://doi.org/10.1007/s10750-006-0400-0 (2007).Article 

    Google Scholar 
    70.Zambrano, L., Valiente, E. & Vander Zanden, M. J. food web overlap among native axolotl (Ambystoma mexicanum) and two exotic fishes: carp (Cyprinus carpio) and tilapia (Oreochromis niloticus) in Xochimilco, Mexico City. Biol. Invasions 12, 3061–3069. https://doi.org/10.1007/s10530-010-9697-8 (2010).Article 

    Google Scholar 
    71.Córdova-Tapia, F., Contreras, M. & Zambrano, L. Trophic niche overlap between native and non-native fishes. Hydrobiologia 746, 291–301. https://doi.org/10.1007/s10750-014-1944-z (2015).Article 

    Google Scholar 
    72.Portz, D. E. & Tyus, H. M. Fish humps in two Colorado River fishes: a morphological response to cyprinid predation?. Environ. Biol. Fishes 71, 233–245. https://doi.org/10.1007/s10641-004-0300-y (2004).Article 

    Google Scholar 
    73.Pennock, C. A. et al. Predicted and observed responses of a nonnative Channel Catfish population following managed removal to aid the recovery of endangered fishes. N. Am. J. Fish. Mgmt. 38, 565–578. https://doi.org/10.1002/nafm.10056 (2018).Article 

    Google Scholar 
    74.Hedden, S. C. et al. Quantifying consumption of native fishes by nonnative Channel Catfish in a desert river. N. Am. J. Fish. Manag. https://doi.org/10.1002/nafm.10514 (2020).Article 

    Google Scholar 
    75.Nogueira, M. G., Oliveira, P. C. R. & Britto, Y. T. Zooplankton assemblages (Copepoda and Cladocera) in a cascade of reservoirs of a large tropical river (SE Brazil). Limnetica 27, 151–170 (2008).
    Google Scholar 
    76.Slaveska-Stamenković, V. et al. Factors affecting distribution pattern of dominant macroinvertebrates in Mantovo Reservoir (Republic of Macedonia). Biologia 67, 1129–1142. https://doi.org/10.2478/s11756-012-0102-1 (2012).Article 

    Google Scholar 
    77.Behn, K. E. & Baxter, C. V. The trophic ecology of a desert river fish assemblage: influence of season and hydrologic variability. Ecosphere 10, e02583. https://doi.org/10.1002/ecs2.2583 (2019).Article 

    Google Scholar 
    78.Glenn, E. P., Lee, C., Felger, R. & Zengel, S. Effects of water management on the wetlands of the Colorado River Delta, Mexico. Conserv. Biol. 10, 1175–1186. https://doi.org/10.1046/j.1523-1739.1996.10041175.x (1996).Article 

    Google Scholar 
    79.Sykes, G. The Colorado River Delta. Publication no. 460. (Carnegie Institution of Washington, D.C. 1937).80.Dalrymple, G. B. & Hamblin, W. K. K-Ar of Pleistocene lava dams in the Grand Canyon in Arizona. Proc. Natl. Acad. Sci. U.S.A. 95, 9744–9749. https://doi.org/10.1073/pnas.95.17.9744 (1998).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    81.Minckley, W. L. Status of the razorback sucker, Xyrauchen texanus (Abbott), in the Lower Colorado River Basin. Southwest. Nat. 28, 165–187. https://doi.org/10.2307/3671385 (1983).Article 

    Google Scholar 
    82.Doi, H. Spatial patterns of autochthonous and allochthonous resources in aquatic food webs. Popul. Ecol. 51, 57–64. https://doi.org/10.1007/s10144-008-0127-z (2009).Article 

    Google Scholar 
    83.Thorp, J. H. & Delong, M. D. Dominance of autochthonous autotrophic carbon in food webs of heterotrophic rivers. Oikos 96, 543–550. https://doi.org/10.1034/j.1600-0706.2002.960315.x (2002).Article 

    Google Scholar 
    84.Rennie, M. D., Sprules, W. G. & Johnson, T. B. Resource switching in fish following a major food web disruption. Oecologia 159, 789–802. https://doi.org/10.1007/s00442-008-1271-z (2009).ADS 
    Article 
    PubMed 

    Google Scholar 
    85.Cummings, B. M. & Schindler, D. E. Depth variation in isotopic composition of benthic resources and assessment of sculpin feeding patterns in an oligotrophic Alaskan lake. Aquat. Ecol. 47, 403–414. https://doi.org/10.1007/s10452-013-9453-0 (2013).CAS 
    Article 

    Google Scholar 
    86.Fera, S. A., Rennie, M. D. & Dunlop, E. S. Broad shifts in the resource use of a commercially harvested fish following the invasion of dreissenid mussels. Ecology 98, 1681–1692. https://doi.org/10.1002/ecy.1836 (2017).Article 
    PubMed 

    Google Scholar 
    87.Pennock, C. A., McKinstry, M. C. & Gido, K. B. Razorback Sucker movement strategies across a river-reservoir habitat complex. Trans. Am. Fish. Soc. 149, 620–634. https://doi.org/10.1002/tafs.10262 (2020).Article 

    Google Scholar 
    88.Vatland, S. & Budy, P. Predicting the invasion success of an introduced omnivore in a large heterogeneous reservoir. Can. J. Fish. Aquat. Sci. 64, 1329–1345. https://doi.org/10.1139/f07-100 (2007).Article 

    Google Scholar 
    89.Romanuk, T. N., Hayward, A. & Hutchings, J. A. Trophic level scales positively with body size in fishes. Glob. Ecol. Biogeogr. 20, 231–240. https://doi.org/10.1111/j.1466-8238.2010.00579.x (2011).Article 

    Google Scholar 
    90.Franssen, N. R., Gilbert, E. I., Gido, K. B. & Propst, D. L. Hatchery-reared endangered Colorado pikeminnow (Ptychocheilus lucius) undergo a gradual transition to piscivory after introduction to the wild. Aquat. Conserv. 29, 24–38. https://doi.org/10.1002/aqc.2995 (2019).Article 

    Google Scholar 
    91.Hoeinghaus, D. J., Winemiller, K. O. & Agostinho, A. A. Hydrogeomorphology and river impoundment affect food-chain length of divers Neotropical food webs. Oikos 117, 984–995. https://doi.org/10.1111/j.2008.0030-1299.16458.x (2008).Article 

    Google Scholar 
    92.Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221. https://doi.org/10.1038/s41586-019-1111-9 (2019).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    93.Pennock, C. A. & Gido, K. B. Spatial and temporal dynamics of fish assemblages in a desert reservoir over 38 years. Hyrdobiologia 848, 1231–1248. https://doi.org/10.1007/s10750-021-04514-z (2021).Article 

    Google Scholar 
    94.Oliveira, E. F., Minte-Vera, C. V. & Goulart, E. Structure of fish assemblages along spatial gradients in a deep subtropical reservoir (Itaipu Reservoir, Brazil-Paraguay border). Environ. Biol. Fish. 72, 283–304. https://doi.org/10.1007/s10641-004-2582-5 (2005).Article 

    Google Scholar 
    95.Buckmeier, D. L., Smith, N. G., Fleming, B. P. & Bodine, K. A. Intra-annual variation in river-reservoir interface fish assemblages: implications for fish conservation and management in regulated rivers. River Res. Appl. 30, 780–790. https://doi.org/10.1002/rra.2667 (2014).Article 

    Google Scholar 
    96.Albrecht, B. A., Holden, P. B., Kegerries, R. B. & Golden, M. E. Razorback sucker recruitment in Lake Mead, Nevada-Arizona, why here?. Lake Reserv. Manage. 26, 336–344. https://doi.org/10.1080/07438141.2010.511966 (2010).Article 

    Google Scholar  More