Ecology

Wheat cultivars and growing conditions
In this study, pot and field experiments with the shade-tolerant cultivar Henong825 and the shade-sensitive cultivar Kenong9204 were performed. These two winter wheat cultivars were identified with different degrees of shade tolerance by our previous study17. Both cultivars are released by Hebei Province, China, which are the most widely planted wheat cultivars in North China Plain. The parental combination of Henong825 and Kenong9204 is Linyuan95-3091/Shi4185, SA502/6021, respectively. Henong825 is characterized by strong lodging resistance. Kenong9204 is characterized by suitable for moderate water and fertilizer. Two field experiments were conducted during the 2016–2017 and 2017–2018 wheat-growing seasons in the Luancheng agro-ecosystem experimental station of the Chinese Academy of Sciences, Hebei Province (37° 53′ N and 114° 41′ E; elevation at 50 m). The climate characterizing of the study region is summer monsoon. The mean temperature, total precipitation, and solar radiation in both the winter wheat-growing seasons are shown in Table 5. The soil used in the experiments was loam containing 21.41 g kg−1 organic matter, 109.55 mg kg−1 alkaline nitrogen (N), 1.44 g kg−1 total N, 15.58 mg kg−1 available phosphorus (P), and 220 mg kg−1 rapidly available potassium (K). In both seasons, soils were fertilized with urea (N, 46%) and complete fertilizer (N–P, 21–54%) at 300 kg ha−1 and 375 kg ha−1. Seeds were sown by hand on October 6, 2016 and October 17, 2017, then the seedlings emerged 1 week later. In 2017 growing season, the YM stage was on April 15, and anthesis stage was on May 1 in both cultivars. In 2018 growing season, the YM stage was on April 16, and anthesis stage was on May 2 in both cultivars. The seedling density was 166 m−2, which is the norm in this region.
Table 5 The monthly mean temperature (°C), total precipitation (mm), and solar radiation (MJ m−2 day−1) during the two growing seasons of winter wheat in 2016–2017 and 2017–2018.
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Experimental design
This study was a combination of field experiment and pot experiment to investigate the effect of different shading intensity and duration during YM stage on grain components and photosynthetic characteristics. Pot experiment was supplement to field experiment.
Field experiments
The experiments were arranged in a randomized split-split plot design with three replicates. The main plots were split into three subplots subjected to one of three shading intensities: 100% (CK, control), 40% (S1), and 10% (S2) of natural light. Each subplot was split into four sub-subplots, which were randomly allocated to one of four durations: 1 day (D1), 3 days (D3), 5 days (D5), and 7 days (D7) during the YM stage. The shading treatments were conducted in these periods and replicated three times. Each plot size was 6 m long and 2 m wide, with 40 rows. There were 72 plots. Different degrees of artificial shade were provided by using black polyethylene screens horizontally installed at a height of 2 m above the ground.
Determination of YM stage
The YM stage roughly corresponds to Zadok’s scale from Z37 (main stem with flag leaf is visible) to Z39 (flag leaf ligule is noticeable). According to previous researches of YM stage, the estimated measurement of the YM stage was based on the auricle distance (AD, the distance between the auricle of the flag leaf and the auricle of the penultimate leaf) of main stem43,44. In order to keep the relationship between the occurrence of YM and AD unchanged, the field management practices, adequate irrigation was the same in two growing-seasons. Moreover, for each experiment, at the onset of appearance of the flag leaf of the main stem, 30 anthers of ten main stem spike of wheat were randomly sampled to establish the timing of YM stage initiation1. The correlation of the AD with the development of the YM stage in the florets of the two cultivars was measured and observed using microscope (Fig. 9). The cultivar Henong825 reached the YM stage at 1–2 cm, whereas Kenong9204 reached the YM stage at − 1 to 0 cm. To capture the YM stage in the shading condition, the plants were subjected to shading stress ahead of the YM stage occurrence. When more than 50% of the plants in each plot reached − 2 cm in Henong825 and − 4 cm in Kenong9204, the main stem of the plants was tagged, and shading stress was applied in each plot. Each experimental plot for Henong825 and Kenong9204 was independently subjected to shading stress on April 15, 2017 and April 16, 2018. When the shading stress treatments ended, the shade screens were removed and were exposed to natural light until they matured. Air temperature, light intensity, and relative humidity above the canopy were recorded using a portable weather station (ECA-YW0501; Beijing, China) during the shading period. Light spectral was measured using a portable geographic spectrometer (PSR + 3500, USA). The irradiance of spectral wavelength ranging from 350 to 2,500 nm was measured. The proportions of blue light (B/T), green light (G/T), red light (R/T), far-red light (FR/T), and red/far red (R/FR) were calculated according to their irradiance at 400–500 nm, 500–600 nm, 600–700 nm, and 700–800 nm, respectively. Following the local field management practices, adequate irrigation was conducted three times during the overwinter, jointing, and anthesis stages of the wheat-growing season. Weeds, fungal diseases, and insect pests were controlled through spraying of conventional herbicides, fungicides, and insecticides, correspondingly.
Figure 9

The relationship between anther development and shading period in two wheat cultivars.

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Pot experiments
The pot experiments were conducted in a temperature-controlled glasshouse. Vernalized seedlings of the two wheat cultivars were transplanted to pots (45 cm in length, 28.5 cm in width and 20 cm in height; 18 plants in each pot; three pots for each treatment group) containing a mixture of vermiculite and nutritional soil (1:1). All wheat seedlings were grown at a day temperature of 25 °C, night temperature of 15 °C, and light intensity of 800 μmol m−2 s−1. When the AD of the main stems of Henong825 and Kenong9204 cultivars were approximately − 2 cm and − 4 cm, respectively, the main stem of the plants was tagged, and shading stress was applied in each treatment. Shading treatments groups were the different shading intensities and shading durations previously mentioned. The shading condition in glasshouse was simulated with black polyethylene screen to keep up with the experimental methods in the field. After shading stress, the shading nets were removed, until the crops matured.
Sampling and measurements
Photosynthetic rate, stomatal conductance, intercellular carbon dioxide, and chlorophyll fluorescence parameters
In field experiments, three randomly selected flag leaves on the tagged main stems of plants in each plot were analyzed to determine Pn, Gs, Ci, and chlorophyll fluorescence. For each shading treatment group, Pn, Gs, and Ci were measured using an LI-6400XT portable system (LI-COR Biosciences, Nebraska, USA), and the chamber of which was equipped with a red/blue LED light source (LI6400-02B) before the shading stress was removed. Before measurement, the machine was preheated for 30 min, and checked, adjusted to zero, calibrated according to the instructions. Moreover, the light intensity in measured chamber was equivalent of shading treatment conditions. The flow rates was set at 500 μmol s−1, The temperature in chamber was set 25 °C. The CO2 concentration was set to 400 μmol mol−1, which was provided by carbon dioxide cylinders to maintain a stable CO2 environment. The chlorophyll fluorescence of flag leaves on the tagged main stems of plants were measured using a modulate chlorophyll fluorescence imaging system (Imaging-PAM; Hansatech, UK) in each plot. The primary light energy conversion efficiency of PSII (Fv/Fm) and actual photochemical quantum efficiency (YII) were measured after 30 min of dark adaptation. The saturation irradiance (PARsat) and maximum electron transport (Jmax) of flag leaves in each treatment were calculated using a modified rectangular hyperbola. On the day next to shading removal, the Pn of three flag leaves from each replicate plot were measured.
Chlorophyll content
For glasshouse pot experiments, nine flag leaves (three leaves were randomly selected per pot from three pots in each treatment group) tagged main stems of plant were selected prior to the removal of shading. The flag leaves were then sliced following the removal of the main vein. After the sliced fresh leaves were weighed to 0.1 g, the chlorophyll content of leaves was extracted with 80% acetone for 48 h and analyzed through micro-determination (Thermo Varioskan Flash, USA). The absorbance of chlorophyll a (chl a) and chlorophyll b (chl b) was read at 663 and 646 nm, respectively (Thermo Varioskan Flash, USA), and the chlorophyll contents were calculated according to following equations: chl a (mg/g) = (12.7 × A663 nm–2.69 × A646 nm)/(100 × M); and chl b (mg/g) = (22.9 × A646 nm–4.68 × A663 nm)/(100 × M) where A663 and A646 are absorption levels at 663 and 646 nm, respectively; M is leaf fresh weight. The total chlorophyll (chl a + chl b) values were calculated by chl a and chl b values.
Leaf anatomy and surface characteristics
The approximately 2-mm2 leaf sections in D7 treatments and one day after recovery were harvested from the center of three flag leaves on the tagged main stems of plants using a scalpel and were rapidly fixed in electron microscope fixation fluid at 4 °C overnight. Stomatal apertures and chloroplast ultrastructure were observed by Servicebio (Wuhan) using a scanning electron microscope (SU8100; Hitachi) and a transmission electron microscope (HT7700; Hitachi). Simultaneously, the fully expanded flag leaves collected from plants in each treatment were fixed with FAA solution and embedded in paraffin to measure the leaf anatomical structure. The embedded wax block were sectioned to a thickness of 8 μm, then following dewaxing in environmental transparent solution and rehydration in a series of graded alcohol solutions. Finally, the tissue samples were stained with safranin and fast green, observed under a Leica DM6 microscope (Leica, Germany), and the respective images were obtained.
Grain yield, yield components, and aboveground biomass
At harvest in the field experiments during both growing seasons, 60 tagged plants per replicate were randomly sampled to determine grain yield components. The harvested plants were naturally dried to a grain water content of approximately 11%. Each tagged plant was then threshed using a single plant threshing machine to determine the grain number and grain yield needed for the estimation of the average grain weight. In addition, 30 tagged winter wheat plants were uprooted randomly and gradually by hand from each plot. Each plant was cut from the root and was dried at 80 °C. Aboveground biomass was measured using a precision digital balance (model BSA3202S; Sartorius, Germany) with a precision of 0.01 g.
Statistical analysis
The experimental data for grain yield, yield components, biomass and chlorophyll fluorescence parameters were analyzed using a general linear model procedure (GLM) in SPSS 22.0 for a split-split plot design. The significant differences among treatment mean values were determined by the least significance difference analysis (LSD, P  More

IR1 invades colonies of other bacteria on low-nutrient agar plates
A screening was made for bacteria that interacted when in close proximity with IR1 colonies on ASWBLow agar plates. The source of the bacteria was the same as IR1: sediment and the brown alga Fucus vesiculosus from brackish water near Rotterdam Harbour (NL), after storage of original samples at −80 °C for 5 years (Tables S1 and S2 for strains used). The most common form of interaction found was that motile, gliding cells from colonies of IR1 overgrew and degraded some adjacent colonies. The bacteria that were vulnerable to IR1 were identified on the basis of 16S rRNA sequencing and found to be Moraxella osloensis, Staphylococcus pasteuri, Pseudomonas spp., Pedobacter spp. and Enterobacteria cloacae. The latter were repeatedly isolated and strain B12 was chosen for further work. In contrast, successful competition by IR1 over B12 was not seen in liquid culture or a submerged biofilm model (Supplementary Fig. S1). SC was also not observed in liquid culture nor biofilms.
Competition between IR1 and B12 co-inoculated on an agar surface
Further competition experiments were performed between IR1 and GFP-expressing B12(pGFP) on low-nutrient agar plates to determine the basis of the competitiveness of IR1. The two strains were co-inoculated as a 10 µl spot on ASWBLow plates, which were then incubated at 22 °C for up to 2 days. Within the area of inoculation, IR1 reduced the numbers of viable B12 to below the initial inoculation level, suggesting an active killing mechanism. Replacing the cells of B12 with similar numbers of fluorescein-labelled latex spheres (0.2–2 µm diameter) resulted in no significant redistribution of the spheres by growing IR1. This suggests that IR1 was not simply pushing bacterial-sized objects outside the imaging area. Outside the area of (co-)inoculation, the more motile IR1 dominated completely (Fig. S2b, c) and was able to disengage from B12 and form axenic gliding groups. Imaging of B12(pGFP) and IR1 indicated that B12 was not present within emerging masses of IR1 (Fig. S2b, c).
IR1 grows on living cells of B12 on starvation medium suggesting predation
The interaction between IR1 and B12 was tested on agar plates that contained insufficient nutrients for the growth of either strain alone (starvation medium). IR1 was inoculated directly on a starvation plate previously spread with either dead or alive B12 (that had been repeatedly washed to avoid carry-over of nutrients). Both dead and living B12 cells supported progressive colony expansion (up to 0.5 mm day−1) by IR1 over a period of 12 days, compared to starvation medium alone (Fig. 1 and Fig. S2d). Live strain B12, in the absence of IR1, did not show a high level of propidium iodide (PI) staining on starvation medium and ASWLow suggesting that autolysis was not occurring (Fig. S3a, b). Therefore, IR1 appeared to be growing at the expense of B12; that scavenging (use of dead cells as nutrients) and predation (use of live cells of another species as nutrients) both occurred.
Fig. 1: Colony expansion of IR1 on starvation medium in the presence or absence of B12.

IR1 colony expansion rates (average of n = 3) were calculated over two weeks. Shaded circles, IR1 on agar without B12. Solid circles, IR1 on agar covered with living B12. Open circles, IR1 on agar with dead B12.

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Invasion of B12 by IR1 is first by infiltration and then by undercutting of B12
In order to visualize the early stages in predation, an assay was created where spots of IR1 and B12(pGFP) were inoculated 3 mm apart on ASWBLow agar. This “encounter” assay allowed growth of both strains, motility of IR1 but not B12, and monitoring by microscopy of the early interactions upon contact. Initially, IR1 expanded equally in all directions, showing no directed movement towards the B12 colony. Contact between two colonies (on the mm scale) was therefore driven by gliding IR1 and was accidental, not directed. After contact, the following stages in predation were observed:
Stage 1 (1–4 h after contact)
Cells of IR1 infiltrated the B12 colony. The IR1 cells were flexible (Movie S1) and moved through dense masses of B12. In addition, IR1 cells moved around the periphery of the B12 colony to surround it, as detectable by the SC displayed by IR1 (Fig. 2a).
Fig. 2: IR1 invades and predates adjacent colonies of B12.

a Inoculation of IR1 adjacent to B12(pGFP) on ASWBFLow plates (ASWBLow agar supplemented with 0.5% w/v fucoidan), showing the result 10 h after contact between the spreading colony of IR1 and the static mass of B12. IR1 surrounds the B12 colony (w) and creates breaches (x) in the thicker edge of the B12 colony and a shift from dull purple/red SC typical of growth on ASWBFLow to green (y). IR1, IR1 colony; B12, B12 colony. b–d Images 4 h after contact with invading IR1. b Illumination from side showing white B12, with a thicker colony at the periphery (z) and SC from IR1 (bright pinpoints of colour including deep within the B12 colony) (y). c Fluorescence image showing GFP expressed by B12. d Merged (b) and (c). e–g are similar to b–d but after 9 h showing more extensive clearing of B12 cells and major breaches at periphery of the B12 colony (x). h and i show an experiment where B12 is inoculated in a droplet on to starvation medium, allowed to dry and then IR1 inoculated inside B12. h Result after 4 days showing expansion of the IR1 colony (IR1, showing predominantly green SC) to breach the periphery of the B12 colony (opaque white) from within. i Result of the same colony as (h) after 8 days showing progressive destruction of the B12 colony and movement around the periphery of B12 to engulf it. Scale bar indicates 0.4 mm for (a), 0.15 mm for (b–g) and 0.5 mm for (h) and (i).

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Stage 2 (4–20 h after contact)
Channels were created through the periphery of the B12 colony by groups of IR1 (Fig. 2a–d).
Stage 3 (after 20 h)
Penetration of IR1 cells into the B12 colony interior occurred through increasingly large breaches at the periphery of the prey colony, spreading to hollow it out. In this stage, groups of hundreds to thousands of cells of IR1 moved into B12, in an arrangement reminiscent of roots pushing through soil (Figs. 2e–g, 3 and Movie S2). Initial progress through the B12 colony was rapid, up to 60% of the rate at which IR1 spread over agar in the absence of B12, i.e., up to 5 mm h−1.
Fig. 3: Invasion of B12 by IR1 imaged by confocal microscopy.

a–c Three images taken from a Z-slice of a colony of B12(pGFP) during predation by IR1 (unstained, lines of advance shown with white arrows). From left to right the three slices show B12 cells at the agar surface, then 5, and 10 µm heights. d Overview image assembled from multiple contiguous images showing IR1 penetrating a colony of B12(pGFP). IR1 (not stained, visible as dark root-like regions but with an overall invasion route of top right to bottom left) is moving into a colony of GFP-expressing B12. White arrows show the direction of movement of some of the IR1 masses. Propidium iodide (red) is staining damaged cells (predominantly B12) within 20 μm of the major lines of advance of IR1. The scale bar in (d) indicates 50 µm when applied to (a–c) and 80 µm when applied to (d).

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Because of the intense SC displayed, shifts in the organization of IR1 cells could be inferred from alterations in colour visible during invasion of the B12 colony. When the agar medium contained high levels of fucoidan, the predominant colour displayed by IR1 was a dull red purple/red (Fig. 2a, b). However, SC was more noticeable when IR1 contacted B12 and particularly an intense green colour within the B12 colony. This suggested a high degree of local organization, as a 2DPC [1], when IR1 was interacting with B12. It was notable that both the steps in predation described above, and formation of the 2DPC, were unaffected by illumination (using a broad-spectrum white LED which was optimal for viewing SC) over a 48 h period.
Inoculation of IR1 inside a larger spot of B12 on starvation medium resulted in the growth of both strains (particularly IR1); IR1 both formed a uniform SC and degraded the B12 until it reached the edge of the colony (Fig. 2h, i). At this point, IR1 then rapidly moved around the periphery of the B12 colony in less than a day, effectively engulfing it (Fig. 2h, i).
Confocal microscopy of B12(pGFP) at leading edges of IR1 during stages 2 and 3, at different depths, indicated that groups of cells of the invading IR1 were able to undercut B12 (Fig. 3a–c); i.e., the front edge of IR1 made the greatest progress into dense masses of B12 at the agar surface. IR1 interposed a dense mass of cells between the nutrient-containing surface and the mass of B12 cells above. However, after that point (50 µm behind the leading edge) IR1 cells extended from bottom to top of the colony, i.e., over 20 µm in height. This was the case for a high-density colony (inoculation of at least 5 × 108 cells cm−2) of B12.
The killing of B12 by IR1 is short range and inhibited by excess nutrients
On rich medium, i.e., ASWBC or ASWB agar (both containing 5 g l−1 peptone, the former containing 5 g l−1 κ-carageenan in addition to the other components of ASWBLow agar), IR1 was motile but failed to predate B12 during the first 4 days of contact. On ASWBLow plates, during invasion of a B12 colony confocal microscopy of B12(pGFP) cells immediately adjacent to the invading IR1 did not reveal any change in morphology of B12 (Movie S2 and Fig. S3). In order to investigate the action of IR1 on B12, predation assays were created in which B12(pGFP) and IR1 were inoculated adjacently as before, but PI was used to stain damaged cells [27]. Imaging by confocal microscopy suggested that the cells of B12(pGFP) were absent from the main invading groups of IR1. The cells of B12 in close proximity to the leading masses of IR1 (108 cfu of IR1 cells were spotted within 5 mm, in which case motility appeared directed towards IR1 (Fig. 6). This suggests a degree of sensing and targeting of IR1; unlike the interaction of IR1 and B12, in which the initial collision between the strains appeared accidental, with the only specific interactions occurring after this event. Using a co-inoculation assay the ability of PIR4 to predate WT and mutant strains of IR1 (Fig. 6) was quantified. No significant differences were found, suggesting that motility and formation of a 2DPC did not provide resistance.
Fig. 6: Predation of IR1 by Rhodococcus spp. PIR4.

a Images of PIR4 (P, white) apparently moving towards and degrading a colony of IR1 (IR1 SC green) after 30 and 48 h (left and right, respectively). Scale bar indicates 5 mm. b Quantification of predation of GFP-expressing strains (WT and mutants) of IR1 by PIR4. C indicates a control (WT without PIR4). Replicates were threefold in arbitrary units of fluorescence; error bars indicate SD from the mean.

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As we hypothesized, the applicability of using SPM as source for fish eDNA metabarcoding has been confirmed and used for first time in this study. Fish species were found in all samples, irrespective of the location or characteristics of the SPM sampled.
Comparing the different extraction methods used, the eDNA extracted from SPM samples using a modified protocol of the DNeasy PowerSoil Kit, presented the highest purity (260/280 nm ratio) in combination with high DNA concentration, therefore it was the method selected for metabarcoding the eDNA extracted from the nine sampling sites. The isolation method was chosen due to its simplicity and scalability to perform a high number of extractions. However all tested methods resulted in high DNA concentration, making them suitable for metabarcoding, even if post extraction cleanup would have been needed (e.g. the Magnetic Forensic kit showed lower purity 1.44 (260/280 nm ratio)).
While eDNA-based fish monitoring from filtered water samples has been widely used and described and has cheap setup costs, it provides only a snapshot of the diversity at the sampling point, while continuous integration and eDNA settling in time-integrative sampled SPM would provide a better reflection of long-term site occupancy15,16,17,18. On the other hand, eDNA extraction from water samples using filters are laborious and extractions yields are low. The process of particles sinking or binding of eDNA (or residues of, e.g. fish tissue, feces or shales containing eDNA) to organic or mineral particles in SPM18 may result in a progressive accumulation of eDNA in the SPM. This statement was confirmed in our study. The results showed that using one SPM sample yielded higher DNA amounts per extraction (400–2,500 ng) than what is reported for eDNA extracted from an individual water sample using filters (30–560 ng)18,21,22,23,24,25. Here a small amount of SPM (~ 250 mg) is sufficient to extract high amounts of eDNA, which is of particular importance for the detection of rare fish species, where the concentration of their DNA is expected to be low. For example, Salmo salar which is classified as endangered in German rivers26, was detected in the Koblenz, Weil, and Blankenese SPM samples. Another main advantage of using SPM (in particular archived in the ESB), is that it is possible to retrieve and reanalyze the source material, allowing repeats and other complementary analyses e.g. chemical analysis to determine the presence of contaminants or stressors responsible for changes in fish populations. This kind of repeat analysis are not possible with filtered water samples, unless multiple samples are taken in parallel or the water itself is retained, both costly options.
Here, eDNA metabarcoding of the 9 riverine sites detected a total of 29 fish species. Most taxa found belong to commonly detected species in large rivers in Germany. For example, Abramis brama, Rutilus rutilus, Barbus barbus, Squalius cephalus, and Perca fluviatilis and are largely overlapping with the regulatory monitoring data from the Water Framework Directive (WFD)27. This coherence of fish species identified from eDNA extracted from SPM with the commonly detected fish species demonstrated the suitability of this approach. However, the number of fish species found in the ESB samples is similar or lower to what was found using traditional fish monitoring techniques, e.g. electro- and netfishing under the WFD27. For example ,with regard to monitoring sites in Germany between 27 and 57 fish species have been detected in 2012 and 2013 along the Rhine28, between 19 and 24 fish taxa were counted in 2007 at four sites of the river Elbe and between 27 and 29 fish species were detected at three sites of the Danube29. However, it needs to be considered that the number of WFD surveillance monitoring sites is much higher than the ESB sampling sites investigated in this study.
The fish community analysis also evidenced the presence of two contaminant species: Danio rerio and Oryzias latipes. For this reason, the extractions from the 9 sampling sites were repeated retrieving new subsamples from SPM, and before sequencing the absence of contaminant species (e.g. Danio rerio) was validated using specific qPCR primers (See Supplementary information). The specie-specific qPCR and the metabarcoding results showed successful removal of exogenous lab- contaminant fish species (See Supplementary information). The detection of those reads in the first samples strongly suggests cross-contamination in the laboratory since Danio rerio is a specie that we used commonly in our facilities for other purposes. It is well known that the most serious pitfall of metabarcoding eDNA is the risk of contamination with exogenous DNA30,31.
At the stage of PCR during library preparation, several samples exhibited unspecific amplification (double banding), Prossen, Weil, Bimmen and Dessau, which might be indicative of bacterial amplification. This additional bacterial amplification might have resulted in less efficient fish-specific sequencing and in consequence, a lower number of species found in those samples (5–9 species found compared to 8–17 species found in the non-contaminated samples). However, the richness is not only attributable to the presence or absence of contamination but might be also inherent to the sample. Contamination of reagents with bacterial DNA, or contamination with exogenous DNA in the laboratory (e.g. Danio rerio), in combination with the bacteria inherent to the sample itself, is a major problem exacerbated by the highly sensitive nature of the PCR, in particular when using universal primers. Therefore, even minor presence of these species in the lab equipment (like pipettes, surfaces, etc.) might result in large non-target amplification. To avoid such risk, we performed decontamination procedures for laboratory spaces and equipment (with UV radiation) and physically separated pre- and post-PCR workspaces.
The results of this proof-of-concept study will open the door for the retrospective evaluation of SPM samples to study, for example, seasonal and temporal trends of invasive species. The present study can be regarded as a first step towards more comprehensive investigations using eDNA extracted from archived SPM of freshwater fauna, flora and microorganisms. The fish taxa detected in this study complement well with species sampled in fish monitoring with traditional methods, e.g. nets, fykes and electrofishing. However, to study the fish community of a particular sampling site and draw conclusions on differences among sites, further investigations and more stringent analyses are required. The definition of a methodology should include an eDNA extraction strategy considering, for example, SPM extraction volume, the number of replicate extractions, the number of independent sequencing analyses required vs pooling the extracted DNA, etc. In order to validate this proof-of-concept study, future work will focus on method optimization and comparisons with established monitoring approaches. More

1.
Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).
ADS  PubMed  CAS  Google Scholar 
2.
Wallace, B. P. et al. Global conservation priorities for marine turtles. PLoS ONE 6, e24510 (2011).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

3.
Davidson, A. D. et al. Drivers and hotspots of extinction risk in marine mammals. Proc. Natl. Acad. Sci. USA 109, 3395–3400 (2012).
ADS  PubMed  CAS  Google Scholar 

4.
Dulvy, N.K., et al. Extinction risk and conservation of the world’s sharks and rays. eLife 3, e00590 (2014).

5.
Slooten, E. & Davies, N. Hector’s dolphin risk assessments: old and new analyses show consistent results. J. R. Soc. NZ 42, 49–60 (2012).
Google Scholar 

6.
Cagnazzi, D., Parra, G. J., Westley, S. & Harrison, P. L. At the heart of the industrial boom: Australian snubfin dolphins in the Capricorn Coast, Queensland, need urgent conservation action. PLoS ONE 8, e56729 (2013).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

7.
Parra, G. J. & Cagnazzi, D. Conservation status of the Australian humpback dolphin (Sousa sahulensis) using the IUCN Red List criteria. Adv. Mar. Biol. 73, 157–192 (2016).
PubMed  Google Scholar 

8.
Turvey, S. T. et al. First human-caused extinction of a cetacean species?. Biol. Lett. 3, 537–540 (2007).
PubMed  PubMed Central  Google Scholar 

9.
Taylor, B. L. et al. Extinction is imminent for Mexico’s endemic porpoise unless fishery bycatch is eliminated. Conserv. Lett. 10, 588–595 (2017).
Google Scholar 

10.
Gormley, A. M. et al. First evidence that marine protected areas can work for marine mammals. J. Appl. Ecol. 49, 474–480 (2012).
Google Scholar 

11.
Edgar, G. J. et al. Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216 (2014).
ADS  PubMed  CAS  Google Scholar 

12.
Hoyt, E. Marine Protected Areas for Whales, Dolphins and Porpoises: A World Handbook for Cetacean Habitat Conservation and Planning 2nd edn. (Earthscan, London, 2011).
Google Scholar 

13.
di Sciara, G. N. et al. Place-based approaches to marine mammal conservation. Aquat. Conserv. 26, 85–100 (2016).
Google Scholar 

14.
Gregr, E. J., Baumgartner, M. F., Laidre, K. L. & Palacios, D. M. Marine mammal habitat models come of age: The emergence of ecological and management relevance. Endanger Species Res. 22, 205–212 (2013).
Google Scholar 

15.
Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).
PubMed  PubMed Central  Google Scholar 

16.
Hooker, S. K. et al. Making protected area networks effective for marine top predators. Endanger Species Res. 13, 203–218 (2011).
Google Scholar 

17.
Dryden, J., Grech, A., Moloney, J. & Hamann, M. Rezoning of the Great Barrier Reef World Heritage Area: Does it afford greater protection for marine turtles?. Wildl Res 35, 477–485 (2008).
Google Scholar 

18.
Cleguer, C., Grech, A., Garrigue, C. & Marsh, H. Spatial mismatch between marine protected areas and dugongs in New Caledonia. Biol. Conserv. 184, 154–162 (2015).
Google Scholar 

19.
Oh, B. Z. L., Sequeira, A. M. M., Meekan, M. G., Ruppert, J. L. W. & Meeuwig, J. J. Predicting occurrence of juvenile shark habitat to improve conservation planning. Conserv. Biol. 31, 635–645 (2017).
PubMed  Google Scholar 

20.
Liu, M., Bejder, L., Lin, M., Zhang, P., Dong, L. & Li, S. Determining important habitats of the world’s second largest humpback dolphin population: Implications for place-based conservation and management. Aquat. Conserv. Mar. Freshw. Ecosyst. 1–11 https://doi.org/10.1002/aqc.3253 (2019).

21.
Tardin, R. H. et al. Modelling habitat use by the Guiana dolphin, Sotalia guianensis, in south-eastern Brazil: Effects of environmental and anthropogenic variables, and the adequacy of current management measures. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 775–786 (2020).
Google Scholar 

22.
Worm, B. Marine conservation: How to heal an ocean. Nature 543, 630–631 (2017).
ADS  PubMed  CAS  Google Scholar 

23.
Wood, L. J., Fish, L., Laughren, J. & Pauly, D. Assessing progress towards global marine protection targets: shortfalls in information and action. Oryx 42, 340–351 (2008).
Google Scholar 

24.
Devillers, R. et al. Reinventing residual reserves in the sea: are we favouring ease of establishment over need for protection?. Aquat. Conserv. 25, 480–504 (2015).
Google Scholar 

25.
Bottrill, M. C. & Pressey, R. L. The effectiveness and evaluation of conservation planning. Conserv. Lett. 5, 407–420 (2012).
Google Scholar 

26.
Agardy, T. Justified ambivalence about MPA effectiveness. ICES J. Mar. Sci. 75, 1183–1185 (2018).
Google Scholar 

27.
CALM & MPRA. Management Plan for the Ningaloo Marine Park and Muiron Islands Marine Management Area, 2005–2015. Western Australian Government Department of Conservation and Land Management, and Marine Parks and Reserve Authority, Perth, Western Australia (2005). https://www.dpaw.wa.gov.au/images/documents/parks/management-plans/decarchive/ningaloo_mp_01_2005_withmaps.pdf.

28.
UNESCO. United Nations Educational, Scientific and Cultural Organisation. Decisions adopted by the World Heritage Committee at its 35th session, Paris, 7 July 2011. WHC-11/35.COM/20 (2011). https://whc.unesco.org/en/decisions/4278.

29.
Hanf, D. M., Hunt, T. N. & Parra, G. J. Humpback dolphins of Western Australia: a review of current knowledge and recommendations for future management. Adv. Mar. Biol. 73, 193–218 (2016).
PubMed  Google Scholar 

30.
Jefferson, T.A. & Rosenbaum, H.C. Taxonomic revision of the humpback dolphins (Sousa spp.), and description of a new species from Australia. Mar. Mamm. Sci. 30, 1494–1541 (2014).

31.
Parra, G. J., Corkeron, P. J. & Marsh, H. Population sizes, site fidelity and residence patterns of Australian snubfin and Indo-Pacific humpback dolphins: Implications for conservation. Biol. Conserv. 129, 167–180 (2006).
Google Scholar 

32.
Cagnazzi, D. D. B., Harrison, P. L., Ross, G. J. B. & Lynch, P. Abundance and site fidelity of Indo-Pacific humpback dolphins in the Great Sandy Strait, Queensland, Australia. Mar. Mamm. Sci. 27, 255–281 (2011).
Google Scholar 

33.
Palmer, C. et al. Estimates of abundance and apparent survival of coastal dolphins in Port Essington harbour, Northern Territory, Australia. Wildl. Res. 41, 35–45 (2014).
Google Scholar 

34.
Brown, A. M., Bejder, L., Pollock, K. H. & Allen, S. J. Site-specific assessments of the abundance of three inshore dolphin species to inform conservation and management. Front. Mar. Sci. 3, 4. https://doi.org/10.3389/fmars.2016.00004 (2016).
Article  Google Scholar 

35.
Brooks, L., Palmer, C., Griffiths, A. D. & Pollock, K. H. Monitoring variation in small coastal dolphin populations: An example from Darwin, Northern Territory, Australia. Front. Mar. Sci. 4, 94. https://doi.org/10.3389/fmars.2017.00094 (2017).
Article  Google Scholar 

36.
Hunt, T. N. et al. Demographic characteristics of Australian humpback dolphins reveal important habitat toward the southwestern limit of their range. Endanger Species Res. 32, 71–88 (2017).
Google Scholar 

37.
Brown, A. M. et al. Population differentiation and hybridisation of Australian snubfin (Orcaella heinsohni) and Indo-Pacific humpback (Sousa chinensis) dolphins in north-western Australia. PLoS ONE 9, e101427 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

38.
Parra, G. J. Resource partitioning in sympatric delphinids: space use and habitat preferences of Australian snubfin and Indo-Pacific humpback dolphins. J. Anim. Ecol. 75, 862–874 (2006).
PubMed  Google Scholar 

39.
Parra, G., Cagnazzi, D., Perrin, W. & Braulik, G.T. Sousa sahulensis. The IUCN Red List of Threatened Species 2017: e.T82031667A82031671. https://www.iucnredlist.org/details/82031667/0. (2017).

40.
Hunt, T.N., Allen, S.J., Bejder, L. & Parra, G.J. Assortative interactions revealed in a fission-fusion society of Australian humpback dolphins. Behav Ecol 1–14. https://doi.org/10.1093/beheco/arz029 (2019).

41.
Hanf, D.M. Species Distribution Modelling of Western Pilbara Inshore Dolphins. MRes thesis. Murdoch University, Perth, Western Australia (2015).

42.
Allen, S. J., Cagnazzi, D. D., Hodgson, A. J., Loneragan, N. R. & Bejder, L. Tropical inshore dolphins of north-western Australia: Unknown populations in a rapidly changing region. Pac. Conserv. Biol. 18, 56–63 (2012).
Google Scholar 

43.
Bejder, L., Hodgson, A., Loneragan, N. & Allen, S. J. Coastal dolphins in north-western Australia: The need for re-evaluation of species listings and short-comings in the Environmental Impact Assessment process. Pac. Conserv. Biol. 18, 22–25 (2012).
Google Scholar 

44.
Rob, D. & Barnes, P. Whale Shark Management Annual Report: 2016 Whale Shark Season. Progress report for the Department of Parks and Wildlife, Wildlife Management Program No. 57. (2016). Report available on request.

45.
Brown, A., Bejder, .L, Cagnazzi, D., Parra, G.J. & Allen, S.J. The North West Cape, Western Australia: A potential hotspot for Indo-Pacific humpback dolphins Sousa chinensis? Pac Conserv Biol 18, 240–246 (2012).

46.
Raudino, H. C., Hunt, T. N. & Waples, K. Records of Australian humpback dolphins (Sousa sahulensis) from an offshore island group in Western Australia. Mar. Biodivers. Rec 11, 14. https://doi.org/10.1186/s41200-018-0147-0 (2018).
Article  Google Scholar 

47.
Palmer, C., Parra, G. J., Rogers, T. & Woinarski, J. Collation and review of sightings and distribution of three coastal dolphin species in waters of the Northern Territory, Australia. Pac. Conserv. Biol. 20, 116–125 (2014).
Google Scholar 

48.
Parra, G. J., Schick, R. & Corkeron, P. J. Spatial distribution and environmental correlates of Australian snubfin and Indo-Pacific humpback dolphins. Ecography 29, 396–406 (2006).
Google Scholar 

49.
Cagnazzi, D. Conservation status of Australian snubfin dolphin, Orcaella heinsohni, and Indo-Pacific humpback dolphin, Sousa chinensis, in the Capricorn Coast, Central Queensland, Australia. PhD thesis. Southern Cross University, Lismore, Australia (2011).

50.
Cagnazzi, D. Review of coastal dolphins in central Queensland, particularly Port Curtis and Port Alma regions. Report produced for the Ecosystem Research and Monitoring Program Advisory Panel as part of Gladstone Ports Corporation’s Ecosystem Research and Monitoring Progra. Gladstone Ports Corporation, Queensland, Australia (2013).

51.
Beasley, I. et al. Observations on Australian humpback dolphins (Sousa sahulensis) in waters of the Pacific Islands and New Guinea. Adv. Mar. Biol. 73, 219–271 (2016).
PubMed  Google Scholar 

52.
Corkeron, P. J., Morissette, N. M., Porter, L. & Marsh, H. Distribution and status of hump-backed dolphins, Sousa chinensis, Australian waters. Asian Mar. Biol. 14, 49–59 (1997).
Google Scholar 

53.
Parra, G. J., Corkeron, P. J. & Marsh, H. The Indo-Pacific humpback dolphin, Sousa chinensis (Osbeck, 1765), in Australian waters: A summary of current knowledge. Aquat. Mamm. 30, 197–206 (2004).
Google Scholar 

54.
Jefferson, T. A. & Curry, B. E. Humpback dolphins: A brief introduction to the genus Sousa. Adv. Mar. Biol. 72, 1–16 (2015).
PubMed  Google Scholar 

55.
Koper, R. P., Karczmarski, L., du Preez, D. & Plön, S. Sixteen years later: Occurrence, group size, and habitat use of humpback dolphins (Sousa plumbea) in Algoa Bay, South Africa. Mar. Mamm. Sci. 32, 490–507 (2016).
Google Scholar 

56.
Palmer, C. Conservation biology of dolphins in coastal waters of the Northern Territory, Australia. PhD thesis. Charles Darwin University, Northern Territory, Australia (2014).

57.
Heithaus, M. R. & Dill, L. M. Food availability and tiger shark predation risk influence bottlenose dolphin habitat use. Ecology 83, 480–491 (2002).
Google Scholar 

58.
Benoit-Bird, K. J. et al. Prey patch patterns predict habitat use by top marine predators with diverse foraging strategies. PLoS ONE 8, e53348 (2013).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

59.
Pirotta, E. et al. Predicting the effects of human developments on individual dolphins to understand potential long-term population consequences. Proc. R Soc. B 282, 20151209 (2015).
Google Scholar 

60.
Parra, G. J. & Jedensjö, M. Stomach contents of Australian snubfin (Orcaella heinsohni) and Indo-Pacific humpback dolphins (Sousa chinensis). Mar. Mamm. Sci. 30, 1184–1198 (2014).
Google Scholar 

61.
Downie, R. A., Babcock, R. C., Thomson, D. P. & Vanderklift, M. A. Density of herbivorous fish and intensity of herbivory are influenced by proximity to coral reefs. Mar. Ecol. Prog. Ser. 482, 217–225 (2013).
ADS  Google Scholar 

62.
Fitzpatrick, B. M., Harvey, E. S., Langlois, T. J., Babcock, R. & Twiggs, E. Effects of fishing on fish assemblages at the reefscape scale. Mar. Ecol. Prog. Ser. 524, 241–253 (2015).
ADS  Google Scholar 

63.
Smith, F., Allen, S. J., Bejder, L. & Brown, A. M. Shark bite injuries on three inshore dolphin species in tropical northwestern Australia. Mar. Mamm. Sci. 34, 87–99. https://doi.org/10.1111/mms.12435 (2017).
Article  Google Scholar 

64.
Best, B. D. et al. Online cetacean habitat modeling system for the US east coast and Gulf of Mexico. Endanger Species Res. 18, 1–15 (2012).
Google Scholar 

65.
Bancroft, K. & Sheridan, M. The major marine habitats of the Ningaloo Marine Park and the proposed southern extension. Marine Conservation Branch, Department of Conservation and Land Management, Perth, Western Australia. MMS/PI/NMP&NSE- 26/2000 (2000).

66.
Zanardo, N., Parra, G. J., Passadore, C. & Möller, L. M. Ensemble modelling of southern Australian bottlenose dolphin Tursiops sp. distribution reveals important habitats and their potential ecological function. Mar. Ecol. Prog. Ser. 569, 253–266 (2017).

67.
Palacios, D. M., Baumgartner, M. F., Laidre, K. L. & Gregr, E. J. Beyond correlation: integrating environmentally and behaviourally mediated processes in models of marine mammal distributions. Endanger Species Res. 22, 191–203 (2013).
Google Scholar 

68.
Torres, L. G., Read, A. J. & Halpin, P. Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?. Ecol. Appl. 18, 1702–1717 (2008).
PubMed  Google Scholar 

69.
Hastie, G. D., Wilson, B., Wilson, L. J., Parsons, K. M. & Thompson, P. M. Functional mechanisms underlying cetacean distribution patterns: hotspots for bottlenose dolphins are linked to foraging. Mar. Biol. 144, 397–403 (2004).
Google Scholar 

70.
Hunt, T.N. Demography, habitat use and social structure of Australian humpback dolphins (Sousa sahulensis) around the North West Cape, Western Australia: Implications for conservation and management. PhD thesis. College of Science and Engineering, Flinders University, Adelaide, Australia (2018).

71.
Mitchell, J. D. et al. Quantifying shark depredation in a recreational fishery in the Ningaloo Marine Park and Exmouth Gulf, Western Australia. Mar. Ecol. Prog. Ser. 587, 141–157 (2018).
ADS  Google Scholar 

72.
Smallwood, C. B., Beckley, L. E., Moore, S. A. & Kobryn, H. T. Assessing patterns of recreational use in large marine parks: A case study from Ningaloo Marine Park, Australia. Ocean Coast Manag 54, 330–340 (2011).
Google Scholar 

73.
Great Sandy Marine Park Zoning Plan. Marine Parks (Great Sandy) Zoning Plan 2017. Marine Parks Act 2004. Queensland Government. https://www.legislation.qld.gov.au/view/pdf/inforce/current/sl-2017-0155 (2017).

74.
Smith, H., Frère, C., Kobryn, H. & Bejder, L. Dolphin sociality, distribution and calving as important behavioural patterns informing management. Anim. Conserv. 19, 462–471 (2016).
Google Scholar 

75.
Sala, E. & Giakoumi, S. No-take marine reserves are the most effective protected areas in the ocean. ICES J. Mar. Sci. 75, 1166–1168 (2018).
Google Scholar 

76.
Davies, H. N. et al. Integrating climate change resilience features into the incremental refinement of an existing marine park. PLoS ONE 11, e0161094 (2016).
PubMed  PubMed Central  Google Scholar 

77.
Cassata, L. & Collins, L. B. Coral reef communities, habitats, and substrates in and near sanctuary zones of Ningaloo Marine Park. J. Coast Res. 24, 139–151 (2008).
Google Scholar 

78.
Mann, J. Behavioral sampling methods for cetaceans: a review and critique. Mar. Mamm. Sci. 15, 102–122 (1999).
Google Scholar 

79.
Connor, R. C., Mann, J., Tyack, P. L. & Whitehead, H. Social evolution in toothed whales. Trends Ecol. Evol. 13, 228–232 (1998).
PubMed  CAS  Google Scholar 

80.
Redfern, J. et al. Techniques for cetacean–habitat modeling. Mar. Ecol. Prog. Ser. 310, 271–295 (2006).
ADS  Google Scholar 

81.
Kobryn, H. T., Wouters, K., Beckley, L. E. & Heege, T. Ningaloo reef: shallow marine habitats mapped using a hyperspectral sensor. PLoS ONE 8, e70105 (2013).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

82.
Geoscience Australia. Bathymetry Grids of Carnarvon Shelf. https://www.ga.gov.au (2008).

83.
Geoscience Australia. Australian Bathymetry and Topography Grid, June 2009. https://www.ga.gov.au (2009).

84.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org (2015).

85.
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).
Google Scholar 

86.
Naimi, B., Hamm, N., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling?. Ecography 37, 191–203 (2014).
Google Scholar 

87.
MacLeod, C.D. An Introduction to Using GIS in Marine Mammal Research. Course Manual. Adelaide, South Australia, 15–19 July 2013 Fremantle, Western Australia, 22–26 July 2013 (2013).

88.
Gu, W. & Swihart, R. K. Absent or undetected? Effects of non-detection of species occurrence on wildlife–habitat models. Biol. Conserv. 116, 195–203 (2004).
Google Scholar 

89.
Barbet-Massin, M., Thuiller, W. & Jiguet, F. How much do we overestimate future local extinction rates when restricting the range of occurrence data in climate suitability models?. Ecography 33, 878–886 (2010).
Google Scholar 

90.
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
PubMed  Google Scholar 

91.
Gottschalk, T. K., Aue, B., Hotes, S. & Ekschmitt, K. Influence of grain size on species-habitat models. Ecol. Model. 222, 3403–3412 (2011).
Google Scholar 

92.
Hanberry, B. B. Finer grain size increases effects of error and changes influence of environmental predictors on species distribution models. Ecol. Inform. 15, 8–13 (2013).
Google Scholar 

93.
Passadore, C., Möller, L. M., Diaz-Aguirre, F. & Parra, G. J. Modelling dolphin distribution to inform future spatial conservation decisions in a marine protected area. Sci. Rep. 8, 1–14 (2018).
CAS  Google Scholar 

94.
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186 (2000).
Google Scholar 

95.
Elith, J. & Graham, C. H. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography 32, 66–77 (2009).
Google Scholar 

96.
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K. & Thuiller, W. Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib. 15, 59–69 (2009).
Google Scholar 

97.
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol Evol 22, 42–47 (2006).
PubMed  Google Scholar 

98.
Franklin, J. Mapping species distributions: spatial inference and prediction (Cambridge University Press, Cambridge, 2010).
Google Scholar 

99.
Grenouillet, G., Buisson, L., Casajus, N. & Lek, S. Ensemble modelling of species distribution: the effects of geographical and environmental ranges. Ecography 34, 9–17 (2011).
Google Scholar 

100.
Sun, Y. Crested ibis in a dynamic and increasingly human-dominated landscape. PhD Thesis. Faculty of Geo-Information Science and Earth Observation, Univeristy of Twente, The Netherlands (2016).

101.
Oppel, S. et al. Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biol. Conserv. 156, 94–104 (2012).
Google Scholar 

102.
Gårdmark, A. et al. Biological ensemble modeling to evaluate potential futures of living marine resources. Ecol Appl 23, 742–754 (2013).
PubMed  Google Scholar 

103.
Pikesley, S. K. et al. Modelling the niche for a marine vertebrate: A case study incorporating behavioural plasticity, proximate threats and climate change. Ecography 38, 001–010 (2015).
Google Scholar 

104.
Abrahms, B., H. et al. Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species. Divers. Distrib. 25, 1182–1193 (2019).

105.
Pérez-Jorge, S. et al. Can static habitat protection encompass critical areas for highly mobile marine top predators? Insights from coastal East Africa. PLoS ONE 10, e0133265 (2015).
PubMed  PubMed Central  Google Scholar 

106.
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).
Google Scholar 

107.
Guisan, A., Edwards, T. C. Jr. & Hastie, T. Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecol. Model. 157, 89–100 (2002).
Google Scholar 

108.
Friedman, J., Hastie, T. & Tibshirani, R. Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28, 337–407 (2000).
MATH  Google Scholar 

109.
De’ath, G. & Fabricius, K. E. Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81, 3178–3192 (2000).
Google Scholar 

110.
Hastie, T., Tibshirani, R. & Buja, A. Flexible discriminant analysis by optimal scoring. J. Am. Stat. Assoc. 89, 1255–1270 (1994).
MathSciNet  MATH  Google Scholar 

111.
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
MATH  Google Scholar 

112.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 

113.
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar 

114.
Becker, E.A., et al. Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees. Ecol Evol. https://doi.org/10.1002/ece3.6316 (2020)

115.
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).
Google Scholar 

116.
Peterson, A.T., et al. Ecological Niches and Geographic Distributions (MPB-49). (Princeton University Press, 2011).

117.
Hood, G. PopTools version 3.2. 5. https://www.poptools.org. (2011).

118.
Manly, B. F. Randomization, bootstrap and Monte Carlo methods in biology 3rd edn. (Chapman & Hall, London, 2007).
Google Scholar  More

1.
Liu, X. et al. Curr. Biol. 29, 499–505.e4 (2019).
CAS  Article  Google Scholar 
2.
Sardain, A. et al. Nat. Sustain. 2, 274–282 (2019).
Article  Google Scholar 

3.
Review of Maritime Transport 2019 (United Nations, 2019).

4.
Leigh, E. G. et al. Biol. Rev. 89, 148–172 (2014).
Article  Google Scholar 

5.
Seebens, H. et al. Proc. Natl Acad. Sci. USA 113, 5646–5651 (2016).
CAS  Article  Google Scholar 

6.
Galil, B. et al. Manag. Biol. Invasion 8, 141–152 (2017).
Article  Google Scholar 

7.
Spanier, E. & Galil, B. S. Endeavour 15, 102–106 (1991).
Article  Google Scholar 

8.
Ruiz, G. M. et al. Smithson. Contrib. Mar. Sci. 38, 73–93 (2009).
Google Scholar 

9.
Muirhead, J. R. et al. Divers. Distrib. 21, 75–87 (2015).
Article  Google Scholar 

10.
Galil, B. S. et al. Biol. Invasion 17, 973–976 (2015).
Article  Google Scholar 

11.
Azzurro, E. et al. Biol. Invasion 18, 2761–2772 (2016).
Article  Google Scholar 

12.
Sharpe, D. et al. Ecology 98, 412–424 (2017).
CAS  Article  Google Scholar 

13.
Salgado, J. et al. Sci. Total Environ. 729, 138444 (2020).
CAS  Article  Google Scholar 

14.
Informe sobre la Aplicación y Eficiencia de Medidas de Mitigación para el Estudio de Impacto Ambiental del Proyecto “Ampliación del Canal de Panamá -Tercer Juego de Esclusas” (Panama Canal Authority, accessed 23 April 2020); https://go.nature.com/2FvcWMF

15.
Miller, A. W. & Ruiz, G. M. Nat. Clim. Change 4, 413–416 (2014).
Article  Google Scholar 

16.
Cramer, W. et al. Nat. Clim. Change 8, 972–980 (2018).
Article  Google Scholar 

17.
Ballew, N. G. et al. Sci. Rep. 6, 32169 (2016).
CAS  Article  Google Scholar 

18.
Savva, I. et al. J. Fish. Biol. 97, 148–162 (2020).
Article  Google Scholar 

19.
Shine, C. EPPO Bull. 37, 103–113 (2007).
Article  Google Scholar 

20.
Ballast Water Management (IMO, accessed 20 May 2020); https://go.nature.com/2DUkI2t

21.
GloFouling (IMO, accessed 20 May 2020); https://www.glofouling.imo.org/

22.
Jouffray, J.-B. et al. One Earth 2, 43–54 (2020).
Article  Google Scholar 

23.
Peleg, O. & Guy-Haim, T. Nature 575, 287 (2019).
CAS  Article  Google Scholar 

24.
Balasingham, K. D. et al. Mol. Ecol. 27, 112–127 (2018).
CAS  Article  Google Scholar 

25.
Martignac, F. et al. Fish. Fish. 16, 486–510 (2014).
Article  Google Scholar 

26.
Putland, R. L. & Mensinger, A. F. Rev. Fish. Biol. Fish. 29, 789–807 (2019).
Article  Google Scholar 

27.
Dennis, C. E. III et al. Biol. Invasion 21, 2837–2855 (2019).
Article  Google Scholar 

28.
Sepulveda, A. J. et al. Trends Ecol. Evol. 35, 668–678 (2020).
Article  Google Scholar 

29.
van Rijn, I. et al. Can. J. Fish. Aquat. Sci. 77, 752–761 (2020).
Article  Google Scholar  More

1.
Collins, M. et al. In Climate Change 2013—The Physical Science Basis (ed. Intergovernmental Panel on Climate Change) 1029–1136 (Cambridge University Press, Cambridge, 2013).
2.
Ackerly, D. D. et al. The geography of climate change: implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).
Google Scholar 

3.
Bradshaw, W. E. & Holzapfel, C. M. Evolutionary response to rapid climate change. Science 312, 1477–1478 (2006).
CAS  PubMed  Google Scholar 

4.
Chu, C., Mandrak, N. E. & Minns, C. K. Potential impacts of climate change on the distributions of several common and rare freshwater fishes in Canada. Divers. Distrib. 11, 299–310 (2005).
Google Scholar 

5.
Princé, K. & Zuckerberg, B. Climate change in our backyards: the reshuffling of North America’s winter bird communities. Glob. Change Biol. 21, 572–585 (2015).
ADS  Google Scholar 

6.
Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).
PubMed  Google Scholar 

7.
Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ 18 O records. Paleoceanography 20, PA1003 (2005).
ADS  Google Scholar 

8.
Dyke, A. S. An outline of the deglaciation of North America with emphasis on central and northern Canada. Quat. Glaciat. Chronol. Part II 2b, 373–424 (2004).
Google Scholar 

9.
Thompson, L. G. et al. Late glacial stage and Holocene tropical ice core records from Huascaran, Peru. Science 269, 46–50 (1995).
ADS  CAS  Google Scholar 

10.
Johnsen, S. J. et al. Oxygen isotope and palaeotemperature records from six Greenland ice-core stations: camp century, dye-3, GRIP, GISP2, Renland and NorthGRIP. J. Quat. Sci. 16, 299–307 (2001).
Google Scholar 

11.
Kawamura, K. et al. Northern Hemisphere forcing of climatic cycles in Antarctica over the past 360,000 years. Nature 448, 912–916 (2007).
ADS  CAS  PubMed  Google Scholar 

12.
Dyke, A. S. Late quaternary vegetation history of Northern North America based on pollen, macrofossil, and faunal remains. Géogr. Phys. Quat. 59, 211–262 (2005).
Google Scholar 

13.
Froese, D. et al. Fossil and genomic evidence constrains the timing of bison arrival in North America. Proc. Natl Acad. Sci. USA 114, 3457–3462 (2017).
ADS  CAS  PubMed  Google Scholar 

14.
Palkopoulou, E. et al. Holarctic genetic structure and range dynamics in the woolly mammoth. Proc. R. Soc. B Biol. Sci. 280, 20131910 (2013).
Google Scholar 

15.
Debruyne, R. et al. Out of America: ancient DNA evidence for a new world origin of late quaternary woolly mammoths. Curr. Biol. 18, 1320–1326 (2008).
CAS  PubMed  Google Scholar 

16.
Shapiro, B. et al. Rise and fall of the Beringian Steppe Bison. Science 306, 1561–1565 (2004).
ADS  CAS  PubMed  Google Scholar 

17.
Campos, P. F. et al. Ancient DNA analyses exclude humans as the driving force behind late Pleistocene musk ox (Ovibos moschatus) population dynamics. Proc. Natl Acad. Sci. USA 107, 5675–5680 (2010).
ADS  CAS  PubMed  Google Scholar 

18.
Chang, D. et al. The evolutionary and phylogeographic history of woolly mammoths: a comprehensive mitogenomic analysis. Sci. Rep. 7, 44585 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

19.
Heintzman, P. D. et al. Bison phylogeography constrains dispersal and viability of the ice free corridor in western Canada. Proc. Natl Acad. Sci. USA 113, 8057–8063 (2016).
CAS  PubMed  Google Scholar 

20.
Zazula, G. D. et al. American mastodon extirpation in the Arctic and Subarctic predates human colonization and terminal Pleistocene climate change. Proc. Natl Acad. Sci. USA 2014, 6–11 (2014).
Google Scholar 

21.
Zazula, G. D. et al. A case of early Wisconsinan “over-chill”: New radiocarbon evidence for early extirpation of western camel (Camelops hesternus) in eastern Beringia. Quat. Sci. Rev. 171, 48–57 (2017).
ADS  Google Scholar 

22.
Saunders, J. J. et al. Paradigms and proboscideans in the southern Great Lakes region, USA. Quat. Int. 217, 175–187 (2010).
Google Scholar 

23.
Oltz, D. F. & Kapp, R. O. Plant remains associated with Mastodon and Mammoth remains in central Michigan. Am. Midl. Nat. 70, 339–346 (1963).
Google Scholar 

24.
Dreimanis, A. Extinction of Mastodons in Eastern North America: testing a new climatic-environmental hypothesis. Ohio J. Sci. 68, 257–272 (1968).
Google Scholar 

25.
Shoshani, J. Understanding proboscidean evolution: a formidable task. Trends Ecol. Evol. 13, 480–487 (1998).
CAS  PubMed  Google Scholar 

26.
Teale, C. L. & Miller, N. G. Mastodon herbivory in mid-latitude late-Pleistocene boreal forests of eastern North America. Quat. Res. 78, 72–81 (2012).
Google Scholar 

27.
Green, J. L., DeSantis, L. R. G. & Smith, G. J. Regional variation in the browsing diet of Pleistocene Mammut americanum (Mammalia, Proboscidea) as recorded by dental microwear textures. Palaeogeogr. Palaeoclimatol. Palaeoecol. 487, 59–70 (2017).
Google Scholar 

28.
Birks, H. H. et al. Evidence for the diet and habitat of two late Pleistocene mastodons from the Midwest, USA. Quat. Res. 91, 792–812 (2019).
CAS  Google Scholar 

29.
Owen-Smith, N. Pleistocene extinctions: the pivotal role of megaherbivores. Paleobiology 13, 351–362 (1987).
Google Scholar 

30.
Barnosky, A. D. et al. Variable impact of late-quaternary megafaunal extinction in causing ecological state shifts in North and South America. Proc. Natl Acad. Sci. USA 113, 856–861 (2016).
ADS  CAS  PubMed  Google Scholar 

31.
Widga, C. et al. Late pleistocene proboscidean population dynamics in the North American midcontinent. Boreas 46, 772–782 (2017).
Google Scholar 

32.
Godfrey-Smith, D., Grist, A. & Stea, R. Dosimetric and radiocarbon chronology of a pre-Wisconsinan mastodon fossil locality at East Milford, Nova Scotia, Canada. Quat. Sci. Rev. 22, 1353–1360 (2003).
ADS  Google Scholar 

33.
Enk, J. et al. Mammuthus population dynamics in late pleistocene North America: divergence, phylogeogrpaphy and introgression. Front. Ecol. Evol. 4, 1–13 (2016).
Google Scholar 

34.
Ishida, Y., Georgiadis, N. J., Hondo, T. & Roca, A. L. Triangulating the provenance of African elephants using mitochondrial DNA. Evol. Appl. 6, 253–265 (2013).
CAS  PubMed  Google Scholar 

35.
Fernando, P., Pfrender, M. E., Encalada, S. E. & Lande, R. Mitochondrial DNA variation, phylogeography and population structure of the Asian elephant. Heredity 84, 362–372 (2000).
CAS  PubMed  Google Scholar 

36.
Fisher, D. In The Proboscidea: Evolution and Paleoecology of Elephants andtheir Relatives (eds. Shoshani, J. & Tassy, P.) 296–315 (Oxford University Press, Oxford, 1996).

37.
Fisher, D. C. Paleobiology of pleistocene proboscideans. Annu. Rev. Earth Planet. Sci. https://doi.org/10.1146/annurev-earth-060115-012437 (2018).

38.
Rohland, N. et al. Genomic DNA sequences from mastodon and woolly mammoth reveal deep speciation of forest and savanna elephants. PLoS Biol. 8, e1000564 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

39.
Muhs, D. R., Ager, T. A. & Begét, J. E. Vegetation and paleoclimate of the last interglacial period, central Alaska. Quat. Sci. Rev. 20, 41–61 (2001).
ADS  Google Scholar 

40.
Jass, C. N. & Barrón-Ortiz, C. I. A review of quaternary proboscideans from Alberta, Canada. Quat. Int. 443, 88–104 (2017).
Google Scholar 

41.
Shapiro, B. et al. A Bayesian phylogenetic method to estimate unknown sequence ages. Mol. Biol. Evol. 28, 879–887 (2011).
CAS  PubMed  Google Scholar 

42.
Drummond, A. J. & Stadler, T. Bayesian phylogenetic estimation of fossil ages. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150129 (2016).
Google Scholar 

43.
Plint, T., Longstaffe, F. J. & Zazula, G. Giant beaver palaeoecology inferred from stable isotopes. Sci. Rep. 9, 7179 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

44.
Yalden, D. W. The history of British mammals 12–27 (T & A D Poyser Ltd, Berkhamsted, 1999).

45.
Schreve, D. C. A new record of Pleistocene hippopotamus from River Severn terrace deposits, Gloucester, UK—palaeoenvironmental setting and stratigraphical significance. Proc. Geol. Assoc. 120, 58–64 (2009).
Google Scholar 

46.
Stoffel, C. et al. Genetic consequences of population expansions and contractions in the common hippopotamus (Hippopotamus amphibius) since the late Pleistocene. Mol. Ecol. 24, 2507–2520 (2015).
PubMed  Google Scholar 

47.
Tape, K. D., Gustine, D. D., Ruess, R. W., Adams, L. G. & Clark, J. A. Range expansion of moose in Arctic Alaska linked to warming and increased shrub habitat. PLoS ONE 11, e0152636 (2016).
PubMed  PubMed Central  Google Scholar 

48.
Tape, K. D., Jones, B. M., Arp, C. D., Nitze, I. & Grosse, G. Tundra be dammed: beaver colonization of the Arctic. Glob. Change Biol. 24, 4478–4488 (2018).
ADS  Google Scholar 

49.
Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).
ADS  CAS  PubMed  Google Scholar 

50.
Glocke, I. & Meyer, M. Extending the spectrum of DNA sequences retrieved from ancient bones and teeth. Genome Res. 27, 1–8 (2017).
Google Scholar 

51.
Kircher, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, 1–8 (2012).
Google Scholar 

52.
Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, 1–10 (2010).
Google Scholar 

53.
Gansauge, M.-T. & Meyer, M. Single-stranded DNA library preparation for the sequencing of ancient or damaged DNA. Nat. Protoc. 8, 737–748 (2013).
PubMed  Google Scholar 

54.
Gansauge, M.-T. et al. Single-stranded DNA library preparation from highly degraded DNA using T4 DNA ligase. Nucleic Acids Res. 45, 1–10 (2017).
Google Scholar 

55.
Renaud, G., Stenzel, U. & Kelso, J. leeHom: adaptor trimming and merging for Illumina sequencing reads. Nucleic Acids Res. https://doi.org/10.1093/nar/gku699 (2014).

56.
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

57.
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

58.
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772–772 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

59.
Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

60.
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

61.
Baele, G., Lemey, P. & Suchard, M. A. Genealogical working distributions for Bayesian model testing with phylogenetic uncertainty. Syst. Biol. 65, 250–264 (2016).
PubMed  Google Scholar 

62.
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).
PubMed  PubMed Central  Google Scholar 

63.
Stuiver, M. & Reimer, P. J. Extended 14C database and revised CALIB radiocarbon calibration program. Radiocarbon 35, 215–230 (1993).
Google Scholar 

64.
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
CAS  Google Scholar 

65.
Colleoni, F., Wekerle, C., Näslund, J.-O., Brandefelt, J. & Masina, S. Constraint on the penultimate glacial maximum Northern Hemisphere ice topography (≈140 kyrs BP). Quat. Sci. Rev. 137, 97–112 (2016).
ADS  Google Scholar  More

1.
Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: how many parasites? How many hosts? Proc. Natl Acad. Sci. USA 105, 11482–11489 (2008).
CAS  PubMed  Google Scholar 
2.
Mideo, N. Parasite adaptations to within-host competition. Trends Parasitol. 25, 261–268 (2009).
PubMed  Google Scholar 

3.
Greischar, M. A. et al. Evolutionary consequences of feedbacks between within-host competition and disease control. Evol. Med. Public Health 2020, 30–34 (2020).
PubMed  PubMed Central  Google Scholar 

4.
Wale, N. et al. Resource limitation prevents the emergence of drug resistance by intensifying within-host competition. Proc. Natl Acad. Sci. USA 114, 13774–13779 (2017).
CAS  PubMed  Google Scholar 

5.
Bhattacharya, A., Toro Díaz, V. C., Morran, L. T. & Bashey, F. Evolution of increased virulence is associated with decreased spite in the insect-pathogenic bacterium Xenorhabdus nematophila. Biol. Lett. 15, 20190432 (2019).
PubMed  PubMed Central  Google Scholar 

6.
Susi, H., Barrès, B., Vale, P. F. & Laine, A.-L. Co-infection alters population dynamics of infectious disease. Nat. Commun. 6, 5975 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Read, A. F. & Taylor, L. H. The ecology of genetically diverse infections. Science 292, 1099–1102 (2001).
CAS  PubMed  Google Scholar 

8.
Hawley, D. M. & Altizer, S. M. Disease ecology meets ecological immunology: understanding the links between organismal immunity and infection dynamics in natural populations. Funct. Ecol. 25, 48–60 (2011).
Google Scholar 

9.
Hoverman, J. T., Hoye, B. J. & Johnson, P. T. J. Does timing matter? How priority effects influence the outcome of parasite interactions within hosts. Oecologia 173, 1471–1480 (2013).
PubMed  Google Scholar 

10.
Zhan, J. & McDonald, B. A. Experimental measures of pathogen competition and relative fitness. Annu. Rev. Phytopathol. 51, 131–153 (2013).
CAS  PubMed  Google Scholar 

11.
Hellard, E., Fouchet, D., Vavre, F. & Pontier, D. Parasite–parasite interactions in the wild: how to detect them? Trends Parasitol. 31, 640–652 (2015).
PubMed  Google Scholar 

12.
Tollenaere, C., Susi, H. & Laine, A. L. Evolutionary and epidemiological implications of multiple infection in plants. Trends Plant Sci. 21, 80–90 (2015).
PubMed  Google Scholar 

13.
Budischak, S. A. et al. Competing for blood: the ecology of parasite resource competition in human malaria–helminth co-infections. Ecol. Lett. 21, 536–545 (2018).
PubMed  Google Scholar 

14.
Griffiths, E. C., Pedersen, A. B., Fenton, A. & Petchey, O. L. Analysis of a summary network of co-infection in humans reveals that parasites interact most via shared resources. Proc. R. Soc. B 281, 20132286 (2014).
PubMed  Google Scholar 

15.
Ezenwa, V. O. Helminth–microparasite co-infection in wildlife: lessons from ruminants, rodents and rabbits. Parasite Immunol. 38, 527–534 (2016).
CAS  PubMed  Google Scholar 

16.
Lello, J., Boag, B., Fenton, A., Stevenson, I. R. & Hudson, P. J. Competition and mutualism among the gut helminths of a mammalian host. Nature 428, 840–844 (2004).
CAS  PubMed  Google Scholar 

17.
Chung, E., Petit, E., Antonovics, J., Pedersen, A. B. & Hood, M. E. Variation in resistance to multiple pathogen species: anther smuts of Silene uniflora. Ecol. Evol. 2, 2304–2314 (2012).
PubMed  PubMed Central  Google Scholar 

18.
Halliday, F. W., Umbanhowar, J. & Mitchell, C. E. A host immune hormone modifies parasite species interactions and epidemics: insights from a field manipulation. Proc. R. Soc. B 285, 20182075 (2018).
PubMed  Google Scholar 

19.
Eswarappa, S. M., Estrela, S. & Brown, S. P. Within-host dynamics of multi-species infections: facilitation, competition and virulence. PLoS ONE 7, e38730 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

20.
Zélé, F., Magalhães, S., Kéfi, S. & Duncan, A. B. Ecology and evolution of facilitation among symbionts. Nat. Commun. 9, 4869 (2018).
PubMed  PubMed Central  Google Scholar 

21.
Jenner, E. An Inquiry into the Causes and Effects of the Variolae Vaccinae, a Disease Discovered in Some of the Western Countries of England, Particularly Gloucestershire, and Known by the Name of “The Cow Pox” (1798) Vol. 84 (R. Lier, 1923).

22.
Fulton, R. W. Practices and precautions in the use of cross protection for plant virus disease control. Annu. Rev. Phytopathol. 24, 67–81 (1986).
Google Scholar 

23.
Van Loon, L. C. Induced resistance in plants and the role of pathogenesis-related proteins. Eur. J. Plant Pathol. 103, 753–765 (1997).
Google Scholar 

24.
Conrath, U. et al. Priming: getting ready for battle. Mol. Plant Microbe Interact. 19, 1062–1071 (2006).
CAS  PubMed  Google Scholar 

25.
Pieterse, C. M. J. et al. Induced systemic resistance by beneficial microbes. Annu. Rev. Phytopathol. 52, 347–375 (2014).
CAS  PubMed  Google Scholar 

26.
Spoel, S. H., Johnson, J. S. & Dong, X. Regulation of tradeoffs between plant defenses against pathogens with different lifestyles. Proc. Natl Acad. Sci. USA 104, 18842–18847 (2007).
CAS  PubMed  Google Scholar 

27.
Kliebenstein, D. J. & Rowe, H. C. Ecological costs of biotrophic versus necrotrophic pathogen resistance, the hypersensitive response and signal transduction. Plant Sci. 174, 551–556 (2008).
CAS  Google Scholar 

28.
Koornneef, A. et al. Kinetics of salicylate-mediated suppression of jasmonate signaling reveal a role for redox modulation. Plant Physiol. 147, 1358–1368 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

29.
Glazebrook, J. Contrasting mechanisms of defense against biotrophic and necrotrophic pathogens. Annu. Rev. Phytopathol. 43, 205–227 (2005).
CAS  PubMed  Google Scholar 

30.
Ezenwa, V. O., Etienne, R. S., Luikart, G., Beja-Pereira, A. & Jolles, A. E. Hidden consequences of living in a wormy world: nematode‐induced immune suppression facilitates tuberculosis invasion in African buffalo. Am. Nat. 176, 613–624 (2010).
PubMed  Google Scholar 

31.
Clay, P. A., Cortez, M. H., Duffy, M. A. & Rudolf, V. H. W. Priority effects within coinfected hosts can drive unexpected population‐scale patterns of parasite prevalence. Oikos 128, 571–583 (2019).
Google Scholar 

32.
Clay, P. A., Duffy, M. A. & Rudolf, V. H. W. Within-host priority effects and epidemic timing determine outbreak severity in co-infected populations. Proc. R. Soc. B 287, 20200046 (2020).
PubMed  Google Scholar 

33.
Clark, P., Ward, W., Lang, S., Saghbini, A. & Kristan, D. Order of inoculation during Heligmosomoides bakeri and Hymenolepis microstoma coinfection alters parasite life history and host responses. Pathogens 2, 130–152 (2013).
PubMed  PubMed Central  Google Scholar 

34.
Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).
Google Scholar 

35.
Vannette, R. L. & Fukami, T. Historical contingency in species interactions: towards niche-based predictions. Ecol. Lett. 17, 115–124 (2014).
PubMed  Google Scholar 

36.
Halliday, F. W., Umbanhowar, J. & Mitchell, C. E. Interactions among symbionts operate across scales to influence parasite epidemics. Ecol. Lett. 20, 1285–1294 (2017).
PubMed  Google Scholar 

37.
Johnson, P. T. J., de Roode, J. C. & Fenton, A. Why infectious disease research needs community ecology. Science 349, 1259504 (2015).
PubMed  PubMed Central  Google Scholar 

38.
Karvonen, A., Jokela, J. & Laine, A.-L. Importance of sequence and timing in parasite coinfections. Trends Parasitol. 35, 109–118 (2019).
PubMed  Google Scholar 

39.
Mordecai, E. A., Gross, K. & Mitchell, C. E. Within-host niche differences and fitness trade-offs promote coexistence of plant viruses. Am. Nat. 187, E13–E26 (2016).
PubMed  Google Scholar 

40.
Kuris, A. M., Blaustein, A. R. & Alio, J. J. Hosts as islands. Am. Nat. 116, 570–586 (1980).
Google Scholar 

41.
Rynkiewicz, E. C., Pedersen, A. B. & Fenton, A. An ecosystem approach to understanding and managing within-host parasite community dynamics. Trends Parasitol. 31, 212–221 (2015).
PubMed  Google Scholar 

42.
Sousa, W. P. Interspecific interactions among larval trematode parasites of freshwater and marine snails. Am. Zool. 32, 583–592 (1992).
Google Scholar 

43.
Graham, A. L. Ecological rules governing helminth microparasite coinfection. Proc. Natl Acad. Sci. USA 105, 566–570 (2008).
CAS  PubMed  Google Scholar 

44.
Seabloom, E. W. et al. The community ecology of pathogens: coinfection, coexistence and community composition. Ecol. Lett. 18, 401–415 (2015).
PubMed  Google Scholar 

45.
Cobey, S. & Lipsitch, M. Pathogen diversity and hidden regimes of apparent competition. Am. Nat. 181, 12–24 (2013).
PubMed  Google Scholar 

46.
Greischar, M. A. & Koskella, B. A synthesis of experimental work on parasite local adaptation. Ecol. Lett. 10, 418–434 (2007).
PubMed  Google Scholar 

47.
Hoeksema, J. D. & Forde, S. E. A meta-analysis of factors affecting local adaptation between interacting species. Am. Nat. 171, 275–290 (2008).
PubMed  Google Scholar 

48.
Burdon, J. J. & Laine, A.-L. Evolutionary Dynamics of Plant Pathogen Interactions (Cambridge Univ. Press, 2019).

49.
Lambrechts, L., Fellous, S. & Koella, J. C. Coevolutionary interactions between host and parasite genotypes. Trends Parasitol. 22, 12–16 (2006).
PubMed  Google Scholar 

50.
Ferro, K. et al. Experimental evolution of immunological specificity. Proc. Natl Acad. Sci. USA 116, 20598–20604 (2019).
CAS  PubMed  Google Scholar 

51.
Westman, S. M., Kloth, K. J., Hanson, J., Ohlsson, A. B. & Albrectsen, B. R. Defence priming in Arabidopsis—a meta-analysis. Sci. Rep. 9, 13309 (2019).
PubMed  PubMed Central  Google Scholar 

52.
Pedersen, A. B. & Fenton, A. Emphasizing the ecology in parasite community ecology. Trends Ecol. Evol. 22, 133–139 (2007).
PubMed  Google Scholar 

53.
Pedersen, A. B. & Fenton, A. The role of antiparasite treatment experiments in assessing the impact of parasites on wildlife. Trends Parasitol. 31, 200–211 (2015).
PubMed  Google Scholar 

54.
Laine, A. L. Context-dependent effects of induced resistance under co-infection in a plant–pathogen interaction. Evol. Appl. 4, 696–707 (2011).
PubMed  PubMed Central  Google Scholar 

55.
Conrath, U., Beckers, G. J. M., Langenbach, C. J. G. & Jaskiewicz, M. R. Priming for enhanced defense. Annu. Rev. Phytopathol. 53, 97–119 (2015).
CAS  PubMed  Google Scholar 

56.
Douma, J. C., Vermeulen, P. J., Poelman, E. H., Dicke, M. & Anten, N. P. R. When does it pay off to prime for defense? A modeling analysis. N. Phytol. 216, 782–797 (2017).
CAS  Google Scholar 

57.
Mauch-Mani, B., Baccelli, I., Luna, E. & Flors, V. Defense priming: an adaptive part of induced resistance. Annu. Rev. Plant Biol. 68, 485–512 (2017).
CAS  PubMed  Google Scholar 

58.
Budischak, S. A. et al. Resource limitation alters the consequences of co-infection for both hosts and parasites. Int. J. Parasitol. 45, 455–463 (2015).
PubMed  Google Scholar 

59.
Borer, E. T., Laine, A.-L. & Seabloom, E. W. A multiscale approach to plant disease using the metacommunity concept. Annu. Rev. Phytopathol. 54, 397–418 (2016).
CAS  PubMed  Google Scholar 

60.
Bushnell, W. R. in The Powdery Mildews: A Comprehensive Treatise (eds Belanger, R. R. et al.) 1–12 (APS, 2002).

61.
Warton, D. I., Wright, S. T. & Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3, 89–101 (2012).
Google Scholar 

62.
Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. Mvabund—an R package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012).
Google Scholar 

63.
Benesh, D. P. & Kalbe, M. Experimental parasite community ecology: intraspecific variation in a large tapeworm affects community assembly. J. Anim. Ecol. 85, 1004–1013 (2016).
PubMed  Google Scholar 

64.
Mucha, J. et al. Effect of simulated climate warming on the ectomycorrhizal fungal community of boreal and temperate host species growing near their shared ecotonal range limits. Microb. Ecol. 75, 348–363 (2018).
CAS  PubMed  Google Scholar 

65.
Chang, A. L., Brown, C. W., Crooks, J. A. & Ruiz, G. M. Dry and wet periods drive rapid shifts in community assembly in an estuarine ecosystem. Glob. Change Biol. 24, e627–e642 (2018).
Google Scholar 

66.
David, A. S., Seabloom, E. W. & May, G. Disentangling environmental and host sources of fungal endophyte communities in an experimental beachgrass study. Mol. Ecol. 26, 6157–6169 (2017).
PubMed  Google Scholar 

67.
Penczykowski, R. M., Parratt, S. R., Barrès, B., Sallinen, S. K. & Laine, A. L. Manipulating host resistance structure reveals impact of pathogen dispersal and environmental heterogeneity on epidemics. Ecology 99, 2853–2863 (2018).
PubMed  Google Scholar 

68.
Pieterse, C. M. J. et al. Hormonal modulation of plant immunity. Annu. Rev. Cell Dev. Biol. 28, 489–521 (2012).
CAS  PubMed  Google Scholar 

69.
Susi, H. & Laine, A.-L. The effectiveness and costs of pathogen resistance strategies in a perennial plant. J. Ecol. 103, 303–315 (2015).
Google Scholar 

70.
Höckerstedt, L. Evolutionary and Ecological Dimensions of Disease Resistance. PhD dissertation, Univ. of Helsinki (2020); https://helda.helsinki.fi/handle/10138/314983

71.
Macke, E. et al. Diet and genotype of an aquatic invertebrate affect the composition of free-living microbial communities. Front. Microbiol. 11, 380 (2020).
PubMed  PubMed Central  Google Scholar 

72.
Wagner, M. R. et al. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat. Commun. 7, 12151 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

73.
Koch, H. & Schmid-Hempel, P. Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc. Natl Acad. Sci. USA 108, 19288–19292 (2011).
CAS  PubMed  Google Scholar 

74.
Biere, A. & Goverse, A. Plant-mediated systemic interactions between pathogens, parasitic nematodes, and herbivores above- and belowground. Annu. Rev. Phytopathol. 54, 499–527 (2016).
CAS  PubMed  Google Scholar 

75.
Little, T. J., Watt, K. & Ebert, D. Parasite–host specificity: experimental studies on the basis of parasite adaptation. Evolution 60, 31–38 (2006).
PubMed  Google Scholar 

76.
Seybold, H. et al. A fungal pathogen induces systemic susceptibility and systemic shifts in wheat metabolome and microbiome composition. Nat. Commun. 11, 1910 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

77.
Cui, J. et al. Pseudomonas syringae manipulates systemic plant defenses against pathogens and herbivores. Proc. Natl Acad. Sci. USA 102, 1791–1796 (2005).
CAS  PubMed  Google Scholar 

78.
Mideo, N., Alizon, S. & Day, T. Linking within- and between-host dynamics in the evolutionary epidemiology of infectious diseases. Trends Ecol. Evol. 23, 511–517 (2008).
PubMed  Google Scholar 

79.
Pedersen, A. B. & Greives, T. J. The interaction of parasites and resources cause crashes in a wild mouse population. J. Anim. Ecol. 77, 370–377 (2008).
PubMed  Google Scholar 

80.
Laine, A.-L., Barrès, B., Numminen, E. & Siren, J. P. Variable opportunities for outcrossing result in hotspots of novel genetic variation in a pathogen metapopulation. eLife 8, e47091 (2019).
PubMed  PubMed Central  Google Scholar 

81.
Vaumourin, E. & Laine, A.-L. Role of temperature and coinfection in mediating pathogen life-history traits. Front. Plant Sci. 9, 1670 (2018).
PubMed  PubMed Central  Google Scholar 

82.
Numminen, E., Vaumourin, E., Parratt, S. R., Poulin, L. & Laine, A.-L. Variation and correlations between sexual, asexual and natural enemy resistance life-history traits in a natural plant pathogen population. BMC Evol. Biol. 19, 142 (2019).
PubMed  PubMed Central  Google Scholar 

83.
Tack, A. J. M., Thrall, P. H., Barrett, L. G., Burdon, J. J. & Laine, A.-L. Variation in infectivity and aggressiveness in space and time in wild host–pathogen systems: causes and consequences. J. Evol. Biol. 25, 1918–1936 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

84.
Penczykowski, R. M., Laine, A. L. & Koskella, B. Understanding the ecology and evolution of host–parasite interactions across scales. Evol. Appl. 9, 37–52 (2016).
PubMed  Google Scholar 

85.
Rynkiewicz, E. C., Fenton, A. & Pedersen, A. B. Linking community assembly and structure across scales in a wild mouse parasite community. Ecol. Evol. 9, 13752–13763 (2019).
PubMed  PubMed Central  Google Scholar 

86.
Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).
PubMed  PubMed Central  Google Scholar 

87.
Siefert, A. Incorporating intraspecific variation in tests of trait-based community assembly. Oecologia 170, 767–775 (2012).
PubMed  Google Scholar 

88.
Laughlin, D. C. et al. A predictive model of community assembly that incorporates intraspecific trait variation. Ecol. Lett. 15, 1291–1299 (2012).
PubMed  Google Scholar 

89.
Shaw, D. J. & Dobson, A. P. Patterns of macroparasite abundance and aggregation in wildlife populations: a quantitative review. Parasitology 111, S111–S133 (1995).
PubMed  Google Scholar 

90.
Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. M. Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005).
CAS  PubMed  PubMed Central  Google Scholar 

91.
Sagar, G. R. & Harper, J. L. Plantago major L., P. media L. and P. lanceolata. J. Ecol. 52, 189–221 (1964).
Google Scholar 

92.
Ross, M. D. Inheritance of self-incompatibility in Plantago lanceolata. Heredity (Edinb.) 30, 169–176 (1973).
Google Scholar 

93.
Ojanen, S. P., Nieminen, M., Meyke, E., Pöyry, J. & Hanski, I. Long-term metapopulation study of the Glanville fritillary butterfly (Melitaea cinxia): survey methods, data management, and long-term population trends. Ecol. Evol. 3, 3713–3737 (2013).
PubMed  PubMed Central  Google Scholar 

94.
Tollenaere, C. & Laine, A. L. Investigating the production of sexual resting structures in a plant pathogen reveals unexpected self-fertility and genotype-by-environment effects. J. Evol. Biol. 26, 1716–1726 (2013).
CAS  PubMed  Google Scholar 

95.
Tack, A. & Laine, A. Ecological and evolutionary implications of spatial heterogeneity during the off‐season for a wild plant pathogen. N. Phytol. 65, 297–308 (2014).
Google Scholar 

96.
Laine, A. L. & Hanski, I. Large-scale spatial dynamics of a specialist plant pathogen in a fragmented landscape. J. Ecol. 94, 217–226 (2006).
Google Scholar 

97.
Jousimo, J. et al. Ecological and evolutionary effects of fragmentation on infectious disease dynamics. Science 344, 1289–1293 (2014).
CAS  PubMed  Google Scholar 

98.
Laine, A. L. Resistance variation within and among host populations in a plant-pathogen metapopulation: implications for regional pathogen dynamics. J. Ecol. 92, 990–1000 (2004).
Google Scholar 

99.
Penczykowski, R. M., Walker, E., Soubeyrand, S. & Laine, A.-L. Linking winter conditions to regional disease dynamics in a wild plant-pathogen metapopulation. N. Phytol. 205, 1142–1152 (2015).
Google Scholar 

100.
Laine, A. L. Pathogen fitness components and genotypes differ in their sensitivity to nutrient and temperature variation in a wild plant–pathogen association. J. Evol. Biol. 20, 2371–2378 (2007).
PubMed  Google Scholar 

101.
Tollenaere, C. et al. SNP design from 454 sequencing of Podosphaera plantaginis transcriptome reveals a genetically diverse pathogen metapopulation with high levels of mixed-genotype infection. PLoS ONE 7, e52492 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

102.
Nicot, P. C., Bardin, M. & Dik, A. J. in The Powdery Mildews: A Comprehensive Treatise (eds Belanger, R. R. et al.) 83–99 (APS, 2002).

103.
Parratt, S. R., Barrès, B., Penczykowski, R. M. & Laine, A.-L. Local adaptation at higher trophic levels: contrasting hyperparasite–pathogen infection dynamics in the field and laboratory. Mol. Ecol. 26, 1964–1979 (2017).
CAS  PubMed  Google Scholar 

104.
R: A Language and Environment for Statistical Computing v.3.5.2 (R Core Team, 2015); https://doi.org/10.1007/978-3-540-74686-7

105.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2014).
Google Scholar 

106.
Lenth, R. et al. Emmeans: Estimated marginal means, aka least-squares means. R package version 1.3.3 (2018).

107.
Hui, F. K. C. boral—Bayesian ordination and regression analysis of multivariate abundance data in R. Methods Ecol. Evol. 7, 744–750 (2016).
Google Scholar 

108.
Bedward, M. ggboral: View BORAL model results with ggplot. R package version 0.1.6 (2019).

109.
Ploner, M. & Heinze, G. coxphf: Cox regression with Firth’s penalized likelihood. R package version 1.13 (2015). More

1.
Höglund, J. & Alatalo, R. V. Leks (Princeton University Press, Princeton, 1995).
Google Scholar 
2.
Alcock, J. Leks and hilltopping in insects. J. Nat. Hist. 21, 319–328 (1987).
Google Scholar 

3.
Bradbury, J. W. Lek mating behavior in the hammer-headed bat. Z. Tierpsychol. 45, 225–255 (1977).
Google Scholar 

4.
Bradbury, J. W. Contrast between insects and vertebrates in the evolution of male display, female choice, and lek mating. Fortschr. Zool. 31, 273–289 (1985).
Google Scholar 

5.
Shelly, T. E. Sexual selection on leks: A fruit fly primer. J. Insect Sci. https://doi.org/10.1093/jisesa/iey048 (2018).
Article  PubMed  PubMed Central  Google Scholar 

6.
Shelly, T. E. & Whittier, T. S. Lek behavior of insects. Evol. Mating Syst. Insects Arachnids https://doi.org/10.1017/cbo9780511721946.017 (2010).
Article  Google Scholar 

7.
Dodson, G. Lek mating system and large male aggressive advantage in a gall-forming tephritid fly (Diptera: Tephritidae). Ethology 72, 99–108 (1986).
Google Scholar 

8.
Andersson, S., Rydell, J. & Svensson, M. G. E. Light, predation and the lekking behaviour of the ghost swift Hepialus humuli (L.) (Lepidoptera: Hepialidae). Proc. R. Soc. B Biol. Sci. 265, 1345–1351 (1998).
Google Scholar 

9.
Alem, S., Koselj, K., Siemers, B. M. & Greenfield, M. D. Bat predation and the evolution of leks in acoustic moths. Behav. Ecol. Sociobiol. 65, 2105–2116 (2011).
Google Scholar 

10.
de Souza, A. R., Lino-Neto, J., Tibbetts, E. A., Turillazzi, S. & Beani, L. The leks of Polistes dominula paper wasps: tiny abdominal spots play a critical role in male attacks toward potential rivals. Ethol. Ecol. Evol. 29, 410–419 (2017).
Google Scholar 

11.
Kimsey, L. S. The behaviour of male orchid bees (Apidae, Hymenoptera, Insecta) and the question of leks. Anim. Behav. 28, 996–1004 (1980).
Google Scholar 

12.
Wickman, P. O. & Jansson, P. An estimate of female mate searching costs in the lekking butterfly Coenonympha pamphilus. Behav. Ecol. Sociobiol. 40(5), 321–328 (1997).
Google Scholar 

13.
Lederhouse, R. C. Territorial defense and lek behavior of the black swallowtail butterfly, Papilio polyxenes. Behav. Ecol. Sociobiol. 10, 109–118 (1982).
Google Scholar 

14.
Alcock, J. Consistency in the relative attractiveness of a set of landmark territorial sites to two generations of male tarantula hawk wasps (Hymenoptera: Pompilidae). Anim. Behav. 31, 74–80 (1983).
Google Scholar 

15.
Beehler, B. M. & Foster, M. S. Hotshots, hotspots, and female preference in the organization of lek mating systems. Am. Nat. 131, 203–219 (1988).
Google Scholar 

16.
Bradbury, J. & Gibson, R. M. Leks and mate choice. Mate choice (1983).

17.
Stillman, R. A., Cutton-Brock, T. H. & Sutherland, W. J. Black holes, mate retention, and the evolution of ungulate leks. Behav. Ecol. 4, 1–6 (1993).
Google Scholar 

18.
Karino, K. Female mate preference for males having long and symmetric fins in the bower-holding cichlid Cyathopharynx furcifer. Ethology 103, 883–892 (1997).
Google Scholar 

19.
Carbone, C. & Taborsky, M. Mate choice or harassment avoidance? A question of female control at the lek. Behav. Ecol. 7, 370–378 (1996).
Google Scholar 

20.
Pilastro, A., Benetton, S. & Bisazza, A. Female aggregation and male competition reduce costs of sexual harassment in the mosquitofish Gambusia holbrooki. Anim. Behav. 65, 1161–1167 (2003).
Google Scholar 

21.
Gibson, R. M., Bradbury, J. W. & Vehrencamp, S. L. Mate choice in lekking sage grouse revisited: the roles of vocal display, female site fidelity, and copying. Behav. Ecol. 2, 165–180 (1991).
Google Scholar 

22.
Cordoba-Aguilar, A. & Cordero-Rivera, A. Evolution and ecology of Calopterygidae (Zygoptera:Odonata ): status of knowledge and research perspectives. Neotrop. Entomol. 34, 861–879 (2005).
Google Scholar 

23.
Pajunen, V. I. Aggressive behaviour and territoriality in a population of Calopteryx virgo L. (Odon, Calopterygidae). Ann. Zool. Fennici 3, 201–214 (1966).
Google Scholar 

24.
Guillermo-Ferreira, R. & Bispo, P. C. Male and female interactions during courtship of the Neotropical damselfly Mnesarete pudica (Odonata: Calopterygidae). Acta Ethol. 15, 173–178 (2012).
Google Scholar 

25.
Günther, A., Hilfert-Rüppell, D. & Rüppell, G. Reproductive behaviour and the system of signalling in Neurobasis chinensis: a kinematic analysis. Int. J. Odonatol. 17, 37–41 (2014).
Google Scholar 

26.
Córdoba-Aguilar, A. & Cordero-Rivera, A. Evolution and ecology of Calopterygidae (Zygoptera: Odonata): status of knowledge and research perspectives. Neotrop. Entomol. 34, 861–879 (2005).
Google Scholar 

27.
Outomuro, D., Söderquist, L., Johansson, F., Ödeen, A. & Nordström, K. The price of looking sexy: visual ecology of a three-level predator–prey system. Funct. Ecol. 31, 707–718 (2017).
Google Scholar 

28.
Contreras-Garduño, J., Buzatto, B. A., Serrano-Meneses, M. A., Nájera-Cordero, K. & Córdoba-Aguilar, A. The size of the red wing spot of the American rubyspot as a heightened condition-dependent ornament. Behav. Ecol. 19, 724–732 (2008).
Google Scholar 

29.
Koskimäki, J., Rantala, M. J., Taskinen, J., Tynkkynen, K. & Suhonen, J. Immunocompetence and resource holding potential in the damselfly, Calopteryx virgo L. Behav. Ecol. 15, 169–173 (2004).
Google Scholar 

30.
Guillermo-Ferreira, R. & Del-Claro, K. Resource defense polygyny by Hetaerina rosea Selys (Odonata: Calopterygidae): influence of age and wing pigmentation. Neotrop. Entomol. 40, 78–84 (2011).
CAS  PubMed  Google Scholar 

31.
Plaistow, S. & Siva-Jothy, M. T. Energetic constraints and male mate-securing tactics in the damselfly Calopteryx splendens xanthostoma (Charpentier). Proc. R. Soc. B Biol. Sci. 263, 1233–1239 (1996).
ADS  Google Scholar 

32.
Córdoba-Aguilar, A., Serrano-Meneses, M. A., Contreras-Garduño, J. & Raihani, G. The lek mating system of Hetaerina damselflies (Insecta: Calopterygidae). Behaviour 146, 189–207 (2009).
Google Scholar 

33.
Dumont, H. J. et al. phylogenetic relationships, divergence time estimation, and global biogeographic patterns of calopterygoid damselflies (odonata, zygoptera) inferred from ribosomal DNA sequences. Syst. Biol. 54, 347–362 (2005).
PubMed  Google Scholar 

34.
Dijkstra, K. D. B., Kalkman, V. J., Dow, R. A., Stokvis, F. R. & Van Tol, J. Redefining the damselfly families: a comprehensive molecular phylogeny of Zygoptera (Odonata). Syst. Entomol. 39, 68–96 (2014).
Google Scholar 

35.
Guillermo-Ferreira, R., Gorb, S. N., Appel, E., Kovalev, A. & Bispo, P. C. Variable assessment of wing colouration in aerial contests of the red-winged damselfly Mnesarete pudica (Zygoptera, Calopterygidae). Sci. Nat. 102, 13 (2015).
Google Scholar 

36.
Weatherhead, P. & Robertson, R. male behavior and female recruitment in the red-winged blackbird. Wilson Bull. (1977).

37.
Rantala, M. J., Koskimäki, J., Taskinen, J., Tynkkynen, K. & Suhonen, J. Immunocompetence, developmental stability and wingspot size in the damselfly Calopteryx splendens L. Proc. Biol. Sci. 267, 2453–2457 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

38.
Siva-Jothy, M. T. Male wing pigmentation may affect reproductive success via female choice in a calopterygid damselfly (Zygoptera). Behaviour 136, 1365–1377 (1999).
Google Scholar 

39.
Heymer, A. Étude du comportement reproducteur et analyse des mécanismes déclencheurs innés (MDI) optiques chez les Calopterygidae (Odon. Zygoptera). Ann. Soc. Entomol. Fr. 9, 219–225 (1973).
Google Scholar 

40.
Cordero, A. Forced copulations and female contact guarding at a high male density in a calopterygid damselfly. J. Insect Behav. 12, 27–37 (1999).
Google Scholar 

41.
Córdoba-Aguilar, A. A female evolutionary response when survival is at risk: male harassment mediates early reallocation of resources to increase egg number and size. Behav. Ecol. Sociobiol. 63, 751–763 (2009).
Google Scholar 

42.
Córdoba-Aguilar, A. & González-Tokman, D. M. Male harassment and female energetics in the territorial damselfly hetaerina Americana (fabricius) (zygoptera: Calopterygidae). Odonatologica 40, 1–15 (2011).
Google Scholar 

43.
Takahashi, Y. & Watanabe, M. Female reproductive success is affected by selective male harassment in the damselfly Ischnura senegalensis. Anim. Behav. 79, 211–216 (2010).
Google Scholar 

44.
Córdoba-Aguilar, A. Seasonal variation in genital and body size, sperm displacement ability, female mating rate, and male harassment in two calopterygid damselflies (Odonata: Calopterygidae). Biol. J. Linn. Soc. 96, 815–829 (2009).
Google Scholar 

45.
Van Gossum, H., Stoks, R. & De Bruyn, L. Frequency-dependent male mate harassment and intra-specific variation in its avoidance by females of the damselfly Ischnura elegans. Behav. Ecol. Sociobiol. 51, 69–75 (2001).
Google Scholar 

46.
Gosden, T. P. & Svensson, E. I. Density-dependent male mating harassment, female resistance, and male mimicry. Am. Nat. 173, 709–721 (2009).
PubMed  Google Scholar 

47.
Yasukawa, K. Male quality and female choice of mate in the red-winged blackbird (Agelaius phoeniceus). Ecology 62, 922–929 (1981).
Google Scholar 

48.
Jennings, D. J., Carlin, C. M., Hayden, T. J. & Gammell, M. P. Investment in fighting in relation to body condition, age and dominance rank in the male fallow deer, Dama dama. Anim. Behav. 79, 1293–1300 (2010).
Google Scholar 

49.
Murai, M., Koga, T. & Yong, H. S. The assessment of female reproductive state during courtship and scramble competition in the fiddler, Uca Paradussumieri. Behav. Ecol. Sociobiol. 52, 137–142 (2002).
Google Scholar 

50.
Contreras-Garduño, J., Canales-Lazcano, J. & Córdoba-Aguilar, A. Wing pigmentation, immune ability, fat reserves and territorial status in males of the rubyspot damselfly, Hetaerina americana. J. Ethol. 24, 165–173 (2006).
Google Scholar 

51.
Corbet, P. S. Dragonflies: Behaviour and Ecology of Odonata (Harley books, Essex, 1999).
Google Scholar 

52.
Guillermo-Ferreira, R., Therézio, E. M., Gehlen, M. H., Bispo, P. C. & Marletta, A. The role of wing pigmentation, UV and fluorescence as signals in a neotropical damselfly. J. Insect Behav. 27, 67–80 (2014).
Google Scholar 

53.
Altmann, J. Observational study of behavior: sampling methods. Behavior 49, 227–267 (1974).
CAS  Google Scholar 

54.
Utzeri, C. Female” refusal display” versus male” threat display” in Zygoptera: is it a case of intraspecific imitation?. Odonatologica 17, 45–54 (1988).
Google Scholar 

55.
Koskimäki, J., Rantala, M. J. & Suhonen, I. Wandering males are smaller than territorial males in the damselfly Calopteryx virgo (L.) (zygoptera: calopterygidae). Odonatologica 38, 159–165 (2009).
Google Scholar 

56.
Bürkner, P. C. Advanced Bayesian multilevel modeling with the R package brms. R J. 10, 395–411 (2018).
Google Scholar 

57.
Wickham, H. ggplot2: elegant graphics for data analysis. J. R. Stat. Soc. A https://doi.org/10.1007/978-3-319-24277-4 (2016).
Article  MATH  Google Scholar 

58.
R Core Team. (2017). R: a language and environment for statistical computing. https://www.R-project.org/. More