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    Short-term sedimentation dynamics in mesotidal marshes

    No plants were collected or harmed during this study, and all research involving plants followed relevant national, and international guidelines and legislation.Study areaThe study site encloses a wetland area bordering Ramalhete Channel, in the western part of the Ria Formosa lagoon, a mesotidal system located in southern Portugal (Fig. 1). Lunar tides are semi-diurnal, with a mean tidal range of about 2 m that can reach up to 3.5 m during spring tides. Offshore waves have no major propagation inside the lagoon33,34. Water circulation inside the lagoon is mostly driven by tides. The lagoon extends over 55 km along the coast and is connected to the ocean through six tidal inlets35. The three westmost inlets of the system (Ancão, Faro-Olhão, and Armona), which together capture ca. 90% of the total prism, are highly interconnected, with a strong residual circulation from Faro-Olhão Inlet directed towards Ancão and Armona inlets (located in Fig. 1), during both spring and neap tides36. The tidal currents in Ramalhete Channel, connecting the Faro-Olhão and Ancão Inlet, have high tidal asymmetry and shifts in tidal dominance, from flood to ebb. There are no significant fluvial inputs into the lagoon, with a yearly average terrestrial sediment influx of around 2 × 105 m3/yr37, reaching the system through small streams. The main sediment delivery to the system is through the inlets, though there are few studies assessing related fluxes. The net sediment entry through the stabilized Faro-Olhão Inlet is estimated at 1.4 × 105 m3/year38. Recent sedimentation rates in the marsh of the westmost edge of the lagoon were estimated at 1.1 ± 0.1 mm/yr39.The lagoon system is composed of large salt marsh patches, tidal flats and a complex net of natural, and partially dredged tidal channels. The tidal flats (vegetated and non-vegetated) and salt marshes represent more than 2/3 of the total lagoon area. The salt marshes comprise silt and fine sand40, while coarser (sand to shingle) shell-rich sediment, of marine provenance, is found on tidal channels and the lower domain of intertidal flats41. The dominant intertidal species are Spartina maritima and the seagrass Zostera noltei, the latter occupying an estimated area of 1304 ha, which represent 45% of the total intertidal area42.Figure 1Location of the field site in the Ria Formosa lagoon western sector over a satellite image collected in 2019 (South Portugal; upper panel); zoom to monitoring stations S1 to S4 (left lower panel); and field view of the studied site (right lower panel). Map generated with ArcGIS 10.8 (http://www.esri.com) and Adobe Illustrator 2022. Map data: Google Earth 7.3, image Landsat / Copernicus.Full size imageExperimental setup and data analysisAn experimental setup was deployed in the study area to assess dominant local topography, hydrodynamics (water levels and current velocities), Suspended Sediment Concentrations (SSCs), Deposition Rates (DRs), vegetation characteristics, and bed sediment grain size and organic matter content. Measurements were made during a full tide cycle, on a spring tide (tidal range = 3.2 m), and on a neap tide (tidal range = 1.8 m). Sampling was conducted in four wetland stations: S1 and S2 in a vegetated tidal flat comprising Zostera noltei; S3 in the low marsh comprising Spartina maritima; and S4 in the mid-upper marsh with the most abundant species of Sarcocornia perennis and Atriplex portucaloides (see S1 to S4, Fig. 1); the tidal flat is interrupted by a small oblique secondary tidal creek that flows near S2 station.Stations of sediment sampling and equipment deployment along the transect are illustrated in Fig. 2. During neap tide there was no data collection in S4, since the inundation time of the station was very short. The profile elevation was measured using Real Time Kinematic Differential Global Positioning System (RTK-DGPS, Trimble R6; vertical error in the order of few centimetres), and the slope of each habitat within a transect was calculated and expressed in percentage (%). Vegetation at each point was characterized by the canopy height, calculated as the average shoot length.Suspended Sediment Samplers (SSSs) were installed during low tide in the monitored stations using siphon samplers (Fig. 2) and recovered in the next low tide. These samplers consist of 0.5 L bottles with two holes on the cap, one for water intake and the other for air exhaust, according to the method described in13. Each intake tube is adjusted to form a siphon (i.e., inverse U), allowing to control the water level at which intake starts. Siphons were aligned at the same elevation along the transect for spring and neap tides, which means that all SSSs were collecting at the same time within the tidal cycle. During spring tide, in S1 and S2 at the tidal flat, SSSs were sampling at 0.1, 0.9, and 1.2 m from the bed, while at S3 SSSs were sampling at 0.7 and 1.0 m from the bed, and at S4 the SSS was sampling at 0.1 m from the bed (Fig. 2). During neap tide, in S1 and S2, SSSs were sampling at 0.1 and 0.9 m from the bed, while at S3 the SSS was sampling at 0.7 m from the bed.Surficial sediment samples were collected in each habitat to characterize the sediment grain size (d50) and content of organic matter (% OM). Sediment traps were installed in 3 replicates, during low tide, at each sampling point to measure the short-term sediment deposition rate (i.e., deposition over a tidal cycle, following procedures of43). Traps consisted of 3 cm diameter pre-labeled cylindrical tubes (Falcon® tubes, 50 ml). Traps and sediment samples were transported to the laboratory and maintained in a fridge. The sediment content was washed, and both the inorganic and organic weights were determined.The measured inorganic DR (g/m2/hr) was calculated as:$${text{DR}} = {raise0.7exhbox{${{text{W}}_{{{text{DS}}}} }$} !mathord{left/ {vphantom {{{text{W}}_{{{text{DS}}}} } {{text{A}} cdot {text{T}}}}}right.kern-0pt} !lower0.7exhbox{${{text{A}} cdot {text{T}}}$}}$$
    (1)
    where WDS is the weight of deposited sediment (in grams), A is the area of the sediment trap opening (m2), and T is in hours. Two different tide durations were considered to compute DRs, one assuming T equal to the hydroperiod in each station, and one assuming T equal to the entire tide duration (~ 12.4 h). These measured DRs are hereon mentioned as flood and tide DRs (DRflood and DRtide, respectively). The former is an expression of the actual deposition rate within the flood phase, during the period in which each station is inundated (and therefore active deposition can take place). The latter is the value used to compare with DRs in literature, which typically corresponds to values averaged over multiple tidal cycles (thus accounting for the entire tide duration).Tide levels were measured in the field using pressure sensors (PT, InSitu Inc. Level TROLL; ~ 2 cm from the bed), deployed from S2 towards S4 (Fig. 2). Velocity currents were measured at 20 cm from the bed, using an electromagnetic current meter (EMCM; Infinity Series JFE Advantech Co., Ltd; in S2 to S4; Fig. 2), and raw data (recording interval: 30 s) were filtered using a 10 min moving average for cross-shore and longshore components. To identify tidal asymmetry and assess the related phase dominance, tidal current skewness was calculated through the formula described in44 by which:
    $$Sk_{U} = frac{{frac{1}{N – 1}mathop sum nolimits_{t = 1}^{N} left( {U_{t} – overline{U}} right)^{3} }}{{left( {frac{1}{N – 1}mathop sum nolimits_{t = 1}^{N} left( {U_{t} – overline{U}} right)^{2} } right)^{{{raise0.7exhbox{$3$} !mathord{left/ {vphantom {3 2}}right.kern-0pt} !lower0.7exhbox{$2$}}}} }}$$
    (2)
    where N is the number of recordings, Ut is the input velocity signal and (overline{U}) is the mean velocity. Positive/negative skewness indicates flood/ebb dominance (assuming that flood currents are positive).Figure 2Deployment of the sediment traps, SSSs and devices (electromagnetic current meter EMCM; pressure transducer PT) in the stations (S1 to S4) during spring tide (sketch is exaggerated in the vertical).Full size imageComplementary to the measured DRs, theoretical DRs were also determined from the data, allowing us to link the sediment and flow data collected, and validate the deposition patterns from the traps. The theoretical deposition rate was determined based on45 formula:$${text{DR}} = left{ {begin{array}{*{20}c} {{text{C}}_{{text{b}}} cdot {text{w}}_{{text{s}}} cdot left( {1 – frac{{{uptau }_{{text{b}}} }}{{{uptau }_{{{text{cd}}}} }}} right)} & {{uptau }_{{text{b}}} < {uptau }_{{{text{cd}}}} } \ 0 & {{uptau }_{{text{b}}} ge {uptau }_{{{text{cd}}}} } \ end{array} } right.$$ (3) where Cb is the SSC at the bed, ws is the flock settling velocity, τb is the bed shear stress and τcd is the corresponding critical value for deposition.To determine the settling rate of the flocculates, the modified Stokes’ velocity for cohesive sediment was used, taking shape factors α and β (α = β = 1 for perfectly spherical particles):$${text{w}}_{{text{s}}} = frac{{upalpha }}{{upbeta }} cdot frac{{left( {{uprho }_{{text{s}}} - {uprho }_{{text{w}}} } right) cdot {text{g}} cdot {text{D}}_{50}^{2} }}{{{uprho }_{{text{w}}} cdot 18 cdot {upnu }}}$$ (4) where ρw and ρs are the densities of the water and sediment, respectively and ν is the kinematic viscosity of water (~ 106 m2/s).The bed shear stress τb was calculated from the measured current magnitude, |U| using the law of the wall:$$begin{array}{*{20}c} \ {{uptau }_{{text{b}}} = {uprho }_{{text{w}}} cdot {text{u}}_{*}^{2} , {text{u}}_{*} = frac{left| U right| cdot kappa }{{ln left( {{raise0.7exhbox{$z$} !mathord{left/ {vphantom {z {z_{0} }}}right.kern-0pt} !lower0.7exhbox{${z_{0} }$}}} right)}} } \ end{array} { }$$ (5) where κ is the von Kármán constant (~ 0.4) and z0 is the roughness length. For Zostera noltei, the roughness length was estimated at 5 mm46, value that was also used in the other stations, in lack of related estimate for marsh plants.The critical shear for deposition, τcd, was calculated using the formula47:$$sqrt {frac{{{uptau }_{{{text{cd}}}} }}{{{uprho }_{{text{w}}} }}} = left{ {begin{array}{*{20}c} {0.008} & {{text{w}}_{{text{s}}} le 5 cdot 10^{ - 5} {text{m}}/{text{s}}} \ {0.094 + 0.02 cdot {text{log}}_{10} left( {{text{w}}_{{text{s}}} } right)} & {3 cdot 10^{ - 4} le {text{w}}_{{text{s}}} le 5 cdot 10^{ - 5} {text{m}}/{text{s}}} \ {0.023} & {{text{w}}_{{text{s}}} ge 3 cdot 10^{ - 4} {text{m}}/{text{s}}} \ end{array} } right.$$ (6) Theoretical values of minimum SSCs needed for these DRs were also calculated, assuming that there is constant deposition (i.e., setting τb = 0), and compared with the field results. More

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    Understanding the role of natural and anthropogenic forcings in structuring the periphytic algal assemblages in a regulated river ecosystem

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    Heterogeneity of interaction strengths and its consequences on ecological systems

    Now consider a generalized model in which the species interactions are heterogeneous. A natural way of introducing heterogeneity in the system is by having a species diversify into subpopulations with different interaction strengths12,13,14,15. This way of modeling heterogeneity is useful as it can describe different kinds of heterogeneity. For example, the subpopulations could represent polymorphic traits that are genetically determined or result from plastic response to heterogeneous environments. A population could also be divided into local subpopulations in different spatial patches, which can migrate between patches and may face different local predators. We can also model different behavioral modes as subpopulations that, for instance, spend more time foraging for food or hiding from predators. We study several kinds of heterogeneity after we introduce a common mathematical framework. By studying these different scenarios using variants of the model, we show that our main results are not sensitive to the details of the model.We focus on the simple case where only the prey species splits into two types, (C_1) and (C_2), as illustrated in Fig. 1b. The situation is interesting when predator A consumes (C_1) more readily than predator B and B consumes (C_2) more readily than A (i.e., (a_1 / a_0 > b_1 / b_0) and (b_2 / b_0 > a_2 / a_0), which is equivalent to the condition that the nullclines of A and B cross, see section “Resources competition and nullcline analysis”). The arrows between (C_1) and (C_2) in Fig. 1b represent the exchange of individuals between the two subpopulations, which can happen by various mechanisms considered below. Such exchange as well as intraspecific competition between (C_1) and (C_2) result from the fact that the two prey types remain a single species.The system is now described by an enlarged Lotka-Volterra system with four variables, A, B, (C_1), and (C_2): $$begin{aligned} dot{A}&= varepsilon _A ,alpha _{A1} , A , C_1 + alpha _{A2} , A , C_2 – beta _A , A end{aligned}$$
    (3a)
    $$begin{aligned} dot{B}&= varepsilon _B , alpha _{B1} , B , C_1 + alpha _{B2} , B , C_2 – beta _B , B end{aligned}$$
    (3b)
    $$begin{aligned} dot{C_1}&= C_1 , (beta _C – alpha _{CC} , C)-alpha _{A1} , C_1 A-alpha _{B1} , C_1 B – sigma _1 , C_1 + sigma _2 , C_2 end{aligned}$$
    (3c)
    $$begin{aligned} dot{C_2}&= C_2 , (beta _C – alpha _{CC} , C) -alpha _{A2} , C_2 A -alpha _{B2} , C_2 B + sigma _1 , C_1 – sigma _2 , C_2 end{aligned}$$
    (3d)
    The parameters in these equations and their meanings are listed in Table 1. Here we assume that the prey types (C_1) and (C_2) have the same birth rate and intraspecific competition strength, but different interaction strengths with A and B. Note that (C_1) and (C_2) are connected by the (sigma _i) terms, which represent the exchange of individuals between these subpopulations through mechanisms studied below; these terms indicate a major difference between our model of a prey with intraspecific heterogeneity and other models of two prey species. For the convenience of analysis, we transform the variables (C_1) and (C_2) to another pair of variables C and (lambda), where (C equiv C_1 + C_2) is the total population of C as before, and (lambda equiv C_2 / (C_1 + C_2)) represents the composition of the population (Fig. 1c). After this transformation and rescaling of variables (described in “Methods”), the new dynamical system can be written as: $$begin{aligned} dot{A}&= A , big ( C , (a_1 (1-lambda ) + a_2 lambda ) – a_0 big ) end{aligned}$$
    (4a)
    $$begin{aligned} dot{B}&= B , big ( C , (b_1 (1-lambda ) + b_2 lambda ) – b_0 big ) end{aligned}$$
    (4b)
    $$begin{aligned} dot{C}&= C , big ( 1 – C – A (a_1 (1-lambda ) + a_2 lambda ) – B (b_1 (1-lambda ) + b_2 lambda ) big ) end{aligned}$$
    (4c)
    $$begin{aligned} dot{lambda }&= lambda (1-lambda ) , big ( A (a_1 – a_2) + B (b_1 – b_2) big ) + eta _1 (1-lambda ) – eta _2 lambda end{aligned}$$
    (4d)
    Here, (a_i) and (b_i) are the (rescaled) feeding rates of the predators on the prey type (C_i); (a_0) and (b_0) are the death rates of the predators as before; (eta _1) and (eta _2) are the exchange rates of the prey types (Table 1). The latter can be functions of other variables, representing different kinds of heterogeneous interactions that we study below. Notice that Eqs. (4a–4c) are equivalent to the homogeneous Eqs. (2a–2c) but with effective interaction strengths (a_text {eff} = (1-lambda ) , a_1 + lambda , a_2) and (b_text {eff} = (1-lambda ) , b_1 + lambda , b_2) that both depend on the prey composition (lambda) (Fig. 1c).Table 1 Model parameters (before/after rescaling) and their meanings.Full size tableThe variable (lambda) can be considered an internal degree of freedom within the C population. In all of the models we study below, (lambda) dynamically stabilizes to a special value (lambda ^*) (a bifurcation point), as shown in Fig. 3a. Accordingly, a new equilibrium point (P_N) appears (on the line (mathscr {L}) in Fig. 2), at which all three species coexist. For comparison, Fig. 3b shows the equilibrium points if (lambda) is held fixed at any other values, which all result in the exclusion of one of the predators. Thus, heterogeneous interactions give rise to a new coexistence phase (see Fig. 4 below) by bringing the prey composition (lambda) to the value (lambda ^*), instead of having to fine-tune the interaction strengths. The exact conditions for this new equilibrium to be stable are detailed in “Methods”.Figure 3(a) Time series of (lambda) for systems with each kind of heterogeneity. All three systems stabilize at the same (lambda ^*) value, which is the bifurcation point in panel (b). (b) Equilibrium population of each species (X = A), B, or C, with (lambda) held fixed at different values. Solid curves represent stable equilibria and dashed curves represent unstable equilibria (see Eq. (9) in “Methods”). The vertical dashed line is where (lambda = lambda ^*), which is also the bifurcation point. Notice that the equilibrium population of C is maximized at this point (for (a_1 > a_2) and (b_2 > b_1)). Parameters used here are ((a_0, a_1, a_2, b_0, b_1, b_2, rho , eta _1, eta _2, kappa ) = (0.25, 0.5, 0.2, 0.4, 0.2, 0.6, 0.5, 0.05, 0.05, 50)).Full size imageInherent heterogeneityWe first consider a scenario where individuals of the prey species are born as one of two types with a fixed ratio, such that a fraction (rho) of the newborns are (C_2) and ((1-rho )) are (C_1). This could describe dimorphic traits, such as the winged and wingless morphs in aphids12 or the horned and hornless morphs in beetles13. We call this “inherent” heterogeneity, because individuals are born with a certain type and cannot change in later stages of life. The prey type given at birth determines the individual’s interaction strength with the predators. This kind of heterogeneity can be described by Eq. (4d) with (eta _1 = rho (1-C)) and (eta _2 = (1-rho ) (1-C)) (see “Methods”).Figure 4Phase diagrams showing regions of parameter space identified by the stable equilibrium points. Yellow region represents (P_C) (predators A, B both extinct), red represents (P_A) (A excludes B), blue represents (P_B) (B excludes A), and green represents (P_N) (A, B coexist). The middle point (black dot) is where the preferences of the two predators are identical, (a_2/a_0=b_2/b_0) and (b_1/b_0=a_1/a_0). The coexistence phase appears in all three kinds of heterogeneity modeled here. (a–d) Inherent heterogeneity: Individuals of the prey population are born in two types with a fixed composition (rho). In the extreme cases of (rho = 0) and 1, the prey is homogeneous and there is no coexistence of the predators. (e–h) Reversible heterogeneity: Individual prey can switch types with fixed switching rates (eta _1) and (eta _2). As the switching rates increase, the coexistence region shrinks because the prey population becomes effectively homogeneous (the occasional green spots are numerical artifacts because the time to reach the equilibrium becomes long in this limit). (i–l) Adaptive heterogeneity: The switching rates (eta _i) dynamically adapt to the predator densities, so as to maximize the growth rate of the prey. As the sharpness (kappa) of the sigmoidal decision function is increased, the prey adapts more optimally and the region of coexistence expands. Parameters used here are ((a_0, a_1, b_0, b_2) = (0.3, 0.5, 0.4, 0.6)).Full size imageThe stable equilibrium of the system can be represented by phase diagrams that show the identities of the species at equilibrium. We plot these phase diagrams by varying the parameters (a_2) and (b_1) while keeping (a_1) and (b_2) constant. As shown in Fig. 4a–d, the equilibrium state depends on the parameter (rho). In the limit (rho = 0) or 1, we recover the homogeneous case because only one type of C is produced. The corresponding phase diagrams (Fig. 4a, d) contain only two phases where either of the predators is excluded, illustrating the competitive exclusion principle. For intermediate values of (rho), however, there is a new phase of coexistence that separates the two exclusion phases (Fig. 4b, c). There are two such regions of coexistence, which touch at a middle point and open toward the bottom left and upper right, respectively. The middle point is at ((a_2/a_0 = b_2/b_0, b_1/b_0 = a_1/a_0)), where the feeding preferences of the two predators are identical (hence their niches fully overlap). Towards the origin and the far upper right, the predators consume one type of C each (hence their niches separate). The coexistence region in the bottom left is where the feeding rates of the predators are the lowest overall. There can be a region (yellow) where both predators go extinct, if their primary prey type alone is not enough to sustain each predator. Increasing the productivity of the system by increasing the birth rate ((beta _C)) of the prey eliminates this extinction region, whereas lowering productivity causes the extinction region to take over the lower coexistence region. Because the existence and identity of the phases is determined by the configuration of the equilibrium points (Fig. 2, see also section “Mathematical methods”), the qualitative shape of the phase diagram is not sensitive to changes of parameter values.The new equilibrium is not only where the predators A and B can coexist, but also where the prey species C grows to a larger density than what is possible for a homogeneous population. This is illustrated in Fig. 3b, which shows the equilibrium population of C if we hold (lambda) fixed at different values. The point (lambda = lambda ^*) is where the system with a dynamic (lambda) is stable, and also where the population of C is maximized (when A and B prefer different prey types). That means the population automatically stabilizes at the optimal composition of prey types. Moreover, the value of (C^*) at this coexistence point can even be larger than the equilibrium population of C when there is only one predator A or B. This is discussed further in section “Multiple-predator effects and emergent promotion of prey”. These results suggest that heterogeneity in interaction strengths can potentially be a strategy for the prey population to leverage the effects of multiple predators against each other to improve survival.Reversible heterogeneityWe next consider a scenario where individual prey can switch their types. This kind of heterogeneity can model reversible changes of phenotypes, i.e., trait changes that affect the prey’s interaction with predators but are not permanent. For example, changes in coat color or camouflage14,16,17, physiological changes such as defense15, and biomass allocation among tissues18,19. One could also think of the prey types as subpopulations within different spatial patches, if each predator hunts at a preferred patch and the prey migrate between the patches20,21. With some generalization, one could even consider heterogeneity in resources, such as nutrients located in different places, that can be reached by primary consumers, such as swimming phytoplankton22. We can model this “reversible” kind of heterogeneity by introducing switching rates from one prey type to the other. In Eq. (4d), (eta _1) and (eta _2) now represent the switching rates per capita from (C_1) to (C_2) and from (C_2) to (C_1), respectively. Here we study the simplest case where both rates are fixed.In the absence of the predators, the natural composition of the prey species given by the switching rates would be (rho equiv eta _1 / (eta _1 + eta _2)), and the rate at which (lambda) relaxes to this natural composition is (gamma equiv eta _1 + eta _2). Compared to the previous scenario where we had only one parameter (rho), here we have an additional parameter (gamma) that modifies the behavior of the system. Fig. 4e–h shows phase diagrams for the system as (rho) is fixed and (gamma) varies. We again find the new equilibrium (P_N) where all three species coexist. When (gamma) is small, the system has a large region of coexistence. As (gamma) is increased, this region is squeezed into a border between the two regions of exclusion, where the slope of the border is given by (eta _1/eta _2) as determined by the parameter (rho). However, this is different from the exclusion we see in the case of inherent heterogeneity, which happens only for (rho rightarrow 0) or 1, where the borders are horizontal or vertical (Fig. 4a,d). Here the predators exclude each other despite having a mixture of prey types in the population.This special limit can be understood as follows. For a large (gamma), (lambda) is effectively set to a constant value equal to (rho), because it has a very fast relaxation rate. In other words, the prey types exchange so often that the population always maintains a constant composition. In this limit, the system behaves as if it were a homogeneous system with effective interaction strengths (a_text {eff} = (1-rho ) , a_1 + rho , a_2) and (b_text {eff} = (1-rho ) , b_1 + rho , b_2). As in a homogeneous system, there is competitive exclusion between the predators instead of coexistence. This demonstrates that having a constant level of heterogeneity is not sufficient to cause coexistence. The overall composition of the population must be able to change dynamically as a result of population growth and consumption by predators.An interesting behavior is seen when we examine a point inside the shrinking coexistence region as (gamma) is increased. Typical trajectories of the system for such parameter values are shown in Fig. 5. As (gamma) increases, the system relaxes to the line (mathscr {L}) quickly, then slowly crawls along it towards the final equilibrium point (P_N). This is because increasing (gamma) increases the speed that (lambda) relaxes to (lambda ^*), and when (lambda rightarrow lambda ^*), (mathscr {L}) becomes marginally stable. Therefore, the attraction to (mathscr {L}) in the perpendicular direction is strong, but the attraction towards the equilibrium point along (mathscr {L}) is weak. This leads to a long transient behavior that makes the system appear to reach no equilibrium in a limited time23,24. It is especially true when there is noise in the dynamics, which causes the system to diffuse along (mathscr {L}) with only a weak drift towards the final equilibrium (Fig. 5). Thus, the introduction of a fast timescale (quick relaxation of (lambda) due to a large (gamma)) actually results in a long transient.Figure 5Trajectories of the system projected in the A-B plane, with parameters inside the coexistence region (by holding the position of (P_N) fixed). As (gamma) increases, the system tends to approach the line (mathscr {L}) quickly and then crawl along it. The grey trajectory is with independent Gaussian white noise ((sim mathscr {N}(0,0.5))) added to each variable’s dynamics. Noise causes the system to diffuse along (mathscr {L}) for a long transient period before coming to the equilibrium point (P_N). Parameters used here are ((a_0, a_1, a_2, b_0, b_1, b_2) = (0.2, 0.8, 0.5, 0.2, 0.6, 0.9)), chosen to place (P_N) away from the middle of (mathscr {L}) to show the trajectory drifting toward the equilibrium.Full size imageAdaptive heterogeneityA third kind of heterogeneity we consider is the change of interactions in time. By this we mean an individual can actively change its interaction strength with others in response to certain conditions. This kind of response is often invoked in models of adaptive foraging behavior, where individuals choose appropriate actions to maximize some form of fitness25,26. For example, we may consider two behaviors, resting and foraging, as our prey types. Different predators may prefer to strike when the prey is doing different things. In response, the prey may choose to do one thing or the other depending on the current abundances of different predators. Such behavioral modulation is seen, for example, in systems of predatory spiders and grasshoppers27. Phenotypic plasticity is also seen in plant tissues in response to consumers28,29,30.This kind of “adaptive” heterogeneity can be modeled by having switching rates (eta _1) and (eta _2) that are time-dependent. Let us assume that the prey species tries to maximize its population growth rate by switching to the more favorable type. From Eq. (4c), we see that the growth rate of C depends linearly on the composition (lambda) with a coefficient (u(A,B) equiv (a_1 – a_2) A + (b_1 – b_2) B). Therefore, when this coefficient is positive, it is favorable for C to increase (lambda) by switching to type (C_2). This can be achieved by having a positive switching rate (eta _2) whenever (u(A,B) > 0). Similarly, whenever (u(A,B) < 0), it is favorable for C to switch to type (C_1) by having a positive (eta _1). In this way, the heterogeneity of the prey population constantly adapts to the predator densities. We model such adaptive switching by making (eta _1) and (eta _2) functions of the coefficient u(A, B), e.g., (eta _1(u) = 1/(1+mathrm {e}^{kappa u})) and (eta _2(u) = 1/(1+mathrm {e}^{-kappa u})). The sigmoidal form of the functions means that the switching rate in the favorable direction for C is turned on quickly, while the other direction is turned off. The parameter (kappa) controls the sharpness of this transition.Phase diagrams for the system with different values of (kappa) are shown in Fig. 4i–l. A larger (kappa) means the prey adapts its composition faster and more optimally, which causes the coexistence region to expand. In the extreme limit, the system changes its dynamics instantaneously whenever it crosses the boundary where (u(A,B) = 0), like in a hybrid system31. Such a system can still reach a stable equilibrium that lies on the boundary, if the flow on each side of the boundary points towards the other side32. This is what happens in our system and, interestingly, the equilibrium is the same three-species coexistence point (P_N) as in the previous scenarios. The region of coexistence turns out to be largest in this limit (Fig. 4l).Our results suggest that the coexistence of the predators can be viewed as a by-product of the prey’s strategy to maximize its own benefit. The time-dependent case studied here represents a strategy that involves the prey evaluating the risk posed by different predators. This is in contrast to the scenarios studied above, where the prey population passively creates phenotypic heterogeneity regardless of the presence of the predators. These two types of behavior are analogous to the two strategies studied for adaptation in varying environments, i.e., sensing and bet-hedging33,34. The former requires accessing information about the current environment to make optimal decisions, whereas the latter relies on maintaining a diverse population to reduce detrimental effects caused by environmental changes. Here the varying abundances of the predators play a similar role as the varying environment. From this point of view, the heterogeneous interactions studied here can be a strategy of the prey species that is evolutionarily favorable. More

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    Pathogen evasion of social immunity

    Ant hostWe used workers of the invasive Argentine ant, Linepithema humile, as host species. As typical for invasive ants, populations of this species lack territorial structuring and instead consist of interconnected nests forming a single supercolony with constant exchange of individuals between nests40. We collected L. humile queens, workers and brood in 2011, 2016 and 2022 from its main supercolony in Europe that extends more than 6,000 km along the coasts of Portugal, Spain and France40,41,42, from a field population close to Sant Feliu de Guíxols, Spain (41° 49’ N, 3° 03’ E). Field-collected ants were reared in large stock colonies in the laboratory. For the experiments, we sampled worker ants from outside the brood chambers and placed them into petri dishes with plastered ground (Alabastergips, Boesner, BAG), subjected to their respective treatments as detailed below. Experiments were carried out in a temperature- and humidity-controlled room at 23 °C, 65% relative humidity and a 12 h day/night light cycle. During experiments, ants were provided with ad libitum access to a sucrose-water solution (100 g l−1) and plaster was watered every 2–3 d to keep humidity high.Collection of this unprotected species from the field was in compliance with international regulations, such as the Convention on Biological Diversity and the Nagoya Protocol on Access and Benefit-Sharing (ABS, permit numbers ABSCH-IRCC-ES-260624-1 ESNC126 and SF0171/22). All experimental work followed European and Austrian law and institutional ethical guidelines.Fungal pathogensAs pathogen, we used the obligate-killing entomopathogenic fungus Metarhizium, whose infectious conidiospores naturally infect ants43,44,45 by penetrating their cuticles, killing them and growing out to produce highly infectious sporulating carcasses23,46. We used a total of six strains of the two species M. robertsii and M. brunneum, all isolated from the soil of the same natural population—an agricultural field at the Research Centre Årslev, Denmark27,47, which makes host co-infections with these sympatric strains in the field likely. As in ref. 24, we used three strains of M. robertsii (R1: KVL 12-36, R2: KVL 12-38, R3: KVL 12-35) and three of M. brunneum (B1: KVL 13-13, B2: KVL 12-37, B3: KVL 13-14), all obtained from the University of Copenhagen, Denmark (B. M. Steinwender, J. Eilenberg and N. V. Meyling).We started our selection experiment by exposing the ants to a mix of the six strains in equal proportions. To this end, each strain was grown separately from monospore cultivates from its long-term storage (43% glycerol (Sigma-Aldrich, G2025) in skimmed milk, −80 °C) on SDA plates (Sabouraud-4% dextrose agar, Sigma-Aldrich, 84088-500G) at 23 °C until sporulation. Conidiospores (abbreviated to ‘spores’) were collected by suspending them in sterile 0.05% Triton X-100 (Sigma-Aldrich, X-100; in milliQ water, autoclaved) and mixed in equal amounts to a total concentration of 1 × 106 spores ml−1. Before mixing, we confirmed that all strains had ≥98% germination.We exposed worker ants individually to the fungal pathogen by dipping them into the spore suspension using clean forceps (Gebrüder Martin; bioform, B32d). Afterwards, each ant was brieftly placed on filter paper (Whatman; VWR, 512-1027) to remove excess liquid before being placed into its experimental Petri dish.Serial passage experimentWe tested for the long-term effect of social immunity on pathogen selection, in which the pathogen was serially cycled through the host in the absence or presence of social immunity while the host population remained constant.Experimental design and procedureAfter exposure to the fungal spore mix, worker ants were either kept alone (individual host treatment, n = 10 replicate lines) or together with two untreated nestmates (social host treatment, n = 10 replicate lines; Fig. 1a). Individual ants could only protect themselves by individual immunity (selfgrooming behaviour and their physiological immune system), while the attended ants experienced both individual and social immunity due to the additional allogrooming by their caregiving nestmates. Thus, comparing the two host conditions revealed the effect of social immunity.As sanitary care by the nestmates reduces the pathogens’ success to kill the exposed individuals, we had to set up more experimental dishes of the social host treatment to obtain equal numbers of sporulating carcasses under both selection treatments, from which we then collected the spores for the next host infection cycle. For the individual treatment, we exposed an average of 23 workers per cycle, while an average of 40 workers per cycle were exposed in the social host treatment. The experiment was run for 10 host passages, that is, 27 weeks. In total, 6,312 workers (2,299 in the individual and 4,013 in the social host treatment) were exposed during the course of the experiment, and 8,026 nestmates were used. To obtain the spore suspensions for the next steps, we then collected and pooled the outgrowing spores of the first 8 carcasses produced per replicate line and cycle (that is, a total of n = 800 carcasses from the individual and n = 800 carcasses from the social host treatment, over the 10 host passages). Dead nestmates were not considered (see below).In detail, at each host cycle, the freshly exposed ants were placed into Petri dishes with plastered, humidified ground (Ø 3.5 cm for the individual and Ø 6 cm for the social host condition; both Bioswisstec AG, 10035 and 10060) in the absence (individual host treatment) or presence (social host treatment) of two untreated nestmates. We checked survival daily for 8 d. Ants that died within 24 h after exposure were excluded from the experiment as their mortality could not yet have resulted from infection, but rather from treatment procedures. Ants dying from days 2 to 8 were checked for internal Metarhizium infections by surface-sterilization (washing the carcass in 70% ethanol (Honeywell; Bartelt, 24194-2.5l; diluted with water) for a few seconds, rinsing it in distilled water, incubating in 3% bleach (Sigma-Aldrich, 1056142500) in sterile 0.05% Triton X-100 for 3 min and rinsing it again three times in water48), followed by incubation in a Petri dish on humidified filter paper at 23 °C until day 13, when they were checked for Metarhizium spore outgrowth. This timeline was chosen as preliminary work showed that the exposed ants die mostly on days 4 to 8 (median day 5, for both individual and social host treatments) after exposure and that sporulation required no longer than 5 d in our experimental conditions, so that a duration of 13 d per cycle also allowed for the later dying ants to complete sporulation. Preliminary work further revealed that in cases where nestmates contracted the disease, they died at a delayed timepoint and with spore outgrowth mostly around the mouthparts. These characteristics were used to distinguish between the directly exposed ants and infected nestmates in the experiment where ants were not colour-marked. The carcasses of sporulating nestmates were excluded from further procedures. An additional control experiment using 120 sham-treated ants showed no Metarhizium outgrowth, so that all Metarhizium outgrowth in our experiment could be attributed to our experimental infections. Carcasses with saprophytic outgrowth were not considered. For each host passage and each replicate line, we collected the spores of the first 8 ants dying after day 1 from their Metarhizium-sporulating carcasses at day 13 in 0.05% Triton X-100, pooled and counted them using an automated cell counter (Cellometer Auto M10, Nexcelom Bioscience). The concentration of each pool was then adjusted to 1 × 106 spores ml−1, and was used directly (that is, in the absence of any intermediate fungal growth step on agar plates) for exposing the ants in the next host infection cycle. The ants of each host passage were thus dipped in the same spore concentration. The remaining spore suspension was frozen at −80 °C in a long-term storage for further analysis.Pathogen diversity and strain compositionWe analysed which strains were present and in which proportion after 5 and 10 passages in each of the 10 individual and 10 social replicate lines. To this end, we first extracted total DNA from the respective spore pools (n = 40), which we analysed (1) quantitatively for the respective representation of M. robertsii vs M. brunneum (using species-specific real-time PCR targeting the PR1-gene sequence; detailed below) and (2) qualitatively for which of the 6 original strains were still present in the pool (using strain-specific microsatellite analysis; detailed below). We used this first estimate of remaining strain diversity and composition of each pool to determine how many spores we had to analyse separately for their strain identity after individualization by FACS sorting and growing them individually as colony forming units (c.f.u.s). This clone-level strain identification was again performed using microsatellite analysis (n = 1,347 individualized clones from the 40 spore mixes, in addition to n = 27 spores from the 6 ancestral strains; detailed below). Such clonal separation was needed since expansion of the spore mix by growth on SDA plates was not representative of the genetic composition of the strains in the pool, due to strong strain–strain growth inhibition when growing in a mix.In detail, we extracted the DNA of the 6 ancestral strains and the 40 spore mixes (10 each for individual and social lines at passages 5 and 10), as well as of 27 individualized clones of the ancestral strains and 1,374 clones from the 40 pools of passages 5 and 10, by centrifuging 100 µl of the spore suspensions in 1.5 ml tubes (Eppendorf, 0030120086) at full speed for 1 min and discarding the supernatant. Nuclease-free water (50 µl) was added and the spores were crushed in a bead mill (Qiagen TissueLyser II, 85300) at 30 Hz for 10 min using acid-washed glass beads (425–600 µm; Sigma-Aldrich, G8772). DNA was extracted using a DNeasy blood and tissue kit (Qiagen, 69506) following the manufacturer’s instructions, using a final elution volume of 50 µl buffer AE.For the quantitative species-level analysis of the pools, we performed quantitative real-time PCR (qPCR) using primers and differently labelled probes24 that we had developed on the basis of the sequence of the PR1 gene49 (forward: 5′ TCGATATTTTCGCTCCTG, reverse 5′-TTGTTAGAGCTGGTTCTGAAG, PR1 probe M. brunneum: 5′-(6-carboxyfluorescein (6FAM))TATTGTACCTACCTCGATAAGCTTAGAGAC(BHQ1), PR1 probe M. robertsii: 5′-(hexachloro-fluorescein (HEX))AGTATTGTACCTCGATAAGCTCGGAGAC(BHQ1)). Reactions were performed in 20 μl volumes using 10 μl iQ Multiplex Powermix (Bio-Rad, 1725849), with 600 nM of each primer (Sigma-Aldrich), 200 nM of each probe (Sigma-Aldrich) and 2 μl of extracted DNA. The amplification programme was initiated with a first step at 95 °C for 3 min, followed by 40 cycles of 10 s at 95 °C and 45 s at 60 °C. Primer efficiency was above 92% for both primer/probe combinations using standard curves of 10-fold dilutions of known input amounts. Data were analysed using Bio-Rad CFX Manager software.For the strain-specific analysis of both the pools and the individualized clones, we used two microsatellite loci, Ma30750 and Ma205451. Microsatellite locus Ma307 (forward: 5′-(6FAM)CATGCTCCGCCTTATTCCTC-3′, reverse: 5′-GGGTGGCGAAGAAGTAGACG-3′) allowed distinction of all strains except two of the M. brunneum strains (B1 and B3), which were distinguished by microsatellite locus Ma2054 (forward: 5′-(6FAM)GCCTGATCCAGACTCCCTCAGT-3′, reverse: 5′-GCTTTCGTACCGAGGGCG-3′). We analysed the microsatellites by E-Gel high-resolution 4% agarose gels (ILife Technologies, G501804) and fragment length analysis (done by Eurofins MWG) using Peak Scanner software 2.For clone individualization, we used flow cytometry to sort single spores out of the 40 spore pools (and the 6 ancestral strains for comparison) on 96-well plates (TPP; Biomedica, TP-92696) containing SDA (100 µl per well). The unstained spore population was detected using the FSC (forward scatter)/SSC (side scatter) in linear mode (70 μm nozzle, FACS ARIA III, BD Biosciences, as exemplified in Supplementary Fig. 1). Purity mode was set to ‘single cell’ and spore clones were obtained by sorting 1 particle event into each well. Sorting and data analysis were performed using Diva 6.2 software. The number of spores that we obtained for microsatellite analysis varied for each replicate, as it was adjusted to the remaining strain diversity estimate that we obtained from the quantitative and qualitative analysis of the pools. In total, we analysed 4–5 clones per ancestral strain (total n = 27) and a median of 5, but up to 101 different clones for the pools (total n = 1,347), as we intensified analysis for the strains that were revealed to be present at low frequency on the basis of previous analysis.Common garden experimentExperimental design and procedureWe then tested whether the successful lines at the end of the experiment (that is, after 10 host passages) differed in their virulence (induced host mortality) and investment into transmission stages (produced spore number) depending on their selection history (individual vs social), when current host social context either reflected the selection history or not. This common garden experiment thus led to 20 matched combinations of selection history and current condition (10 each of the individual lines in current individual host conditions (individual–individual) and the social lines in current social host conditions (social–social)) and 20 non-matched conditions (10 each of the individual lines in current social host conditions (individual–social) and the social lines in current individual host conditions (social–individual)).We obtained the lines for performance of the common garden experiment by the following procedure: (1) for the 16 out of the 20 replicate lines, where a single strain was the sole remaining representative at the end of the experiment (Fig. 1b), we expanded one of the c.f.u.s grown after FACS sorting (see above) by plating on SDA; (2) for the 4 remaining replicates in which two strains had remained (two individual and two social replicate lines), we expanded one c.f.u. of each of the remaining strains and mixed the spores in their representative proportion, as determined above.Virulence and transmissionFor the 10 individual and 10 social lines, we determined the induced host mortality as a measure of virulence and the outgrowing spore number as transmission stage production under their matched and non-matched current host conditions. We exposed the workers as in the selection treatment, kept them either alone or with two untreated nestmates, and monitored their mortality daily for 8 d. Again, ants dying in the first 24 h after treatment and dying nestmates were excluded from the analysis. In total, we obtained survival data of 797 ants (19–20 ants exposed for each of the 10 replicates from each of 4 combinations of selection history and current host condition). Dead ants were treated as above and their outgrowing spores collected by a needle dipped in sterile 0.05% Triton X-100 directly from the carcass, and resuspended in 100 µl of sterile 0.05% Triton X-100. The number of spores per carcass was counted individually using the automated cell counter, as described above (n = 215; median of 5 per replicate). We excluded one outlier carcass(from replicate I5) where we expected a counting error as this single carcass showed approx. 100-fold higher spore count than the other carcasses of this replicate. Exclusion of this outlier did not affect the statistical outcome. The proportion of ants dying per replicate line for each combination of selection history and current host condition and the number of spores produced by all carcasses per replicate were respectively used as measures of virulence and transmission (mean carcass spore load per replicate plotted in Fig. 2).Allogrooming elicitation by the fungal linesWe determined the allogrooming elicited by the individual and the social lines. To this end, we exposed workers as above and observed the allogrooming performed by two untreated nestmates towards the exposed ant. In detail, we performed 3 biological replicates for each of the 20 replicate lines (n = 10 individual and 10 social lines, resulting in a total of 60 videos), where the exposed ant was placed with two untreated nestmates within 10 min after exposure, and filmed with Ueye cameras for 30 min (whereby 4 cameras were used in parallel, each filming 3 replicates simultaneously, and using StreamPix 5 software (NorPix 2009-2001) for analysis). Videos were obtained in a randomized manner and labels did not contain treatment information so that the observer was blind to both the selection history and individual treatment during the behavioural annotations. For each ant, we observed both self- and allogrooming. Start and end times for each grooming event were determined, supported by use of the software BioLogic (Dimitri Missoh, 2010 (https://sourceforge.net/projects/biologic/)).As the ants in our serial passage and common garden experiments were not colour-marked, we also used unmarked ants for this behavioural experiment to keep conditions the same. This was possible as preliminary data with colour-coded nestmates (n = 18 videos) had shown that exposure alters the ant’s behaviour and that of its untreated nestmates in a predictable way that allows reliable classification of the pathogen-exposed individuals from the untreated nestmates; we used the following rules to classify an ant as the exposed individual: (1) the individual spent >5% more time (of the 30 min observation period) selfgrooming than the other individuals; (2) if the difference in selfgrooming time between the individuals was More

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    Fieldwork: how to gain access to research participants

    Anna Lena Bercht interviewed fishers in Lofoten, Norway, to assess how climate change was affecting their livelihoods.Credit: Anna Lena Bercht

    I remember February 2011, when, in the Chinese megacity of Guangzhou, an older man finally overcame his scepticism about being interviewed and invited me to sit down next to him on a stone bench under a shady tree. I held my notebook on my lap, and we sat on either side of a translator and talked about his life and world for more than two hours. It was one of the most informative and revealing interviews that I had done during my fieldwork in the city.
    Making it in the megacity
    One of the most fundamental challenges in qualitative fieldwork is gaining access to research participants. This is often time-consuming and labour-intensive, particularly when the topic requires in-depth methods and addresses a sensitive subject.Advice that goes beyond the usual recommendations of establishing relationships with gatekeepers, ensuring anonymity for interviewees and relying on the snowball sampling technique (in which one research participant suggests further ones) is rare. In this light, I’m happy to share some simple, but often neglected, examples from my qualitative fieldwork in the lively Guangzhou (where I worked for 12 months)1 and on the remote, Arctic island chain of Lofoten, Norway (done over 4 months)2, that might offer some inspiration and encouragement.I have a background in human geography, and did my PhD on experiences of stress, coping and resilience among the Chinese population of Guangzhou in the face of the city’s rapid urbanization. I travelled there five times to help to establish research cooperation with Chinese scholars, make field observations, select a case-study site and interview locals. I, together with other PhD students, stayed in a typical Chinese high-rise apartment in a neighbourhood that wasn’t a common choice for expatriates. Living side-by-side with the locals gave us a perfect opportunity to experience genuine everyday life and Chinese culture.My first postdoctoral project after my PhD brought me to Lofoten, where I looked at psychological barriers to climate adaptation in small-scale coastal fisheries. I went to Lofoten twice. On my first visit, I travelled across the whole archipelago by bus for one month to get a profound overview of the fishing villages and local living conditions, and to conduct first interviews. During my second visit, I stayed for a total of three months in rental locations near fishing harbours, and conducted more extensive interviews.In both China and Norway, I used in-depth interviews to learn about the challenges that people face. I asked people about unemployment, about the possibility of being forced to move elsewhere and about how climate change might affect their livelihoods. This required a sensitive and thoughtful approach to ‘getting invited’ into people’s lives. In Guangzhou, German- and English-speaking Chinese students assisted me as translators (and interpreters, when needed). On Lofoten, I conducted the interviews myself in English.There are two ways to access research participants: physical access, which refers to the ability of the researcher to get in direct face-to-face contact with people, and mental access. Successful mental access means that interlocutors open up about why they think, feel and behave as they do. Physical access is a necessary condition for mental access; however, in my experience, both are equally valuable.

    Chinese interviewees in Guangzou shared their feelings about the rapid urbanization of their city.Credit: Anna Lena Bercht

    Compared with Lofoten, it took longer to get physical access to local inhabitants in China. Presumably, this was because of the language barrier and reliance on translators, as well as cultural differences. Trust is considered a central tenet in Chinese relationships, and time and effort are needed to let it grow. During my time in Guangzhou, I occasionally benefited from being a foreigner: people were touched that someone from abroad showed genuine interest in their well-being. In Lofoten, fishers appreciated talking to a social scientist instead of a natural scientist who would have mainly asked questions about fishing quotas and catch volume.My advice for other social scientists hoping to gain access to research participants falls into those two categories.How to get good physical accessUse local public transport. Using local public transport creates many unexpected opportunities to bump into people, get into conversations and gain relevant information. For example, while waiting at a bus stop in Lofoten, I came across an art-gallery owner from a fishing village. He wondered why I was travelling out of the peak tourism season. I ended up with an invitation to his gallery, where he introduced me to two retired fishers whom he had also invited. Without the gallerist and his proactive networking, I probably would not have been given the chance to interview these two very informative and engaging fishers.In a metro station in Guangzhou, a toddler kept staring at me and tried to touch my light hair. This small interaction led me to chat to the toddler’s father, who recommended that I talk to a local teacher to learn more about the area’s history. His advice opened up important insights into urban-restructuring processes that I would have missed otherwise.
    Nine ‘brain food’ tips for researchers
    Use local media. In Norway, a journalist was at the harbour to get first-hand information on the year’s cod catch, when he saw me interviewing fishers. He became curious and eager to learn more about my work. In the end, he wrote an article about my research, which was published a few days later across Lofoten. His article was a door-opener for me.People recognized me from my photo in the article and contacted me to tell me about their lives and the cod fisheries. They also invited me on their vessels and put me in touch with other key informants.Change your workplace. During fieldwork, a workplace is often needed for interview transcription, literature research and interim data analysis. Moving the workplace outside wherever you are staying during a field trip allows you to immerse yourself in the daily lives of local people and interact with them more easily. For me, such agile ‘mini-office’ locations were cafes, public libraries and picnic tables. In this way, I was able to recruit interview partners on the spot.How to create deeper mental accessWear appropriate outfits. First impressions count, always. Researchers are judged not only on what they say and how they say it, but also on how they look. Certain clothes, such as those with a political slogan or religious symbol, have certain meanings and connotations. Depending on the context and whom you talk to, your appearance could promote or impede making connections and building rapport. For instance, whereas my practical ‘outdoorsy’ get-dirty outfit was appropriate for interviews on fishing vessels, a modest appearance (non-branded clothes and a simple style) was useful in rural areas of Guangzhou.Show respect. Just like in any other relationship, respect and humility play a crucial part in building a trustworthy interviewer–interviewee relationship. Showing respect can be subtly embedded in conversations in many ways, including in the content of questions and the manner in which they are asked. When interviewees started to close down when asked about painful issues, such as underemployment or loss of identity, I upheld their privacy, comfort and security by not probing when given an evasive answer. Instead, I changed the interview focus and, when appropriate, cautiously reapproached the sensitive issue by using interview techniques such as roleplaying. Interviewees were asked to put themselves in the position of someone else, such as a spatial planner or politician, and assess the issue at hand from this perspective. Taking such an imaginary role can help to make the interviewees feel more secure and face pain more openly.Be humble. Having a modest view of yourself is essential to communicate at eye level with people. As a scientist, you can easily fall into the trap of thinking that your thoughts and concepts are somehow more valuable because you are well-educated and established. However, you are the one asking questions — and the interviewees, whether they are fishers, farmers or homeless people, often know more about many things than you do. Being aware of this is an expression of humility. I let the interviewees know that they were the local experts and I was the foreign learner.Use small talk. Small talk — including non-verbal communication, such as smiling, or connective gestures, for example handing out a handkerchief or offering some tea — has an essential bonding function. Talking about ‘safe’ topics can help the interviewee to overcome the feelings of otherness, newness and discomfort that can emerge in an interview, and fosters social cohesiveness. This can help to counteract the asymmetrical power relationship between the researcher (who asks) and the researched (who answers). For example, before substantive questioning, I created shared experiences by talking about last night’s storm or the world cod-fishing championship, which takes place every year in Lofoten. This took the relationship to a greater level of intimacy and togetherness — which small talk after finishing the interview can strengthen. I remember joking about my stamina for eating properly with chopsticks to one interviewee.Use self-disclosure. Revealing selected information about yourself and sharing your own thoughts with interlocutors can help to create and reaffirm a sphere of confidentiality and trust. Fishers in Norway would, for instance, often ask “What interested you in Lofoten coastal fisheries?” or “Why do you ask me and not the scientists from Tromsø University?” I answered such questions honestly, which assisted in creating a more balanced relationship, encouraging the interviewees to address sensitive subjects more openly and readily.Change interview sites. In several interviews, I found that the answers given tended to depend on where the interview was held and which identity that site evoked for the interviewee. For example, a fisher did not talk about climate-change concerns on his fishing vessel (any concern was masked by his existential fear of losing his livelihood as a coastal fisher), but he later that day freely discussed his worries in his home. Changing the interview site can be a helpful technique to access hidden thoughts and feelings.Above all, be realistic. You will probably make mistakes; I regretted not dressing warmly enough on a fishing vessel in Arctic weather. Locals will find you amusing, weird or impolite. They will keep out of your way, and you will never know why. And they will terminate interviews prematurely with no excuse. And that’s all right. In the end, fieldwork is a combination of planning, resources, time, skills, hard work, commitment, headache, joy — and luck. Learn from your mistakes, and accept the things you cannot change. More

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    Tamarixia radiata global distribution to current and future climate using the climate change experiment (CLIMEX) model

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