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JSATS acoustic receiver deploymentsTwo types of acoustic receivers were deployed for this study: cabled and autonomous acoustic receivers (Table 1). These systems were deployed in the Snake River in eastern Washington State and in the lower Columbia River along the Oregon/Washington border. Two hydropower dams were outfitted with cabled acoustic receiver arrays: LGS and LMN. This allowed passage routes to be defined at each of these dams for each of the tagged fish. Transects of autonomous acoustic receivers (i.e., receiver arrays) were deployed immediately upstream and downstream of each of these dams for computing dam passage survival, and at several other locations to estimate cumulative mortality. From the release site to the final autonomous acoustic receiver array, fish detected at the final array will have passed through LGS, LMN, Ice Harbor, McNary, John Day, The Dalles, and Bonneville Dams. Each of these dams feature powerhouses with large, vertical Kaplan runners19 and spillways utilizing radial or vertical lift spillway gates. Most also include juvenile bypass systems (JBS; Table 1) and surface spill weirs20 to provide safer passage routes for fish.Before deployment, all hydrophones and receivers were evaluated in an acoustic tank lined with anechoic materials at the PNNL Bio-Acoustics & Flow Laboratory (BFL21). The BFL is accredited by the American Association for Laboratory Accreditation to ISO/IEC 17025:2005, which is the international standard for calibration and testing laboratories. The accreditation scope (Certificate Number 3267.01) includes hydrophone sensitivity measurements and power-level measurements of sound sources for frequencies from 50 to 500 kHz for both military equipment and commercial components. The evaluation involved simulating transmissions from tags located at increasing distances. This allowed the performance of each receiver to be validated prior to deployment to ensure the expected detection range will likely be achieved.Cabled receivers – deployment, hardware, and data processingA JSATS cabled acoustic receiver system2 consists of up to four narrowband hydrophones; various types of hydrophone cables (e.g., four-channel “deck” cables, “y-blocks” that split the “deck” cables to individual connectors, and “wet” cables that run from the surface down into the water); a signal-conditioning, variable-gain amplifier; a data acquisition card that features a high-speed, analog-to-digital converter, a digital signal processor and field-programmable gate array; a GPS receiver for synchronizing time among multiple systems; a data acquisition computer; and software for detecting22 and decoding23,24 the acoustic waveforms. Deploying these systems within the forebay of a hydropower dam typically involves rigidly mounting slotted pipes to the upstream edge of the pier nose between the powerhouse and spillway bays. The cabled hydrophones are mounted on “trolleys” that have an L-shaped arm that protrudes and rides in the slot in the pipe and allows the hydrophone to be mounted pointing upstream. Conical baffles containing anechoic material are installed around the hydrophones to block noise coming from behind the hydrophone—either noise from the dam or reflections off the concrete. To obtain the locations of hydrophones that have been lowered below the water surface, survey equipment is used to sight the tip of the hydrophone as it is lowered down the pier nose. This provides the true direction and slope of the pier nose, which is used, along with the length of braided steel cable attached to the trolley to lower it into place, to calculate the 3-D location in space for each hydrophone. The individual “wet” cables for each hydrophone are routed to the forebay deck, where they are combined into “deck” cables carrying four signals using the “y-block” cables. The deck cables are routed to mobile trailers that house the acoustic receiver equipment. Acoustic beacons that send out JSATS tag-code signals every 15 to 60 s are deployed alongside several of the hydrophones across the array. These beacons are used primarily for quality control, to monitor (typically through the internet from an off-site location) the performance of each hydrophone to determine whether there is a reduction in performance so that any malfunctioning hydrophones can be repaired as soon as possible.Two main programs run on the JSATS cabled receiver data acquisition computer. The first program is an energy-based detector software22 that collects the raw acoustic waveforms whenever the hydrophone signal meets a prescribed set of criteria. The second is decoder software24 that processes the waveforms saved by the detector to determine whether there is a valid detection (i.e., a decoded signal that has a valid CRC). If there is a valid detection, the decoded tag-code is saved to a text file, along with the detection time and other metadata. The detector software writes the binary waveform files to the hard drive using the *.bwm file type. The decoder is configured to wait for *.bwm files to be generated. Once a new *.bwm file is detected, the decoder will open the file, decode the data contained in the file, and then change the file extension to *.com to indicate that this file was decoded. If the detector saves data faster than the decoder can process it, for example at a hydraulic structure that is generating large amounts of acoustic noise (e.g., spillways with vertical lift gates), the decoder is configured to skip waveform files to avoid falling behind. Although these files may be skipped, the data contained within these files will still be used because the two different file extensions allow for readily identifying which files were not decoded in real time so that they can be decoded offline in a separate processing step after retrieving the data. The data is physically collected every 1–2 weeks, by swapping out the data collection hard drives. To make sure that all detection waveforms are processed by the decoder, the collected hard drives are put into data processing machines to decode any files that still have the *.bwm file extension. After confirming that all files have been decoded, either in real time or through post-processing after data retrieval, the decoded data from every hydrophone is checked for gaps in data. If a hydrophone is functional, there should be no large gaps in decoded data, since there are multiple stationary acoustic beacons deployed with the cabled hydrophone receiver array.Data is filtered to remove potential false positive decodes. Data filtering for the JSATS cabled acoustic receivers (Fig. 2a) begins with a multipath filter, which removes decodes from multipath signal propagations (e.g., acoustic reflections off the surface/bottom). The multipath filter is used on the data from each individual hydrophone. Any decodes of the same tag-code that occur a very short time (e.g., typically 45 m), too far away temporally from adjacent points ( >10 min), and that result in unrealistic velocities (~2 m/s for the size of fish we studied). Further quality assurance filters remove points that occur before a transmitter was released, after the PIT tag in the fish is detected downstream, and after the acoustic transmitter is detected by downstream autonomous receiver arrays.Once the 3-D trajectories have been computed, they are used to assign passage routes through the dam for each tagged fish (Fig. 3a). The route assignment for each tagged fish is divided into three parts: main route, subroute, and hole (Table 2). The main route describes the part of the general dam structure through which the tagged fish passed. This includes the powerhouse, the spillway, and a generic category, “dam,” which is used for rare scenarios where there is confidence that the fish was physically present but a lack of confidence in specifically where the fish passed the dam. The subroute further divides the main passage route into different subcategories. For a main route of “spillway,” the two subroutes are the traditional (deep) spillbays and special surface weirs (surface spillbays20). The surface weirs reside within one of the spillbays and assignment to either of these two subroutes is made directly using the acoustic telemetry results. For a main route of “powerhouse,” the two subroutes are turbine and JBS. Assignment of the JBS subroute requires that the PIT tag of a fish assigned to the powerhouse was detected by the PIT tag readers within the JBS system; otherwise the fish is assigned the turbine subroute. PIT tag detections at dams where cabled hydrophone arrays have been deployed can be used to assign the JBS subroute, and PIT tag detections at the other dams along the migration route can serve as additional detection events. The hole assignment defines the specific powerhouse intake or spillbay where the passage occurred.Table 2 Dam passage routes (main route, subroute, and hole) at LGS and LMN Dams.Full size tablePassage routes are assigned using the last 3D tracked location and the last detection. Two methods are used because the ability to consistently track the transmitter can diminish as the tagged fish approaches or passes through the plane containing the hydrophones, and the last decoded transmission could be later than the last 3-D tracked location.When the last 3-D tracked location is used, a route is assigned according to whether the last tracked point is within a specific area. This area spans the entire dam plus 25 m on each side and extends from the dam face to 30 m upstream into the forebay. If the last tracked point is within this boundary, the 3-D track passage route is assigned to the bay corresponding to the Y coordinate in the local dam coordinate system. If the last tracked point is outside the piers on either side of the dam, the passage route is assigned to the nearest bay.Route assignment based on the last detection uses the last transmission that was detected by multiple hydrophones. The detections associated with this transmission are sorted by time, and the pier numbers for the two hydrophones on different piers that first detected this transmission are averaged; the passage bay corresponding to this average pier is assigned as the last detection passage route. The default final route assignment is the 3-D tracked route assignment. However, when the two methods indicate different main routes, subroutes, or a different hole that is more than two bays away, the 3-D tracks are manually reviewed, and a decision is made regarding which method should be relied on for the final route assignment.After the final route assignment, a final quality assurance step is to compare the final route assignment to the dam operations. In case a tag-code has been assigned to a closed passage route, the 3-D tracks are reviewed to consider the trajectory of the tagged fish and the location of the nearest open passage route. As previously mentioned, the ability to consistently track a transmitter is diminished as it approaches or passes through the plane containing the hydrophones. An example of when a tagged fish could initially be assigned to a closed passage route would be when a passage route with a strong attractive flow (e.g., surface spill weir) is adjacent to a closed passage bay.Autonomous receivers – deployment, hardware, and data processingA JSATS autonomous receiver (SR5000, Advanced Telemetry Systems [ATS], USA), along with the necessary deployment accessories, consists of a hydrophone that is connected to a cylindrical, positively buoyant, self-contained, battery-powered, autonomous acoustic receiver; a submerged buoy line; an acoustic release; a braided stainless-steel cable; and a steel anchor. These receivers are typically deployed according to the methods described by Titzler et al.10, which involves using a 34 kg or 57 kg (depending on flow) steel anchor to deploy the autonomous receiver system to the river bottom. The anchor is attached to the release side of an acoustic release using a braided stainless-steel cable. The fixed end of the acoustic release is attached to the autonomous receiver using a submerged buoy line. When the acoustic release is remotely triggered, it detaches from the anchor line, and the combined buoyancy of the acoustic receiver and the submerged buoy line bring the system up to the surface. To maintain the receiver orientation in the water column during deployment, a thin plastic sheet is folded around the cylindrical body of the receiver, creating an airfoil-like shape that keeps the receiver oriented in the flow direction. Attached to each autonomous receiver is a JSATS beacon that is similar to the JSATS beacons deployed with the cabled hydrophone receiver arrays and used in the same way. The length of time that the JSATS autonomous receivers can be deployed is largely dependent on the battery life, with data retrieval and battery changes typically done every 2–3 weeks.Although it is possible to use autonomous receivers to conduct 3-D tracking, the process is much more challenging than using the cabled hydrophone receivers because the receivers are not fixed at well-defined locations (e.g., changes in river currents could change the receiver’s depth and horizontal location relative to the anchor) and are not time synchronized with each other. The autonomous receivers are typically used to detect presence of tagged fish, although recent research has investigated methods to improve the ability to conduct 3-D tracking25. Deploying an autonomous receiver array entails deploying several receivers in a line spanning the width of a river with the detection ranges of the receivers overlapping slightly. This creates a virtual detection gate that can be used to determine when a tagged fish passes through this location in the river. In addition to simply determining the migration timing of tagged fish, the autonomous receiver arrays are typically used for analyzing dam passage survival and near-dam behavior (e.g., forebay residence time, tailrace egress time).The data collected by the autonomous receivers is filtered similarly to data from the cabled receivers (Fig. 2b). The primary difference is that the data from each individual autonomous receiver is processed entirely by itself. As a result, the single-hydrophone filter is not used, and a single-node PRI filter is used instead of the message PRI filter.After the event histories for both the cabled and the autonomous acoustic receivers have been determined, individual routes were cross-checked by tracing the chronology of detections of every tagged fish as it was detected along the river in the sequence of acoustic receiver arrays. Upstream movement past a dam or out-of-sequence detections were deemed anomalous detection events. These anomalous detection events could be a few receptions resulting from noise or repeated detections of a transmitter that had been dropped near a receiver array after fish or bird predation. If the apparent behavior was impossible for a live fish, the anomalous detection was excluded from the detection history used for subsequent analysis.JSATS transmittersThe injectable transmitters used in this study (Fig. 1b) were manufactured by PNNL. Each transmitter (model microV26, which is licensed to, and currently commercially available from, Advanced Telemetry Systems as Model SS400) was 15 mm long, had an outside diameter of 3.35 mm, a volume of 0.111 mL, and a mass of 0.216 g in air and 0.105 g in water. The transmitters are generally cylindrical; excess epoxy was eliminated to reduce the weight, and epoxy surrounding the transducer element was minimized. The transmitters had a nominal transmission rate of 1 pulse every 4.2 s. Nominal transmitter life was expected to be about 28 d at a 4.2 s pulse rate. The acoustic signal is transmitted using a carrier frequency of 416.7 kHz, a source level of approximately 156 dB (ref. to 1 µPa at 1 m), and a total signal duration of 477 µs. The transmitter emits a uniquely coded 31-bit signal2, resulting in more than 65,000 individual tag-codes, using binary phase-shift keyed (BPSK) signal encoding.Each fish also bore a PIT tag (HPT12, Biomark, USA; 12.5 mm x Ø2.03 mm; 0.106 g in air). PIT tag detections were used to assign fish to passage through the JBS at LGS and LMN to distinguish between fish that were assigned to a main route of powerhouse and the turbine or JBS subroutes.Tagged fishFor this study, 682 subyearling Chinook salmon (Oncorhynchus tshawytscha) were tagged with the injectable acoustic transmitter and released upstream of LGS Dam on the Snake River in Washington State, USA (Fig. 1a). The fish were obtained from the JBS at LMN Dam and selected using existing fish screening criteria utilized in previous juvenile salmon survival studies26. The fish selected for the study were held in holding tanks for 18 to 30 hours prior to tagging, and for 10 to 25 hours after tagging prior to release. The size criteria for tagged fish was also identical to other recent juvenile salmon survival studies26. For this study the fork-lengths ranged from 95 to 143 mm, and the weights ranged from 7.5 to 29.3 g (see Tagged Fish Data for information on each tagged fish).Tagging procedureWhile each anesthetized fish was at the data station for recording physical parameters, a second person inserted both a disinfected PIT tag and an injectable acoustic transmitter, assigned to a specific fish, into a sterilized 8-gauge stainless-steel hypodermic needle17. First, the injectable transmitter was placed into the needle, battery-end first. The PIT tag was then also inserted in the same needle. A sanitized plastic cap was then placed over each end of the needle to retain the tags. Once both tags had been placed in the needle, the tag loaded needle was handed to the surgeon working at the tagging station. Additional details for the tagging procedure are documented by Deng et al.11.Release procedureThe fish implanted with the injectable acoustic transmitters were released using the same methods as fish tagged with commercially available acoustic transmitters for a separate large-scale survival study26. All fish were tagged at LMN and transported in insulated totes by truck to the single release site (Fig. 1a). There were five release locations across the river at the release site, and equal numbers of fish were released at each of the five locations. Releases occurred for 11 consecutive days (between 22 June and 2 July, 2013) and were staggered between day and night.Data managementUse of JSATS can generate a large volume of data. An integrated suite of science-based tools known as the Hydropower Biological Evaluation Toolset (HBET; https://hydropassage.org/hbet)27 was developed to assist the characterization of hydraulic conditions at hydropower structures and to understand the potential impacts on aquatic life. HBET was initially developed to be utilized to facilitate use of the autonomous sensor technology known as Sensor Fish28. HBET allows researchers to use previously collected Sensor Fish data to design studies to evaluate hypotheses, archive field-collected data, process raw sensor data, compare different hydraulic structures or operating conditions, and to estimate the biological response for species with known dose-response relationships. More recently, HBET was adapted to also provide the functionality of archiving new or previously collected acoustic telemetry data and to produce visualizations from that data. Although it is not necessary to visualize the data set associated with this manuscript, PNNL offers free government and academic use of the HBET software package in the U.S. and a free 90-day trial version of the package to interested parties. More

SamplingSamples were collected on 28 August 2018 during a field campaign to Lake Cadagno29, Switzerland. In situ measurements and water collection was performed at the deepest part of the lake (21 m). Water was collected using a pump CTD system as described in Di Nezio et al.36. Online in situ data were obtained during a continuous downcast of the CTD-system from the water surface down to ~17.5 m depth. During the upcast, discrete water samples were collected from a total of 20 depths (between 12 m and 17 m) above, in, and below the chemocline for chemical analyses and from 3 depths for incubation experiments (13.7 m, 14 m, and 15.5 m).In Lake Cadagno, wind-driven internal waves lead to vertical shifts of the water masses and their corresponding physicochemical parameters56. While sampling, it was apparent that the depths of the individual water masses had slightly shifted between the down- and the upcast. Therefore, we corrected the water depths of the samples collected during the upcast so that the physicochemical parameters during sampling best matched those of the continuous downcast, to ensure that samples were assigned to the respective water mass that they originated from. A custom R script was employed for the depth correction. In brief, all parameters measured by the CTD-system during the upcast and the downcast were normalized to percent (with 100% as the maximum observed value, and 0% the minimum observed value). Per individual sampling depth (during the upcast, where the pump cast CTD remained stationary for some time), average values of conductivity, temperature, and pressure were calculated and converted to percent values. Then, the depth from the downcast profile was identified that best matched all calculated percent values. This was achieved by subtracting the percent values per parameter from all respective data points of the downcast profile. Absolute values of the calculated differences per data row were summed. The depth with the lowest resulting sum, i.e., with the most similar physicochemical parameters, was then chosen as the corrected depth.Chemical analyses, flux calculations, and rate determinationsFor chemical analyses, lake water from the individual sampling depths was sterile-filtered (0.2 µm, cellulose acetate filter) and frozen at −20 °C until analysis. Samples were analyzed with a QuAAtro39 autoanalyzer (Seal Analytical) using the methods described in Strickland and Parsons57 to determine concentrations of dissolved inorganic phosphorus (PO43−), nitrite (NO2−), nitrate (NO3−), and reactive silica (Si). Ammonium concentrations were determined from the same filtered samples using the colorimetric analysis described in Kempers et al.58. Molybdenum concentrations were determined from filtered samples after acidification with 1% HNO3 (69%, ROTIPURAN®, Roth) using an ICP-MS 7900 (Agilent, Santa Clara, USA). Molybdenum was analyzed on mass 95 in He-mode using a multi-element calibration SRM (21 elements, Bernd Kraft). The SRM NIST 1643f was analyzed in parallel to guarantee the quality of analyses. Concentrations of sulfide were determined colorimetrically from unfiltered Lake water samples, following Cline59.To calculate the turbulent flux (J) of ammonium into the chemocline, we assumed a steady-state using Fick’s first law: J = −D∂C/∂x. A turbulent diffusion coefficient (D) of 1.6 × 10−6 m2 s−1 was used, corresponding to turbulence at the Lake Cadagno chemocline boundaries60. The change in concentration (∂C) was calculated over 14.25 m to 14.77 m depth, where the steepest ammonium gradient was observed. Ammonium uptake rates were calculated for the chemocline by integrating this flux over the chemocline from 13.45 m to 14.45 m depth.To quantify N2 fixation and primary production (i.e., CO2 fixation) rates, stable isotope incubations with 15N2 and 13CO2 were performed using established protocols61. Briefly, lake water from three different depths of the chemocline was sampled directly from the CTD pump system into five 250 ml serum bottles per depth. Water was filled into the bottles from bottom to top, allowing 1–2 bottle volumes to overflow to minimize oxygen contamination before crimp-sealing the bottles headspace-free with butyl rubber stoppers. Back in the field laboratory, no more than 8 h after sampling, one bottle per depth was filtered onto pre-combusted (460 °C, 6 h) glass microfiber filters (GF/F, Whatman®, UK) for in situ natural abundance of C and N. 13C-labeled sodium bicarbonate (NaH13CO3, 98 atom% 13C, dissolved in autoclaved MilliQ water; Sigma-Aldrich) was injected (320 µL) into three bottles per depth, to achieve a final concentration of 160 µmol L−1. Then, a volume of 5 ml 15N2 gas (Cambridge Isotope Laboratories, >98 atom% 15N, Lot #: I-19197/AR0586172) was injected as a bubble into the same bottles and shaken for 20 min to equilibrate the 15N2 gas. Sulfide solution was injected aiming for a final concentration of approximately 2 µM to remove trace oxygen contamination in the incubation bottles. Finally, the 15N2 gas bubble was replaced by anoxic in situ lake water from the respective depth. The bottles, together with one untreated control bottle per depth (containing unamended lake water), were incubated for a full light-dark cycle (13 h light, 11 h dark) under natural light conditions (0–8267 Lux, average: 247 Lux, median: 10.8 Lux, as determined by a HOBO pendant data logger, Onset Computer Corporation, Bourne, USA) in a water bath kept at ~12 °C.After incubation, samples were filtered onto pre-combusted GF/F filters. The filters were dried at room temperature and frozen at −20 °C for transport and storage. In addition, subsamples for nanoscale secondary ion mass spectrometry (nanoSIMS) analysis and for the determination of 13C and 15N enrichments in the substrate pools were taken from all bottles amended with 13C and 15N. NanoSIMS samples were fixed with 2% (final w/v) formaldehyde solution for 1 h at room temperature, prior to filtration onto gold-sputtered 0.22 µm polycarbonate membrane filters (GTTP IsoporeTM, Merck Millipore, USA). Subsamples for label% determinations were taken in gas-tight glass vials (Exetainer Labco, UK) and biological activity was terminated with HgCl2.Samples on GF/F filters were analyzed for C and N content and the respective isotopic composition by an elemental analyzer (Thermo Flash EA, 1112 Series) coupled to a continuous-flow isotope ratio mass spectrometer (Delta Plus XP IRMS; Thermo Finnigan, Dreieich, Germany). Enrichment of 15N in the N2 pool was determined using a membrane inlet mass spectrometer (MIMS; GAM200, IPI). Enrichment of 13C in the dissolved inorganic carbon pool was determined from 13C/12C-CO2 ratios after sample acidification with phosphoric acid using cavity ring-down spectroscopy (G2201-I coupled to a Liaison A0301, Picarro Inc., connected to an AutoMate Prep Device, Bushnell, USA). In addition, we tested the used 15N2 gas bottle for contamination with 15N-ammonia62. Briefly, a 2 ml subsample of the used 15N2 gas was injected into a 12 ml gas-tight glass vial (Exetainer) filled with MilliQ (pH 95% sequence identity to any of the MAG NifD/NifK sequences were identified with a blastp search74 to the NCBI nr database. Multiple sequence alignments were obtained with MAFFT87. All full-length sequences were used to construct base trees with RAxML88 and 100 bootstraps in ARB90. The ARB Parsimony function was employed to add partial sequences to the base trees.The resulting trees were visualized in iTOL91.FISH, cell counts, and cell sizesFrom each incubation depth, 10–30 ml lake water was filtered onto 0.22 µm polycarbonate membrane filters (GTTP IsoporeTM, Merck Millipore, USA). The filters were fixed in 2% formaldehyde solution in sterile-filtered lake water for 10–12 h at 4 °C and then washed with MilliQ water. The filters were frozen and stored at −20 °C until further processing.The 16S rRNA FISH probe “Thiosyn459” (Table S7), exclusively targeting T. syntrophicum Cad16, was designed in ARB90. In addition, two competitor probes and four helper probes92 were designed (Table S7) to ensure efficient and specific binding of the probe to the target. All FISH probes and respective formamide concentrations are listed in Table S7. Probes, but not helpers and competitors, were double-labeled with either Atto488 or Atto594 fluorophores. Samples were embedded in 0.05% low melting point agarose. Cells were permeabilized with lysozyme (1.5 mg ml−1) for 30 min at 37 °C. Hybridization was performed for 2–4.5 h at 46 °C. Washing included 15 min in washing buffer at 48 °C and 20 min in 1× PBS buffer at room temperature. We used the hybridization and washing buffers described in Barrero-Canosa et al.93 to reduce background fluorescence. Cells were counterstained with DAPI.Samples were analyzed using a Zeiss Axio Imager.M2 microscope equipped with a Zeiss Axiocam 506 mono camera. Z-stack images were taken and the number of fluorescently labeled cells per image was counted for the individual probes. For each PSB population, we analyzed ≥38 randomly selected fields of view and ≥54 target cells, on one filter replicate each (see Supplementary File S1). Total cell counts were obtained in triplicates through flow cytometry as described in Danza et al.94.For cluster-forming organisms (Thiodictyon syntrophicum, Lamprocystis purpurea, Lamprocystis roseopersicina, and Lamprocystis spp.), the cell size (length and width, for biovolume and C-content calculations, see section below) of 100 cells per population was determined from the maximum-intensity projection of the z-stack images using the Zeiss Zen blue software 3.2.Single-cell analysis with nanoSIMSFor nanoSIMS analyses, we chose the replicate sample from 13.7 m depth that exhibited the highest bulk N2 fixation rate. Random spots were marked with a laser microdissection microscope (6000 B, Leica) on the gold-sputtered GTTP filter covered with cells incubated with 15N2 and 13CO2. After laser marking, FISH was performed as described above. For analysis of Thiodictyon cells, no permeabilization was performed, while for analysis of the other population’s permeabilization was reduced to 15 min at 37 °C using 2 mg ml−1 Lysozyme. Within one hybridization reaction, we simultaneously applied Apur453 with S453D and Laro453 with Cmok453, each probe double labeled with different fluorescent dyes (Atto488 and Atto594).Single-cell 15N- and 13C-assimilation from incubation experiments with 15N2 and 13CO2 was measured using a nanoSIMS 50 L instrument (CAMECA), as described in Martínez-Pérez et al.53. Briefly, instrument precision was monitored regularly on graphite planchet. Samples were pre-sputtered with a Cs+ beam (~300 pA) before the measurements with a beam current of around 1.5 pA. The diameter of the primary beam was tuned More

Modeling frameworkWe consider S species distributed in L distinct habitat patches. The patches form a linear latitudinal chain going around the globe, with dispersal between adjacent patches (Fig. 1). The state variables are species’ local densities and local temperature optima (the temperature at which species achieve maximum intrinsic population growth). This temperature optimum is a trait whose evolution is governed by quantitative genetics18,19,20,21,22: each species, in every patch, has a normally distributed temperature optimum with a given mean and variance. The variance is the sum of a genetic and an environmental contribution. The genetic component is given via the infinitesimal model23,24, whereby a very large number of loci each contribute a small additive effect to the trait. This has two consequences. First, a single round of random mating restores the normal shape of the trait distribution, even if it is distorted by selection or migration. Second, the phenotypic variance is unchanged by these processes, with only the mean being affected25 (we apply a reduction in genetic variance at very low population densities to prevent such species from evolving rapidly; see the Supplementary Information [SI], Section 3.4). Consequently, despite selection and the mixing of phenotypes from neighboring patches, each species retains a normally-shaped phenotypic distribution with the same phenotypic variance across all patches—but the mean temperature optimum may evolve locally and can therefore differ across patches (Fig. 1).Fig. 1: Illustration of our modeling framework.There are several patches hosting local communities, arranged linearly along a latitudinal gradient. Patch color represents the local average temperature, with warmer colors corresponding to higher temperatures. The graph depicts the community of a single patch, with four species present. They are represented by the colored areas showing the distributions of their temperature optima, with the area under each curve equal to the population density of the corresponding species. The green species is highlighted for purposes of illustration. Each species has migrants to adjacent patches (independent of local adaptedness), as well as immigrants from them (arrows from and to the green species; the distributions with dashed lines show the trait distributions of the green species’ immigrant individuals). The purple line is the intrinsic growth rate of a phenotype in the patch, as a function of its local temperature optimum (this optimum differs across patches, which is why the immigrants are slightly maladapted to the temperature of the focal patch.) Both local population densities and local adaptedness are changed by the constant interplay of temperature-dependent intrinsic growth, competition with other species in the same patch, immigration to or emigration from neighboring patches, and (in certain realizations of the model) pressure from consumer species.Full size imageSpecies in our setup may either be resources or consumers. Their local dynamics are governed by the following processes. First, within each patch, we allow for migration to and from adjacent patches (changing both local population densities and also local adaptedness, due to the mixing of immigrant individuals with local ones). Second, each species’ intrinsic rate of increase is temperature-dependent, influenced by how well their temperature optima match local temperatures (Fig. 2a). For consumers, metabolic loss and mortality always result in negative intrinsic growth, which must be compensated by sufficient consumption to maintain their populations. Third, there is a local competition between resource species, which can be thought of as exploitative competition for a set of shared substitutable lower-level resources26. Consumers, when present, compete only indirectly via their shared resource species. Fourth, each consumer has feeding links to five of the resource species (pending their presence in patches where the consumer is also present), which are randomly determined but always include the one resource which matches the consumer’s initial mean temperature optimum. Feeding rates follow a Holling type II functional response. Consumers experience growth from consumption, and resource species experience loss due to being consumed.Fig. 2: Temperature optima and climate curves.a Different growth rates at various temperatures. Colors show species with different mean temperature optima, with warmer colors corresponding to more warm-adapted species. The curves show the maximum growth rate achieved when a phenotype matches the local temperature, and how the growth rate decreases with an increased mismatch between a phenotype and local temperature, for each species. The dashed line shows zero growth: below this point, the given phenotype of a species mismatches the local temperature to the extent that it is too maladapted to be able to grow. Note the tradeoff between the width and height of the growth curves, with more warm-tolerant species having larger maximum growth at the cost of being viable for only a narrower range of temperatures62,63. b Temperature changes over time. After an initial establishment phase of 4000 years during which the pre-climate change community dynamics stabilize, temperatures start increasing at t = 0 for 300 years (vertical dotted line, indicating the end of climate change). Colors show temperature change at different locations along the spatial gradient, with warmer colors indicating lower latitudes. The magnitude and latitudinal dependence of the temperature change is based on region-specific predictions by 2100 CE, in combination with estimates giving an approximate increase by 2300 CE, for the IPCC intermediate emission scenario27.Full size imageFollowing the previous methodology, we derive our equations in the weak selection limit22 (see also the Discussion). We have multiple selection forces acting on the different components of our model. Species respond to local climate (frequency-independent directional selection, unless a species is at the local environmental optimum), to consumers and resources (frequency-dependent selection), and competitors (also frequency-dependent selection, possibly complicated by the temperature-dependence of the competition coefficients mediating frequency dependence). These different modes of selection do not depend on the parameterization of evolution and dispersal, which instead are used to adjust the relative importance of these processes.Communities are initiated with 50 species per trophic level, subdividing the latitudinal gradient into 50 distinct patches going from pole to equator (results are qualitatively unchanged by increasing either the number of species or the number of patches; SI, Section 5.9–5.10). We assume that climate is symmetric around the equator; thus, only the pole-to-equator region needs to be modeled explicitly (SI, Section 3.5). The temperature increase is based on predictions from the IPCC intermediate emission scenario27 and corresponds to predictions for the north pole to the equator. The modeled temperature increase is represented by annual averages and the increase is thus smooth. Species are initially equally spaced, and adapted to the centers of their ranges. We then integrate the model for 6500 years, with three main phases: (1) an establishment period from t = −4000 to t = 0 years, during which local temperatures are constant; (2) climate change, between t = 0 and t = 300 years, during which local temperatures increase in a latitude-specific way (Fig. 2b); and (3) the post-climate change period from t = 300 to t = 2500 years, where temperatures remain constant again at their elevated values.To explore the influence and importance of dispersal, evolution, and interspecific interactions, we considered the fully factorial combination of high and low average dispersal rates, high and low average available genetic variance (determining the speed and extent of species’ evolutionary responses), and four different ecological models. These were: (1) the baseline model with a single trophic level and constant, patch- and temperature-independent competition between species; (2) two trophic levels and constant competition; (3) single trophic level with temperature-dependent competition (where resource species compete more if they have similar temperature optima); and (4) two trophic levels as well as temperature-dependent competition. Trophic interactions can strongly influence diversity in a community, either by apparent competition28 or by acting as extra regulating agents boosting prey coexistence29. Temperature-dependent competition means that the strength of interaction between two phenotypes decreases with an increasing difference in their temperature optima. Importantly, while differences in temperature adaptation may influence competition, they do not influence trophic interactions.The combination of high and low genetic variance and dispersal rates, and four model setups, gives a total of 2 × 2 × 4 = 16 scenarios. For each of them, some parameters (competition coefficients, tradeoff parameters, genetic variances, dispersal rates, consumer attack rates, and handling times; SI, Section 6) were randomly drawn from pre-specified distributions. We, therefore, obtained 100 replicates for each of these 16 scenarios. While replicates differed in the precise identity of the species which survived or went extinct, they varied little in the overall patterns they produced.We use the results from these numerical experiments to explore patterns of (1) local species diversity (alpha diversity), (2) regional trends, including species range breadths and turnover (beta diversity), (3) global (gamma) diversity, and global changes in community composition induced by climate change. In addition, we also calculated the interspecific community-wide trait lag (the difference between the community’s density-weighted mean temperature optima and the current temperature) as a function of the community-wide weighted trait dispersion (centralized variance in species’ density-weighted mean temperature optima; see Methods). The response capacity is the ability of the biotic community to close this trait lag over time30 (SI, Section 4). Integrating trait lag through time31 gives an overall measure of different communities’ ability to cope with changing climate over this time period; furthermore, this measure is comparable across communities. The integrated trait lag summarizes, in a single functional metric, the performance and adaptability of a community over space and time. The reason it is related to performance is that species that on average live more often under temperatures closer to their optima (creating lower trait lags) will perform better than species whose temperature optima are far off from local conditions in space and/or time. Thus, a lower trait lag (higher response capacity) may also be related to other ecosystem functions, such as better carbon uptake which in turn has the potential to feedback to global temperatures32.Overview of resultsWe use our framework to explore the effect of species interactions on local, regional, and global biodiversity patterns, under various degrees of dispersal and available genetic variance. For simplicity, we focus on the dynamics of the resource species, which are present in all scenarios. Results for consumers, when present, are in the SI (Section 5.8). First, we display a snapshot of species’ movement across the landscape with time; before, during, and after climate change. Then we proceed with analyzing local patterns, followed by regional trends, and finally, global trends.Snapshots from the time series of species’ range distributions reveal useful information about species’ movement and coexistence (Fig. 3). Regardless of model setup and parameterization, there is a northward shift in species’ ranges: tropical species expand into temperate regions and temperate species into polar regions. This is accompanied by a visible decline in the number of species globally, with the northernmost species affected most. The models do differ in the predicted degree of range overlap: trophic interactions and temperature-dependent competition both lead to broadly overlapping ranges, enhancing local coexistence (the overlap in spatial distribution is particularly pronounced with high available genetic variance). Without these interactions, species ranges overlap to a substantially lower degree, diminishing local diversity. Below we investigate whether these patterns, observed for a single realization of the dynamics for each scenario, play out more generally as well.Fig. 3: Species’ range shift through time, along a latitudinal gradient ranging from polar to tropical climates (ordinate).Species distributions are shown by colored curves, with the height of each curve representing local density in a single replicate (abscissa; note the different scales in the panels), with the color indicating the species’ initial (i.e., at t = 0) temperature adaptation. The model was run with only 10 species, for better visibility. The color of each species indicates its temperature adaptation at the start of the climate change period, with warmer colors belonging to species with a higher temperature optimum associated with higher latitudes. Rows correspond to a specific combination of genetic variance and dispersal ability of species, columns show species densities at different times (t = 0 start of climate change, t = 300 end of climate change, t = 2500 end of simulations). Each panel corresponds to a different model setup; a the baseline model, b an added trophic level of consumers, c temperature-dependent competition coefficients, and d the combined influence of consumers and temperature-dependent competition.Full size imageLocal trendsTrophic interactions and temperature-dependent competition indeed result in elevated local species richness levels (Fig. 4). The fostering of local coexistence by trophic interactions and temperature-dependent competition is in line with general ecological expectations. Predation pressure can enhance diversity by providing additional mechanisms of density regulation and thus prey coexistence through predator partitioning28,29. In turn, temperature-dependent competition means species can reduce interspecific competition by evolving locally suboptimal mean temperature optima22, compared with the baseline model’s fixed competition coefficients. Hence with temperature-dependent competition, the advantages of being sufficiently different from other locally present species can outweigh the disadvantages of being somewhat maladapted to the local temperatures. If competition is not temperature-dependent, interspecific competition is at a fixed level independent of the temperature optima of each species. An important question is how local diversity is affected when the two processes act simultaneously. In fact, any synergy between their effects is very weak, and is even slightly negative when both the available genetic variance and dispersal abilities are high (Fig. 4, top row).Fig. 4: Local species richness of communities over time, from the start of climate change to the end of the simulation, averaged over replicates.Values are given in 100-year steps. At each point in time, the figure shows the mean number of species per patch over the landscape (points) and their standard deviation (shaded region, extending one standard deviation both up- and downwards from the mean). Panel rows show different parameterizations (all four combinations of high and low genetic variance and dispersal ability); columns represent various model setups (the baseline model; an added trophic level of consumers; temperature-dependent competition coefficients; and the combined influence of consumers and temperature-dependent competition). Dotted vertical lines indicate the time at which climate change ends.Full size imageRegional trendsWe see a strong tendency for poleward movement of species when looking at the altered distributions of species over the spatial landscape (Fig. 3). Indeed, looking at the effects of climate change on the fraction of patches occupied by species over the landscape reveals that initially cold-adapted species lose suitable habitat during climate change, and even afterwards (Fig. 5). For the northernmost species, this always eventuate to the point where all habitat is lost, resulting in their extinction. This pattern holds universally in every model setup and parameterization. Only initially warm-adapted species can expand their ranges, and even they only do so under highly restrictive conditions, requiring both good dispersal ability and available genetic variance as well as consumer pressure (Fig. 5, top row, second and third panel).Fig. 5: Range breadth of each species expressed as the percentage of the whole landscape they occupy (ordinate) at three different time stamps (colors).The mean (points) and plus/minus one standard deviation range (colored bands) are shown over replicates. Numbers along the abscissa represent species, with initially more warm-adapted species corresponding to higher values. The range breadth of each species is shown at three time stamps: at the start of climate change (t = 0, blue), the end of climate change (t = 300, green), and at the end of our simulations (t = 2500, yellow). Panel layout as in Fig. 4.Full size imageOne can also look at larger regional changes in species richness, dividing the landscape into three equal parts: the top third (polar region), the middle third (temperate region), and the bottom third (tropical region). Region-wise exploration of changes in species richness (Fig. 6) shows that the species richness of the polar region is highly volatile. It often experiences the greatest losses; however, with high dispersal ability and temperature-dependent competition, the regional richness can remain substantial and even increase compared to its starting level (Fig. 6, first and third rows, last two columns). Of course, change in regional species richness is a result of species dispersing to new patches and regions as well as of local extinctions. Since the initially most cold-adapted species lose their habitat and go extinct, altered regional species richness is connected to having altered community compositions along the spatial gradient. All regions experience turnover in species composition (SI, Section 5.1), but in general, the polar region experiences the largest turnover, where the final communities are at least 50% and sometimes more than 80% dissimilar to the community state right before the onset of climate change—a result in agreement with previous studies as well7,33.Fig. 6: Relative change in global species richness from the community state at the onset of climate change (ordinate) over time (abscissa), averaged over replicates and given in 100-year steps (points).Black points correspond to species richness over the whole landscape; the blue points to richness in the top third of all patches (the polar region), green points to the middle third (temperate region), and yellow points to the last third (tropical region). Panel layout as in Fig. 4; dotted horizontal lines highlight the point of no net change in global species richness.Full size imageGlobal trendsHence, the identity of the species undergoing global extinction is not random, but strongly biased towards initially cold-adapted species. On a global scale, these extinctions cause decreased richness, and the model predicts large global biodiversity losses for all scenarios (Fig. 6). These continue during the post-climate change period with stable temperatures, indicating a substantial extinction debt which has been previously demonstrated34. Temperature-dependent competition reduces the number of global losses compared to the baseline and trophic models.A further elucidating global pattern is revealed by analyzing the relationship between the time-integrated temperature trait lag and community-wide trait dispersion (Fig. 7). There is an overall negative correlation between the two, but more importantly, within each scenario (unique combination of model and parameterization) a negative relationship is evident. Furthermore, the slopes are very similar: the main difference between scenarios is in their mean trait lag and trait dispersion values (note that the panels do not share axis value ranges). The negative trend reveals the positive effect of more varied temperature tolerance strategies among the species on the community’s ability to respond to climate change. This is analogous to Fisher’s fundamental theorem35, stating that the speed of the evolution of fitness r is proportional to its variance: dr/dt ~ var(r). More concretely, this relationship is also predicted by trait-driver theory, a mathematical framework that focuses explicitly on linking spatiotemporal variation in environmental drivers to the resulting trait distributions30. Communities generated by different models reveal differences in the magnitude of this relationship: trait dispersion is much higher in models with temperature-dependent competition (essentially, niche differentiation with respect to temperature), resulting in lower trait lag. The temperature-dependent competition also separates communities based on their spatial dispersal ability, with faster dispersal corresponding to greater trait dispersion and thus lower trait lag. Interestingly, trophic interactions tend to erode the relationship between trait lag and trait dispersion slightly (R2 values are lower in communities with trophic interactions, both with and without temperature-dependent competition). We have additionally explored the relationship between species richness and trait dispersion, finding a positive relationship between the two (SI, Section 4.1).Fig. 7: The ability of communities in four different models (panels) to track local climatic conditions (ordinate), against observed variation in traits within those communities (abscissa).Larger values along the ordinate indicate that species’ temperature optima are lagging behind local temperatures, meaning a low ability of communities to track local climate conditions. Both quantities are averaged over the landscape and time from the beginning to the end of the climate change period, yielding a single number for every community (points). The greater the average local diversity of mean temperature optima in a community, the closer it is able to match the prevailing temperature conditions. Species’ dispersal ability and available genetic variance (colors) are clustered along this relationship.Full size image More

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