DOI: 10.5061/dryad.h1893200d
title: Data from: Global warming confers performance advantages to a non-native predator

description: Anthropogenic impacts manifest in rising temperatures worldwide, with
 drastic consequences for communities and ecosystems. Ectotherms tend to
 reach a smaller size at maturity in warmer environments; however, whether
 these responses elicit performance advantages between native and invasive
 species remains poorly understood. Here, we compared foraging efficiency
 and growth of aquatic predators (invasive racer goby, Babka
 gymnotrachelus; native European bullhead, Cottus gobio) in current and
 predicted future water temperatures (15^ °C; 23 °^C) facing two types of
 prey: live and frozen gammarids (Gammarus jadzewskii). We show that the
 growth rate of both predators was negatively affected by a temperature
 increase, but was consistently higher for the invasive species compared to
 the native. This was driven by changes in predators’ foraging efficiency,
 increased in the invasive, and decreased in the native species. Fish
 foraging was also shaped by prey motility, with active prey consumed more
 often than inactive ones. The results indicate that a temperature increase
 may shift the competitive balance between invasive and native species,
 with the former being more robust to temperature increase under a limited
 food supply than native comparators. This could further modify the impact
 of invaders on the local communities, with implications for ecosystem
 stability.
description: Methods <em>Animal collecting and
 housing</em> We used the invasive racer goby and
 the native European bullhead as model organisms. Both species are
 shelter-associated bottom dwellers, with burst-and-hold swimming mode,
 feeding on a variety of macroinvertebrates (mostly insects, crustaceans)
 and co-existing in habitats with rocky and gravelly substrates (Kottelat
 and Freyhof 2007, Kakareko et al. 2016, Janáč et al. 2018, Teletchea and
 Beisel 2018, Płąchocki et al. 2020). Fish were collected in the summer
 (June) of 2024, from the Brda River (central Poland, 53° 08'
 52.5" N 17° 58' 10.5" E) by diving, using an aquarium net.
 The fish were kept in the laboratory in single-species 120-L stock tanks,
 with a maximum density of 20 individuals per tank, and divided according
 to the experimental temperatures (i.e., two groups per species), ensuring
 a similar size range of individuals in both groups. Tanks were filled with
 conditioned tap water (mean ± SD: temperature 19.1 ± 0.4, pH 8.1 ± 0.2,
 electrical conductivity 621 ± 23 µS/cm, oxygen level 8.2 ± 0.5 mg/l;
 measured with a Multi 340i Meter, WTW, Weilheim, Germany). The fish were
 acclimated to temperature treatments by gradually increasing or decreasing
 water temperature by 1<sup>o</sup>C/day. All of the stock
 tanks were equipped with aquarium filters, aerators and ceramic and stony
 shelters, with no other bottom substrate. The photoperiod was set at a
 12:12h light:dark cycle, with lights turned on at 07:00 and off at
 19:00. We chose <em>Gammarus
 jazdzewskii</em> (Rudolph, Coleman, Mamos & Grabowski, 2018)
 as a representative prey species, being commonly predated by the tested
 fish species in the wild (Mills and Mann 1983, Grabowska and Grabowski
 2005). Gammarids were collected from the Zielona Struga, a forest stream
 in central Poland (52<sup>o</sup>99'72.1"N,
 18<sup>o</sup>45'04.7"E), with a hand net, in June
 and July 2024. The prey individuals were kept in the laboratory in 60-l
 tanks equipped with aquarium filters, aerators, and woody debris from the
 collection site, and filled with conditioned tap water at 18.5 ± 0.5
 <sup>o</sup>C. The gammarids were acclimated to the
 experimental temperatures in the same manner as the test fish.
 All animals had 2 weeks to acclimate to experimental temperatures
 before use. The fish were fed daily <em>ad libitum</em> with
 frozen chironomids, and the gammarids were fed every two days with frozen
 chironomids and decomposing leaves. The water in the stock tanks was
 exchanged once a week (ca. 30% of water volume).
 <em>Experimental design</em> To
 investigate the effects of elevated temperature on fish foraging
 efficiency we selected two water temperatures: ambient
 (15<sup>o</sup>C) and elevated (23<sup>o</sup>C).
 The ambient temperature corresponds to the mean river water temperature
 between May and October in Polish rivers over the 1971-2015 period (Graf
 and Wrzesiński 2020). Despite the fact that we compare performance of a
 primarily cold-water tolerant native species with a higher temperature
 tolerant invader, both species coexist in the sampled system, experiencing
 the same temperature regime at the collection site for 10-20 years
 (Kostrzewa and Grabowski 2003, Radtke et al. 2015). Further, the
 temperature of 23<sup>o</sup>C, according to the IPCC report
 (2023) and our model for the fish collection site, is predicted to be the
 common mean summer water temperature in European freshwaters by the end of
 2100. We collected the fish in the lower section of a tributary of the
 Vistula River. In the Vistula, in summer (July) the water temperature
 reaches a maximum of 23.6°C already (Marszelewski and Pius 2014).
 Moreover, it is likely that a similar maximum occurs in the shallow water
 within tributary sampled system. Thus, while acknowledging the large
 temperature increment in our study, comparing both species’ performance at
 the higher selected temperature will provide valuable information on how
 the species could respond to ongoing global warming and microhabitat
 contexts. We used live (active) and frozen (inactive)
 prey individuals to assess fish foraging efficiency concerning the
 prey's ability to actively escape. To produce frozen prey, live
 gammarids were collected from holding tanks, allocated numbers of prey
 individuals were placed into 50 ml containers assigned to particular
 experimental tanks, and frozen at -80<sup>o</sup>C.
 We used opaque experimental tanks (51 × 38 × 30 cm, length ×
 width × height) filled with 40 l of conditioned (constantly aerated for 48
 h) tap water as experimental units. The bottom of each tank was covered
 with a 1-cm deep layer of sand, and constant aeration and water filtering
 were provided. Two weeks before the start of the experiment, each fish
 individual was weighed (to the nearest 0.1 g using Radwag WLC 2.X2 scale,
 N = 56), assigned among the temperature treatments, and placed
 individually in the experimental tanks. Each fish individual was assigned
 to a particular experimental tank and stayed inside throughout the
 experiment. The ambient temperature in the experimental tanks was
 maintained using air conditioning within the laboratory. To maintain the
 elevated water temperature, we placed half of the experimental tanks
 inside larger 60-l tanks (58 × 39 × 35 cm, length × width × height) filled
 with tap water and containing an aquarium heater with a thermostat (i.e.
 the heater was placed outside the experimental tank).
 Fish were acclimated to live gammarid prey in their respective
 arenas by feeding them <em>ad libitum</em> for two weeks.
 Next, they were starved for 48h before the first experimental trial. On
 the test day, live gammarids were collected from the holding tanks, and
 allocated numbers of them were placed into 50 ml containers assigned to
 particular experimental tanks scheduled to receive active prey on that
 day. At the same time, inactive gammarids were defrosted to be used in the
 experimental tanks scheduled to receive inactive prey on that day. Then,
 the aeration in the experimental tanks was turned off and filters were
 pulled out. Each trial started between 09:00 and 10:00. The gammarids were
 flushed into experimental tanks by spreading them evenly over the water
 column. The fish had 24h (12:12h light:dark cycle, with light turned off
 at 19:00 and on at 7:00) to forage, after which the fish were removed and
 gammarids remaining in each experimental tank were counted. After that,
 the fish were starved for 24h before the next trial. Note that each fish
 was exposed to all prey densities by the end of the experiment, and thus
 had a similar overall feeding regime. Each fish was thus tested 12 times,
 with six densities (5, 10, 15, 20, 25 and 40 individuals) of both types of
 prey (active/inactive) provided in a random order. All the experimental
 tanks (13-16 replicates per species per temperature) were set up at the
 same time. Tanks assigned to different fish species and water temperature
 treatments were randomly distributed in the laboratory. The water in the
 experimental tanks was exchanged once a week (ca. 30% of water
 volume). <em>Statistical
 analysis</em> Overall body sizes before the
 experiment between the racer goby and the European bullhead were compared
 with Wilcoxon rank sum exact tests. Feeding rates (expressed as a
 proportion of prey killed over the initial prey density per replicate)
 were analysed using generalised linear mixed models (GLMM). These models
 followed a binomial error family, and considered proportions of killed
 prey as a function of species, temperature, prey type, and fish initial
 body mass, alongside all of their possible interactions. Initial prey
 density was included as a further explanatory variable. Individual fish
 identities were included as a random effect, because individuals were
 reused across prey density and prey type treatments during the experiment.
 This approach allowed us to capture variations in body mass regarding raw
 feeding rates of fish, in interaction alongside species, temperature, and
 prey type. Residuals were checked for overdispersion through diagnostics
 with simulations (Hartig 2022). Post-hoc tests were computed using
 estimated marginal means for significant effects (Lenth 2023).
 Functional responses were characterised using logistic regression
 of the proportion of prey eaten as a function of prey density for each
 fish species, temperature, and prey type separately. We applied the Type
 II Rogers’ random predator equation (Rogers 1972) to account for prey
 depletion during the experiments: (1) where <em>N</em><sub>e</sub> is the number of prey eaten, <em>N</em><sub>0</sub> the initial prey density, <em>a</em> the predator attack rate (classically interpreted as the search efficiency), <em>h</em> the predator handling time (defined as the time spent pursuing, subduing, and consuming each prey item plus the time spent preparing to search for the next prey item), and <em>T</em> the duration of the experiment. Functional responses were compared using non-parametric bootstrapped 95% confidence intervals, with 2000 iterations per functional response curve (Pritchard et al. 2017). Fish growth rate was analysed using generalised linear models (GLM) with a Gaussian error family. We log-transformed final and initial fish body mass to linearize the relationship between them. and modelled final body mass as a function of species, temperature, and initial fish body mass with all of their possible interactions. The total consumption (expressed as the total number of prey individuals eaten in all experimental trials) was included as a covariate. The Wilcoxon rank sum exact test was performed using the base “stats” package (R Core Team 2024). Both GLMM and GLM were fitted with “glmmTMB” R package (Brooks et al. 2017). Residuals were checked for overdispersion through diagnostics with residual simulations using the “DHARMa” package (Hartig 2022). Slopes and means estimates for significant interaction terms were compared using “emmeans” package (Lenth 2023). Functional response models were fitted with the “frair” R package (Pritchard et al. 2017). All statistical analyses were performed using R 4.4.0 (R Core Team 2024). Reference list: Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M., & Bolker, B. M. (2017). glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal, 9(2). https://doi.org/https://doi.org/10.3929/ethz-b-000240890 Grabowska, J., & Grabowski, M. (2005). Diel-feeding activity in early summer of racer goby Neogobius gymnotrachelus (Gobiidae): A new invader in the Baltic basin. Journal of Applied Ichthyology, 21(4), 282–286. https://doi.org/10.1111/j.1439-0426.2005.00676.x Graf, R., & Wrzesiński, D. (2020). Detecting patterns of changes in riverwater temperature in Poland. Water, 12(5), 1327. https://doi.org/10.3390/W12051327 Hartig F. (2022). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.6, https://CRAN.R-project.org/package=DHARMa. Janáč, M., Roche, K., Šlapanský, L., Polačik, M., & Jurajda, P. (2018). Long-term monitoring of native bullhead and invasive gobiids in the Danubian rip-rap zone. Hydrobiologia, 807, 263–275. https://doi.org/10.1007/s10750-017-3398-6 Kakareko, T., Kobak, J., Poznańska, M., Jermacz, Ł., & Copp, G. H. (2016). Underwater evaluation of habitat partitioning in a European river between a non-native invader, the racer goby and a threatened native fish, the European bullhead. Ecology of Freshwater Fish, 25(1), 60–71. https://doi.org/10.1111/eff.12191 Kostrzewa, J., & Grabowski, M. (2003). Opportunistic feeding strategy as a factor promoting the expansion of racer goby (Neogobius gymnotrachelus Kessler, 1857) in the Vistula basin. Lauterbornia, 48, 91–100. https://www.researchgate.net/publication/259000131 Kottelat, M., & Freyhof, J. (2007). Handbook of European freshwater fishes. Cornol, Switzerland: Publications Kottelat. Lenth R (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.8.7, https://CRAN.R-project.org/package=emmeans. Marszelewski, W., & Pius, B. (2014). The impact of changes in air temperature upon the thermal regime of the river Vistula in Toruń (Poland) over the years 1961-2012. In P. Gâştescu, W. Marszelewski, & P. Bretcan (Eds.), Water resources and wetlands (pp. 231–236). http://www.limnology.ro/water2014/proceedings/30_Marszelewski.pdf Mills, C. A., & Mann, R. H. K. (1983). The bullhead cottus gobio, a versatile and successful fish. In Fifty-first annual report for the year ended 31st March 1983 (pp. 76–88). Freshwater Biological Association. https://agris.fao.org/agris-search/search.do?recordID=AV2012055893 Płąchocki, D., Kobak, J., Poznańska-Kakareko, M., & Kakareko, T. (2020). Environmental factors associated with the occurrence of the Ponto–Caspian gobies in a lowland river belonging to the central European invasion corridor. River Research and Applications, 36(1), 25–35. https://doi.org/10.1002/rra.3543 Pritchard, D. W., Paterson, R. A., Bovy, H. C., & Barrios-O’Neill, D. (2017). frair: an R package for fitting and comparing consumer functional responses. Methods in Ecology and Evolution, 8(11), 1528–1534. https://doi.org/10.1111/2041-210X.12784 R Core Team (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Radtke, G., Bernaś, R., Dębowski, P., Morzuch, J., & Skóra, M. (2015). Ichtiofauna systemu rzeki Brdy. Rocz.Nauk.PZW, 25, 31–47. https://doi.org/10.12823/sapaa.0860-648X.15003 Rogers, D. (1972). Random search and insect population models. The Journal of Animal Ecology, 41(2), 369. https://doi.org/10.2307/3474 Rudolph, K., Coleman, C. O., Mamos, T., & Grabowski, M. (2018). Description and post-glacial demography of Gammarus jazdzewskii sp. nov. (Crustacea: Amphipoda) from Central Europe. Systematics and Biodiversity, 16(6), 587–603. https://doi.org/10.1080/14772000.2018.1470118
description: # Data from: Global warming confers performance advantages to a non-native
 predator Dataset DOI: [10.5061/dryad.h1893200d](10.5061/dryad.h1893200d)
 ## Description of the data and file structure Data collected from the
 functional response experiment are stored in "Global warming
 confers.xlsx" file. The compressed .zip file also includes the code
 necessary to recreate all statistical analyses related to the study with R
 open software. ### Files and variables #### File:
 Global_warming_confers_-_data_and_code.zip **Description:**  1\.
 "Global warming confers.xlsx" - the file contains data collected
 during the functional response experiments. Two sheets are included: Sheet
 1: "Foraging efficiency data" - this sheet contains raw data
 representing foraging efficiency of tested fish facing two experimental
 temperatures and two types of prey. Dataset was used to calculate total
 foraging efficiency and to assess functional response curves for predators
 tested. Variables: id - unique fish identifier, as fish were reused during
 the experiment species - categorical; species identity: European
 bullhead/racer goby temperature - categorical; indicates experimental
 temperature: 15^ ^°C / 23^ ^°C initial.mass - numeric; fish body mass at
 the start of the experimental period, expressed in grams test.day -
 numeric; indicates specific test day: from 1 to 12 for each fish tested
 prey.type - numeric; indicates type of prey offered at specific day:
 active/inactive initial.prey.density - categorical; number of prey
 individuals offered at the beggining of specific test day;
 5/10/15/20/25/40 prey.left - numeric; number of prey individuals remained
 at the end of the experimental period prey.eaten - numeric; number of prey
 individuals eaten by the fish tested ( i.e. difference between number of
 prey individuals offered and left at the end of the experimental period)
 Sheet 2: "Growth data" - this sheet contains initial and final
 fish body mass, alongside the total number of prey individuals eaten
 throughout the whole experimental period. Dataset was used to calculate
 fish growth during experimental period. Variables: id - unique fish
 identifier - each value corresponds to specific fish from "Foraging
 efficiency data" sheet species - categorical; species identity:
 European bullhead/racer goby temperature - categorical; indicates
 experimental temperature: 15 °C / 23 °C initial.mass - numeric; fish body
 mass at the start of the experimental period, expressed in grams
 final.mass - numeric; fish body mass at the end of the experimental
 period, expressed in grams consumption - numeric; total number of prey
 individuals eaten during the whole experimental period (sum of the number
 of prey individuals eaten in each specific test day stored in
 "Foraging efficiency data: sheet) ## Code/software The compressed
 .zip folder contains „Global warming confers.Rproj” file, which is a
 project created in R open software to analyze experimental data. Specific
 parts of Rcode are provided in .R files: 1. "Global warming confers
 FR model.R" - this file contains code neccesary to recreate all
 analyses used to assess fish foraging efficiency. 2. "Global warming
 confers growth model.R" - this file contains code neccesary to
 recreate all analysis of fish growth during the experimental period. All
 code provided was created and used with R open software, ver 4.4.0. The
 list of packages needed is provided at the beginning of the each code.