DOI: 10.5061/dryad.zpc866tk0
title: Temporal niche dynamics of spreading native invertebrates underlie doubling of richness in pristine temperate streams

description: While biodiversity loss is undeniably a global phenomenon, an increase in
 taxonomic richness has recently been reported from some ecosystems and
 spatial scales. A striking increase in abundance and/or species richness
 has been documented from temperate rivers over the last 25 years, with
 many of the expanding species (i.e. winners) being native species.
 However, the lack of repeatedly collected local environmental data
 prevents the exploration of their niche dynamics and also makes it
 difficult to distinguish between possible causes. We fill this gap by
 using species occurrence data from 65 pristine Czech rivers sampled in
 1997-2000 and 2015. The same methods were used for sampling
 macroinvertebrates and measuring environmental parameters in both periods.
 We selected 43 winners, defined as taxonomically validated and originally
 non-rare native macroinvertebrate species whose occupancy increased by at
 least six sites between the time periods. We searched for consistent
 patterns of niche dynamics (i.e. (stability, expansion, and restriction)
 among species that might contribute most to the overall increase in
 species richness. Using several biological traits, we also compared the
 winners with the other 253 taxa collected to look for differences.
 Analysis of the occurrence data showed that niche stability was by far the
 predominant pattern of the niche dynamics. This clearly indicates that the
 winners fill their original niche, with a limited contribution of niche
 shift or expansion, depending on the species. As no significant
 differences in either temperature preferences or the other biological
 traits were found between the winners and the other taxa, there is no
 unique set of functional traits that explain the success of the winners.
 The observed mechanism of filling the original niche space by the
 spreading native species not only explains the increase in local species
 richness, but also contributes to support the hypothesis of a
 climate-driven increase in ecosystem energy flow from a new perspective.
 The increased metabolism of the system may relax interspecific competition
 allowing to carry more individuals and species, even without the need for
 an increase in nutrients and ecosystem recovery.
description: Data on aquatic macroinvertebrates and local environmental
 variables were collected from 65 streams of 2–7th stream Strahler order
 and altitudes of 150–800 m a.s.l. across all main river basins of the
 Czech Republic (map available in Zhai et al. 2023). The sites represented
 the least modified sites in terms of physical conditions, water quality
 and riparian zone and floodplain characteristics met the requirements for
 type-specific reference conditions according to the Water Framework
 Directive, 2000/60/EC30 (Nijboer et al. 2004). The initial selection of
 these sites also considered their long-term water quality development
 prior to the first sampling campaign (Kokeš et al. 2006), which resulted
 in the exclusion also of streams affected by acidification in the 1980s.
 This selection is the reason why both high-elevation streams and large
 lowland rivers are poorly represented in the dataset as a fraction of them
 fulfil the requirements to be considered pristine or at least near natural
 in case of low-altitude rivers. The sampling of
 macroinvertebrates was done during spring (April–May) and autumn
 (September–October) in two periods: 1997–2000 (initial period of the first
 sampling campaign) and 2015 (final period, the last sampling), using the
 same standardized methodology of a multi-habitat sampling method. For
 details on data collection and sample processing see Zhai et al. (2023).
 Data from spring and autumn samples of the same period were pooled for
 each site. The identification was done to species whenever possible,
 except for Diptera, some Oligochaeta and Coleoptera larvae that were
 identified to genera. Due to uncertainties of identifications in the
 initial period, all Chironomidae taxa had to be excluded from the dataset
 and several species of Ephemeroptera, Plecoptera, Trichoptera, and
 Oligochaeta had to be pooled into operational taxonomic units. This study
 did not require ethical approval, nor were any licenses or permits needed
 to carry out the work. Three criteria were applied to
 define the winners suitable for temporal niche change exploration: (a)
 unequivocal and reliable identification to species level, (b) occurrence
 frequency in the initial period of ≥6 (i.e., 10% frequency), (c) increase
 in the occurrence frequency in the terminal period at least by 6 sites.
 From the total of 296 taxa, these three criteria were met by 43 of them
 (Table S1, Fig. S1b of this study). On the contrary, there were only five
 species that could be considered “losers” based on the same numerical
 criteria. Because such rarity of losers in our study system, we did not
 analyse their niche dynamic. To define the
 environmental niches of winners we included three groups of environmental
 variables: (i) local environmental parameters, as biological oxygen demand
 (BOD5), concentrations of total phosphorus (TP), nitrate-nitrogen
 (NO<sub>3</sub>-N), and ammonium-nitrogen
 (NH<sub>4</sub>-N), roughness of bed substrate (Phi), and
 share of riffles (Riff), all counted as means of the values measured six
 times during the year of macroinvertebrate sampling; (ii) proxies of land
 use (from 250 m wide river side strip across 15 km of stream network above
 the site), i.e. areas of unfavourable surfaces (Un_surf) and forested area
 (Fore), computed based on the information from the CORINE Land Cover
 system (see Zhai et al. 2023 for details on how the parameters were
 measured); and (iii) climatic variables, such as mean air temperatures in
 January and July (T_Jan, T_Jul), and annual precipitation (Prec), which
 were calculated based on values of daily averages in the year of sampling
 and two consecutive years prior to sampling, using gridded data provided
 by the Czech Hydrometeorological Institute (Polášek et al.
 2017). Each of 296 taxa included in the analysis was
 characterised by seven biological traits describing body size, feeding
 behaviour, locomotion, respiration, dispersal strategy and potential, and
 life cycle (Table S1 of this study). Trait data were taken from published
 sources (Tachet et al. 2000; Sarremejane et al. 2020) and online databases
 (Schmidt-Kloiber and Hering 2015; Polášek et al. 2017). Traits with 29
 categories<em> </em>were coded using the fuzzy approach to
 assign the affinity of species to a particular trait category (Chenevet et
 al. 1994). We also tested whether winners were more warm-adapted than the
 other species using the approach of Haase et al. (2019), to associate
 expansion of winners with warming. This approach defines the temperature
 niche of species based on their affinities to river zones (Illies and
 Botosaneanu, 1963). For more details and literature, see Haase et al.
 (2019). The data were available for all winners and for 232 of 253
 remaining taxa. Chevenet, F., S. Dolédec,
 and D. Chessel. 1994. A fuzzy coding approach for the analysis of
 long-term ecological data. Freshw. Biol. <strong>31</strong>:
 295–309. Haase, P., F. Pilotto, F. Li, A. Sundermann,
 A. W. Lorenz, J. D. Tonkin, and S. Stoll. 2019. Moderate warming over the
 past 25 years has already reorganized stream invertebrate communities.
 Sci. Total Environ. <strong>658</strong>:
 1531-1538. Illies, J., and L. Botosaneanu. 1963.
 Problèmes et méthodes de la classification et de la zonation écologique
 des eaux courantes, considerées surtout du point de vue faunistique: Avec
 18 figures dans le texte et en supplément. Int. Ver. Theor. Angew. Limnol.
 <strong>12</strong>: 1–57. Nijboer, R.C.,
 R. K. Johnson, P. F. M. Verdonschot, M. Sommerhäuser, and A. Buffagni.
 2004. Establishing reference conditions for European streams.
 Hydrobiologia <strong>516</strong>: 91–105.
 Polášek, M., S. Zahrádková, D. Němejcová, M. Straka, M. Bareš,
 and L. Opatřilová. 2017. Monitoring of long-term changes in the
 biodiversity of running waters at the time of climate change: proposal,
 implementation and incorporation into the ARROW public information system”
 (EHP-CZ02-OV-1-018-2014). T. G. Masaryk Water Research Institute,
 Brno. Sarremejane, R., and others. 2020. DISPERSE, a
 trait database to assess the dispersal potential of European aquatic
 macroinvertebrates. Sci. Data <strong>7</strong>:
 386. Schmidt-Kloiber A., and D. Hering. 2015.
 www.freshwaterecology.info – An online tool that unifies, standardized and
 codifies more than 20,000 European freshwater organisms and their
 ecological preferences. Ecol. Indic. <strong>53</strong>:
 271–282. Tachet, H., P. Richoux, and M. Bournaud.
 2000. Invertébrés d’Eau Douce. Systématique, biologie, écologie. CNRS,
 Paris. Zhai, M., J. Bojková, D. Němejcová, M.
 Polášek, V. Syrovátka, and M. Horsák. 2023. Climatically promoted
 taxonomic homogenization of macroinvertebrates in unaffected streams
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description: # Temporal niche dynamics of spreading native invertebrates underlie
 doubling of richness in pristine temperate streams
 [https://doi.org/10.5061/dryad.zpc866tk0](https://doi.org/10.5061/dryad.zpc866tk0) ## Description of the data and file structure Data on aquatic macroinvertebrates and local environmental variables collected from 65 streams in two periods: 1997–2000 (initial period of the first sampling campaign) and 2015 (final period, the last sampling), using the same standardized methodology of a multi-habitat sampling method. Each of 296 taxa included in the analysis is characterised by biological traits describing body size, feeding behaviour, locomotion, respiration, dispersal strategy and potential, and life cycle. The file contains five sheets. 1 & 2) The first two sheets contain species occurrence data from the initial ("occurrence initial") and the final ("occurrence final") sampling periods. For species abbreviation see the last sheet "biological_traits". 3 & 4) The third and fourth sheets contain environmental data to the same sites as for species data. Again from the initial ("environment initial") and final (environment final") sampling periods. Abbreviation and full names of the variables: * prec_3_C, annual precipitation [mm]; * phi_sqrt, roughness of bed substrate [-]; * NO3_log, concentrations of nitrate-nitrogen [mg/l]; * riffles_share, share of riffles [%]; * forest_km2_log, forested area [km2]; * unfav_surf_perc_log, areas of unfavourable surfaces [%]; * TP_log, concentrations of total phosphorus [mg/l]; * BOD5_sqrt, biological oxygen demand [mg/l]; * NH4_log, concentrations of ammonium-nitrogen [mg/l]; * temp_jan_mean, mean air temperatures in January [°C]; * temp_jul_mean, mean air temperatures in July [°C]. Transformation details and descriptive statistics see Table S3 in Supporting information of this study. More information on data measurements and their sources are in M&M of the paper. 5\) The fifth sheet contains data on biological traits. The first column "winners" indicate the species classified in the study as winners. Biological traits, their abbreviations and modality (i.e. qualitative or quantitative range of expressions that a trait can exhibit) are shown. Modality is provided in fuzzy coding (i.e. 0, 1, ..., 10), in which modalities are used to represent the degree to which an organism exhibits a particular trait expression. * body size intervals (mm): &lt;5, 5-10, 10-20, 20-40, &gt;40; * feeding_beh, feeding behaviour classes: GRSC, grazer/scraper; SHRE, shredder; GACO, gatherer/collector; FILT, filter feeder; PRED, predator; * locomotion type classes: SWSD, swimming/scatting; BURW, burrowing; SPWA, sprawling/walking; SMSE, semisessile; * respiration type classes: TEG, tegument; GIL, gills; PLS, plastron; AER, aerial; * life-cycle types: SEMV, semivoltine; UNIV, univoltine; PLUV, plurivoltine; * dissem_strat, dispersal mode classes: AQP, aquatic passive; AQA, aquatic active; AEP, aerial passive; AEA, aerial active; OTHR, others. * dissem_pot, dispersal via water intervals (m): &lt;10, 10-100, 100-1000, &gt;1000. * temperature preference (°C), calculated temperature preferences based on Haase et al. (2019). "na" stands for "not available", i.e. the given trait was not available in the use databases for the given taxon.