Long-Term Trends and Drivers of Aquatic Insects in the Netherlands

LONG-TERM TRENDS AND DRIVERS OF AQUATIC INSECTS IN THE NETHERLANDS

2021 39

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LONG-TERM TRENDS AND DRIVERS OF AQUATIC INSECTS IN THE NETHERLANDS

CASPAR A. HALLMANN EELKE JONGEJANS

2021 39

PB 1 Water beetle - Cybister lateralimarginalis - tuimelaar Photo: Bureau Biota COLOPHON

STOWA-number 2021-39 ISBN 978.90.5773.943.9

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2 Plecoptera Nemoura cinerea larvae Photo:Bureau Biota

Authors Design Caspar A. Hallmann1 & Eelke Jongejans1,18 Shapeshifter.nl | Utrecht

In cooperation with Photography cover Ineke Barten6, Harry Boonstra14, Theo Cuijpers17, Hans de Kroon1, Ben Eenkhoorn11, Bureau Biota Boris Everwijn3, Ronald Gylstra15, Bram Koese2, John P.M. Lensen7, Mieke Moeleker12, Left: bush-tailed caddisfly - Sericostoma personatum - kokerjuffer Astra Ooms11, Leo Posthuma1,8, Jaap Postma9, Ernst Raaphorst10, Dorien Roubos13, Right: common darter - Sympetrum striolatum - bruinrode heidelibel Carlo Rutjes5, Mark Scheepens6, Henk Siepel1, Jaap Slootweg8, Wilco C.E.P. Verberk1, Bas van der Wal3, Gert van Ee11, Barend van Maanen4, Tessa van Vreeswijk16, Copyright Danneke Verhagen-Bakker16 & Theo Zeegers2 The information in this publication may be used, provided the source is acknowledged. The knowledge in this report, developed or accumulated, is available free of charge. Costs 1 Radboud University, Radboud Institute for Biological and Environmental Sciences, STOWA charges for publications, are only costs for design, printing and shipping. Nijmegen, The Netherlands 2 EIS Kenniscentrum Insecten, Leiden, The Netherlands Disclaimer 3 STOWA Foundation for Applied Water Research, Amersfoort, The Netherlands This report is based on current insights and has been put together with care. Nevertheless, 4 Waterschap Limburg, Roermond, The Netherlands the results should be critically considered during application. The authors and STOWA 5 Waterschap Aa en Maas, ’s Hertogenbosch, The Netherlands may not be held liable for any damages resulting from the application of the information 6 Waterschap De Dommel, Boxtel, The Netherlands in this report. 7 Waterschap Rijn en IJssel, Doetinchem, The Netherlands 8 RIVM National Institute for Public Health and the Environment, Bilthoven, The Netherlands 9 Ecofide, Weesp, The Netherlands 10 Hoogheemraadschap van Delfland, Delft, The Netherlands 11 Hoogheemraadschap Hollands Noorderkwartier, Heerhugowaard, The Netherlands 12 AQUON, Tiel, The Netherlands 13 Waterschap Vallei en Veluwe, Apeldoorn, The Netherlands 14 Wetterskip Fryslân, Leeuwarden, The Netherlands 15 Waterschap Rivierenland, Tiel, The Netherlands 16 Hoogheemraadschap De Stichtse Rijnlanden, Houten, The Netherlands 17 Hoogheemraadschap van Schieland en de Krimpenerwaard, Rotterdam, The Netherlands 18 Netherlands Institute for Ecology, Wageningen, The Netherlands

2 33 Banded demoiselle - Calopteryx splendens - weidebeekjuffer Photo: Wil Leurs TABEL OF CONTENTS

2 COLOPHON

90 ABOUT STOWA

4 6 SAMENVATTING 30 RESULTS 6 Ecologisch herstel Nederlandse wateren leidt tot grotere 30 Abundance trends of freshwater macrofauna insects insectendiversiteit en lagere aantallen 33 Genus-level trends 7 Monsters verzameld door waterschappen tussen 1990 en 2017 37 Trends in diversity 7 Dansmuggen en haften namen af, kokerjuffers en libellen toe 37 Trends in drivers 7 Overheersende effecten van verminderde eutrofiëring 37 Effects of potential drivers on the abundance en verminderde toxiciteit pesticiden of freshwater insects 7 Herstel van waterinsecten-gemeenschappen 39 Effects of potential drivers on the trends of 8 Nederlandstalige bijschriften van figuren en tabellen macroinvertebrate insects 40 Trend-trait relationships 10 ABSTRACT 48 DISCUSSION 12 INTRODUCTION 49 What caused insect abundance to be halved and diversity to increase? 16 MATERIALS AND METHODS 53 Comparison with aquatic and terrestrial insect 17 Freshwater macroinvertebrate data trends elsewhere 18 Sampling 55 Limnodata Neerlandica: opportunities, 19 Species identification limitations and recommendations 19 Macroinvertebrate models 57 Topics for future research 20 Taxonomic group models 58 Conclusions 20 Genus-level models 59 Acknowledgements 20 Diversity models 21 Explanatory variables 60 LITERATURE CITED 221 Water type 22 Land use and land cover 64 SUPPLEMENTARY INFORMATION 23 Weather variables 65 Appendix A: Interpolation of weather data 23 Physical/chemical properties 66 Appendix B: Spatio-temporal estimation and interpolation 24 Toxic pressure of mixtures of chemical pollutants of chemical water properties 26 Principal Component Analysis 68 Appendix C: Scale-dependent analysis 26 Estimated relative effects of explanatory variables 69 Supplementary Tables on abundance, diversity and trends 74 Supplementary Figures 26 Trend-trait relationships

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Larva of cranefly - Phalacrocera replicata - buismug Photo: Bureau Biota SAMENVATTING

ECOLOGISCH HERSTEL NEDERLANDSE WATEREN LEIDT TOT GROTERE INSECTENDIVERSITEIT EN LAGERE AANTALLEN Monitoring door de Nederlandse waterschappen biedt nieuwe inzichten. Ondanks dat het totale aantal individuen van aquatische insecten de af- gelopen drie decennia halveerde, is de diversiteit juist toegenomen. De grote afname kan vooral toegeschreven worden aan bepaalde dansmuggen, die voorheen profiteerden van voedselrijke omstandigheden door vermesting en vervuiling.

Verontrusting over de afname van het aantal landinsecten roept de vraag op hoe het gaat met in het water levende insecten. Langer-lopende monitoring zoals de macrofauna-bemonstering

6 van de Nederlandse waterschappen biedt de mogelijkheid om te achterhalen OVERHEERSENDE EFFECTEN VAN VERMINDERDE van welke groepen insecten het aantal individuen afneemt of juist toeneemt, en EUTROFIËRING EN VERMINDERDE TOXICITEIT PESTICIDEN welke milieufactoren en beheermaatregelen daarbij het meest van invloed zijn. Bij het vergelijken met data van meerdere omgevingsvariabelen, zagen de on- De onderzoeksresultaten zijn bemoedigend en ondersteunen het beheer van de derzoekers dat de aantalstrends het best verklaard worden door de verbeterde waterschappen dat erop gericht is de waterkwaliteit en biodiversiteit verder te waterkwaliteit. Halvering van de stikstof- en fosforconcentraties was positief verhogen. gecorreleerd met het aantal individuen van de meeste groepen insecten, maar verlaagde juist het totale aantal dansmuggen en kriebelmuggen. De flinke afna- MONSTERS VERZAMELD DOOR WATERSCHAPPEN TUSSEN 1990 EN 2017 me in de toxische druk van pesticiden in het water (uitgedrukt in meer-stoffen Om insectentrends in de Nederlandse oppervlaktewateren te bepalen, analyseer- PAF) over de drie decennia had een vergelijkbaar positief effect op de diversiteit den we de langlopende, maar complexe, dataset van monsters verzameld door de aan waterinsecten. waterschappen. De acht bestudeerde waterschappen lagen in het zuiden (Limburg en Oost-Brabant), oosten (Achterhoek en Twente), en westen (zuidwestelijke helft HERSTEL VAN WATERINSECTEN-GEMEENSCHAPPEN van Zuid-Holland). In onze analyses hebben we rekening gehouden met mogelij- Dit onderzoek geeft duidelijke aanwijzingen dat de gemeenschappen van ke verschillen tussen de waterschappen en de veldmedewerkers die de monsters waterinsecten zich aan het herstellen zijn van de slechte waterkwaliteit in de verzameld hebben, als ook het totale aantal meters dat bemonsterd is. Weer- en jaren tachtig (o.a. door vervuiling, vermesting en organische belasting door waterkwaliteitsgegevens werden ruimtelijk geïnterpoleerd naar de 1.709 locaties overstorten en directe lozingen van huishoudens). Insecten die karakteristiek waar in de bestudeerde periode 12.087 macrofauna-monsters verzameld zijn. zijn voor gezonde rivieren meerecosystemen namen toe, terwijl negatieve indicatorgroepen juist afnamen. Zo deden insecten die van helder en stro- DANSMUGGEN EN HAFTEN NAMEN AF, KOKERJUFFERS EN LIBELLEN TOE mend water houden het beduidend beter dan insecten die stilstaand water en Het aantal aquatische insecten is in 27 jaar tijd met 53% afgenomen. Niet alle slibbodems prefereren. insectengroepen droegen daar evenveel aan bij. Vooral de sterke afnames in de meest talrijke groepen waren bepalend: vooral dansmuggen, maar ook haften. Tegelijkertijd zijn op veel locaties de concentraties van nutriënten en toxische Waterwantsen en -kevers namen ook af, maar waren sowieso al minder talrijk stoffen nog steeds boven de vastgestelde normen, terwijl de diversiteit aan en hadden dus minder invloed op het totale aantal insecten. Dat laatste geldt insecten gedurende de hele studieperiode toenam. Onze resultaten bieden dus ook voor kokerjuffers en libellen, die juist in aantal toenamen. Trendanalyses op het perspectief dat verdergaande verbetering van de waterkwaliteit effectief het niveau van taxonomische geslachten laten een geheel andere patroon zien: kan zijn voor het herstel van waterinsecten-gemeenschappen. Helaas kon het van 66% van 213 insecten-geslachten nam het aantal juist toe. De meest talrijke effect van oever- en waterbeheer niet meegenomen worden in de analyses, geslachten kwamen minder voor, terwijl van de zeldzamere geslachten juist maar de verwachting is dat natuurvriendelijk beheer een zeer positieve bijdra- steeds meer individuen aangetroffen werden. Dit zorgde voor een steeds gelijkere ge kan leveren. Het is echter niet duidelijk hoever de waterinsecten-gemeen- verdeling van de aantallen individuen over de insecten-geslachten. Aangezien ook schappen nog afstaan van historische situaties, omdat referentie-datasets die het aantal geslachten per monster toenam, leidt dat tot een grotere diversiteit, goed inzicht geven in de aantallen ontbreken. hetgeen gunstig is.

6 7 NEDERLANDSTALIGE BIJSCHRIFTEN VAN FIGUREN EN TABELLEN geven volgens de modelvoorspellingen op basis van standaardinstellingen. De volgorde van onderstaande bijschriften is dezelfde als die van de figuren en Rechtsonder worden de jaarlijkse veranderingen (in percentages) gegeven per tabellen in dit rapport. taxonomische groep. • Figuur 1: Schema van de uitgevoerde analyses in deze studie. • Figuur 5: gemiddelde abundantie trend (in percentages verandering per jaar) • Figuur 2: A. Kaart van de waterschappen waarvan data meegenomen zijn voor de tien taxonomische groepen van macrofauna insecten apart voor drie in deze studie. B. Aantal macrofauna-monsters in de gebruikte dataset per regio’s (verschillende kleuren). West (W): HHD & WHD, Zuid (S): WAM, WD, jaar. C. Aantal macrofauna-monsters per locatie. De acht waterschappen WPM & WRO, Oost (E): WRD & WRIJ. zijn HHD (Hoogheemraadschap van Delfland), WHD (Waterschap Hollandse • Figuur 6: Trends in dichtheden (aantal macrofauna-insecten) per genus. A) His- Delta), WAM (Waterschap Aa en Maas), WD (Waterschap De Dommel), WPM togram van ln-lambda schattingen op het niveau van genera. Positieve trends (Waterschap Peel en Maasvallei, tegenwoordig onderdeel van Waterschap (’abundance trend’ groter dan 0) geven aan dat het aantal individuen in een Limburg), WRD (Waterschap Regge en Dinkel, tegenwoordig onderdeel genus toenam over de jaren van de studie. Rode kleuren vertegenwoordigen de van Waterschap Vechtstromen), WRIJ (Waterschap Rijn en IJssel) en WRO 73 genera van de dansmuggen, blauwe kleuren de 173 andere genera waarvoor (Waterschap Roer en Overmaas, tegenwoordig onderdeel van Waterschap we betrouwbare trend-schattingen konden berekenen. B) Relatie tussen de Limburg). trend (ln-lambda) op genus niveau en de abundantie van de individuen van die • Tabel 1: Overzicht van de verklarende factoren die meegenomen zijn in de genera in de jaren negentig. De abundantie in de periode 1980-1989 is simpel- analyses. De waarden van dynamische variabelen kunnen veranderen over de weg benaderd door alle getelde individuen op te tellen over die periode. De jaren. helling van de gefitte regressielijn is zeer significant: trend=0.11650-0.01377* • Tabel 2: Aantal macrofauna-monster locaties per watertype en waterschap. ln(abundantie), p<0.001. Of een genus wel of niet tot de chironomiden (dans- Alleen locaties waarvoor monsters bekend waren uit tenminste 3 jaren zijn muggen) behoort (dus een binomiale factor) had geen effect op helling of inter- meegenomen. cept van de regressielijn. • Tabel 3: Aantal waterkwaliteits-monsters en -locaties waarop de interpolatie • Figuur 7: Trends in insect-macrofauna diversiteit. A Gemiddelde soorten- voor elke fysisch-chemische covariabele gebaseerd is. rijkdom. B Shannon’s diversiteits-index. C Simpson’s diversiteits-index. D • Tabel 4: Aantal waterkwaliteits-monsters en -locaties waarvoor de toxische Shannon’s index van gelijkmatigheid. Voor elke index geeft de blauwe lijn druk (uitgedrukt in meer-stoffen PAF) is berekend. het gemiddelde over de 8 waterschappen, terwijl de andere lijnen de variatie • Figuur 3: Trends in dichtheden (aantallen per bemonsterde meter) aan macro- tussen de waterschappen tonen. NB: in de beginjaren was het aantal monsters fauna-insecten. A: Trend in totale dichtheid aan insect-macrofauna (opgeteld in sommige waterschappen laag, waardoor de WA-specifieke lijnen voor die over de 10 taxonomische groepen en 8 waterschappen). De 95% betrouwbaar- jaren meer variatie vertonen. heids-interval is geplot rond de trendlijn. B: Gemiddelde jaarlijkse verandering • Figuur 8: Trends in nutriëntenconcentraties: A) ammonium, B) fosfor totaal, C) (in percentages) in het aantal macrofauna-insecten per waterschap. stikstof totaal, en D) biochemisch zuurstofverbruik. Blauwe lijnen met knopen • Figuur 4: Trends in totale dichtheden (aantallen per bemonsterde meter) aan geven het gemiddelde over de waterschappen, terwijl de andere lijnen de macrofauna-insecten voor elk van 10 taxonomische groepen. Blauwe lijnen ge- afzonderlijke WAs vertegenwoordigen. ven de trends per waterschap weer, terwijl de blauwe punten de overall trend • Figuur 9: Gemiddelde veranderingen in de oppervlaktes van verschillende

8 landgebruiksklasses van 1996 tot en met 2015, binnen een straal van 1km om gatief) hangt af het teken van de correlatie tussen de aantalstrends en scores elk van de macrofauna monsters. voor een eigenschap op het niveau van de genera. De geteste eigenschappen • Figuur 10: Gemiddelde verandering in toxiciteit-indices tussen 1990 en 2017. zijn in figuur 14 geprefereerd substraat, voortbeweging, kenmerkend nutriën- A: jaarlijks maximum van de potentieel aangetaste fractie op basis van pesti- tenniveau, stroomsnelheid, transversale verspreiding (gradiënt vanaf rivieren ciden. B: jaarlijks maximum van de potentieel aangetaste fractie op basis van landinwaarts), en longitudinale verspreiding (van bovenstroom tot zee); industriële stoffen. C: jaarlijks maximum van de potentieel aangetaste fractie in figuur 15 aquatische stadia, voedsel, wijze van eten, maximum grootte, op basis van verbrandingsverbindingen. Blauwe lijnen met knopen geven het levenscyclusduur, en saprobiteit (een maat van waterkwaliteit); in figuur 16 gemiddelde over de waterschappen, terwijl de andere lijnen de afzonderlijke geprefereerde pH, reproductie, verbreiding, stadia waarin moeilijk omstan- WAs vertegenwoordigen. De toxische druk van mengsels van chemische stof- digheden overbrugd worden, ademhaling, hoogte, het aantal levenscycli per fen is gekwantificeerd met de jaarlijkse maxima van msPAF-NOEC waarden. jaar, en temperatuur. • Figuur 11: Terug-getransformeerde effecten (z -scores) van covariabelen op • Figuur 17: Relatie tussen aantalstrends op genus-niveau en verschillende de abundantie van insecten. Getoonde effecten vatten de effecten op de 10 indicator waarden van soorten in die genera. A: Er blijkt geen verschil in de insectengroep samen. De horizontale balken laten dus de variatie zien in gemiddelde aantalstrend van genera waar van de soorten wel, danwel niet, effectgroottes tussen de taxonomische groepen. risico lopen door effecten van microverontreinigingen volgens de SPEAR me- • Figuur 12: Terug-getransformeerde effecten (z -scores) van covariabelen op de thodiek. 31 genera waren ’at risk’, 203 genera waren ’not at risk’. In panels abundantie van insecten. Effecten zijn samengevat over de 10 insectengroe- B en C staat de gemiddelde aantalstrend uitgezet tegen indicatorklasse voor pen, apart voor de 3 regio’s: A) Oost, B) Zuid, C) West. doeltypen van respectievelijk rivieren (B) en meren (C). De indicatorklasses • Figuur 13: Effecten van veranderingen door de tijd in de waarden van cova- zijn: N (”Negatief”, soorten in een genus zijn voornamelijk negatieve indi- riabelen op de trend in de abundantie van insecten, apart voor elk van 10 catoren rivieren meer-doeltypen), K (”Kenmerkend”, soorten in een genus taxonomische groepen. Gele pijlen geven de richting (toename of afname) en zijn voornamelijk karakteristieke taxa), P (”Positief”, voornamelijk positieve relatieve grootte van de veranderingen (van 1990 tot 2017) aan voor elke van indicatoren wanneer soorten dominant zijn), en 0 (genera zonder indicator de covariabelen. De zwarte balken kwantificeren het effect van de verande- soorten of met een mix van soorten met N, K en P indicaties). Zwarte balken ring in een covariabele op de abundantie-trend. De blauwe, onderbroken lijn geven significante verschillen aan. geeft de som van de zwarte balken weer (soms buiten beeld). De eenheden zijn op de log-λ schaal. In deze grafieken zijn de resultaten voor de 8 waterschappen samengenomen, ongewogen naar de relatieve abundantie van de insectengroepen in die waterschappen. • Figuren 14, 15 en 16: Relaties tussen aantalstrends op genus-niveau en verschillende eigenschappen van die genera. Hier geplot staan de herverdelin- gen van de t-waardes van de mixed-effect modellen waarin steeds één van de eigenschappen als covariabele opgenomen zijn. Zwarte balken geven significante effecten aan (t-value > 1.96). De richting van de balken (positief of ne-

8 9 Sewer overflow Photo: Eelke Jongejans ABSTRACT

World-wide concerns about declines in insect abundance have renewed appreciation for existing insect monitoring schemes. Longer-running schemes present the opportunity to learn which insect groups are doing better than others, and to look for environmental factors and management options that influence the abundance trends of insects.

To study insect trends in Dutch surface waters, we here analyse a long-running but complex dataset of insects in macroinvertebrate samples collected by Dutch Water Authorities (WAs). Prior quality checks resulted in a subset of one third of the WAs, covering parts of the south and east of the Netherlands as well as a large region in the

10 vicinity of Rotterdam. To arrive at a database based on a reasonably consistent Seemingly in contrast with the observed overall decline in insect abundance, sampling protocol, we used a subset of years: from 1990 until 2018. Identity of the positive trends were found for 66% of the 213 insect genera for which we were person collecting the insects in a water body, the WAs, and the length of the sam- able to determine abundance trends. Looking at the number of insect genera in pling transect were included as factors in our analyses. Weather data and water the macroinvertebrate samples, and their evenness and diversity, we see clear quality samples were spatially interpolated to 1,709 macroinvertebrate sampling increases over time. This increase in biodiversity is partly due to the decrease in locations (with a total of 12,087 samples). abundance of the most abundant taxa, but also due to the increase of some of the previously more rare taxa. Our analyses show that taxonomic (genus level) richness and diversity increased over the course of 28 years, while the overall abundance of water insects declined Linking the genus-level trends to traits of insects in those groups showed that by 53%. Although dragonflies and caddisflies increased in abundance, most other insects preferring clear, running water had more positive abundance trends insect orders declined in abundance. In our final models of insect abundance, than those preferring standing waters and sludge. This is confirmed by patterns the negative effects of nitrogen, phosphorus and pesticides (i.e. there were fewer among genera with indicator species. Taxa characteristic for healthy river and insects when nutrient concentrations or the combined toxicity of pesticides were lake communities showed positive trends, while negative indicators declined in high) were amongst the strongest effects in most groups. In contrast, non-biting abundance. midges (Chironomidae) and black flies (Simuliidae) showed positive responses to these factors. The presence of greenhouses and proximity of sewer outlets also The emerging pattern is that, in general, water insect communities are recover- had negative effects on the abundance of most groups. Other factors that showed ing from poor water conditions (high nutrient loads and pollutions) in the 1980s. up as important for the measured abundance of at least some groups included As a result of the halving of the nitrogen and phosphorus concentrations and biochemical oxygen demand, wet natural areas, and growing degree days (a meas- pesticide toxicity during the study period, groups like non-biting midges and ure of accumulated heat). black flies have decreased, which generally have opportunistic life history strate- gies and are indicators of poor (eutrophic) water quality. Overall, diversity indices We also quantified how much environmental changes over the course of the continuously improved from 1990 till 2017. Since proper reference datasets are study period contributed to the abundance trend of various insect groups. Our lacking, it remains unclear to what degree insect communities have recovered. models showed that the halving of N and P concentrations from 1990 to 2018, However, management aimed at restoring water quality, which was the last phase generally had a strongly positive effect on insect abundance trends, as had of implementing sanitation measures (sewage systems), appears to have had a the threefold drop in combined toxicity of measured pesticide levels. Again, clear positive effect on the diversity of aquatic insects, favouring indicator species indicators of eutrophic and polluted conditions, Chironomidae and Simulii- of healthy ecosystems. Seeing these encouraging developments, there is every dae, showed opposing responses to the decreases in nutrients and pesticides. reason to continue with the efforts to further improve water quality, as nutrient Land cover variables had not changed much over this time period and thus did and toxicity levels are still above their maximally allowed concentrations in many not contribute much to abundance trends; nor did most weather variables. In 7 water bodies. At the same time there is a need to research how management of out of 10 insect groups, the abundance trends were mainly caused by unknown water and vegetation can fully restore pristine insect communities, especially factors. with respect to taxa-specific abundances.

10 11 Migrant hawker - Aeschna mixta - paardenbijter Photo: Michel de Beer INTRODUCTION

Recent reports in Europe and other parts of the world (Biesmeijer et al. 2006; Dirzo et al. 2014; Hallmann et al. 2017, 2019; Sánchez-Bayo and Wyckhuys 2019; Seibold et al. 2019; van Klink et al. 2020b; Zattara and Aizen 2021) suggest that terrestrial insects are declining at alarm- ing rates, but knowledge on community-wide abundance trends of insects as well as on causal factors driving insect decline remain far from complete. Furthermore, how insect communities respond to changes in potential drivers remains uncertain, partially because of the large number of potential drivers (Wagner et al. 2021), and the different spatial and temporal scales at which they operate. Large-scale, fine-grained, multi-taxon and long-term standardized data are imperative to assess which groups of insects are in decline most, where such insect declines occur, and why.

12 Freshwater invertebrates are known to be influenced by an array of chemical and environmental changes. However, even when successful, new monitoring will physical water properties (Von Der Ohe and Liess 2004; Beketov et al. 2008, 2013; obviously not uncover trends and effects of the past decades. Therefore, we take Posthuma et al. 2019; Birk et al. 2020; Lemm et al. 2021), pesticides (Liess et al. on an even bigger challenge in this report: we set out to integrate multiple, 2021), climate (Burgmer et al. 2007; Verberk et al. 2016; Jourdan et al. 2018; Baranov mostly disconnected datasets that have been constructed for different purposes et al. 2020; Roth et al. 2020; Outhwaite et al. 2020) and land use (Richards and Host than detecting and understanding abundance trends of insect macrofauna. The 1994; Weijters et al. 2009). Water quality was very poor in many (but not all) places challenges for such an integration are multifold, even in the Netherlands where in The Netherlands halfway the 20th century, but important measures were al- different monitoring schemes have high spatial resolution and are relatively ready taken in the 1970s and 1980s with the construction of new sewage systems long-running. and wastewater treatment plants. Also in other parts of Europe improvements in water quality have occurred (Vaughan and Gotelli 2019), further facilitated by the In the case of Dutch data on macrofauna, weather, land use, water quality and introduction of the EU Water Framework Directive in 2000 (Outhwaite et al. 2020). micropollutants, a major challenge is that these variables have been measured In the Netherlands, water quality has indeed improved in recent decades (van at different locations and at different points in time. Weather stations supply Gaalen et al. 2020), with water quality measurements indicating that emissions the most consistent, hourly data, but are relatively sparse in space. Water quality of industrial chemicals, nutrient emissions (N, P) and agricultural pesticides have measurements (e.g. pH, nutrients) are repeatedly (generally several times per year) been lowered, and that wastewater treatment has improved. Notwithstanding measured at the same locations, but these locations do not necessarily match the this marked decrease in total volume of application, the toxic pressure from locations where macrofauna samples are taken. Interpolations to the macrofauna new compounds may have increased at the same time, owing to a much higher sampling locations are therefore required, taking into account temporal trends relative toxicity of currently used pesticides and medicine residues. Additionally, and spatiotemporal covariance structures of these environmental variables, while increased summer droughts, earlier springs and other climatic changes are likely weighting by distance. Substances like pesticides, industrial compounds, and to induce variation in annual insect populations. These various developments combustion products, also known as micropollutants, prove to be particularly may thus have both positive as well as negative effects on total insect abundance. challenging in that respect: due to their large variety and the relative high costs Furthermore, factors like high nutrient concentrations and toxic compounds can of tests, not all micropollutants are tested for, after a water sample is taken. More- have interactive effects, as has recently been shown in some empirical studies over, new agricultural and industrial compounds are being developed and taken (Birk et al. 2020; Lemm et al. 2021). In order to disentangle potentially opposing or into use regularly, while monitoring for new micropollutants in the environment interacting effects, we require large-scale and long-term information not just on generally starts only years after these compounds and their derivatives entered the abundance of macrofauna, but also on the state of potential drivers. the environment. Also, many test results show that concentrations of specific micropollutants are below detection limits, which does not mean that the Designing a new macrofauna monitoring program that is large-scale enough to concentration is zero, nor that low concentrations are irrelevant for macrofauna. cover many regions and types of water bodies nation-wide is a daunting endeav- As interpolation of single micropollutants therefore prove too difficult, novel our. This is particularly challenging as it is pivotal to also measure all environ- integration methods need to be developed to arrive at toxic pressure estimates mental factors relevant now and in the future, and to continue the program for each of different groups of micropollutants (e.g. pesticides, industrial com- consistently for a long enough time to detect macrofauna trends and effects of pounds, and combustion products).

12 13 The macrofauna data themselves pose challenges as well. Originally set up to as- In the present study we scrutinize and bring together long-term and large-scale sess the biodiversity and intactness of macroinvertebrate communities and their datasets on i. freshwater insect macrofauna, ii. weather, iii. land use, iv. physical/ relationships with abiotic factors (van der Hammen 1992; Steenbergen 1993), chemical water properties, and v. toxicity. Our goal is to uncover the trend of the Dutch macrofauna monitoring scheme is accompanied by a clear sampling, freshwater insect-communities over the past three decades in the Netherlands, processing and estimation protocol (Beers et al. 2014). Already before this protocol and to unveil the (potentially opposing) effects of multiple drivers. We integrate was published, macrofauna samples were collected and processed in reasonably two powerful ecological assessment methods into the analysis, that of driver- standardized way. In practice, however, subtle but important differences have change effect-on-trend interpretation, and that of trait-based interpretation of occurred between the different Water Authorities (currently there are 21 WAs in population trends, in order to characterize developments in freshwater insects the Netherlands) and between individual field and lab workers. Proper analyses along trait axes. Specifically, we study trends in abundance of various freshwater of macrofauna trends will have to accommodate all these aspects of sampling insects at two taxonomic levels: we analyse abundance trends at the level of (e.g. how many meters were sampled) and processing (e.g. how larger numbers of genera, and for each of 10 insect groups defined at the order or family level. individuals in a sample have been estimated) in a mechanistic way, rather than Furthermore, we also analyse the species richness diversity of the macrofauna glossing over potential confounding factors. samples. In order to do so we develop powerful statistical models that properly account for the spatiotemporal design of these measurements, i.e. disentangle ecological variation from sampling variation.

14 Southern emerald damselfly - Lestes barbarus - zwervende pantserjuffer Photo: Marga Limbeek

14 1515 Taking macrofauna samples Photo: Ilse van de Kraats MATERIALS AND METHODS

The Materials and Methods section is organized as follows: We first introduce the macroinvertebrate dataset, including the monitoring and sample processing protocols used by the Water Authorities (WAs). We then describe models of insect abundance trends at two different taxonomic levels: i) per genus and ii) integrated over 10 taxonomic groups (e.g. insect orders, see Table S.1), as well as models of trends in diversity indices. Next, we explain our assessment of the effects of a large range of potential explanatory variables on the taxonomic-group abundances, genus-level diversity, and trends therein. Finally, we related abundance trends to characteristic traits of the investigated genera. A flow diagram of all analyses is given in Fig. 1.

16 FIGURE 1 FRESHWATER MACROINVERTEBRATE DATA Schematic diagram of analyses performed. Red box: response data. Blue boxes: Explanatory In the Netherlands, regional Water Authorities (also called Water Boards, and in data. Orange boxes: results. Black arrows: Data link. Blue arrows: spatio-temporal interpo- Dutch: Waterschappen or Hoogheemraadschappen; hereafter: WAs) have amassed lation. Green Arrow: Spatial weighting function. vast datasets on the abundance and distribution of macrofauna (i.e. small invertebrates still detectable without magnifier or microscope) in Dutch surface water bodies (brooks, ditches, ponds, lakes, canals, rivers, etc) since 1980. These data have previously been collated and archived under the name Limnodata Neerland- ica, which are available at http://ipt.nlbif.nl/ipt/resource.do?r=stowa-limnodata. However, variable levels of data curating and metadata archiving, as well as methodological sampling differences between the WAs prohibit a straightforward trend analysis of species abundances with this national database (Netten et al. 2010; Verdonschot and van Oosten-Siedlecka 2010). Notwithstanding the protocol, field workers potentially differ in how exactly they collected macrofauna samples. In the lab, the taxonomic level at which specimens were identified varied somewhat between the people processing the samples, probably due to varying levels of experience and time available. Over the years, better identification guides became available, increasing the possibilities to accurately identify insects at the species level.

However, the developmental stage of the caught insects has a big effect on whether specimens are identified at the species level: in many cases it is not possible to identify species at larval or juvenile stages. Earlier attempts to aggregate the macrofauna data from all Water Authorities suffered from inconsistencies in the recorded abundances and metadata (Netten et al. 2010; Verdonschot and van Oosten-Siedlecka 2010). Therefore, it was necessary for us to go back to the original data of separate WAs and only select those datasets that were sufficiently well-organized and accompanied with necessary metadata (Koese and Zeegers 2018). To arrive at a subset of the macrofauna data with minimal issues, EIS Neth- erlands (Koese and Zeegers 2018) assessed the state of the macrofauna datasets of all WAs. These researchers checked whether for each macrofauna sample the transect length and the identity of the person who collected the sample in the field was known, whether the records were complete (i.e. no obvious groups of

16 17 insects missing) and whether the sample processing protocols appeared to have FIGURE 2 been followed. They eventually selected eight WAs with the most complete and Map of Dutch Water Authorities (WAs) included in this study (A) along with the number of most consistently archived datasets, and identified which subsets of those data- contributing samples per year (B) and the number of sampled years per monitoring location sets were of high-enough quality. Two of these eight WAs have recently (1 January (C). Locations had to have samples from at least 3 years to be included in the study. The 2017) merged, as has happened frequently in the rich history of Dutch WAs. The eight WAs are HHD (Hoogheemraadschap van Delﬂand), WHD (Waterschap Hollandse Del- eight WAs selected based on the quality and availability of their data were: ta), WAM (Waterschap Aa en Maas), WD (Waterschap De Dommel), WPM (Waterschap Peel • HHD: Hoogheemraadschap van Delfland en Maasvallei, nowadays part of Waterschap Limburg), WRD (Waterschap Regge en Dinkel, • WHD: Waterschap Hollandse Delta (also known as WSHD) nowadays part of Waterschap Vechtstromen), WRIJ (Waterschap Rijn en IJssel) and WRO • WAM: Waterschap Aa en Maas (Waterschap Roer en Overmaas, nowadays part of Waterschap Limburg). • WD: Waterschap De Dommel • WPM: Waterschap Peel en Maasvallei (now part of Waterschap Limburg) B • WRD: Waterschap Regge en Dinkel (now part of Waterschap Vechtstromen) 700 • WRIJ: Waterschap Rijn en IJssel 600 • WRO: Waterschap Roer en Overmaas (now part of Waterschap Limburg) 500 400 The spatial distribution of these WAs is given in Figure 2A. We here use a re-com- 300 piled database based on the original data of these eight WAs. The datasets were 200 Number of samples 100 thoroughly examined by entomologists of EIS Netherlands (Koese and Zeegers A 0 2018) for completeness, irregularities, and sufficient level of metadata archiving. 1990 2000 2010 Since then more data and metadata were made available by several WAs (HHD, WPM, WRO, WRIJ) and added to the dataset. These additions required new rounds WRD of data checking to fix inconsistencies. HHD WRIJ C WHD 400 Sampling HHD WPM WD WAM WPM WAM WRD Macrofauna samples were collected by field experts of the WAs following a 300 WD WRIJ prescribed protocol (Beers et al. 2014). To arrive at a multi-habitat sample, locally WHD WRO available microhabitats are sampled. The microhabitats can differ in vegetation, WRO 200 structure, soil properties and the transparency of the water. Often the total 100 length over which samples are taken on a sampling location (i.e. summed over Number of locations the sampled microhabitats) was five meters (5m in 47% of the cases, mean=6.3m, 0 sd=2.6m). Earlier samples were largely collected with the same methodology 3 6 9 13 17 21 25 29 as recorded in the protocol. Deviations in e.g. sampling length are recorded in Number of years

Figure 2: Map of Dutch Water Authorities (WAs) included in this study (A) along with the number 18of contributing samples per year (B) and the number of sampled years per monitoring location (C). Locations had to have samples from at least 3 years to be included in the study. The eight WAs are HHD (Hoogheemraadschap van Delﬂand), WHD (Waterschap Hollandse Delta), WAM (Waterschap Aa en Maas), WD (Waterschap De Dommel), WPM (Waterschap Peel en Maasvallei, nowadays part of Waterschap Limburg), WRD (Waterschap Regge en Dinkel, nowadays part of Waterschap Vecht- stromen), WRIJ (Waterschap Rijn en IJssel) and WRO (Waterschap Roer en Overmaas, nowadays part of Waterschap Limburg).

further processed in the laboratory, where they were identiﬁed at the lowest taxonomic level possible,

and conserved.

For the trend analyses we selected all sites with at least 3 sampling occasions. In total, 12,087

22 the metadata. Standard macrofauna-nets, with a 20×30cm rectangle opening uals required to be identified taxonomically. For example, for chironomids the and 0.5-mm mesh size, were used for sampling the water column and substrate minimum is set at 150 individuals, for Lepidoptera at 25 individuals. In prac- (Greijdanus-Klaas 1999). All macrofauna specimens caught were collected, sorted, tice, however, the number of individuals that are actually identified to species preserved, and further processed in the laboratory, where they were identified at level varies somewhat between samples, but is dispersed regularly around the the lowest taxonomic level possible, and conserved. prescribed minimum number of individuals for each taxonomic group. Sub-sampling within abundant taxa affects the species richness, as this parameter scales For the trend analyses we selected all sites with at least 3 sampling years. In total, non-linearly with sample size (i.e. number of individuals in a sample). A further 12,087 unique sampling instances were used in our analysis, distributed over 1,709 complication that arises in 57% of the macroinvertebrate samples is that the unique monitoring locations, over the period 1990-2018. The number of samples number of individuals actually determined to species level is not archived, or is have increased with time, from around 200 samples per year in the 1990s, to at not certain. In such cases, only the extrapolated numbers abundance is archived, least 500 samples per year after 2010 (Fig. 2B). Half of the locations (52%) were sam- meaning that there is no way of telling whether or not species-level abundances pled in 3, 4 or 5 years, but the analysed database also included longer time series: have been partially extrapolated or by how much. Properly dealing with counted 16% of the locations were sampled in at least ten years, while 2% of the locations and extrapolated counts is also important for the certainty of the model param- was sampled 20 years or more (Fig. 2C). Macrofauna samples were mainly collected eters. In the present study, we take the approach of modelling these sampling during spring, summer and early autumn, and rarely during winter. processes explicitly, in order to propagate errors of uncertainty to the points of interest, such as the temporal dynamics of the focal species. Species identification Sample processing was always done according to a nationally standardized pro- MACROINVERTEBRATE MODELS tocol (Beers et al. 2014). Following this protocol, live macroinvertebrate samples Our modelling strategy to derive trends in abundance of insect macrofauna involved were first divided into major groups, which were separately stored in appropri- two sets of models: one set at the level of taxonomic groups (e.g. insect orders, see ate preservation fluids. Next, specimens were identified at species level where Supplementary Table S.1) and one set at the genus level. Additionally, we derived possible, or at some higher taxonomic level if not. The percentage of specimens trend models for four genus-diversity indices. We first ran models on the sum of that were identified at species level is variable over time, with a slight increase in each taxonomic group per macroinvertebrate sample. These models were used to taxonomic depth: from approximately 65% identified at species level in 1990 to derive general trends in total abundance for each taxonomic group. Secondly, we 70% in 2018 (Figure S1). Taxa with very high numbers were treated as follows: a developed and ran models per genus to assess spatio-temporal changes at that lower random subset up to a taxon-specific maximum was identified at the lowest-pos- taxonomic level. We explain each of the two sets of models separately below. sible taxonomic level. The abundance of the remaining individuals was counted or estimated, after which the abundances of the identified taxa are extrapolated Given the variation in how macroinvertebrate samples were collected and how proportionally. In many of the datasets these extrapolations are indicated specif- samples were processed at the species level in the lab, we developed additive ically. For instance, for chironomids numbers surpassed the prescribed threshold mixed-effects model to accommodate sources of variation in both ecological in approximately 30% of the samples. Depending on the taxonomic group, the and sampling processes. Generally, there are five sources of sampling variation laboratory protocols (Beers et al. 2014) prescribe a minimum number of individ- present in the data:

18 19 oﬀset for log-sampling length. Our null model only included a single environmental variable: water

type, while our full models involved a number of additional covariates, such as soil type, land use

and cover variables, weather variables, chemical and physical water properties, and measures of total 1. variation in taxonomic depth of species identiﬁcation over time and between WAs, toxicity (see Table 1). Further details on covariates and preprocessing are given in section Explanatory 2. variation due to sub-sampling for taxonomic identiﬁcation, variables.

3. variation in sampling-transect length, Genus-level models

4. variation between field experts who collected the macrofauna samples, and 1. variation in taxonomic depth of species identification over time and At genus level,and physical we fitted water a similar properties, model and to measures the null taxonomic-groupof total toxicity (see models Table 1). (i.e. Further including only between WAs, details on covariates and pre-processing are given in section Explanatory variables. 5. slight variation in data processing protocols between WAs. water type as covariate; eq. 1) We did not fit genus-level trend models for genera for which less than 2. variation due to sub-sampling for taxonomic identification, 3. variation in sampling-transect length, 500 individualsGenus-level were recordedmodels in total, as there needs to be ample data to estimate reliable rates For each of these sources of variation, an attempt was made to ameliorate potential bias in our 4. variation between field experts who collected the macrofauna samples, and At genus level, we fitted a similar model to the null taxonomic-group models (i.e. of increase. And even when overall numbers were above that threshold, still some models did not estimates.5. Sources slight of variation abovementioned in data processing variation protocols 1 and 2 are between not of WAs. influence on models at taxonomic including only water type as covariate; eq. 1). We did not fit genus-level trend converge onmodels reliable for trend genera estimates for which for less some than genera. 500 individuals In those cases were where recorded a trend in total, estimate had very group-level,For but each are of certainly these sources of influence of variation, in models an attempt at the genuswas made level, to andameliorate in estimates poten of- diversity. as there needs to be ample data to estimate reliable rates of increase. And even tial bias in our estimates. Sources of abovementioned variation 1 and 2 are not of wide confidencewhen intervals,overall numbers we excluded were above it from that further threshold, analyses still of some the genus-levelmodels did ratenot con of increase.- Sources 3-5 are of influence on all modeling estimes, and need to be dealt with explicitly. influence on models at taxonomic group-level, but are of influence in models at verge on reliable trend estimates for some genera. In those cases where a trend the genus level, and in estimates of diversity. Sources 3-5 are of influence on all estimate had very wide confidence intervals, we excluded it from further analyses Diversity models Taxonomicmodelling group modelsestimates. of the genus-level rate of increase.

We considered genus richness, Shannon’s index of diversity, Simpson’s index of diversity, and Shan- We ﬁtted twoTaxonomic generalized group mode additivels models (GAMs, Wood, 2017) to the summed counts for each Diversity models We fitted two generalized additive models (GAMs, Wood 2017) to the summed non’s evennessWe considered index, to describe genus richness, trends in Shannon’s genus-level index diversity of diversity, over time Simpson’s and per WA.index Unit of analysis taxonomiccounts group. for Preliminary each taxonomic results group. indicated Preliminary that response results dataindicated were that overdispersed response relative to a of diversity, and Shannon’s evenness index, to describe trends in genus-level was the index for each sampling instance, and as such we infer on the average diversity per sample. Poisson process,data were and overdispersed hence we used relative a negative-binomial to a Poisson process, distributions and hence in ourwe used GAMs, a neg with- a log link diversity over time and per WA. Unit of analysis was the index for each sampling ative-binomial distribution in our GAMs, with a log link to the predictors. Our instance, and as such we infer on the average diversity per sample. Shannon’s Shannon’s diversity index scales between 0 (a single species) to log(S) and depends on both the to the predictors.models Our(one modelsfor each (one of the for 10 each taxonomic of the 10groups), taxonomic were groups),of the form were of the form diversity index scales between 0 (a single species) to log(S) and depends on both number of generathe number and theirof genera relative and abundances their relative in abundances a sample. in a sample.

log(ni)=α + bijxij + f(t WA)+fcc(s)+fsp(x, y)+ri + oi (1) (1) | S H = (pilog(pi)) (2) (2) − i where i denotes sample, α a global intercept, x covariate levels, along with the corresponding linear where i denotes sample, α a global intercept,ij xij covariate levels, along with the

corresponding linear effects (bi j). f (t|WA) is a smooth annual trend function, The evenness is calculated as H/log(S). The Simpson’s index of diversity is calculat- eﬀects (bij). f(t WA) is a smooth annual trend function, conditioned on the identity of the Water conditioned| on the identity of the Water Authority (WA), fcc(s) is a cubic spline The evennessed is as calculated as H/log(S). The Simpson’s index of diversity is calculated as

Authority (WA),functionf of(s day-number,) is a cubic splinefsp(x, y) functiondenotes a of spatial day-number, smooth fspline(x, yof) denotessampling a loca spatial- smooth cc sp 26 tion coordinates. ri denotes a random effect for observer and finally oi is the offset spline of samplingfor log-sampling location length. coordinates. Our nullri denotesmodel only a random included effect a single forobserver environmental and finally oi is the J =1 (p2) (3) (3) − i variable: water type, while our full models involved a number of additional covariates, such as soil type, land use and cover variables, weather variables, chemical 25 and reflects the probability that two independent draws of an individual in a sample belong to different

genera. 20 Each of the four indices was was modelled using generalized additive models. We assumed a

negative-binomial error structure with a log link for richness. For other indices, we assumed Gaussian

errors, but we used a log transformation of H, and a logit transformation of evenness and Simpsons

index. Our models for each index (k) read as

l(θ )=α + b x + f(t WA)+f (s)+f (x, y)+f (log(n)) + γlog(p)+r + o (4) k ij ij | cc sp r i i

where l(.) is the link function speciﬁc to index k. Parameters and smooth functions bij, ri, oi, as well

as smooth functions f(.), fcc(.), fsp(.) are deﬁned in this model as in equation 1, while fr(.) denotes

a smooth function of total abundance (i.e. mimicking rarefaction) and γ measures a linear eﬀect on

the log-proportion of the sample identiﬁed to at least genus level.

Explanatory variables

To uncover drivers of variation in abundance (at taxonomic group level) and diversity, as well as

trends therein, we expanded the set of explanatory covariates (xij) in equations 1 and 4, by including

land cover and land use, soil properties, physical/chemical water properties, toxicity, and weather

variables. In doing so, we opted to obtain time-varying covariates (where necessary), which allows to

examine, besides their relative eﬀect on the each response value, also how changes in each variable

27 The evenness is calculated as H/log(S). The Simpson’s index of diversity is calculated as

J =1 (p2) (3) − i

and reflectsand the reflects probability the thatprobability two independent that two draws independent of an individual draws in of a samplean individual belong to in different a TABLE 1 sample belong to different genera. Overview of explanatory variables included in the analyses. The values of dynamic variables genera. can change over the duration of the study period. EachEach of the of fourthe indicesfour indices was was was modelled modelled using using generalized generalized additive additive models. models. We assumed We a assumed a negative-binomial error structure with a log link for richness. For oth- Parameter Characterization Years Source negative-binomial error structure with a log link for richness. For other indices, we assumed Gaussian er indices, we assumed Gaussian errors, but we used a log transformation of H, Water type Static - WAs; CBS 2019 errors, butand we a usedlogit atransformation log transformation of evenness of H, and and a logit Simpsonstransformation index.of Our evenness models and for Simpsons each Protected areas Static - Nationaal Georegister index (k) read as Soil type Static - Grondsoortenkaart index. Our models for each index (k) read as Land cover Dynamic/Scale- 1996-2019 CBS 2019; Kadaster dependent 2019 l(θ )=α + b x + f(t WA)+f (s)+f (x, y)+f (log(n)) + γlog(p)+r + o (4) Distance to sewage treatment plant Dynamic 1990-2018 WAs k ij ij | cc sp r i i Weather variables Dynamic 1990-2018 KNMI Physical/Chemical properties Dynamic 1990-2018 WAs where l(.) is the link function specific to index k. Parameters and smooth functions bij, ri, oi, as well where l(.) is the link function specific to index k. Parameters and smooth func- Toxicity Dynamic 1990-2018 WAs

as smoothtions functions bij, ri, ofi,( .as), fwellcc(.), asfsp smooth(.) are defined functions in this f (.), model fcc(.), asfsp(.) in are equation defined1, while in thisfr( .)model denotes as in equation 1, while f (.) denotes a smooth function of total abundance (i.e. a smooth function of total abundancer (i.e. mimicking rarefaction) and γ measures a linear effect on mimicking rarefaction) and γ measures a linear effect on the log-proportion of the log-proportionthe sample of identified the sample identifiedto at least to genus at least level. genus level. Water type Owing to the large variety in types of surface water bodies, a water classification EXPLANATORY VARIABLES system was deemed necessary to characterize the sampled water bodies. Our clas- Explanatory variables To uncover drivers of variation in abundance (at taxonomic group level) and sification was largely based on established Water Framework Directive definitions,

To uncoverdiversity, drivers as of well variation as trends in abundance therein, (atwe taxonomicexpanded groupthe set level) of explanatory and diversity, covariates as well as which classifies water bodies according to major classes (e.g. lakes, ponds, rivers,

(xij) in equations 1 and 4, by including land cover and land use, soil properties, canals, streams and ditches), with further subdivision depending on size, depth or trends therein, we expanded the set of explanatory covariates (x ) in equations 1 and 4, by including physical/chemical water properties, toxicity, and weatherij variables. In doing so, flow-speed classes, or in some cases on soil type. A sizeable part of the macrofauna land coverwe andopted land to use,obtain soil time-varying properties, physical/chemical covariates (where water necessary), properties, which toxicity, allows and weatherto monitoring locations (24%) is not situated in a KRW-water body, and therefore examine, besides their relative effect on each response value, also how changes have not been assigned a KRW-definition. For these locations we derived addition- variables. In doing so, we opted to obtain time-varying covariates (where necessary), which allows to in each variable contributed to changes in abundance. Furthermore, we used al information from the national land cover map (TOP10NL: see www.pdok.nl), examine,a besidesdata-driven their relativeapproach eﬀect in ondetermining the each response the spatial value, scale also howat which changes land in use each and variable that classifies water bodies as ditch, waterways of 3-6 and 6-12 meter, rivers, and land-cover variables affected taxonomic group abundances. An overview of the lakes. An overview of the number of macroinvertebrate sampling locations per wa- explanatory variables is given in Table27 1. Below we describe the source of each of ter type is given in table 2. A full account of the relation between watertype-classi- these covariates and how we handled the data prior to the analyses. fication in this study with original water type classification from the two sources is given in table S.2.

20 21 TABLE 2 Sewage treatment plants are a source of water contamination in many parts of Number of macroinvertebrate sampling locations per water type and Water Authority (WA). the world. In the Netherlands around 327 facilities exist, out of which 140 are Only locations with at least three sampling years were included in the study. WFD = Water located within the study area. To include possible effects of sewage pollution we Framework Directive. used the proximity (i.e. the inverse of distance) of each measurement location to the nearest treatment plant. While doing so, the proximity was set to zero for all HHD WAM WD WHD WPM WRD WRIJ WRO locations not likely to be influence by sewage water, such as for example sam- Ditch 100 32 14 1 0 2 11 0 pling points in different water-catchment areas than that of the treatment plant, Slow running water 5 68 118 4 162 153 78 38 or sampling points clearly located upstream prior to sewage discharge points. Fast running water 0 5 0 0 10 17 0 79 Canals 141 25 6 68 10 5 13 0 Information on land use was extracted from a vector-land use database (CBS 2019; Ponds 0 6 0 0 25 36 0 16 Bestand Bodemgebruik), available for the years 1996, 2000, 2003, 2006, 2008, Small shallow lakes 15 0 0 0 2 7 0 10 2010, 2012 and 2015. Inevitably, we assumed no changes in landscape configura- Large lakes 0 0 0 17 0 0 0 0 tion in the period prior to 1996 and after 2015. Within a radius of 1km from the Brackish water 5 0 0 34 0 0 0 0 sampling location, we considered the area of the following land use categories: Ditch non-WFD 0 0 0 83 0 0 40 0 (i) agricultural land, subdivided into area for greenhouses, and remaining, (ii) for Waterway 3-6m non-WFD 0 0 0 57 0 0 1 0 est area, (iii) dry natural areas, (iv) wet natural areas, and (v) build-up areas. Ad- Waterway 6-12m non-WFD 2 0 0 121 0 0 5 0 ditional subdivision of remaining-agricultural land was achieved by integrating River non-WFD 0 0 0 46 0 0 1 0 the Dutch Topographic Map (Top10NL, Kadaster 2012), which classifies land cover Small lakes non-WFD 1 0 0 12 0 0 1 0 into grassland and cropped land, amongst other classes. For land use defined as remaining-agricultural land, we thus further subdivided it into grassland or cropped land. All computations were raster-based, at a resolution of 25m2, imply- ing that land use was described by over 5000 cells per sampling instance.

Coupling land use (surface data) to monitoring locations (point data) requires Land use and land cover knowledge on the spatial scale at which potential land-cover attributes may affect For each macrofauna sampling location, we extracted information on soil type, insect macrofauna, in order to achieve maximum explanatory power of the mod- amount of protected area, proximity to sewage treatment plants, and land use. els. Instead of relying on an a-priori defined spatial scale (e.g. percentage land-cov- Information on soil type around monitoring locations was extracted from the er in a buffer of 500 meters), we developed a data-driven approach to identify Dutch Soil Map (WUR-Alterra 2006). We recorded which part of this surround- the most appropriate scale at which to measure spatial covariate values for each ing area was protected (e.g. NATURA2000 or lower-level protection areas, nature taxonomic group. Additionally, one may envisage that the effect of a particular parks, etc.) within a radius of 1km around each monitoring location. Data were spatial covariate may diminish with distance to the focal point. To accommodate obtained from the ’Nationaal Georegister’ (www.nationaalgeoregister.nl). this in our models, we used a distance-weighted description of each variable, in

22 a manner by which values of the covariate at close proximity to sampling points growing degree days (i.e., the accumulated heat above 5oC), in order to account contribute more than values at increasing distance from measurement points. for potential phenological effects. To account for lagged effects, we considered the number of frost days in the preceding winter (Dec-Feb), as well as the sum of The relative contributions are described by weights which themselves are precipitation and mean temperature in the previous season (July-September). structured by low-rank parametric kernels, that depend only on scale and shape parameters (two parameters for each category of each variable, see Figure S4). The Physical/chemical properties scale parameters can be interpreted as a measure of the ”effective spatial scale” at Data on concentrations of chemical and organic compounds in the water have which ecological processes are affected by a particular covariate, while the shape been collected by, or commissioned by, the WAs according to fixed protocols as parameters as the steepness and tail length of the weighting function. More part of regional water quality assessments (www.helpdeskwater.nl/onderwer- details on this approach are provided in Appendix C. pen/monitoring/toetsen-beoordelen). Raw data were checked for validity and completeness by RIVM in collaboration with Ecofide as part of a related project, In practice, this approach is exhaustive (i.e. data-hungry), particularly with many and were made available to us by Jaap Slootweg. In total, between 12 thousand covariates and covariate levels, increasing with spatial resolution and range of and 400 thousand samples were included in the present analysis (depending on search. To reduce dimensionality of the computations, we first ran univariate variable), covering between 730 and 6884 unique locations (Table 3). As not all models between response variables (i.e. sums per taxonomic groups) and each water quality variables were measured for each sample, the datasets for specific covariate, and identify the scale and shape of each spatial weighting function. variables were considerably smaller. Despite the remarkably high density of spa- We then fixed weighting parameters for each covariate in all subsequent analyses tio-temporal information, measurements of physical and chemical water prop- involving all (or subsets) of these covariates. Additionally, we used a 25m grid erties are available at approximately 75% of the macrofauna sampling locations. representation of land cover within a radius of 500 meters around each measure- Furthermore, water samples for chemical analyses often were not collected on ment location, with binary values for each category. the same day as the macrofauna sampling. Hence, there is only a partial spatiotemporal overlap between water quality samples and macrofauna samples. As Weather variables the overlap between macrofauna and physical/chemical sampling locations was Weather data from 49 weather stations were obtained from the Royal Dutch Me- only partial, we require a means to arrive at estimates for each parameter at the teorological Institute (www.knmi.nl), where we extracted daily weather informa- location and date of each macrofauna sample (see Table 3). Expectations (usu- tion for the period 1990-2018. We considered ambient temperature, precipitation ally concentrations, but also scales such as water temperature) at macrofauna and potential evapotranspiration. These three weather variables were interpo- sampling points were derived using a combination of Generalized linear models lated to macrofauna monitoring locations using a combination of GAMs and and residual spatio-temporal interpolations. Per WA, we modelled the quantities, spatial interpolation of the residuals (see Supplementary Information: Appendix e.g. log(concentration), as a function of water type, soil type, spatial coordinates, A) for the full time period (i.e. daily, form 1-jan 1989 to 31-Dec-2018 and for each year and day-of-the-year, where the response variables were modelled as smooth- location). From the interpolated data, we derived multiple statistics, mean daily, splines of the space and time variables (x,y,t). We used a tobit-1 link (Tobin 1958) to weekly and monthly values of temperature as well as daily, weekly and monthly account for left censoring, where necessary. With this method we also took care sum of precipitation and of evapotranspiration. Additionally, we considered of potential effects of changing detection limits over time.

22 23 Table 3: Overview of sample sizes for each of the physical/chemical variables included in the analysis. Variable Locations Samples NH4 6495 325862 BOD 4582 204431 Cd 2647 90886 Ca 4331 70782 Cl 6682 436302 Chloro. alpha 3243 126713 Cr 2618 84761 PO4 6495 306819 Sum P 6670 333341 Cu 3055 107218 Pb 2623 85778 Mg 3695 63941 Na 3534 52898 Ni 2728 92242 Suspended matter 4526 155328 NO2+NO3 5400 274879 Sum N 6256 296762 Water temperature 6796 381233 HCO3 3917 52487 Zn 3052 107178 pH 6884 378892 O 6598 356967

link (Tobin, 1958) to account for left censoring, where necessary. With this method we also took care

TABLE 3 of potential eﬀectsOur models of changing read as: detection limits over time. Our models read as: Overview of sample sizes for each of the physical/chemical variables included in the analysis.

Variable Locations Samples l(y)=α + b x + f(t WA)+f (s)+f (x, y)+r (5) (5) ij ij | cc sp i NH 6495 325862 4 BOD 4582 204431

Cd 2647 90886 where α an intercept,where α anbij intercept,and xij coeﬃcients bij and xij coefficients and corresponding and corresponding covariates (water-type,soil-type,covariates (water pre- Ca 4331 70782 type, soil type, precipitation in the previous week and month), f (t|WA) a smooth cipitation in previous week and month), f(t WA) a smooth function of year conditional on WA, f(s) Cl 6682 436302 function of year conditional on |WA, f(s) a seasonal smooth component, fsp(x, y) a Chloro. alpha 3243 126713 spatial smooth component, and r a random intercept for watershed identity. a seasonal smooth component, fsp(x, y) a spatiali smooth component, and ri a random intercept for Cr 2618 84761 watershed identity. PO4 6495 306819 Model residuals were subsequently interpolated in both time and space to mon- Sum P 6670 333341 itoring locations at measurement date. Further details of how we modelled and Model residuals were subsequently interpolated in both time and space to monitoring locations at Cu 3055 107218 interpolated the physical/chemical water properties, as well as how we calculated Pb 2623 85778 measurementresiduals date. Further for measurements details of how below we modelled the detection and interpolatedthreshold, can the be physical/chemical found in the water Mg 3695 63941 Supplementary Information: Appendix B. Results of spatial and temporal correla- Na 3534 52898 tion used in the interpolation of each33 compound, can be found in Table S.3. Ni 2728 92242 Suspended matter 4526 155328 Toxic pressure of mixtures of chemical pollutants

NO 2+NO3 5400 274879 In order to include measures of toxicity of chemical substances originating from Sum N 6256 296762 pesticide application, combustion or industry in our models, and at the same Water temperature 6796 381233 time, to deal with the multidimensionality problem of over 1400 measured

HCO3 3917 52487 substances in surface waters, we relied on multiple-substances Potential Affected Zn 3052 107178 Fraction (hereafter msPAF) for compound mixtures of chemicals (Posthuma et al. pH 6884 378892 2002; de Zwart and Posthuma 2005). The Potentially Affected Fraction (PAF value) O 6598 356967 of a compound is based on large numbers of lab experiments, and is a measure of ecotoxicological risk posed by a substance: it represents the proportion of species that is affected by a substance (Klepper et al. 1998). The combined impact of multiple substances (e.g., for each of the three groups of chemical pollutions assessed here) is expressed as msPAF, of which two variants exists, acute (EC50; half maximum effect concentration) and chronic (NOEC; No Observed Effect Concentration) (Posthuma and de Zwart 2012), of which we used the latter. Fur-

24 To diﬀerentiate sources of toxicity, we categorized the large number of substances available in

three groups (pesticides, industrial substances, and combustion by-products), while several substances

belonging to either pharmaceutical or household-products were excluded due to insuﬃcient samples

sizes. As such, toxicity is described in our models as a three component structure, diﬀerentiating

sources of toxic pressure on insect macrofauna. An overview of the number of samples and locations

used for modelling for the three groups of toxicants can be found in Table 4. Please note that we

decided to leave heavy metals out of these toxic pressure estimates. Instead, heavy metals, whether

occurring naturally or due to pollution, were treated like other water quality variable that were

measured much more consistently than the chemical pollutants for which we expressed mixtures toxic

pressure as msPAF.

We ran General Additive Models (GAMs), assuming a beta distribution, and a logit link, to

derive mean trends per waterboard authority, and per chemical pollutant-group mixture toxic pressure

subcategory, followed subsequently by residual interpolation on an annual basis. Our GAMs read, for

each subcategory θ:

thermore, only substances above detection/reporting limits were included in the logit(msP AF )=α + b x + f(t WA)+f (x, y)+r + γlog(k) (6) (6) θ ij ij | sp i calculations of mixtures toxic pressure.

A further problem in using mixture toxic pressure data, is that the calculation it-where alphawherean intercept, alpha is banij andintercept,xij coeﬃcients bij and xij andcoefficients corresponding and corresponding covariates (water-type,soil-type), covariates

self relies to a large extent on the number of substances analysed in the samples, (water-type,soil-type), f (t|WA) a smooth function of year conditional on WA, fsp(x, f(t WA) a smooth function of year conditional on WA, fsp(x, y) a spatial smooth component, and with usually a strict increase in msPAF value with increasing number of substanc- | y) a spatial smooth component, and ri a random intercept effect for watershed es measured. Additionally, the sets of substances that are tested for varied over identity. Finally, to correct for the number of substances (k) measured at the ri a random intercept effect for watershed identity. Finally, to correct for the number of substances time, among WAs, and even between samples. This renders direct comparison be- maximum per year-location measurement, we included a linear effect of γ in tween samples problematic. As such, we used the maximum value of the msPAF (k) measuredthe at model. the maximum This was pernecessary year-location because measurement, water samples we for included which more a linear chemical effect of γ in the values of measurements per location-year combinations, under the premise that pollutants are tested, tended to have somewhat higher msPAF values. model. This was necessary because water samples for which more different chemical pollutants are toxicity is at least as high as the maximum msPAF value in a given year and at a given location. tested, tendedFurthermore, to have somewhat when macrofauna higher msPAF sampling values. locations and toxicity measurement locations did not match, we spatially interpolated the annual-maximum msPAF To differentiate sources of toxicity, we categorized the large number of substanc- Furthermore,values when to those macrofauna macrofauna sampling sampling locations locations and using toxicity similar measurement methods as locationswith did not es available in three groups (pesticides, industrial substances, and combustion the physical/chemical variables (see Table 4 and Supplementary Information: by-products), while several substances belonging to either pharmaceutical or Appendix B), where serial covariation at35 the daily level was substituted for serial household-products were excluded due to insufficient samples sizes. As such, correlation at the annual level. toxicity is described in our models as a three-component structure, differentiat- ing sources of toxic pressure on insect macrofauna. An overview of the number of samples and locations used for modelling for the three groups of toxicants can be found in Table 4. Please note that we decided to leave heavy metals out of these toxic pressure estimates. Instead, heavy metals, whether occurring naturally or due to pollution, were treated like other water quality variable that were TABLE 4 measured much more consistently than the chemical pollutants for which we Overview of sample sizes for each of the toxic pressure variables (one for each of three expressed mixtures toxic pressure as msPAF. subcategories of chemical pollutants) included in the analysis. Toxic pressure of mixtures is expressed as msPAF values. We ran General Additive Models (GAMs), assuming a beta distribution, and a logit link, to derive mean trends per waterboard authority, and per chemical Variable Locations Samples pollutant-group mixture toxic pressure subcategory, followed subsequently msPAF-Combustion 730 12785 by residual interpolation on an annual basis. Our GAMs read, for each sub- msPAF-Industrial 5688 250343 category θ: msPAF-Pesticide 1376 35324

24 25 match, we spatially interpolated the annual-maximum msPAF values to those macrofauna sampling locations using similar methods as with the physical/chemical variables (see Table 4 and Supplemen- tary Information: Appendix B), where serial covariation at the daily level was substituted for serial correlation at the annual level.

Table 4: Overview of sample sizes for each of the toxic pressure variables (one for each of three subcategories of chemical pollutants) included in the analysis. Toxic pressure of mixtures is expressed as msPAF values. Variable Locations Samples msPAF-Combustion 730 12785 msPAF-Industrial 5688 250343 msPAF-Pesticide 1376 35324

Principal Component Analysis

To account for confounding among interpolated variables, we performed Principal Component Anal- yses (hereafterPrincipal PCA) Component and decorrelated Analysis the variables using the rotation matrix of the PCA. Hence,as calculated when all covariates change from their 1990 to their 2018 mean To account for confounding among interpolated variables, we performed Prin- values (i.e. full temporal predictions). coeﬃcient b in equations 1 and 4, refer to scaled and decorelated variables x . Moreover, in our cipalij Component Analyses (hereafter PCA) and decorrelated the variablesij using full-models,the we rotation retained matrix all 38 of axes the of PCA. the Hence, PCA losing coefficient no information bij in equations by the 1 decorrelationand 4, refer to process. AllTREND-TRAIT RELATIONSHIPS

scaled and decorrelated variables xij. Moreover, in our full-models, we retained all Since we expect considerable variation in the abundance trends among insect variables were38 axes scaled of the to PCA zero-mean losing no and information unit variance by the prior decorrelation to PCA-transformation. process. All varia For- an overviewgroups, we also investigated whether some of that variation could be explained bles were scaled to zero-mean and unit variance prior to PCA-transformation. For by differences in traits of the insect taxa. It could for instance be that environ- of all axes and how variables correlated to PCA-axes see Fig. S5 and for a full correlation matrix see an overview of all axes and how variables correlated to PCA-axes see Fig. S5 and mental changes that took place during the study period have favoured species Fig. S6. for a full correlation matrix see Fig. S6. with certain traits over others. We considered a wide range of traits, from morphology (maximum size), life history (e.g. number of generations per year), Estimated relative effects of explanatory variables on to preferences (e.g. for temperature). We also tested whether insect taxa that are Estimated relative eﬀects of explanatory variables on abundance, diversity and trends abundance, diversity and trends indicators for desired insect communities in rivers or lakes, had different abun- To derive the relative effects of covariates x on abundance and diversity indices, dance trends than taxa that indicate undesirable insect communities. The full To derivewe the used relative the eﬀectsfollowing of covariatesequation x on abundance and diversity indices, we used the followinglist of investigated traits can be found below. equation Ideally, we would relate the variation in trends to traits at the species level. How-

En = Rz (7) (7)ever, for 30% of the sampled individuals, identification was not performed at the species level. Moreover, trait data were not always available at the species level either. Since several of the considered traits have similar values among species where R is the rotation matrix from the PCA analysis, and z the vector of z-scores within the same genus, and since we know the genus of 90% of the sampled indi- where R is the rotation matrix from the PCA analysis, and z the vector of z-scores of coefficients of coefficients from our macrofauna abundance36 and diversity models. viduals, we decided to perform the trend-trait analyses at the genus level. Thus, from our macrofauna abundance and diversity models. we used the ln-lambda values resulting from the genus-level trend analyses as To derive the effects of changes of covariates x on the trends of macrofauna the response variable in the trend-trait analyses. Due to the high number of traits To derivegroupswhere the and effectsR diversity,is ofthe changes rotationwe used of covariatesthe matrix followingx fromon approach: the the trends PCA of macrofauna analysis, andgroupsz andthe diversity, vectorconsidered, of z-scores we oftested coefficients the explanatory power of each trait separately in a series of linear mixed-effect models with insect order as a random effect (to account we used the following approach: from our macrofauna abundance and diversity models. for potential phylogenetic patterns) and weighted by the inverse of the standard E = log(λ¯) log(λ ) (8) (8)error of the ln-lambda estimates. These inverse standard errors were rescaled to a λ − θ To derive the effects of changes of covariates x on the trends of macrofaunamaximum groups of 1 prior and diversity,to the analyses. where λθ is the expected population growth rate in abundance of each taxonomic group when only one wherewe used λ′θ is thethe expected following population approach: growth rate in abundance of each taxonom- Several sources were used for trait data: 21 traits were extracted from Tachet et al. covariateic (x group) changed when from only its one mean covariate value (x in) changed 1990 to itsfrom mean its mean value value in 2018 in 1990 (i.e., to marginal its temporal(2010), 1 trait from the SPEARpesticides database (Liess and Von Der Ohe 2005), ¯ mean value in 2018 (i.e., marginal temporal predictions),Eλ = log(and λ) the log(growthλθ )rate and 2 traits from van der Molen(8) et al. (2018). Here we will describe each in turn, predictions), and λ¯ the growth rate as calculated when all covariates change− from their 1990 to their

2018 mean values (i.e. full temporal predictions). where λθ is the expected population growth rate in abundance of each26 taxonomic group when only one

Trend-traitcovariate relationships (x) changed from its mean value in 1990 to its mean value in 2018 (i.e., marginal temporal predictions), and λ¯ the growth rate as calculated when all covariates change from their 1990 to their Since we expect considerable variation in the abundance trends among insect groups, we also investigated whether2018 mean some of values that variation (i.e. full could temporal be explained predictions). by diﬀerences in traits of the insect taxa.

It could for instance be that environmental changes that took place during the study period have favored speciesTrend-trait with certain traits relationships over others. We considered a wide range of traits, from morphology

(maximum size), life history (e.g. number of generations per year), to preferences (e.g. for tempera- Since we expect considerable variation in the abundance trends among insect groups, we also inves- ture). We also tested whether insect taxa that are indicators for desired insect communities in rivers or lakes, hadtigated diﬀerent whether abundance some trends of that than taxa variation that indicate could undesirable be explained insect by communities. diﬀerences The in traits of the insect taxa. full list ofIt investigated could for traits instance can be be found that below. environmental changes that took place during the study period have

Ideally we would relate the variation in trends to traits at the species level. However, for 30% of favored species with certain traits over others. We considered a wide range of traits, from morphology the sampled individuals identiﬁcation was not performed at the species level. Moreover, trait data (maximum size), life history (e.g. number of generations per year), to preferences (e.g. for tempera- were not always available at the species level either. Since several of the considered traits have similar ture). We also tested whether insect taxa that are indicators for desired insect communities in rivers values among species within the same genus, and since we know the genus of 90% of the sampled or lakes, had diﬀerent abundance trends than taxa that indicate undesirable insect communities. The 37 full list of investigated traits can be found below.

Ideally we would relate the variation in trends to traits at the species level. However, for 30% of

the sampled individuals identiﬁcation was not performed at the species level. Moreover, trait data

were not always available at the species level either. Since several of the considered traits have similar

values among species within the same genus, and since we know the genus of 90% of the sampled

37 starting with the Tachet et al. (2010) database. This database contains fuzzy scores • Altitude: lowlands; piedmont level; alpine level (0, 1, 2 or 3) for 21 traits, each with 2 or more categories. For instance, for the trait • Substrate (preferendum): flags/boulders/cobbles/pebbles; gravel; sand; silt; ’aquatic stages’ the database contains four categories: egg, larva, nymph, and macrophytes; microphytes; twigs/roots; organic detritus/litter; sludge adult. For each taxon in the database fuzzy scores for each of these categories in- • Current velocity (preferendum): null; slow; medium; fast dicate the degree to which those life cycle components are aquatic. The following • Trophic status (preferendum): oligotrophic; mesotrophic; eutrophic list shows the 21 traits and their categories: • Salinity (preferendum): fresh water; brackish water • Temperature: psychrophilic; thermophilic; eurythermic • Maximal potential size: ≤.25cm; >.25-.5cm; >.5-1cm; >1-2cm; >2-4cm; >4-8cm; • Saprobity: xenosaprobic; oligosaprobic; b-mesosaprobic; a-mesosaprobic; poly- >8cm saprobic • Life cycle duration: ≤1 year; >1 year • pH (preferendum): ≤4; >4-4.5; >4.5-5; >5-5.5; >5.5-6; >6 • Potential number of cycles per year: <1; 1; >1 • Aquatic stages: egg; larva; nymph; adult Some genera for which we had ln-lambda estimates did not occur in the Tachet • Reproduction: ovoviviparity; isolated eggs, free; isolated eggs, cemented; et al. (2010) database. In those cases, we checked whether data were available for clutches, cemented or fixed; clutches, free; clutches, in vegetation; clutches, another genus in the same tribe. If not, we subsequently searched for genera in terrestrial; asexual reproduction the same subfamily, family, or infraorder. However, in the end we decided to only • Dispersal: aquatic passive; aquatic active; aerial passive; aerial active include in the analyses those genera for which both the abundance trend and • Resistance forms: eggs, statoblasts; cocoons; housings against desiccation; trait information was directly available at the genus level. diapause or dor mancy; none • Respiration: tegument; gill; plastron; spiracle; hydrostatic vesicle In order to be able to use the fuzzy scores for each of the categories of a specific • Locomotion and substrate relation: flier; surface swimmer; full water swim- trait for the analyses of variation in the genus-level abundance trend (ln-lambda), mer; crawler; burrower; interstitial; temporarily attached; permanently we had to account for the multivariate (i.e. multiple categories) and multinomial attached (i.e. fuzzy scores 0, 1, 2 or 4) nature of the trait data. We therefore used the ’ade4’ • Food: microorganisms; detritus <1mm; dead plant ≥1mm; living microphytes; R package (Dray and Dufour 2007) to perform a Fuzzy Principal Components Anal- living macro phytes; dead animal ≥1mm; living microinvertebrates; living ysis, first using the function ’prep.fuzzy.var’ and then ’dudi.fpca’. macroinvertebrates; vertebrates • Feeding habits: absorber; deposit feeder; shredder; scraper; filter-feeder; pierc- The resulting, mutually-uncorrelated fpca axes were then used as explanatory er; predator; parasite variables in the linear mixed-effect models described above. Afterwards we trans- • Transversal distribution: river channel; banks, connected side-arms; ponds, lated t-values of the fpca axes of the fitted linear mixed-effect model (lme) to the pools, disconnected side-arms; marshes, peat bogs; temporary waters; lakes; original trait categories, in order to make the results easier to interpret. We used groundwaters the loadings of the trait categories on the fpca axes: we rescaled the absolute • Longitudinal distribution: crenon; epirithron; metarithron; hyporithron; values of the loadings of each fpca axis, so they each summed to 1. This way we epipotamon; metapotamon; estuary; outside river system redistributed the t-values of the fpca axes over the trait categories. t-values larger

26 27 than 1.96 were deemed significant. Whenever ln-lambda correlated negatively The third and final source of trait data is the STOWA report by van der Molen et with the fuzzy scores of a trait category, its estimated t-value was depicted as a al. (2018). From the appendices of van der Molen et al. (2018), we extracted infor- negative value. mation about indicator taxa, separately for lakes and rivers. For lakes we included lake types M12, M14, M20, M21 and M23, thus excluding brackish lakes of types The second trait source was the SPEARpesticides (Species At Risk) database (Liess M27 and M31. For rivers we included R5, R6, R7, R12, R13, R14, R15, R16, R17 and and Von Der Ohe 2005). We used their estimates of sensitivity to toxic compounds R18. Each of the species (or their absence) is an indicator of one or more of the in the same way as Liess and Beketov (2011): if a taxon had a sensitivity value river and lake types. The appendix tables in Altenburg et al. (2018) thus contain lower than -0.36 and had three characteristics (low generation time, no possibility for each water type by species combination either a P (positive indicator when to recolonize from a refugium, and exposed at crucial times), we classified that dominant), N (negative indicator when dominant), K (characteristic taxon) or taxon as ’at risk’. All others were ’not at risk’. If the genera for which we had nothing. As multiple species could be listed within a genus, we tallied all the P, N, ln-lambda estimates did not occur in the Spear database, we attempted to make and K within that genus. Whenever one of the letters dominated the tallies (i.e. links at subsequently higher taxonomic levels. This was necessary because links more than 75% of all tallies), the genus was described as being predominantly in at the genus level could only be made for a minority of the genera. The frequen- that category. If none of them dominated, or if no species of a genus occurred in cy of the taxonomic level of these links will be shown in the Results section. the tables, that genus was noted as being not a strong indicator. For clarity: we Whenever at-risk-or-not information was available for multiple species within a performed this classification twice, once for rivers, once for lakes. Afterwards we genus, we chose the most frequently occurring factor level. Afterwards we tested performed linear mixed-effect models with either the lake-indicator factor or the whether being at risk could explain variation in ln-lambda, using the lme struc- river-indicator factor. ture detailed above.

28 Larva of mayfly - Ephemera vulgata - gewone haft Photo: Bureau Biota

28 2929 Variable damselfly - Coenagrion pulchellum - variabele waterjuffer Photo: Michel de Beer RESULTS

ABUNDANCE TRENDS OF FRESHWATER MACROFAUNA INSECTS Trends were calculated based on a series of Gen- eralized Linear Additive models for each of the taxonomic groups. For all insects combined, the trend in total abundance was found to be negative: total insect abundance declined by 52.78% from 1990 till 2017 (Fig. 3A). This corresponds to, on average, a 2.7% decline per year (note that in order to retain a proportion of 0.4722 of the initial abundance after 27 years, you need an annual rate of 0.4722(1/27), which gives 0.9725918 and thus a 2.74082% decline per year).

However, there are noticeable differences in the average rate of change among the 8 WAs (Fig. 3B),

30 with WRIJ, HHD, WD, and WRD experiencing much larger decline rates (between FIGURE 3 -5% and -3% per year; see also Fig. S7) than other WAs, while only WPM had a FigureTrends 3: Trends in total in abundance. total abundance. A: Total insect-macrofaunaA: Total insect-macrofauna abundance (pooled abundance over groups (pooled over groups and WAs) along with 95% conﬁdence intervals over 28 years. B: Average annual percentage change moderate average increase over time. Some of the fluctuations in the trend in per WA.and WAs) along with 95% conﬁdence intervals over 28 years. B: Average annual percentage overall insect abundance (Fig. 3A) are related to fluctuations in specific WAs: for change per WA. instance, the temporary peak in 2000 coincides with a peak in WRD (Fig. S7).

At the level of the different taxonomic groups, opposing trends over time can 1400 A be discerned. While most groups declined in density over the study period (i.e., 1200 over 27 years from 1990 till 2017), increases were observed too (Fig. 4). Positive 1000

trends were found for Trichoptera, Odonata, and Lepidoptera (the latter being a 800 low-abundance group in the studied database). The other seven insect groups had Density negative trends, including the most abundant groups: Chironomidae, Ephemer- 600 optera, Heteroptera, and ’other’ Diptera (i.e., flies and mosquitoes not in the

Chironomidae or Simuliidae families). 400

To see whether the abundance trends of the ten taxonomic groups were consist- 1990 1995 2000 2005 2010 2015 ent across the spatial range of the WAs, we also summarized the trends for each of the three regions (West, South, and East) (Fig. 5). At this regional scale, Trichop- tera, and Lepidoptera show consistently positive trends, while in the other cases B the sign and magnitude of the trends were less consistent. For instance, Ephemer- WRO

optera decreased strongly in abundance in the eastern region (WRD & WRIJ), but WRIJ increased in the western WAs (HHD & WHD) (Fig. 5). What is important to keep WRD in mind though, is that Ephemeroptera abundance is about five times higher in ’East’ than in the other two regions (Fig. S9). The decline in the eastern region is WPM

thus dominating the overall decline shown in Fig. 4. WHD

WD A similar spatial pattern is seen for Chironomidae, with strong declines in the WAM South and East, but an increase in the West, where abundances were the lowest. Heteroptera decreased considerably in West, where it used to be more abundant HHD than the other regions, and showed an increase in the South (Fig. S9). Keep in mind though that different species and genera could be involved in such spatial −10 −8 −6 −4 −2 0 2 4 variation in overall increases and decreases in abundance. Paradoxically, Sim- Percentual annual change

while in the other cases the sign and magnitude of the trends were less consistent. For instance, 30 31 Ephemeroptera decreased strongly in abundance in the eastern region (WRD & WRIJ), but increased

43 FIGURE 4 Trends in total insect-macrofauna abundance for each of the taxonomic groups. The blue right average annual changes (percentages) are shown for each of the taxonomic groups. dots depicts the mean trend as predicted by the models given default settings. In the bottom Trends per WA can be found in the appendix ﬁgures S8 and S9.

32 FIGURE 4 uliidae showed positive trends in each of the three regions, but a negative trend Continued from previous page. overall. For this taxon it is important to notice that the average number of black flies (Simuliidae) per macroinvertebrates sample is low, but also highly variable. Abundance peaks in samples in the 1990s in some WAs are therefore likely responsible for inconsistent overall and regional trend estimates.

Among WAs, the increases in Odonata, Trichoptera and Lepidoptera were mostly pronounced in WHD and the south-eastern WAs (WPM and WRO; Fig. S8). Marked declines in chironomids were observed mainly in WRO, WRIJ, and WRD.

GENUS-LEVEL TRENDS For 213 insect genera we estimated abundance trends. The rates of increase (i.e., the log-transformed per-capita growth rates; ln-lambda) of these genera were in majority (140) above 0, indicating positive trends. The median ln-lambda value was 0.0314, representing 3% annual growth. Due to the somewhat right-skewed distribution (Fig. 6A), the mean ln-lambda is higher than the median: 0.0431. The mean ln-lambda is significantly (p<0.001) higher than 0, mainly due to the high number of ln-lambda values, while the standard deviation (0.1132) is considerably larger than the mean.

Since Chironomidae decreased as a group and had the highest densities per sampled meter (Fig. 4), we also looked at the 68 genera within the Chironomidae family separately (Fig. 6A). Their mean ln-lambda was lower (0.0218 vs 0.0531; t=2.565, P=0.03878) than that of the 145 non-Chironomidae genera. As the mean ln-lambda of the Chironomidae genera was positive, indicating that the abundances of individuals had increased in most genera, one might wonder how the total abundance of Chironomidae could have decreased strongly (Fig. 4). The solution to this apparent paradox is explained in the following paragraph. We related the genus-level ln-lambda values to the initial abundance of these genera, to see if abundant genera had different trends than rare genera (Fig. 6B). As a proxy for initial abundance we simply used the sums of all counts of individuals of a genus during the decade (1980-1989) before the start of our study period

32 33 FIGURE 5 (1990). As figure 6B shows, there is an overall negative relationship: the regres- Average percentual increase or decline among ten insect-macrofauna groups for each of the sion line (fitted to the ln-lambda values) decreased significantly with increasing three WA groups (diﬀerent colors). West (W): HHD & WHD, South (S): WAM, WD, WPM & log-transformed initial abundance estimates, and was below zero for the 17% WRO, East (E): WRD & WRIJ. most abundant genera. The Chirono midae genera showed the same pattern, and did not differ from the other genera in that respect. However, the abundance of the average Chironomidae genus was significantly higher than that of the other genera (t=2.654, p<0.01, based on log-transformed abundance estimates).

E S W This also means that the large, negative contribution of the non-biting midges (Chironomidae) to the overall trend in aquatic insect abundance (Fig. 3A), is main- Lepidoptera ly due to negative trends of a considerable number of formerly highly abundant genera (e.g. Microsepta, Glyptotendipes and Procladius). This is also true for all insect Heteroptera genera combined: even though the median and mean of the abundance trends is positive, the overall trend in insect abundance is negative, mainly because the Ephemeroptera most common genera decreased the most, on average. Considerable declines of Coleoptera formerly abundant species clearly has a stronger effect on the overall insect trend than the net increase of low-abundance genera. Simuliidae

At the same time, it is good to keep in mind that there might be a slight bias, Diptera rem. as it is likely that a genus that occurred at low densities in the 1980s and then Chironomidae decreased in abundance, did not meet our a-priori thresholds for determining a reliable trend, or could have resulted in non-converging models (in which case Trichoptera we did not include it in the analyses of the genus-levels trends). This bias could

Insects rem. have contributed to the on-average positive genus-level trends in genera with low initial abundances (i.e., the left side of Fig. 6B). However, the slope of the fitted Odonata regression line remains significantly negative when genera are omitted with less than 200 individuals counted in the 1980s, indicating that the pattern observed is not solely a statistical artefact. −4 −2 0 2 4

The pattern of net declines of abundant genera and net increase of less-abundant genera (Fig. 6B) might suggest that the diversity of the macrofauna has increased over time. We analyse diversity parameters in the next section.

34 25 A other taxa Chironomidae 20

FIGURE 6 Genus-level abundance trends. A) Histogram of 213 genus-level ln-lambda estimates. Abun- viduals15 in these genera in the nineties. Abundance in the period 1980-1989 is simply approxi- dance trends above 0 indicate increasing numbers of individuals within a certain genus mated by summing all counted individuals recorded in the dataset during that time period. over the years. Reddish color represents 68 genera in the Chironomidae family (i.e., non-bit- The slope10 of the fitted regression line is highly significant: trend=0.11650-0.01377*ln(abun- ing midges), bluish color the 145 other insect genera for which we estimated abundance dance), p<0.001. Whether or not a genus belonged to the Chironomidae did not significantly trends. B) Relationship between the genus-level ln-lambda values and the abundance of indi- affect the slope or intercept of the regression line. number of genera number 5

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 abundance trend

25 A B other taxa 0.4 Chironomidae 0.3 20

0.2 15 0.1

10 0.0

abundance trend abundance −0.1 number of genera number 5 −0.2

0 −0.3 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 10 100 1000 10000 1e+05 abundance trend abundance in 1980−1989

B 0.4

0.3

34 0.2 35

0.1

0.0

abundance trend abundance −0.1

−0.2

−0.3 10 100 1000 10000 1e+05 abundance in 1980−1989 FIGURE 7 FIGURE 8 Trends in insect-macrofauna diversity. A Average taxonomic richness (i.e., number of genera Trends in nutrient concentrations: A Ammonium, B Total phosphorus, C Total nitrogen, and per macroinvertebrate sample). B Shannon index of diversity. C Simpson’s index of diversity. D Biochemical oxygen demand. Blue dots indicate the average trend, while the uninterrupt- D Shannon evenness. B, C and D are also based on abundances per genus. For each index, ed lines show trends for each WA. blue lines depict average over the 8 WAs, while the thinner lines show variation in each index for each of the WAs. Please note that the number of macrofauna samples was low in some WAs in the ﬁrst few years, resulting in more variation among the WA in the early years.

A B 4 3 20 2 Richness Shannon index 10 1 5 0

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

C C 1.0 1.0 0.8 0.8 0.6 0.6 Evennes 0.4 0.4 Simpson index HHD WRD WAM WRIJ 0.2 0.2 WD WRO WHD mean WPM 0.0 0.0

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

36 TRENDS IN DIVERSITY Among the measured nutrients, ammonium, total nitrogen, and total phos- All four of the calculated diversity indices showed positive trends over the period phorus showed declining trends between 1990 and 2018 (Fig. 8ABC): ammoni- 1990 to 2017 (Fig. 7). While variation exists in the trends between the WAs (Fig. 7 um and total phosphorus concentrations halved, while total nitrogen levels and also Figs S10-S13), overall a consistent pattern emerges of increasing diversity dropped from 4 to 2. There is variation among the WAs (see WA-specific lines of macroinvertebrate insect genera over the duration of the study period. The in figure 8), but all WAs see a decline in these nutrient variables. At the same average number of insect genera in the macrofauna samples increased from 13 time, the biochemical oxygen demand (BOD) also declined, from about 3 to 2 to 19 over the 27 years (Fig. 7A). Notwithstanding some fluctuations, taxonomic (Fig. 8D). This decrease in BOD happened between 1990 and 2005, after which it richness continued to increase over the whole period. was relatively stable.

The Shannon index of diversity has increased in a similar way as taxonomic Among land use variables, build up areas and forest increased between 1996 richness (Fig. 7B). The mean Shannon index of genus diversity was about 1 in and 2015, while agricultural land (farmland, grassland and greenhouse) cover 1990 and 2 in 2017. Please note that this index increases when more genera are decreased (Fig. 9). found as well as when individuals are distributed more equally across the genera. Here, both mechanisms are at play: the number of genera has increased and the Toxicity in water, as measured by the annual maximum of the multiple-substance most abundant genera were not as dominant anymore at the end of the study Potentially Affected Fraction indices (maxPAF-NOEC), showed stable trends over period. The latter mechanism also resulted in an increase of the Shannon index time between 1990 and 2018 for combustion compounds, slightly negative trend of evenness, which takes on values between 0 to 1 (Fig. 7D). The Simpson index for industrial compounds, and a strongly negative trend for pesticide compounds of diversity (Fig. 7C) showed the same patterns as the Shannon index of diversity. (Fig. 10). Considerable variation existed between the WA-specific trends in these WA-specific trends in the Shannon an Simpson indices can also be found in figure maxPAF-NOEC parameters. 7D, and in more detail in appendix figures S11, S12 and S13. EFFECTS OF POTENTIAL DRIVERS ON THE ABUNDANCE OF FRESHWATER INSECTS TRENDS IN DRIVERS Inferred from the global models (i.e., based on data from all eight WAs, separately Before we show how environmental variables explained variation in macrofau- per taxonomic group), covariate effects on the abundance appeared to be variable na abundance and trends across sampling locations and over the years, we first between the different insect groups (Fig. 11). Nevertheless, some general patterns show how those environmental variables themselves have changed over the years. could be observed. Among land use covariates, the cover of greenhouses was When a variable has a negative effect on macrofauna abundance, it is important significantly negatively associated with insect abundance across all insect groups, to know whether that variable has decreased over the years, stayed stable, or except for Lepidoptera and Chironomidae (not significant; see Fig. 13 for effects increased. For instance, if a variable has a negative effect on abundance, but has per taxonomic group). The area of arable land near the macrofauna sampling decreased itself over the years, then macrofauna numbers actually go up due to points had a significantly negative effect on the abundance of Trichoptera, the decrease of the variable. remaining Diptera and remaining insects (Fig. 13), but a significantly positive effect in Heteroptera (shown as a deviating point in the bottom row of figure 11. The amount of forest was generally significantly positively associated with

36 37 FIGURE 9 abundance, except for Chironomidae, Coleoptera Average trends in land use variables between 1996 and 2015, within a radius of 1km centered around each of the monitoring and Lepidoptera (not significant) and Ephemerop- locations. Please note the different scales on the y-axes. tera and Heteroptera (significantly negative). Wet natural areas had a significantly positive effect on the abundance of Heteroptera, Coleoptera, Simuliidae, Chironomidae, remaining Diptera, 30 22.0 25.0 and the remaining insect group. Buildup area Farmland 24.5 Grassland 25 21.5 24.0 Effects of nutrients (particularly phosphorus- 20 21.0 23.5 23.0 and nitrogen-related variables) appeared to be % land cover 15 % land cover 20.5 % land cover 22.5 consistently and significantly negatively related 10 20.0 22.0 to the abundance of all insect groups, with the 1996 2003 2008 2012 1996 2003 2008 2012 1996 2003 2008 2012 exception for Chironomidae and Simuliidae (significantly positive). However, taxonomic groups differed in which variables showed the strongest

effect: total nitrogen or NO2+NO3, total phospho-

1.8 1.2 1.20 rus or PO4. Greenhouses 1.1 Open natural wet areas Open natural dry areas 1.6 1.15 1.0 Of the three types of pollutants for which we had 1.4 0.9 1.10 included msPAF-NOEC covariates in the global 0.8 % land cover 1.2 % land cover % land cover 1.05 0.7 models, pesticides show the most consistent 1.0 0.6 1.00 pattern (Fig. 13). In eight out of the ten taxonomic 1996 2003 2008 2012 1996 2003 2008 2012 1996 2003 2008 2012 groups, pesticides had a significant effect on their abundance. The two deviating taxa are Chironomi- dae (no significant effect) and Simuliidae (significantly positive effect).

7.5 Forest Compared to the other types of variables, weather 7.4

7.3 variables showed relatively small effects on abun-

7.2 dance. An exception was the growing degree days

% land cover 7.1 variable (a measure of accumulated heat, here o 7.0 above 5 C), which showed significantly positive 1996 2003 2008 2012

38 effects on Odonata, Chironomidae, Lepidoptera, FIGURE 10 remaining Diptera, remaining insects, and espe- Trends in toxic pressure of mixtures between 1990 and 2018. A Annual maximum of the Potentially Affected Fraction based on cially on Ephemeroptera. On the other hand, high- pesticide compounds (maxPAF-NOEC/pesticide). B Annual maximum of the Potentially Affected Fraction based on industrial er GDD (Growing Degree Days) strongly decreased compounds (maxPAFNOEC/industrial). C Annual maximum of the Potentially Affected Fraction based on combustion com- the abundance of Coleoptera. pounds (maxPAF-NOEC/combustion). Blue lines with dots represent the overall average, while the other lines show the trends per Water Authority (WA). The number of tested substances per water sample are taken into account. Similar models for the three groups (East, South, West) of WAs, showed variation in effect sizes among the regions (Fig. 12). For instance, the negative effect of greenhouses was especially found for the West, while pesticides, nitrogen and phosphorus had consistently negative effects especially in the South and West.

EFFECTS OF POTENTIAL DRIVERS ON THE TRENDS OF MACROINVERTEBRATE INSECTS Besides the abundance responses of taxonomic groups to variation in the considered potential drivers, we also studied how the abundance trends responded to the mean annual change in the values of each potential driver over the 1990-2017 period (Fig. 13). In these analyses we quantified how much the various environmental changes contributed to the trend coefficient according to our global models. With the exception of Odonata, Trichoptera and Heter- optera, the largest contribution to the average annual trend was provided by unknown factors. Otherwise, the most consistent contributions to the abundance trends are made by the decline in pesticides and by the decline in several nutrient concentrations.

38 39 FIGURE 11 The decline in toxic pressure due to pesticides, as quantified by the negative trend Back-transformed effects (z-scores) of covariate values on insect abundances. Effects are sum- in msPAF-NOEC values over the study period, had considerably positive effects marized over the ten taxonomic insect groups. Horizontal boxes therefore show the variation on the abundance trend of eight of the ten insect groups. On the other hand, of effect sizes among the taxonomic groups, based on the global models (i.e., based on data of the decline in toxicity due to pesticides had a considerably negative effect on the all eight WAs). abundance trend of Simuliidae (mainly occurring in streams), and a small negative effect on the trend of Chironomidae (occurring in a variety of water types).

Changes in mean concentrations of the various nutrients provided, together, the largest contribution to the average annual trends. Generally, changes in nutrient concentrations (mainly declining nutrient loads over the study period) had positive effects on the average trends of all taxonomic groups except Chironomidae and Simuliidae. For instance, the decline in total phosphorus (’Sum P’) had a relatively large, positive effect on the abundance trend of Trichoptera. For Odonata, the decline in the nitrogen sum had a positive effect on abundance. The changes in nutrient concentrations also showed mixed effects on the trends of the taxonomic groups, with small positive and small negative effects partially compensating each other. For instance, in Ephemeroptera a dominant positive effect of the declining phosphorus sum is partly buffered by a smaller negative effect of the decrease in phosphate. Decreasing nutrient concentrations had net negative effects on the trends of Simuliidae and Chironomidae.

Changes in weather variables did not appear to contribute consistently to the trends of any of the insect groups. A few exceptions include a strongly positive contribution on the Simuliidae trend by the increase over time of the precipitation in the previous summer. The positive trend in the mean temperature in the month preceding a macroinvertebrate sample, had a positive effect on the trend of the ’remaining insects’ group. Changes in land use parameter hardly contributed to the abundance trends. Changes in heavy metal concentrations generally had relatively small effects.

TREND-TRAIT RELATIONSHIPS Of 213 genera for which we were able to derive abundance trends, 125 also occurred in the trait database of Tachet et al. (2010). For 33 of remaining genera a

40 link could be made at the tribe level, for 40 at the FIGURE 12 subfamily level, for 14 at the family level and for 1 Back-transformed effects (z-scores) of covariate values on insect abundances. Effects are summarized over the ten taxonomic genus a link could only be made at the infraorder insect groups, separately for each of the three groups of Waterboard Authorities (A: East, B: South, C: West). level. However, for the trend-trait analyses shown in figures 14, 15 and 16 we only used the subset of 125 genera for which both abundance trends and trait information were available.

The genus-level abundance trends (ln-lambda) were related to some of the traits considered, but certainly not to all. Please keep in mind that we conserv- atively included insect order as a random factor in our analyses to account for potential phylogenetic patterns.

One of the traits that did show statistically significant effects in the fitted linear mixed-effect models was the preferred substrate that species in a genus normally have. Genera with high fuzzy scores for the categories cobbles/pebbles, gravel, and sand had abundance trends that were significantly above average (Fig. 14A). Genera preferring sludge and macrophytes, on the other hand, per - formed below average, which is consistent with the reduced nutrient load that was observed in the studied water bodies in the period 1990-2017. The trait locomotion showed interesting relationships with the abundance trends (Fig. 14B), with genera with full-water swimmers performing below average. It might well be that this pattern is related to changes in the species compositions in lakes and rivers.

40 41 Odonata Insects rem.

Weather prec. previous summ. Weather prec. previous summ. temp. previous summ. temp. previous summ. evap. month evap. month evap. week evap. week evapotranspiration evapotranspiration prec. month prec. month prec. week prec. week precipitation precipitation Frost days Frost days GDD GDD temp. month temp. month temp. week temp. week temperature temperature Toxicity Pesticide Toxicity Pesticide Industrial Industrial Combustion Combustion Psysical/Chemical properties O Psysical/Chemical properties O pH pH Zn Zn HCO3 HCO3 Water temperature Water temperature Sum N Sum N NO2+NO3 NO2+NO3 Suspended matter Suspended matter Ni Ni Na Na Mg Mg Pb Pb Cu Cu Sum P Sum P PO4 PO4 Cr Cr Chloro. alpha Chloro. alpha Cl Cl Ca Ca Cd Cd BOD BOD NH4 NH4 Land use prox. sewage plant Land use prox. sewage plant Protected Protected wet−natural wet−natural dry−natural dry−natural forest forest FIGURE 13 built−up built−up rem. agric. rem. agric. Greenhouses Greenhouses Effects of changes in covariate values on the abundance change of each of the ten taxonomic ed line signals the sum of the black bars. Units are Arableon the log-λscale. In this graph the results Arable Unknown Unknown insect groups (see also the next two pages). Yellow arrows indicate the direction and relative of the 8 WAs are combined, unweightedUnknown by the relative abundances of insect groups in the Unknown magnitude of the change in each of the covariates from 1990 to 2018. The black bars then separate WAs. quantify the effect of that covariate change on the change in abundance. The blue interrupt- −1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 1.0

OdonataOdonata InsectsInsects rem. rem. Trichoptera Chironomidae

WeatherWeather prec.prec. previous previous summ. summ. WeatherWeather prec.prec. previous previous summ. summ. Weather prec. previous summ. Weather prec. previous summ. temp.temp. previous previous summ. summ. temp.temp. previous previous summ. summ. temp. previous summ. temp. previous summ. evap.evap. month month evap.evap. month month evap. month evap. month evap.evap. week week evap.evap. week week evap. week evap. week evapotranspirationevapotranspiration evapotranspirationevapotranspiration evapotranspiration evapotranspiration prec.prec. month month prec.prec. month month prec. month prec. month prec.prec. week week prec.prec. week week prec. week prec. week precipitationprecipitation precipitationprecipitation precipitation precipitation FrostFrost days days FrostFrost days days Frost days Frost days GDDGDD GDDGDD GDD GDD temp.temp. month month temp.temp. month month temp. month temp. month temp.temp. week week temp.temp. week week temp. week temp. week temperaturetemperature temperaturetemperature temperature temperature ToxicityToxicity PesticidePesticide ToxicityToxicity PesticidePesticide Toxicity Pesticide Toxicity Pesticide IndustrialIndustrial IndustrialIndustrial Industrial Industrial CombustionCombustion CombustionCombustion Combustion Combustion Psysical/ChemicalPsysical/Chemical properties properties O O Psysical/ChemicalPsysical/Chemical properties properties O O Psysical/Chemical properties O Psysical/Chemical properties O pH pH pH pH pH pH Zn Zn Zn Zn Zn Zn HCO3HCO3 HCO3HCO3 HCO3 HCO3 WaterWater temperature temperature WaterWater temperature temperature Water temperature Water temperature SumSum N N SumSum N N Sum N Sum N NO2+NO3NO2+NO3 NO2+NO3NO2+NO3 NO2+NO3 NO2+NO3 SuspendedSuspended matter matter SuspendedSuspended matter matter Suspended matter Suspended matter Ni Ni Ni Ni Ni Ni Na Na Na Na Na Na Mg Mg Mg Mg Mg Mg Pb Pb Pb Pb Pb Pb Cu Cu Cu Cu Cu Cu SumSum P P SumSum P P Sum P Sum P PO4PO4 PO4PO4 PO4 PO4 Cr Cr Cr Cr Cr Cr Chloro.Chloro. alpha alpha Chloro.Chloro. alpha alpha Chloro. alpha Chloro. alpha Cl Cl Cl Cl Cl Cl Ca Ca Ca Ca Ca Ca Cd Cd Cd Cd Cd Cd BODBOD BODBOD BOD BOD NH4NH4 NH4NH4 NH4 NH4 LandLand use use prox.prox. sewage sewage plant plant LandLand use use prox.prox. sewage sewage plant plant Land use prox. sewage plant Land use prox. sewage plant ProtectedProtected ProtectedProtected Protected Protected wet−naturalwet−natural wet−naturalwet−natural wet−natural wet−natural dry−naturaldry−natural dry−naturaldry−natural dry−natural dry−natural forestforest forestforest forest forest built−upbuilt−up built−upbuilt−up built−up built−up rem.rem. agric. agric. rem.rem. agric. agric. rem. agric. rem. agric. GreenhousesGreenhouses GreenhousesGreenhouses Greenhouses Greenhouses ArableArable ArableArable Arable Arable UnknownUnknown UnknownUnknown UnknownUnknown UnknownUnknown Unknown Unknown Unknown Unknown

−1.0−1.0−0.5−0.5 0.00.0 0.50.5 1.01.0 −1.0−1.0−0.5−0.5 0.00.0 0.50.5 1.01.0 −1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 1.0

TrichopteraTrichoptera ChironomidaeChironomidae

WeatherWeather prec.prec. previous previous summ. summ. WeatherWeather prec.prec. previous previous summ. summ. temp.temp. previous previous summ. summ. temp.temp. previous previous summ. summ. 42 evap.evap. month month evap.evap. month month evap.evap. week week evap.evap. week week evapotranspirationevapotranspiration evapotranspirationevapotranspiration prec.prec. month month prec.prec. month month prec.prec. week week prec.prec. week week precipitationprecipitation precipitationprecipitation FrostFrost days days FrostFrost days days GDDGDD GDDGDD temp.temp. month month temp.temp. month month temp.temp. week week temp.temp. week week temperaturetemperature temperaturetemperature ToxicityToxicity PesticidePesticide ToxicityToxicity PesticidePesticide IndustrialIndustrial IndustrialIndustrial CombustionCombustion CombustionCombustion Psysical/ChemicalPsysical/Chemical properties properties O O Psysical/ChemicalPsysical/Chemical properties properties O O pH pH pH pH Zn Zn Zn Zn HCO3HCO3 HCO3HCO3 WaterWater temperature temperature WaterWater temperature temperature SumSum N N SumSum N N NO2+NO3NO2+NO3 NO2+NO3NO2+NO3 SuspendedSuspended matter matter SuspendedSuspended matter matter Ni Ni Ni Ni Na Na Na Na Mg Mg Mg Mg Pb Pb Pb Pb Cu Cu Cu Cu SumSum P P SumSum P P PO4PO4 PO4PO4 Cr Cr Cr Cr Chloro.Chloro. alpha alpha Chloro.Chloro. alpha alpha Cl Cl Cl Cl Ca Ca Ca Ca Cd Cd Cd Cd BODBOD BODBOD NH4NH4 NH4NH4 LandLand use use prox.prox. sewage sewage plant plant LandLand use use prox.prox. sewage sewage plant plant ProtectedProtected ProtectedProtected wet−naturalwet−natural wet−naturalwet−natural dry−naturaldry−natural dry−naturaldry−natural forestforest forestforest built−upbuilt−up built−upbuilt−up rem.rem. agric. agric. rem.rem. agric. agric. GreenhousesGreenhouses GreenhousesGreenhouses ArableArable ArableArable UnknownUnknown UnknownUnknown UnknownUnknown UnknownUnknown

−1.0−1.0−0.5−0.5 0.00.0 0.50.5 1.01.0 −1.0−1.0−0.5−0.5 0.00.0 0.50.5 1.01.0 Odonata Insects rem.

−1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 1.0

Trichoptera Chironomidae DipteraDiptera rem. rem. SimuliidaeSimuliidae

Weather prec. previous summ. Weather prec. previous summ. WeatherWeatherprec. previousprec. previous summ. summ. WeatherWeatherprec. previousprec. previous summ. summ. temp. previous summ. temp. previous summ. temp. temp.previous previous summ. summ. temp. temp.previous previous summ. summ. evap. month evap. month evap. evap.month month evap. evap.month month evap. week evap. week evap. evap.week week evap. evap.week week evapotranspiration evapotranspiration evapotranspirationevapotranspiration evapotranspirationevapotranspiration prec. month prec. month prec. monthprec. month prec. monthprec. month prec. week prec. week prec. weekprec. week prec. weekprec. week precipitation precipitation precipitationprecipitation precipitationprecipitation Frost days Frost days Frost daysFrost days Frost daysFrost days GDD GDD GDD GDD GDD GDD temp. month temp. month temp. temp.month month temp. temp.month month temp. week temp. week temp. temp.week week temp. temp.week week temperature temperature temperaturetemperature temperaturetemperature Toxicity Pesticide Toxicity Pesticide ToxicityToxicity PesticidePesticide ToxicityToxicity PesticidePesticide Industrial Industrial IndustrialIndustrial IndustrialIndustrial Combustion Combustion CombustionCombustion CombustionCombustion Psysical/Chemical properties O Psysical/Chemical properties O Psysical/ChemicalPsysical/Chemical properties propertiesO O Psysical/ChemicalPsysical/Chemical properties propertiesO O pH pH pH pH pH pH Zn Zn Zn Zn Zn Zn HCO3 HCO3 HCO3HCO3 HCO3HCO3 Water temperature Water temperature WaterWater temperature temperature WaterWater temperature temperature Sum N Sum N Sum NSum N Sum NSum N NO2+NO3 NO2+NO3 NO2+NO3NO2+NO3 NO2+NO3NO2+NO3 Suspended matter Suspended matter SuspendedSuspended matter matter SuspendedSuspended matter matter Ni Ni Ni Ni Ni Ni Na Na Na Na Na Na Mg Mg Mg Mg Mg Mg Pb Pb Pb Pb Pb Pb Cu Cu Cu Cu Cu Cu Sum P Sum P Sum PSum P Sum PSum P PO4 PO4 PO4 PO4 PO4 PO4 Cr Cr Cr Cr Cr Cr Chloro. alpha Chloro. alpha Chloro.Chloro. alpha alpha Chloro.Chloro. alpha alpha Cl Cl Cl Cl Cl Cl Ca Ca Ca Ca Ca Ca Cd Cd Cd Cd Cd Cd BOD BOD BOD BOD BOD BOD NH4 NH4 NH4 NH4 NH4 NH4 Land use prox. sewage plant Land use prox. sewage plant Land useLand use prox. sewageprox. sewage plant plant Land useLand use prox. sewageprox. sewage plant plant Protected Protected ProtectedProtected ProtectedProtected wet−natural wet−natural wet−naturalwet−natural wet−naturalwet−natural dry−natural dry−natural dry−naturaldry−natural dry−naturaldry−natural forest forest forestforest forestforest built−up built−up built−upbuilt−up built−upbuilt−up rem. agric. rem. agric. rem. agric.rem. agric. rem. agric.rem. agric. Greenhouses Greenhouses GreenhousesGreenhouses GreenhousesGreenhouses Arable Arable ArableArable ArableArable Unknown Unknown Unknown Unknown UnknownUnknown UnknownUnknown UnknownUnknown UnknownUnknown

−1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 1.0 −1.0−1.0−0.5−0.50.00.0 0.50.5 1.01.0 −1.0−1.0−0.5−0.50.00.0 0.50.5 1.01.0

ColeopteraColeoptera EphemeropteraEphemeroptera

WeatherWeatherprec. previousprec. previous summ. summ. WeatherWeatherprec. previousprec. previous summ. summ. 42 temp. temp.previous previous summ. summ. 43 temp. temp.previous previous summ. summ. evap. evap.month month evap. evap.month month evap. evap.week week evap. evap.week week evapotranspirationevapotranspiration evapotranspirationevapotranspiration prec. monthprec. month prec. monthprec. month prec. weekprec. week prec. weekprec. week precipitationprecipitation precipitationprecipitation Frost daysFrost days Frost daysFrost days GDD GDD GDD GDD temp. temp.month month temp. temp.month month temp. temp.week week temp. temp.week week temperaturetemperature temperaturetemperature ToxicityToxicity PesticidePesticide ToxicityToxicity PesticidePesticide IndustrialIndustrial IndustrialIndustrial CombustionCombustion CombustionCombustion Psysical/ChemicalPsysical/Chemical properties propertiesO O Psysical/ChemicalPsysical/Chemical properties propertiesO O pH pH pH pH Zn Zn Zn Zn HCO3HCO3 HCO3HCO3 WaterWater temperature temperature WaterWater temperature temperature Sum NSum N Sum NSum N NO2+NO3NO2+NO3 NO2+NO3NO2+NO3 SuspendedSuspended matter matter SuspendedSuspended matter matter Ni Ni Ni Ni Na Na Na Na Mg Mg Mg Mg Pb Pb Pb Pb Cu Cu Cu Cu Sum PSum P Sum PSum P PO4 PO4 PO4 PO4 Cr Cr Cr Cr Chloro.Chloro. alpha alpha Chloro.Chloro. alpha alpha Cl Cl Cl Cl Ca Ca Ca Ca Cd Cd Cd Cd BOD BOD BOD BOD NH4 NH4 NH4 NH4 Land useLand use prox. sewageprox. sewage plant plant Land useLand use prox. sewageprox. sewage plant plant ProtectedProtected ProtectedProtected wet−naturalwet−natural wet−naturalwet−natural dry−naturaldry−natural dry−naturaldry−natural forestforest forestforest built−upbuilt−up built−upbuilt−up rem. agric.rem. agric. rem. agric.rem. agric. GreenhousesGreenhouses GreenhousesGreenhouses ArableArable ArableArable UnknownUnknown UnknownUnknown UnknownUnknown UnknownUnknown

−1.0−1.0−0.5−0.50.00.0 0.50.5 1.01.0 −1.0−1.0−0.5−0.50.00.0 0.50.5 1.01.0 DipteraDiptera rem. rem. SimuliidaeSimuliidae

Weather Weatherprec. previousprec. summ. previous summ. Weather Weatherprec. previousprec. summ. previous summ. temp. previoustemp. summ. previous summ. temp. previoustemp. summ. previous summ. evap. monthevap. month evap. monthevap. month evap. weekevap. week evap. weekevap. week evapotranspirationevapotranspiration evapotranspirationevapotranspiration prec. monthprec. month prec. monthprec. month prec. weekprec. week prec. weekprec. week precipitationprecipitation precipitationprecipitation Frost days Frost days Frost days Frost days GDD GDD GDD GDD temp. monthtemp. month temp. monthtemp. month temp. weektemp. week temp. weektemp. week temperaturetemperature temperaturetemperature Toxicity Toxicity Pesticide Pesticide Toxicity Toxicity Pesticide Pesticide Industrial Industrial Industrial Industrial CombustionCombustion CombustionCombustion Psysical/ChemicalPsysical/Chemical properties propertiesO O Psysical/ChemicalPsysical/Chemical properties propertiesO O pH pH pH pH Zn Zn Zn Zn HCO3 HCO3 HCO3 HCO3 Water temperatureWater temperature Water temperatureWater temperature Sum N Sum N Sum N Sum N NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 SuspendedSuspended matter matter SuspendedSuspended matter matter Ni Ni Ni Ni Na Na Na Na Mg Mg Mg Mg Pb Pb Pb Pb Cu Cu Cu Cu Sum P Sum P Sum P Sum P PO4 PO4 PO4 PO4 Cr Cr Cr Cr Chloro. alphaChloro. alpha Chloro. alphaChloro. alpha Cl Cl Cl Cl Ca Ca Ca Ca Cd Cd Cd Cd BOD BOD BOD BOD NH4 NH4 NH4 NH4 Land use Land useprox. sewageprox. plant sewage plant Land use Land useprox. sewageprox. plant sewage plant Protected Protected Protected Protected wet−naturalwet−natural wet−naturalwet−natural dry−naturaldry−natural dry−naturaldry−natural forest forest forest forest built−up built−up built−up built−up FIGURE 13 rem. agric.rem. agric. rem. agric.rem. agric. GreenhousesGreenhouses GreenhousesGreenhouses Continued from previousArable page.Arable Arable Arable Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown

−1.0 −1.0−0.5 −0.50.0 0.00.5 0.51.0 1.0 −1.0 −1.0−0.5 −0.50.0 0.00.5 0.51.0 1.0

ColeopteraColeoptera EphemeropteraEphemeroptera Heteroptera Lepidoptera

prec. previousprec. summ. previous summ. prec. previousprec. summ. previous summ. Weather Weather Weather Weather prec.Weather previous summ. prec.Weather previous summ. temp. previoustemp. summ. previous summ. temp. previoustemp. summ. previous summ. temp. previous summ. temp. previous summ. evap. monthevap. month evap. monthevap. month evap. month evap. month evap. weekevap. week evap. weekevap. week evap. week evap. week evapotranspirationevapotranspiration evapotranspirationevapotranspiration evapotranspiration evapotranspiration prec. monthprec. month prec. monthprec. month prec. month prec. month prec. weekprec. week prec. weekprec. week prec. week prec. week precipitationprecipitation precipitationprecipitation precipitation precipitation Frost days Frost days Frost days Frost days Frost days Frost days GDD GDD GDD GDD GDD GDD temp. monthtemp. month temp. monthtemp. month temp. month temp. month temp. weektemp. week temp. weektemp. week temp. week temp. week temperaturetemperature temperaturetemperature temperature temperature Pesticide Pesticide Pesticide Pesticide Toxicity Toxicity Toxicity Toxicity Toxicity Pesticide Toxicity Pesticide Industrial Industrial Industrial Industrial Industrial Industrial CombustionCombustion CombustionCombustion Combustion Combustion O O O O Psysical/ChemicalPsysical/Chemical properties properties Psysical/ChemicalPsysical/Chemical properties properties Psysical/ChemicalO properties Psysical/ChemicalO properties pH pH pH pH pH pH Zn Zn Zn Zn Zn Zn HCO3 HCO3 HCO3 HCO3 HCO3 HCO3 Water temperatureWater temperature Water temperatureWater temperature Water temperature Water temperature Sum N Sum N Sum N Sum N Sum N Sum N NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 SuspendedSuspended matter matter SuspendedSuspended matter matter Suspended matter Suspended matter Ni Ni Ni Ni Ni Ni Na Na Na Na Na Na Mg Mg Mg Mg Mg Mg Pb Pb Pb Pb Pb Pb Cu Cu Cu Cu Cu Cu Sum P Sum P Sum P Sum P Sum P Sum P PO4 PO4 PO4 PO4 PO4 PO4 Cr Cr Cr Cr Cr Cr Chloro. alphaChloro. alpha Chloro. alphaChloro. alpha Chloro. alpha Chloro. alpha Cl Cl Cl Cl Cl Cl Ca Ca Ca Ca Ca Ca Cd Cd Cd Cd Cd Cd BOD BOD BOD BOD BOD BOD NH4 NH4 NH4 NH4 NH4 NH4 Land use Land useprox. sewageprox. plant sewage plant Land use Land useprox. sewageprox. plant sewage plant Landprox. use sewage plant Landprox. use sewage plant Protected Protected Protected Protected Protected Protected wet−naturalwet−natural wet−naturalwet−natural wet−natural wet−natural dry−naturaldry−natural dry−naturaldry−natural dry−natural dry−natural forest forest forest forest forest forest built−up built−up built−up built−up built−up built−up rem. agric.rem. agric. rem. agric.rem. agric. rem. agric. rem. agric. GreenhousesGreenhouses GreenhousesGreenhouses Greenhouses Greenhouses Arable Arable Arable Arable Arable Arable Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown

−1.0 −1.0−0.5 −0.50.0 0.00.5 0.51.0 1.0 −1.0 −1.0−0.5 −0.50.0 0.00.5 0.51.0 1.0 −1.0 0.0 1.0 −1.0 0.0 1.0

44 FIGURE 13 Genera with oligotrophic species increased in We could not analyse potential effects of the Continued from previous page. abundance (but not significantly so), while those trait preferred salinity in the same way. In the with eutrophic species decreased (Fig. 14C). Again, Tachet trait database salinity has two categories: this clearly is in line with reduced nutrient load fresh water and brackish water. Not surprisingly over the study period. all genera found in the macrofauna samples of the WAs had high scores for fresh water. As only The next three traits, current velocity, transversal one category (brackish water) with variable fuzzy Heteroptera Lepidoptera and longitudinal distribution show related results scores remained, we could not perform the same (Fig. 14DEF). Genera associated with high fuzzy fuzzy principal component analyses as for the oth- prec.Weather previous summ. prec.Weather previous summ. temp. previous summ. temp. previous summ. evap. month evap. month scores for respectively standing waters, lakes, er traits in the Tachet et al. (2010) database. When evap. week evap. week evapotranspiration evapotranspiration and outside river system tended to have lower regressing the genus-level rates of increase against prec. month prec. month prec. week prec. week precipitation precipitation abundance trends. On the other hand, genera the fuzzy scores for brackish water directly, a Frost days Frost days GDD GDD associated with slow and medium current nearly-significant negative effect was found (ln- temp. month temp. month temp. week temp. week temperature temperature velocity had significantly higher than average Lambda=0.0412-.0183*scoreBrackishWater; p-value Toxicity Pesticide Toxicity Pesticide Industrial Industrial abundance trends, which is in agreement with of the slope=0.063), indicated that genera that are Combustion Combustion Psysical/ChemicalO properties Psysical/ChemicalO properties pH pH the positive tendencies of the genera asso- not only found in fresh water but also in brackish Zn Zn HCO3 HCO3 ciated with river channels. It could well be waters performed somewhat worse than genera Water temperature Water temperature Sum N Sum N NO2+NO3 NO2+NO3 that this pattern is related to improved oxygen affiliated with fresh water only. Suspended matter Suspended matter Ni Ni Na Na availability. Mg Mg Pb Pb The risk level derived from the SPEARpesticides Cu Cu Sum P Sum P PO4 PO4 Genera of which the adult stage lives in water database (Liess and Von Der Ohe 2005) did not Cr Cr Chloro. alpha Chloro. alpha performed significantly below average (Fig. 15A). explain variation in the genus-level rates of in- Cl Cl Ca Ca Cd Cd The same is true for deposit feeders, while filter crease (Fig. 17A). The rate of increase did not differ BOD BOD NH4 NH4 feeders had significantly higher than average (p=0.141) between the 26 genera at risk (mean Landprox. use sewage plant Landprox. use sewage plant Protected Protected wet−natural wet−natural abundance trends (Fig. 15C). Longer-lived species ln-lambda=0.048) and the 175 genera not at risk dry−natural dry−natural forest forest performed worse than shorter-lived species (Fig. (ln-lambda=0.041). Please note that there were also built−up built−up rem. agric. rem. agric. Greenhouses Greenhouses 15E). Most of the other traits in the Tachet et al. 12 genera for which we had an abundance trend Arable Arable Unknown Unknown Unknown Unknown (2010) database did not show significant relation- but could not determine the risk level. ships with the abundance trends (Figs. 15 and 16). −1.0 0.0 1.0 −1.0 0.0 1.0 For instance, genus-level abundance trends were Analyses of the effects of the dominant KRW not explained by the preferred temperature range indicator scores per genera (van der Molen et al. of the genera (Fig. 16H). 2018), roughly showed the same patterns for river

44 45 systems and lake systems: genera indicating healthy rivers and lakes increased on average, while negative indicators declined in abundance. In the case of river indicators, most genera were classified as ’K’ (more than 75% of indicator scores being a characteristic taxon; n=96) or 0 (no indicator scores or none of the P, K or N scores having a majority; n=100).

The KRW lake indicator scores showed a somewhat similar but not significant pattern: lower rates of increase for the 9 ’N’ genera (i.e., when these genera occur at high abundance they are negative indicators of desired lake ecosystems) than of the 110 ’0’ genera (Fig. 17C). Those 9 ’N’ genera were, in alphabetical order: Aedes, Anopheles, Callicorixa, Chironomus, Culex, Culiseta, Procladius, Psectrotanypus, and Tanypus. Please keep in mind that we derived the abundance trends based on the entire macrofauna dataset, not separately for e.g. lake and river systems. That would have made the link with the KRW indicators more direct, but would have also meant smaller datasets and less-certain trend analyses for fewer genera.

46 Water strider - Gerris lateralis - rossige schaatsenrijder Photo: Bureau Biota

46 4747 Common backswimmer - Notonecta glauca - gewoon bootsmannetje Photo: Bureau Biota DISCUSSION

Our goal was to uncover how aquatic insect abundance has developed in the Netherlands over the past three decades, making use of the extensive datasets on aquatic stages of insects sampled by Dutch Water Authorities (WAs). Overall, insect abundance (expressed as densities) declined by approximately 53% over 27 years (from 1990 till 2017), which corresponds to a 2.7% decline per year, on average. However, trends differed considerably between and within insect orders. Declines were strongest in the most abundant orders and families (Heteroptera [typical bugs; wantsen], Chironomidae [non-biting midges; dansmuggen], other Diptera [other flies; andere muggen en vliegen]) and Ephemeroptera [mayflies; haften; especially the abundant Cloeon and Caenis genera], while trends in other insect orders varied from small decreases (e.g. Coleop- tera [beetles; kevers]) to increases (e.g. Trichop- tera [caddisflies; kokerjuffers/schietmotten] and Odonata [dragonflies and damselflies; libellen]). Trends also varied across WAs, indicating that regional differences in abiotic conditions and temporal changes therein likely matter.

48 The second objective of our study was to relate trends to potential drivers (chang- Sweden or Eastern Europe. However, such comparisons suffer from confound- es in abiotic conditions) as well as relate trends of insect taxa to their traits. The ing differences in sampling methods and from the fact that many of the rare combined toxicity of the observed pesticide concentrations dropped markedly species found elsewhere do not occur in The Netherlands (Nijboer et al. 2004). In over the study period. Similarly, nitrogen and phosphorus concentrations, which the current study we started our analyses with the starting year 1990, because had strongly negative effects on most insect groups, halved during the study sampling methods were much less consistent before that time. While these period, thus contributing positively to the abundance trends of these groups. trends of improved water quality and increase of characteristic species is hope- On the other hand, the most abundant Chironomidae genera benefited signif- ful it is difficult to gauge how complete the current recovery is without proper icantly from eutrophic conditions, and consequently declined strongly. These data on reference community compositions prior to the strongly eutrophic and patterns are also reflected by some of the traits of the genera for which we were polluted water conditions of the second half of the previous century. It is possible able to determine trends: aquatic insects preferring sand, gravel, cobblestones or that aquatic insect communities are steadily recovering towards historic levels, pebbles performed much better than those preferring sludge or macrophytes as but it is also possible that recovery remains incomplete and stabilizes at a state a substrate to live in. And probably related to this: taxa preferring flowing water impoverished relative to historic levels (but enriched relative to the situation in performed better than those preferring standing water. Indeed, filter feeders per- the 1980s). In other words, we have to keep in mind that we might be affected by formed better than deposit feeders. The improvements in water quality are also shifting baselines (Soga and Gaston 2018; Didham et al. 2020) when interpreting reflected in the finding that taxa characteristic of healthy river and lake commu- these patterns of increasing biodiversity over a time period that started in a time nities increased in abundance, while taxa that are negative indicators of healthy with many disturbances. river and lake communities declined in abundance. WHAT CAUSED INSECT ABUNDANCE TO BE HALVED AND DIVERSITY TO INCREASE? While total insect abundance declined, diversity in insect genera increased. In their review paper, Jactel et al. (2020) expect land use change, pollution, The overall increase in diversity indices reflects both an increase in numbers of climate change, invasive alien species, and interactions between these factors to genera (higher richness) and a higher evenness, due to the decline in the abun- be among the most likely causes of recent insect declines. Of the different sets of dance of individuals of the most abundant genera. It would certainly have been explanatory variables in our analyses, land use variables, pesticide and nutrients interesting to calculate and study species-level diversity, but we feel that that was were generally the strongest explanatory variables to explain local insect abun- not possible in general, as too many caught individuals were identified at only a dance. Effects of land use included consistently negative effects of greenhouses taxonomic level higher than the species level. Furthermore, identification tools on the abundance of ten taxonomic groups considered, mainly in the western and expertise may have increased in the first decade of the study period, leading WAs, where many greenhouse complexes are present. However, as only marginal to more individuals being identified at the species level over time (from 65% to changes in these variables occurred over the past three decades, changes in land 70% of the sampled individuals). use variables contributed little to the overall trend of insect groups. We have to acknowledge, however, that our analyses looked at relative changes in the area It would also have been interesting to compare current diversity levels with those cover of land-use types, meaning that subtle changes like the disappearance of in undisturbed water bodies in the past (e.g. first half of the 20th century) or important small landscape elements like single trees at the water edge are not elsewhere within the same ecoregion, e.g. less disturbed streams in Germany, picked up.

48 49 Contrary to the lack of effect of land use changes, the overall declining trends maxima of indices of the combined toxicity of multiple substances. However, in nutrient concentrations over time most consistently explained, statistically, using annual maxima of toxicity is preferential from ecotoxicological theory: temporal trends in the abundance of macrofauna insects, with positive effects peak toxicity (together with the duration and frequency of peaks) is expected to on most species (but negative on the dominant Chironomidae), which ultimate- have lasting effects throughout the season. Using annual maxima is then a better ly resulted in increases in diversity indices over time. The found halving of the proxy of ecotoxicological effects than daily measurements, as measured pesticide nitrogen and phosphorus concentrations is in line with calculations by Deltares, concentrations tend to fluctuate strongly over time: pesticide exposure tends to which show predominantly downward trends since 2010 in areas dominated occur in ephemeral peaks (e.g. after rainfall resulting in run-off). by agriculture (Buijs et al. 2020a). Sewage water and poorly-functioning sewage sanitation systems used to cause high nutrient levels, as well as high organic load For most locations where macro-invertebrates were sampled, micropollutants which causes oxygen shortage and sludge layers. It was probably this combina- were measured as well. However, for about one quarter of the locations we had tion of nutrients and organic load that provided food for large densities of taxa to spatially interpolate the year-specific maximum-msPAF values. While this preferring eutrophic conditions. While water quality has improved, in 2019, ni- means that there is more uncertainty in those interpolated values than in the trogen levels were still higher than the system-specific water quality norm at 48% maximum-msPAF values that were measured at the same locations, we believe of the sampled locations, while phosphorus concentrations were still too high at that that uncertainty is fairly small. Furthermore, we can argue that, if includ- 40% of the sampled locations (Buijs 2020). ing these locations with interpolated maximum msPAF values had any effect, it would most likely result in more conservative conclusions: uncertainty in the In 81% of water samples from German lowland streams in agricultural areas (in values of an explanatory variable leads to weaker effect sizes. Ways to check this 2018 and 2019) some pesticide concentrations exceeded regulatory acceptable in future research is to also perform the analyses on the data from the locations concentrations (Liess et al. 2021). In the Netherlands, the proportion of cases where macro-invertebrate and micropollutant measurements intersected, or to where pesticide concentrations were found to be above their maximally allowed gradually limit the addition of the remaining locations based on their proximity concentrations has decreased gradually in recent years (2014-2019) (Buijs et al. to the nearest location of micropollutant sampling. Despite the many abiotic 2020b). Similar to the positive effects of the halving of the nitrogen and phos- conditions considered, we could not include all factors. For instance, manage- phorus concentrations, the decline in the toxicity of pesticides over the three ment operations by the Waterboard Authorities (e.g. dredging and mowing), or decades of our study also improved the abundance trends of most of the insect local vege tation characteristics, are known to affect the abundance of the various groups, except for the groups indicative of eutrophic and polluted waters. Over insect species as well (Higler and Verdonschot 1989; Fairchild et al. 2003; Verberk those three decades new pesticides have been introduced in agricultural practice et al. 2005; Verberk and Esselink 2007), but since proper data of the execution of and domestic use (e.g. neonicotinoids), while other types of pesticides have been these operations across the three decades of our study is lacking we could not partly phased out. Given the patchy nature of the dataset on micropollutants (in- include them in the analysis. cluding pesticides), with different sets of substances being measured in different water quality samples, we found that it was not feasible to interpolate pesticide concentrations or their toxicity to the day of sampling, as we did do for the more standardly measured variables of water chemistry. Instead we used annual

50 A: Preferred substrate B: Locomotion

A: PreferredA: Preferredsubstrate substrate 3 B: LocomotionB: Locomotion 3

3 3 3 3 2 2

2 2 2 2 1 1 silt macrophytes org. detritus/litter mud FIGURE 14 flier swimmer full water crawler interstitial 1 1 1 1 Relationship between genus-level abundance trends and various traits of those genera. Black bars indicate signiﬁcant relationships0 (t-value > 1.96). The direction of the bars (pos-0

A: Preferred substrate B: Locomotion t values t values silt macrophytes org. detritus/litter mud flier swimmer full water crawler interstitial flier swimmer full water crawler interstitial silt macrophytes org. detritus/litter mud sand

0 0 gravel Depicted 3here are the redistributed t-values of the linear-mixed3 effect models in which0 each itive or negative) depends on 0the sign of−1 correlation between the trends and the genus-level−1 t values t values t values t values burrower twigs/roots sand

of the traits was included one at a time. sand scores for each trait category. gravel microphytes −1 −1gravel −1 −1

2 2 −2 burrower −2 burrower cobbles/pebbles twigs/roots tempor. attached tempor. surface swimmer surface twigs/roots perman. attached microphytes microphytes −2 −2 −2 −2

1 1 cobbles/pebbles −3 −3 cobbles/pebbles tempor. attached tempor. surface swimmer surface tempor. attached tempor. surface swimmer surface perman. attached perman. attached silt macrophytes org. detritus/litter mud flier swimmer full water crawler interstitial −3 −3 0 A: PreferredA: substrate Preferred0 substrate −3 B: LocomotionB: Locomotion −3 C: Trophic status D: Current velocity t values t values

3 sand 3 3 3 3 3 A: Preferred substrate gravel B: Locomotion C: TrophicC: status Trophic status D: CurrentD: velocity Current velocity −1 −1 burrower

3 3 twigs/roots 3 3 3 3 2 2 microphytes 2 2 2 2 −2 −2 cobbles/pebbles tempor. attached tempor. surface swimmer surface 2 2 2 2 perman. attached 2 2 1 1 1 1 1 1 −3 −3 null eutrophic flier swimmer full water crawler interstitial silt macrophytes org. detritus/litter mud silt macrophytes org. detritus/litter mud 1 1 flier 1 swimmer full water crawler 1 interstitial 1 1 0 0 0 0 0 0 t values t values t values t values t values C: Trophic status t values D: Current velocity null eutrophic flier swimmer full water crawler interstitial silt macrophytes org. detritus/litter mud null eutrophic fast slow sand sand gravel 0 0 gravel 0 0 0 0 −1 −1 −1 −1 −1 −1

3 3 medium t values t values t values t values t values t values burrower burrower fast fast slow slow twigs/roots twigs/roots sand oligotrophic gravel microphytes microphytes −1 −1 −1 −1 −1 −1 mesotrophic medium medium

2 −2 −2 burrower 2 −2 −2 −2 −2 cobbles/pebbles cobbles/pebbles twigs/roots tempor. attached tempor. tempor. attached tempor. surface swimmer surface surface swimmer surface oligotrophic perman. attached oligotrophic perman. attached microphytes mesotrophic mesotrophic −2 −2 −2 −2 −2 −2 −3 −3 cobbles/pebbles 1 −3 −3 1 −3 −3 tempor. attached tempor. surface swimmer surface perman. attached null −3 −3 eutrophic −3 −3 −3 −3 0 C: Trophic statusC: Trophic0 status D: Current velocityD: Current velocity E: Transversal distribution F: Longitudinal distribution t values t values t values of effects on genus−level abundance trends abundance on genus−level of effects t values fast C: Trophic status 3 D:3 Current velocity 3 slow 3 E: TransversalE: Transversal distribution distribution F: 3LongitudinalF: Longitudinal distribution distribution 3 −1 −1 medium t values of effects on genus−level abundance trends abundance on genus−level of effects t values 3 3 trends abundance on genus−level of effects t values 3 3 3 3 oligotrophic 2 2 2 2

2 mesotrophic 2 −2 −2 2 2 2 2 2 2 1 1 1 1 1 1 −3 −3 ponds, pools ponds, peat bogs marshes, temporary waters outside river syst. outside river null eutrophic null 1 1 eutrophic 1 1 1 1 0 0 0 0 0 0 t values t values t values t values t values t values ponds, pools ponds, peat bogs marshes, temporary waters outside river syst. outside river null eutrophic ponds, pools ponds, marshes, peat bogs marshes, temporary waters E: Transversal distribution F: Longitudinal distribution syst. outside river fast fast slow slow lakes

0 trends abundance on genus−level of effects t values 0 0 0 0 0 crenon

−1 −1 −1 −1 −1 −1 estuary medium medium t values t values

t values 3 t values 3 t values t values epirithron fast slow hyporithron lakes lakes metarithron oligotrophic oligotrophic epipotamon mesotrophic mesotrophic river channel river crenon crenon estuary groundwaters −1 −1 estuary −1 −1 −1 −1 metapotamon −2 −2 medium −2 −2 −2 −2 epirithron 2 2 epirithron hyporithron hyporithron metarithron oligotrophic metarithron banks, side−armsbanks, epipotamon epipotamon mesotrophic river channel river river channel river groundwaters metapotamon groundwaters −2 −2 −2 −2 −2 −2 metapotamon 1 −3 −3 1 −3 −3 −3 −3 banks, side−armsbanks, banks, side−armsbanks, ponds, pools ponds, peat bogs marshes, temporary waters −3 −3 −3 −3 syst. outside river −3 −3 0 E: TransversalE: Transversal distribution0 distribution F: LongitudinalF: Longitudinal distribution distribution t values t values t values of effects on genus−level abundance trends abundance on genus−level of effects t values t values of effects on genus−level abundance trends abundance on genus−level of effects t values 3 3 3 3

E: Transversal distribution F: Longitudinallakes distribution crenon

−1 −1 estuary t values of effects on genus−level abundance trends abundance on genus−level of effects t values 50 3 3 epirithron 51 hyporithron 2 metarithron 2 2 2 epipotamon river channel river groundwaters −2 −2 metapotamon 2 2 1 side−armsbanks, 1 1 1 −3 −3 ponds, pools ponds, peat bogs marshes, temporary waters outside river syst. outside river ponds, pools ponds, peat bogs marshes, temporary waters 1 1 syst. outside river 0 0 0 0 t values t values t values t values ponds, pools ponds, peat bogs marshes, temporary waters outside river syst. outside river lakes 0 0 lakes crenon crenon estuary −1 −1 −1 −1 estuary t values t values epirithron epirithron lakes hyporithron hyporithron metarithron metarithron epipotamon epipotamon river channel river crenon river channel river estuary groundwaters

−1 −1 groundwaters metapotamon −2 −2 −2 −2 metapotamon epirithron hyporithron metarithron banks, side−armsbanks, epipotamon banks, side−armsbanks, river channel river groundwaters −2 −2 metapotamon −3 −3 −3 −3 banks, side−armsbanks, −3 −3 A: Aquatic stages B: Food

A: AquaticA: stages Aquatic stages 3 B: Food B: Food 3

3 3 3 3 2 2

2 2 2 2 1 1 FIGURE 15 larva adult 1 1 1 1 dead animal>1mm living microinvert. living macroinvert. vertebrates Relationship between genus-level abundance trends and various traits of those genera. Black bars indicate signiﬁcant relationships0 (t-value > 1.96). The direction of the bars0 (pos- larva adult dead animal>1mm living microinvert. living macroinvert. vertebrates larva adult dead animal>1mm living microinvert. living macroinvert. vertebrates t values t values Depicted here are the redistributed t-values of the linear-mixed effect models in0 which each0 itive or negative) depends0 on the sign0 of correlationegg between the trends and the genus-level nymph t values t values

t values t values −1 −1 egg of the traits was included one at a time. egg scores for each trait category. nymph −1 −1 nymph −1 −1 −2 −2 detritus < 1mm microorganisms dead plant>1mm living microphytes −2 −2 living macrophytes −2 −2 detritus < 1mm detritus < 1mm microorganisms microorganisms dead plant>1mm

−3 dead plant>1mm −3 living microphytes living microphytes living macrophytes living macrophytes −3 −3 −3 −3 A: Aquatic stagesA: Aquatic stages B: Food B: Food C: Feeding habit D: Maximal size

A: Aquatic stages 3 3 B: Food 3 3 C: FeedingC: habit Feeding habit 3 D: MaximalD: size Maximal size 3

3 3 3 3 3 3 2 2 2 2 2 2

2 2 2 2 2 2 1 1 1 1 1 1 larva adult dead animal>1mm living microinvert. living macroinvert. vertebrates > 2−4 cm > 4−8 cm deposit feeder shredder > .25−.5 cm piercer parasite larva adult 1 1 1 1 dead animal>1mm living microinvert. living macroinvert. vertebrates 1 1 0 0 0 0 0 0 > 2−4 cm > 4−8 cm deposit feeder shredder > .25−.5 cm piercer parasite > 2−4 cm > 4−8 cm larva adult deposit feeder shredder > .25−.5 cm dead animal>1mm living microinvert. living macroinvert. vertebrates piercer parasite t values t values t values t values t values t values egg 0 0 egg 0 0 0 0 nymph nymph t values t values scraper t values t values t values t values −1 −1 −1 −1 −1 −1 predator egg > 1−2 cm <= .25 cm > .5−1 cm filter−feeder nymph

−1 scraper −1 −1 −1 −1 scraper −1 predator predator > 1−2 cm > 1−2 cm

−2 −2 <= .25 cm > .5−1 cm −2 −2 detritus < 1mm −2 −2 detritus < 1mm <= .25 cm > .5−1 cm microorganisms microorganisms filter−feeder dead plant>1mm dead plant>1mm filter−feeder living microphytes living microphytes living macrophytes −2 −2 −2 living macrophytes −2 −2 −2 detritus < 1mm microorganisms

−3 −3 dead plant>1mm −3 −3 −3 −3 living microphytes living macrophytes −3 −3 −3 −3 −3 −3 C: Feeding habitC: Feeding habit D: Maximal sizeD: Maximal size E: Life cycle duration F: Saprobity

C: Feeding habit 3 3D: Maximal size 3 3 E: Life cycleE: durationLife cycle duration 3 F: SaprobityF: Saprobity 3

3 3 3 3 3 3 2 2 2 2 2 2

2 2 2 2 2 2 1 1 1 1 1 1 > 2−4 cm > 4−8 cm deposit feeder shredder > .25−.5 cm piercer parasite > 1 year b−mesosaprobic a−mesosaprobic polysaprobic > 2−4 cm > 4−8 cm deposit feeder shredder > .25−.5 cm 1 1 piercer parasite 1 1 1 1 0 0 0 0 0 0 > 1 year b−mesosaprobic a−mesosaprobic polysaprobic > 2−4 cm > 4−8 cm > 1 year b−mesosaprobic a−mesosaprobic polysaprobic deposit feeder shredder > .25−.5 cm piercer parasite t values t values t values t values t values t values 0 0 0 0 0 0 t values t values scraper t values t values t values t values −1 −1 −1 scraper −1 −1 −1 predator predator > 1−2 cm <= 1 year > 1−2 cm <= .25 cm > .5−1 cm <= .25 cm > .5−1 cm filter−feeder filter−feeder −1 −1 −1 scraper −1 −1 −1 oligosaprobic xenosaprobic predator <= 1 year > 1−2 cm −2 −2 −2 −2 <= 1 year −2 −2 <= .25 cm > .5−1 cm filter−feeder oligosaprobic xenosaprobic oligosaprobic −2 −2 −2 −2 −2 xenosaprobic −2 −3 −3 −3 −3 −3 −3

−3 −3 −3 −3 −3 −3 E: Life cycle E:duration Life cycle duration F: Saprobity F: Saprobity

E: Life cycle duration 3 3 F: Saprobity 3 3

3 3 52 2 2 2 2

2 2 1 1 1 1 > 1 year b−mesosaprobic a−mesosaprobic polysaprobic > 1 year 1 1 b−mesosaprobic a−mesosaprobic polysaprobic 0 0 0 0 > 1 year b−mesosaprobic a−mesosaprobic polysaprobic t values t values t values t values 0 0

t values t values −1 −1 −1 −1 <= 1 year <= 1 year −1 −1 oligosaprobic xenosaprobic oligosaprobic xenosaprobic

<= 1 year −2 −2 −2 −2 oligosaprobic −2 −2 xenosaprobic −3 −3 −3 −3

−3 −3 COMPARISON WITH AQUATIC AND TERRESTRIAL INSECT TRENDS ELSEWHERE et al. (2021) found that while total insect biomass decreased by three quarters, A recent meta-analysis (van Klink et al. 2020b) showed that while both rare and species richness (accumulated over a season) decreased by 23% from 1989 to 2014, common taxa of terrestrial insects are declining in abundance conform earlier and even 82% on a daily basis. Clearly these synchronous declines in biomass, reports (Dirzo et al. 2014; Hallmann et al. 2017; Seibold et al. 2019), freshwater abundance and species richness in terrestrial habitats, contrast strongly with our insects appeared to be on the rise in recent decades, likely recovering from low current findings of decreasing abundance linked to increasing species richness numbers related to strong pollution in the second half of the 20th century (Du- and diversity. The combination of strongly eutrophic and polluted starting con- rance and Ormerod 2009). However, the selection of datasets going into this me- ditions and improving water quality during the study have resulted in this more ta-analysis has recently been questioned: both their spatial coverage (Desquilbet promising development. et al. 2020; van Klink et al. 2020a) as their suitability for detecting trends (several monitoring schemes started after a major disturbances) (Jähnig et al. 2021; van LIMNODATA NEERLANDICA: Klink et al. 2021). In our analysis, we did find similar patterns of increase for some OPPORTUNITIES, LIMITATIONS AND RECOMMENDATIONS aquatic insect groups: Trichoptera and Odonata, for instance, increased over time After working with the data from the insect macro-invertebrate samples of the as water conditions became less eutrophic and polluted, which are indications Water Authorities, we have several recommendations for improving existing of improving water quality. The estimated trends for Odonata are consistent with datasets and future data. But first of all, we have to stress that these datasets published trends based on entirely independent data (Termaat et al. 2015), while constitute a wealth of data, providing a spatially dense and historically highly for Trichoptera, the only published reference (Hallmann et al. 2019) showed op- relevant coverage of how insect communities have developed in recent decades. posed (declining) rates. However, the insect orders that contribute most to overall The combined dataset on the abundance of aquatic insects allows for large- insect abundance (Ephemeroptera, Diptera (especially Chironomidae)) showed scale assessments of the effects of both environmental threats and conservation strong declines, leading to an overall 53% decline over 27 years. The decline in measures. At the same time, it is clear that there are important challenges with the total number of individuals of Ephemeroptera might be surprising, as this the current datasets. We have dealt with previously identified inconsistencies in insect order is generally seen as an indicator of good water quality. Six of the ten the recorded abundances and metadata (Netten et al. 2010; Verdonschot and van Ephemeroptera-genera for which we calculated abundance trends increased in Oosten-Siedlecka 2010) by selecting data from a third of the Dutch WAs for which abundance. But the three most abundant Ephemeroptera genera (Cloeon, Baetis datasets were sufficiently well-organized and accompanied with necessary meta- and Caenis), which contained more than 95% of the Ephemeroptera individuals, data (Koese and Zeegers 2018). Furthermore, in our analyses we accounted for decreased in abundance. The most abundant species in those genera are more the ’noise’ caused by methodological differences across WAs, field workers who tolerant to poor water quality than other mayfly species. So, in spite of the collected the samples, and the length of the sampled transect. abundance decline of all aquatic insects combined, richness and other diversity indices such as community evenness showed increases over time. Higher richness The eight WAs were selected based on how organized their data already were. It and diversity most likely reflect a combination of a) diminishing dominance of is likely that parts of the data of other WAs can also meet the criteria for trend genera tolerant to high nutrient loads, and b) recovery of some genera indicative analysis, requiring, however, that the original data sources are accessed to make of improved water quality. In that respect our results on aquatic insect trends the metadata as complete as possible. Information on who collected the sample, differ from recent findings from a terrestrial hoverfly community. Hallmann how many meters were sampled and in which microhabitats, and how the sample

52 53 was processed, might still be available in the original forms and note books. for the location and time of the macroinvertebrate samples. Ideally data on In our analyses we accounted for the identity of the main person collecting a mowing frequencies and dates, dredging, etc. are recorded systematically for particular macrofauna sample in the field. Still, some difficulty arose, e.g. in the all (stretches of) water bodies. Pending such information system, management WPM data, where the team of field workers changed over the course of only a few metadata should be recorded alongside each macroinvertebrate sample. years. Therefore, we cannot fully rule out that the deviating trend (WPM is the • In the lab, metadata about the processing of the samples is required as well: only WA with a positive trend) represents a true increase in total insect abun- who (individual, not just a company name) processed the sample, how much dance, or partly also changing practices in the field. time was spend on processing the sample and whether that restricted the identification process. The macrofauna monitoring scheme of the Dutch Water Authorities is accom- • As not only the level and field of expertise differs between people processing panied by a clear sampling, processing and estimation protocol (Beers et al. the samples, but also between the resources available to them to identify taxa, 2014). Over the selected time period (1990-2018) and for the selected parts of the it would be useful to record which identification keys were available. datasets of the eight WAs that were included in this study, the methods were con- • If not all individuals in a sample are taxonomically identified and/or not all sistent enough to analyse trends in individual abundance, species richness and individuals are counted, the estimation and extrapolation process should diversity. Still, we believe that future analyses would highly benefit from further be described in detail, allowing full reconstruction. Actually counted and standardization of all aspects of sampling aquatic macro-invertebrates, process- additionally extrapolated numbers should be recorded separately. ing the samples, and recording information about both the process (metadata) • Database structures for organizing and collating the macrofauna data should and the taxon-specific counts. To improve future analyses we therefore like to not be hampering the documentation of metadata (e.g., due to restrictive present to following recommendations: data entry portals), but rather enable and steer proper documentation of all • Continue to train field workers to collect macrofauna samples according to relevant aspects of the collection and processing of the macrofauna samples. the protocol in a nationally coordinated course. • It is also advisable to consider conserving and storing the actual samples in • Field workers should minutely document who took the sample, how many a standardised and coordinated way. That would allow checks of potential meters were sampled in total, how many meters in which microhabitat, errors, revisiting samples in the future when new taxonomic insights concurrent water level, development stage of the vegetation, and other could lead to different identifications, or future research on specific taxa or relevant aspects of the sampling conditions. communities. A central, well-organized and accessible database of where these • Ideally water quality samples are collected at the same location and at the samples are is then a requirement, of course. same time as the macro-invertebrate sample. • While it might be important to retain flexibility with respect to new sampling In addition to these recommendations for improving the quality and usefulness locations, the importance of repeat-samples of aquatic insects on exactly the (value) of the macro-invertebrate sampling scheme of the WAs, we also want to same location must also be stressed. briefly discuss alternatives. For instance, taxonomic sorting based on metabar- • Information about macroinvertebrate sample processing in the field should coding (Beentjes et al. 2019), environmental DNA (eDNA) (Goldberg et al. 2016; also be recorded. Valentini et al. 2016; van Bochove et al. 2020) or automated photography and • Key aspects of water and shore vegetation management should be recorded image recognition (Hogeweg et al. 2019; Høye et al. 2021) might be suggested to

54 A: Preferred pH B: Reproduction

3 3

2 2

1 1 <= 4 > 4−4.5 > 4.5−5 > 5−5.5 > 5.5−6 ovoviviparity free isolated eggs, free clutches, clutch:vegetat. clutch:terrestrial 0 0 t values t values > 6 FIGURE 16 −1 A: Preferred pH −1 B: Reproduction −23 −23 Relationship between genus-level abundance trends and various traits of those genera. None of the trait categories had a signiﬁcant t-value (< 1.96). The directionclutch fixed of the bars (pos-

Depicted here are the redistributed t-values of the linear-mixed effect models in which each itive−32 or negative) depends on the sign of correlation−32 between the trendsegg:cemented and the genus-level 1 1 of the traits was included A:one Preferred at a time. pH B: Reproduction scores for each trait category. <= 4 > 4−4.5 > 4.5−5 > 5−5.5 > 5.5−6 ovoviviparity free isolated eggs, A: Preferred pH B: Reproduction C: Dispersal D: Resistance form free clutches, clutch:vegetat. clutch:terrestrial 0 0

3 3 t values t values 3 3 3 > 6 3 −1 −1 2 2 2 2 2 2 −2 −2 1 1 1 1 1 1 clutch fixed egg:cemented clutches, free clutches, clutch:vegetat. clutch:terrestrial <= 4 > 4−4.5 > 4.5−5 > 5−5.5 > 5.5−6 ovoviviparity free isolated eggs, <= 4 > 4−4.5 > 4.5−5 > 5−5.5 > 5.5−6 ovoviviparity free isolated eggs, clutches, free clutches, clutch:vegetat. clutch:terrestrial egg,statoblast cocoons diapause −3 aquatic active aerial active −3 0 0 0 0 0 0 t values t values > 6 t values t values > 6 −1 −1 t values t values −1 A: Preferred pH −1 B: Reproduction −1 C: Dispersal −1 D: Resistance form none −2 −2 −2 −2 clutch fixed 3 3 3 3 clutch fixed −2 −2 aerial pass. egg:cemented egg:cemented

−3 −3 aquat passive −32 −32 −32 −32

1 C: Dispersal 1 D: Resistance form 1 1 C: Dispersal D: Resistance form aerial active egg,statoblast cocoons diapause aquatic active clutches, free clutches, <= 4 > 4−4.5 > 4.5−5 > 5−5.5 > 5.5−6 ovoviviparity free isolated eggs, clutch:vegetat. clutch:terrestrial E: Respiration F: Altitude 3 0 3 0 0 0

3 3 t values t values t values t values

> 6 3 3

2 −1 2 −1 −1 −1 none 2 2 2 2 1 −2 1 −2 −2 −2 clutch fixed aerial pass. egg,statoblast cocoons diapause 1 aquatic active aerial active 1 1 1 aquat passive

0 0 egg:cemented egg,statoblast cocoons diapause aquatic active aerial active lowlands piedmont level alpine level −3 −3 −3 gill spiracle vesicle hydrostatic −3 t values 0 t values 0 0 0 −1 −1 none t values t values t values t values

−1 −1 none E: Respiration F: Altitude −2 C: Dispersal −2 D: Resistance form −1 −1 aerial pass. plastron tegument −23 aquat passive −23 −23 −23 −3 aerial pass. −3 t values of effects on genus−level abundance trends abundance on genus−level of effects t values aquat passive −32 −32 −32 −32 E: Respiration F: Altitude 1 1 1 1 E: Respiration F: Altitude gill spiracle vesicle hydrostatic lowlands piedmont level alpine level egg,statoblast cocoons diapause 3 aquatic active 3 aerial active G: Life cycles per year H: Temperature 0 0 0 0

2 3 2 3 t values t values t values t values 3 3

−1 −1 none −1 −1 1 2 1 2 2 plastron 2 tegument lowlands piedmont level alpine level −2 gill spiracle vesicle hydrostatic −2 −2 −2 aerial pass. 0 1 0 1 trends abundance on genus−level of effects t values 1 1 aquat passive t values t values hydrostatic vesicle hydrostatic lowlands piedmont level alpine level gill spiracle eurythermic −3 > 1 −3 −1 −3 −1 −3 0 0 0 0 1 plastron t values t values t values t values tegument −2 −1 −2 −1 < 1 t values of effects on genus−level abundance trends abundance on genus−level of effects t values E: Respiration F: Altitude −1 G: Life cycles per year −1 H: Temperature

−3 plastron −3 −23 tegument −23 −23 −23 t values of effects on genus−level abundance trends abundance on genus−level of effects t values thermophilic psychrophilic −32 G: Life cycles per year −32 H: Temperature −32 −32

3 1 3 1 1 1 G: Life cycles per year H: Temperature eurythermic > 1 gill spiracle vesicle hydrostatic lowlands piedmont level alpine level 2 0 2 0 0 0 1

3 3 t values t values t values t values < 1 1 −1 1 −1 −1 −1 eurythermic 2 > 1 2 plastron

0 tegument 0 −2 1 −2 −2 −2 t values t values < 1 thermophilic t values of effects on genus−level abundance trends abundance on genus−level of effects t values 1 1 −1 −1 psychrophilic > 1 −3 −3 eurythermic −3 −3 54 0 0 55 −2 1 −2 t values t values < 1 thermophilic psychrophilic −3 −1 G: Life cycles per year −3 −1 H: Temperature −23 −23 thermophilic psychrophilic −32 −32

1 1 eurythermic > 1 0 0 1 t values t values < 1 −1 −1

−2 −2 thermophilic psychrophilic −3 −3 FIGURE 17 A: Risk level B: River indicator C: Lake indicator The abundance trend (rate of increase; ln-lambda) of genera as a function of certain traits: 0.04 A. whether or not the species in a genus are at risk for the effects of micropollutants (n=26 and 175 genera, respectively), and indicator classes for rivers (B) and lakes (C). The indica- 0.4 tor classes are: N (species in a genus are mainly negative indicators of typical river or lake systems), K (species in a genus are mainly characteristic taxa), P (mainly positive indicators 0.03 0.03 when dominant), and 0 (genera with no indicator species or with a mixed bag of N, K and P). The black bars in B and C signal signiﬁcant effects compared to the 0 class. 0.3

0.02 0.02 0.2

0.01 0.1 0.01 Rate of increase Rate of increase Rate of increase

n=9 0.0 n=15 0.00 0.00 n=110 n=85 n=9 n=100 n=96 n=2

−0.1 −0.01 −0.01

−0.2

−0.02 −0.02

At Risk Not at risk 0 N K P 0 N K P

56 augment or even replace the current methodology of collecting aquatic insects causality of the patterns found in this study. from water bodies in the near future. While the technological possibilities are Particular research questions and hypotheses which could be addressed in fol- growing rapidly, the key aspect to evaluate these on is whether they can produce low-up analyses include, amongst many others, and in no particular order: the same data as the current methods: counts per separate taxa. Translating • What are the relative contributions of terrestrial and aquatic environmental metabarcoding data and eDNA samples to the number of individuals present is factors to long-term trends of various taxa? not (yet) straightforward (Beentjes et al. 2018). Even when new methods would • How much improvement in measures (e.g. species richness, evenness, Shannon produce reliable estimates of densities, very thorough, habitat-specific calibration index) of aquatic insect diversity is achievable, given assessments of natural/ would be needed to relate estimates of new and existing methods. As the value of pristine aquatic communities? the macroinvertebrate data of the WAs grows with the years in which the same • Is insect diversity higher in water bodies managed as nature reserves compared protocol is followed, it is advisable to be very careful about changes in methodol- to other water bodies? ogy, and when in doubt, to stick with the existing method with standard macro- • Do wet nature reserves have a positive effect on insect diversity in surrounding, fauna-nets. regular water bodies? • Do algivores decline in those sites where nutrients have reduced and where TOPICS FOR FUTURE RESEARCH there is not much forest cover? In this report we focus on the abundance and diversity trends of all aquatic insects • Are the negative trends in Simuliidae linked to positive trends of their found in the macro-invertebrate samples of the Dutch Water Authorities. For the predators (e.g. hydropsychid caddisflies)? abundance analyses we mainly focused on higher taxonomic levels in order to in- • Can it be shown that short-lived taxa respond stronger to the reduced nutrient clude all insects found. Already when analysing trends and trait-trend relationships concentrations than long-lived taxa? This would indicate that short-lived taxa at the genus level where we forced to leave out some of the counted individuals, be- no longer profit from a larger food base, or in the case of oxygen instead of cause they were identified at a higher taxonomic level only, or because no reliable nutrients: indicating that long-lived taxa are profiting from higher oxygen trends could be estimated for the genera due to lack of data. The diversity indices levels. Another hypothesis is that short-lived species can recover more quickly were calculated at the genus level. Estimating trends and trait-trend relationships from disturbances like strong drops in oxygen availability and vegetation at the species levels would have meant an even further subsetting of the data, and management, which are more frequent in nutrient-rich conditions. fell outside the scope of the research documented in this report. • Are water quality improvements (nutrients, oxygen, pesticides), similar for isolated, standing water sites and connected, running water sites? There are good evidence- and hypothesis-driven reasons, however, to want to • One interesting comparison here could be to compare trends in running and perform trait-trend analyses for subsets of genera or species in follow-up studies. standing waters: do we also see a decline of full-water swimmers in lake sites? For instance, one could imagine that whether or not the decline of genera is Or is the pattern seen here driven by improvements in river habitats making related to nutrient reductions is related to traits such as feeding on algae. Or that them less suitable for lake specialists? insects hunting by sight have different abundance trends than those that can • Analyse the effects of greenhouses on abundances of species at risk, and rely on touch and do not require clear water. This kind of analysis, where trends compare them with the effect size of greenhouses for abundances of species are related to explanatory variables and traits can provide a lot of insight in the not at risk?

56 57 • Are there interactive effects between oxygen parameters and temperature? CONCLUSIONS • How much of the increase in diversity is due to the found increase in species We conclude that water quality improvements since 1990 (e.g. diminished nu- richness, and how much due to the increased evenness? One thing which may trient concentrations and pesticide toxicity) in the Netherlands have positively be informative is to calculate the expected shannon index if all individuals affected insect-macrofauna communities so far. The most abundant groups, that would be equally distributed across the species found. The difference were tolerant to high-nutrient conditions, have declined, while some indicator between this max index and the observed index indicates the effect of groups of healthy communities have increased in abundance. Trends in insect skewed distribution and likely diminishes over time, indicating that as time groups are, however, not fully explained by changing nutrient concentrations progresses, diversity increases more and more because of species additions and toxicity of pesticides, but are explained to a considerable extent by unknown rather than increases in evenness. factors, not included in our analyses. • Can the observation of concurring decreases in nutrient concentrations and shift from shorter-lived to more longer-lived genera be explained solely by It is encouraging to see that the efforts of the WAs and other actors to improve direct effects of food availability, or also through indirect effects of predators? water quality have had clear positive effects on the richness and diversity of It could well be that longer-lived predators are more sensitive to episodic water insects. However, given the abysmal water quality in the 1970s and 1980s, conditions of poor water quality. Analysing patterns per feeding guild could the conditions at the start of our study period should not be used as a baseline. perhaps shed light on this. Thus, the improvements observed reflect recoveries from a severely impover- • Do species differ in their response to pesticides, and can those differences be ished state regarding water quality. We therefore emphasize the need for taking related to traits determining how much they are in direct contact with these further steps to assure the conservation and recovery of freshwater biodiversity. substances? Moths have a waxy cuticle and may therefore have a layer of air Follow-up research will have to show in which direction the composition of insect surrounding their body making them less prone to suffer effects of pesticides communities is developing and how WAs can steer that development in the de- associated with greenhouses. One other group which are not in direct contact sired direction. Our results therefore emphasize the importance of continuing to with water are true bugs (Hemiptera) that dwell at the water surface (e.g., take conservation measures to maintain and restore aquatic biodiversity, and to Gerridae and Velidae). Beetles and heteropterans may be less prone to effects of standardize the sampling of insect macrofauna even more. pesticides but this only applies to the adult stage with sclerotized body parts; the situation is different for their juvenile stages (but not for the surface- dwelling true bugs, making this an interesting subgroup to look into). • Ephemeroptera are particularly sensitive to salt, so although as a group we see a decline in abundance, individual genera may show positive trends, especially where salinity decreased?

58 Caddisfly - Adicella reducta - kokerjuffer Photo: Bureau Biota

ACKNOWLEDGEMENTS i We are grateful to Pauline van Alebeek for digi- tizing some of the data and for proof-reading the final version of this report, and to Harriette Koop for making the Dutch summary understandable for a wider audience.

58 5959 Blue emperor - Anax imperator - grote keizerlibel Photo: Wil Leurs LITERATURE CITED

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62 63 Photo: Michel de Beer

SUPPLEMENTARY INFORMATION

64 Supplementary Information

Appendix A: Interpolation of weather data

We interpolated weather data from up to 49 weather stations to macrofauna monitoring locations

for each particular sampling day and location. We considered temperature, precipitations and evapo-

transpiration. From the interpolated temperature variables we also extracted the average weekly and

average monthly temperature prior to each sampling event, the growing degree-days (GDD: at oC5 de-

grees). We decomposed each weather variable in a mean annual smooth trend, a mean smooth spatial

trend, and a mean seasonal trend (see Supplementary Figure S2), using Generalized Additive Models

assuming Gaussian errors. Using the residuals of the models, we derived the daily mean anomaly over

all weather stations, and subtracted it from the model residuals, i.e. leaving only between-station

daily spatial variation. From these residuals we calculated average daily-semivariance (γs) for spatial

lags up to 300 km, and modelled these using an exponential variogram model: Supplementary Information

APPENDIX A: α D γ = S e− ∗ (S.1) s × AppendixINTERPOLATION A: Interpolation of weather OF data WEATHER DATA

We interpolated weather data from up to 49 weather stations to macrofauna monitoringwhere locationsS is the model sill and α the steepness at which the semivariance reaches the sill over spatial

for each particular sampling day and location. We considered temperature, precipitations andlag evapo-D (See Supplementary Figure S3). To interpolate to macrofauna locations (z), for a particular We interpolated weather data from up to 49 weather stations to macrofauna To interpolate to macrofauna locations (z), for a particular day (t), we predicted transpiration. From the interpolated temperature variables we also extracted the average weeklyday (t and), we predicted the means using each GAM-model (µz,t), added the daily average residuals (ˆrt), monitoring locations for each particular sampling day and location. We consid- the means using each GAM-model (µz,t), added the daily average residuals (rˆt), and ando ﬁnally interpolated the spatial residuals (rz): average monthlyered temperaturetemperature, priorprecipitations to each sampling and evapotranspiration. event, the growing From degree-days the interpolated (GDD: at C5 de- finally interpolated the spatial residuals (rz): temperature variables we also extracted the average weekly and average monthly grees). We decomposedtemperature each prior weather to each variablesampling in event, a mean the annual growing smooth degree-days trend, a(GDD: mean at smooth 5oC). spatial 1 We decomposed each weather variable in a mean annual smooth trend, a mean yˆz,t = µz,t +ˆrt + C1C0− rz (S.2) (S.2) trend, and a mean seasonal trend (see Supplementary Figure S2), using Generalized Additive Models smooth spatial trend, and a mean seasonal trend (see Supplementary Figure S2), assuming Gaussianusing Generalized errors. Using Additive the residuals Models ofassuming the models, Gaussian we derived errors. the Using daily the mean residuals anomaly over

of the models, we derived the daily mean anomaly over all weather stations, and where C0 is the covariance matrix between weather stations, and C1 the covar- where C0 is the covariance matrix between weather stations, and C1 the covariance matrix between all weather stations,subtracted and it from subtracted the model it from residuals, the model i.e. leaving residuals, only between-station i.e. leaving only daily between-station iance matrix between weather and macrofauna locations. Both of these were spatial variation. From these residuals we calculated average daily-semivariance weather andobtained macrofauna by locations. Both of these were obtained by daily spatial variation. From these residuals we calculated average daily-semivariance (γs) for spatial 93 (γs) for spatial lags up to 300 km, and modelled these using an exponential vario- lags up to 300gram km, model: and modelled these using an exponential variogram model: α D Cov(z ,z )=S S e− ∗ i,j (S.3) (S.3) i j − ×

α D γ = S e− ∗ (S.1) (S.1) s × where Di,j whereis the D pairwisei,j is the pairwise distance distance between between two locations two locationszi and z jz.i and zj.

where S is thewhere model S is sillthe andmodelα thesill steepnessand α the steepness at which theat which semivariance the semivariance reaches the reaches sill over spatial the sill over spatial lag D (See Supplementary Figure S3). Appendix B: Spatio-temporal estimation and interpolation of chemical lag D (See Supplementary Figure S3). To interpolate to macrofauna locations (z), for a particular

day (t), we predicted the means using each GAM-model (µz,t), added the daily average residualswater (ˆrt), properties

and ﬁnally interpolated the spatial residuals (rz): Each of the Water Authorities (WAs) have collected measurements of chemical and physical water

characteristics, covering 22 substances between 1990 and 2018 (see table 3). Approximately two thirds 1 yˆz,t = µz,t +ˆrt + C1C− rz (S.2) 64 0 of the65 macrofauna monitoring locations overlap spatially with water quality measurement locations,

but the two monitoring schemes rarely coincide in time. As such, we rely on modelling of the chemical

and physical characteristics of water bodies, and deriving statistical predictions of water quality at

93 the macrofauna locations and time of sampling. We used generalized additive models with Gaussian

errors, where the (log of the) measured concentrations of each compound are modelled as a function

of for example temporal (daily and yearly splines), spatial (nonlinear x- and y-coordinates, as well as

a random eﬀect for catchment area; see equation 5 in main text).

An important aspect in the measurements of physical/chemical properties of the water, are the

detection limits. Detection limits prohibit straightforward modelling, as they pose a left-truncated

variable problem (e.g. below a given detection limit are essentially). Data that are above the limit

provide robust information, while water-concentrations below the limit are left-censored: any positive

value below detection limit is theoretically possible, but is essentially unobserved. Detection limits

94 APPENDIX B: have been lowered by improved analytics over time, in all WAs and for most substances measured SPATIO-TEMPORAL ESTIMATION ANDregularly. INTERPOLATION Ignoring the changing detection limits over time likely results in biased trends in space OF CHEMICAL WATER PROPERTIES and time of the measured substances/parameters. Therefore, we used GAMs with a Tobit error (distribution), followed by spatio-temporal interpolation of model residuals to arrive at predictions for

the macrofauna samples. Tobit regression (Tobin, 1958) allows to deal with the problem of (variable)

detection limits in a straightforward way, and recent developments (Fang, 2017) have allowed to Each of the Water Authorities (WAs) have collected measurements of chemical predictions for the macrofauna samples. Tobit regression (Tobin 1958) allows to and physical water characteristics, covering 22 substances between 1990 and 2018 include thisdeal type with of the error problem distributions of (variable) within detection the framework limits in of a generalizedstraightforward additive way, modelsand (Wood, (see table 3). Approximately two thirds of the macrofauna monitoring locations recent developments (Fang 2017) have allowed to include this type of error distri- overlap spatially with water quality measurement locations, but the two mon- 2017). Below,butions we explainwithin the how framework we interpolated of generalized residuals additive of the tobit-GAMs.models (Wood 2017). Below, itoring schemes rarely coincide in time. As such, we rely on modelling of the we explain how we interpolated residuals of the tobit-GAMs. Let z(x, y, t) be the vector of observation of a given compound, indexed by space and time. Let also chemical and physical characteristics of water bodies, and deriving statistical predictions of water quality at the macrofauna locations and time of sampling. µ(x, y, t)=Letl( θz()x, be y, t the) be correspondingthe vector of observation vector of mean-predictions of a given compound, from equationindexed by5, space and let µ (x ,y ,t ) We used generalized additive models with Gaussian errors, where the (log of the) and time. Let also µ(x, y, t) = l(θ) be the corresponding vector of mean-predictions measured concentrations of each compound are modelled as a function of for ex- be the mean-ﬁttedfrom equation values 5, and atnew let µ′ locations(x′, y′, t′) be ( zthe (x mean-fitted,y ,t )). To values derive at at new full locations predictions (means + ample temporal (daily and yearly splines), spatial (nonlinear x- and y-coordinates, (z′(x′, y′, t′)). To derive at full predictions (means + interpolated residuals) at new interpolated residuals) at new locations, we make use of predicted values µ as well as model residuals as well as a random effect for catchment area; see equation 5 in main text). locations, we make use of predicted values µ′ as well as model residuals r = z − µ An important aspect in the measurements of physical/chemical properties of the r = z µ(indexation(indexation dropped dropped for for simplicity) simplicity) using using − water, are the detection limits. Detection limits prohibit straightforward modelling, as they pose a left-truncated variable problem (e.g. below a given detection 1 limit are essentially). Data that are above the limit provide robust information, zˆ = µ + C C− r (S.4) (S.4) while water-concentrations below the limit are left-censored: any positive value below detection limit is theoretically possible, but is essentially unobserved.

Detection limits have been lowered by improved analytics over time, in all WAs where C thewhere covariance C the covariance matrix between matrix measuredbetween measured concentrations concentrationsC = Cov C(z =, zCov) and(z, z)C and the expected and for most substances measured regularly. Ignoring the changing detection C′ the expected covariance matrix between measurement locations and prediction covariance matrix between measurement locations and prediction locations C = Cov(z, z ). Note limits over time likely results in biased trends in space and time of the measured locations C′= Cov(z, z′). Note that model residuals in the presence of censoring are substances/parameters. Therefore, we used GAMs with a Tobit error (distribu- that modelnot residuals obvious infor theobservations presence below of censoring detection are limits, not obvious and as such for observations we assume for below detection tion), followed by spatio-temporal interpolation of model residuals to arrive at the sake of explanation no censoring for now, but deal with this below. limits, and as such we assume for the sake of explanation no censoring for now, but deal with this

below. 66 Covariance matrices were speciﬁed by low-rank parametric covariance models, so that they can

be represented as simple functions of distance in space d and time u, i.e. Cov(zi,zj)=Cov(dij,uij),

for an arbitrary pair (i and j) of observations. We assumed a separable covariance structure in space

95 and time (Pebesma and Heuvelink, 2016). so that the full spatio-temporal covariance structure can

be represented as a product of separate space- and time-covariance structures.

C(d, u)=σ2 γ(d, u) (S.5) −

where σ2 the estimated residual variance of the tobit-models, and γ(d, u) the semivariance, which in

turn depends on the semivariances in space (γs(d)) and time (γt(u)):

γ(d, u)=σ2(γ (d)+γ (u) γ (d)γ (u)) (S.6) s t − s t

(Pebesma and Heuvelink, 2016). For each substance/parameter, two semvariance models were ﬁtted:

one for time (γt,using only within monitoring-location residuals), and one for space (γs,using only

residuals of measurements within taken the same month at diﬀerent locations). For each of the

two components (spatial and temporal), two alternate semivariance functions were considered for

modelling semivariance, the exponential:

αd γ(d)=v + β(1 e− ) (S.7) − Covariance matrices were specified by low-rank parametric covariance models, and the spherical so that they can be represented as simple functions of distance in space d and and the spherical time u, i.e. Cov(z , z ) = Cov(d , u ), for an arbitrary pair (i and j) of observations. We  and time (Pebesma and Heuvelinki j , 2016ij). soij that the full spatio-temporal covariance structure can 3 assumed a separable covariance structure in space and time (Pebesma and Heuve- v + β 3d d if d<α and time (Pebesma and Heuvelink, 2016). so that the full spatio-temporal covariance structure can 2α − (2α3) beand represented time (Pebesma as a product and Heuvelink of separate, 2016 space-). so that and thetime-covariance full spatio-temporal structures. covariance structure can γ(d)= (S.8) link 2016). so that the full spatio-temporal covariance structure can be represent-  (S.8)  be representeded as as a producta product of of separate separate space- space- and and time-covariancetime-covariance structures.structures. v + β if d α be represented as a product of separate space- and time-covariance structures. ≥   C(d, u)=σ2 γ(d, u) , from(S.5) which we selected the best fitting as judged by RMSE, for each substance and for each of the − C(d, u)=σ2 γ(d, u) (S.5) (S.5) , from which we selected the best fitting as judged by RMSE, for each substance C(d, u)=σ2 − γ(d, u) spatial(S.5) and temporal dimensions. For an overview of estimater parameters see Table S.3. − and for each of the spatial and temporal dimensions. For an overview of estimat- 2 where σ the estimated residual variance of the tobit-models, and γ(d, u) the semivariance, whichTurning in toed the parameters point of censoring, see Table residualsS.3. of concentrations below detection limit are unobserved, 2 2 where σ2 the estimatedwhere σ residualthe estimated variance residual of the variance tobit-models, of the andtobit-models,γ(d, u) the and semivariance, γ(d, u) the which in turnwhere dependsσ the estimated on the semivariances residual variance in space of (theγs(d tobit-models,)) and time ( andγt(u)):γ(d, u) the semivariance, which in semivariance, which in turn depends on the semivariances in space (γ s(d)) and Turning to the point of censoring, residuals of concentrations below detection turn depends on the semivariances in space (γ (d)) and time (γ (u)): turn depends ontime the (γ semivariances(u)): in space (γs(d)) and time (γt(u)): limit are unobserved, just as the actual96 values of the substance are. Here, we used t s t just as the actual values of the substance are. Here, we used the expectations of the residuals for 2 the expectations of the residuals for below-detection measurements, and the γ(d, u)=σ (γs(d)+γt(u) γs(d)γt(u)) (S.6) − just as the actualcalculated values residuals of the for substance non-censored are. Here,values: we used the expectations of the residuals for 2 below-detection measurements, and the calculated residuals for non-censored values: γ(d, u)=σ2(γs(d)+γt(u) γs(d)γt(u)) (S.6) γ(d, u)=σ (γs(d)+γt(u) − γs(d)γt(u)) (S.6) (S.6) − below-detection measurements, and the calculated residuals for non-censored values: (Pebesma and Heuvelink, 2016). For each substance/parameter, two semvariance models were fitted:  zi µi if si =0 (Pebesma and Heuvelink, 2016). For each substance/parameter, two semvariance models were fitted: r =  − (S.9) (onePebesma for time and (γ Heuvelink(Pebesma,using only and, 2016 withinHeuvelink). For monitoring-location each 2016). substance/parameter, For each substance/parameter, residuals), two and semvariance one two for semvariance space models (γ were,using fitted: only i (S.9) t s  zi µi if si =0  − models were fitted: one for time γ( , using only within monitoring-location residu- r = E[zi si = 0] µi si =1 (S.9) one for time (γt,using only within monitoring-locationt residuals), and one for space (γs,using only i | − residualsone for time of measurements (γt,using only within within taken monitoring-location the same month residuals), at different and onelocations). for space For (γs each,using of only the  als), and one for space (γ , using only residuals of measurements within taken the  s  E[zi si = 0] µi si =1 residuals of measurements within taken the same month at different locations). For each of the  | − residuals of measurementssame month at within different taken locations). the same For month each of at the different two components locations). (spatial For eachand of the two components (spatial and temporal), two alternate semivariance functions were consideredwhere forsi denotes a binary variable indexing censoring (si = 1) or not (si = 0), and the expected temporal), two alternate semivariance functions were considered for modelling where s denotes a binary variable indexing censoring (s = 1) or not (s = 0), and the two components (spatial and temporal), two alternate semivariance functions were considered for i i i modellingtwo components semivariance,semivariance, (spatial the and exponential:the temporal), exponential: two alternate semivariance functions were consideredresidualwhere fors isi denotes givenexpected by a binary residual variable is given indexing by censoring (si = 1) or not (si = 0), and the expected d modelling semivariance, the exponential: residual is given by 1 modelling semivariance, the exponential: E[zi si = 0] = (z g(z))dz (S.10) | p d × αd 1−∞ γ(d)=v + β(1 e− ) (S.7) (S.7) E[z s = 0] = (z g(z))dz (S.10) (S.10) − i| i p × αd −∞ γ(d)=v + β(1 e−αd) . (S.7) γ(d)=v + β(1 − e− ) (S.7) − . and the spherical

and the spherical 3 Appendix C: Scale-dependent analysis and the spherical v + β 3d d if d<α 2α − (2α3) γ(d)= 3d d3 (S.8) 3 Appendix C: Scale-dependent analysis v + β 23αd (2dα3) if d<α v + β 3 if d<α z x ,y p  2α − (2α ) For a given response variable x0,y0 at location 0 0 , and a given set of covariate values x,y at γ(d)=v + β − if d α (S.8) { } 66 γ(d)= ≥ (S.8)67  For a given response variable z at location x ,y , and a given set of covariate values p at v + β if d α points x, y , where x, y wex0,y define0 the effect relationship0 0 to be: x,y v + β if d ≥ α { } { }∈ { } , from which we selected the best fitting as judged by RMSE, for≥ each substance and for each of the  points x, y , where x, y we define the effect relationship to be: , from which we selected the best fitting as judged by RMSE, for each substance and for each of the { } { }∈ spatial, from which and temporal we selected dimensions. the best Forfitting an as overview judged of by estimater RMSE, for parameters each substance see Table andS.3 for. each of the z = β (w p ) (S.11) spatial and temporal dimensions. For an overview of estimater parameters see Table S.3. x0,y0 x,y x,y spatial and temporal dimensions. For an overview of estimater parameters see Table S.3. x,y Turning to the point of censoring, residuals of concentrations below detection limit are unobserved, zx0,y0 = β (wx,ypx,y) (S.11) Turning to the point of censoring, residuals of concentrations below detection limit are unobserved, x,y Turning to the point of censoring, residuals of concentrations below detection limit are unobserved, where β defines the sign and magnitude of the effect on the response, wx,y a spatial weight-vector 96 where β defines the sign and magnitude of the effect on the response, w a spatial weight-vector 96 depending on distance to focal measurement point, and itself. The weightsx,y are obtained from a 96 distance-weightingdepending on distance function, to focal which measurement in the present point, study and is defined itself. Theas a weightstwo-dimensional are obtained parametric from a

kernel.distance-weighting The kernel is function, depending which on indisplacement the present distance study is ( deﬁnedd = ( asx ax two-dimensional)2 +(y y )2, d parametric0), a − 0 − 0 ≥ kernel. The kernel is depending on displacement distance (d = (x x )2 +(y y )2, d 0), a scale (σ) and a shape parameter (γ): − 0 − 0 ≥ scale (σ) and a shape parameter (γ):

γ d ) w(d; σ, γ)=Ce−( σ ) (S.12) γ d ) w(d; σ, γ)=Ce−( σ ) (S.12) 97 97 just as the actual values of the substance are. Here, we used the expectations of the residuals for below-detectionjust as the actual measurements, valuesjust as of the the actualand substance the values calculated are. of theHere, residuals substance we used for are. thenon-censored expectations Here, we values: used of the the expectations residuals for of the residuals for below-detection measurements,below-detection and measurements,the calculated residuals and the calculated for non-censored residuals values: for non-censored values:

z µ if s =0 i − i i just as the actual values of the substance are. Here, we used the expectations of the residuals for ri =  (S.9) z µ ifzs =0µ if s =0 below-detection measurements, and the calculated residuals for non-censored values:  i i i i i i r = E[−zi si = 0] rµ=i si =1− (S.9) (S.9) i  | − i  just as the actual values of the substance are. Here, we used the expectations of the residuals for  E[z s = 0] µ s =1 zi µi if si =0 E[zi si = 0] µi si =1i i i i  − | − | − ri = (S.9) below-detection measurements, and the calculated residuals for non-censored values:  where s denotes a binary variable indexing censoring (s= 1) or not (s = 0), and the expected  i  i i E[zi si = 0] µi si =1   | −   residualwhere si isdenotes given by awhere binarys variablei denotes indexing a binary censoring variable (indexingsi = 1) orcensoring not (si (=si 0),= 1) and or the not expected (si = 0), and the expected  zi µi if si =0 where si denotes a binary variable indexing censoring (si = 1) or not (si = 0), and the expected r =  − d (S.9) i  1 residual is given by residual is givenE[z bys = 0] = (z g(z))dz (S.10) residual is given by E[zi si i= 0]i µi si =1 | | − p × d 1 d −∞d E[z s = 0] = (z g(z))dz (S.10) APPENDIX C:1 1 i i | p ×  E[z s = 0] = E([zzi gsi(z=))d 0]z = (z g(z))dz (S.10) (S.10) i| i p ×| p × −∞ .where si denotes a binary variable indexing censoring (si = 1)−∞ or not (si = 0), and the−∞ expected SCALE-DEPENDENT ANALYSIS . .residual is given by . 1 d E[z s = 0] = (z g(z))dz (S.10) Appendix C: Scale-dependent analysis i| i p × Appendix C: Scale-dependent−∞ analysis

For a given response variable zx0,y0 at location x0,y0 , and a given set of covariate values px,y at Appendix. C: Scale-dependentAppendix C: Scale-dependent analysis analysis { } For a given response variable z at location x ,y , and a given set of covariate values p at points x, y , where x, y we define the effect relationship to be: x0,y0 { 0 0} x,y { } { }∈ pointsForAppendix a givenx, y C: response, where Scale-dependentForx, variable ay givenzxwe response0,y analysis0 defineat location variablethe effectxz0x relationship,y0,y00 ,at and location ato given be:x set0,y0 of, covariate and a given values setp ofx,y covariateat values px,y at { } { } zx ,y = β (wx,ypx,y) (S.11) { For} a given response{ }∈ variable zx0,y0 at location {x0, y0}, and a given set of covariate where C is a constant ensuring0 that0 w(d, σ, γ) = 1. Parameter σ can be inter- x,y pointsFor a givenx,values yresponse, where p variable atpoints pointsx, yz x, {atx, y y locationwe,}, where where definex {,yx, thex, yy,} effect and a givenwe relationship define define set of covariatethethe to effecteffect be: values relationshiprelationshipp at toto be:be: preted as the average distance to the measurement point over which the weight- { } x,y { }∈x0{,y0 } { {0 0} }∈ x,y edwhere covariateβ defines values the sign are and summed magnitude of (see the effectFigure on theS4a). response, Parameterwx,y a spatial γ defines weight-vector the shape points x, y , where x, y we define the effectzx relationship,y = β to(w be:x,ypx,y) (S.11) { } { }∈ 0 0 of the declining function. For γ = 1, the shape is a exponential function, for γ = 2 x,y depending on distance to focal measurement point, and itself. The weights are obtained from a z = β (w p ) (S.11) zx0,y0 = β (wx,ypx,yx0),y0 x,y x,y (S.11) (S.11)a distance-weightinghalf-gaussian, function, and further which in with the present γ → study∞ result is defined in a as uniform a two-dimensional distribution parametric (Figure z = β (w p )x,y x,y (S.11) x0,y0 x,y x,y S4b). x,y kernel. The kernel is depending on displacement distance (d = (x x )2 +(y y )2, d 0), a where β defines the sign and magnitude of the effect on the response, wx,y a spatial weight-vector − 0 − 0 ≥ scale (σ) and a shape parameter (γ): where β defines the sign and magnitude of the effect on the response, w a spatial weight-vector dependingwherewhere ββdefinesdefineswhere on the distance β the sign defines signand to magnitude andthe focal sign magnitude measurement of and the effectmagnitude of on the the point, effect response,of the and on effectw the itself.x,y a response,on spatial the The weight-vectorresponse, weightswx,y a w spatialarex,y a obtained weight-vector fromx,y a spatial weight-vector depending on distance to focal measurement point, and γ depending on distance to focal measurement point, and itself. The weights are obtained from a ( d ) ) depending on distancedepending to focal on measurement distance to focalpoint, measurement and itself. The point, weights and itself. are obtained The weights from a are obtained from a w(d; σ, γ)=Ce− σ (S.12) distance-weightingitself. The function,weights are which obtained in the from present a distance-weighting study is defined function, as a two-dimensional which in parametric distance-weighting function, which in the present study is defined as a two-dimensional parametric the presentdistance-weighting study is defined as function, a two-dimensional which in theparametric present studykernel.2 isThe defined kernel as 2 a two-dimensional parametric 97 kernel.distance-weighting The kernel function, is depending which on in displacement the present study distance is defined (d = as(x a two-dimensionalx0) +(y y0) , parametricd 0), a 2 2 kernel. Theiskernel depending is depending on displacement on displacement distance distance (d = (x x0) +(y y0) ,− d ≥ 0),0), a a scale− (σ) ≥ − − ≥ 2 2 2 2 kernel. Theand kernela shapekernel. is parameter depending The (γ kernel on): displacement is depending distance on displacement (d = (x distancex0) +( (dy =y0)(,xd x0)0),+( a y y0) , d 0), a scalescale ( (σσ)) and and a shape a shape parameter parameter (γ): (γ): − − −≥ − ≥ scale (σ) and a shapescale parameter (σ) and ( aγ): shape parameter (γ):

γ ( d ) ) γ w(d; σ, γ)=Ce− σ d ) (S.12) w(d; σ, γ)=Ce−( σ ) (S.12) (S.12)

γ γ d ) ( d ) ) 97w(d; σ, γ)=Ce−( σ )w(d; σ, γ)=Ce− σ (S.12) (S.12) Σ 97

97 97

68 SUPPLEMENTARY TABLES

TABLE S.1 Taxonomic groups used in classifying insect macrofauna by the Dutch Water Authorities. Of some groups only larval stages are found when sampling water bodies.

Code Taxonomic group Dutch names English names

IDCHI Insecta/Diptera - Chironomidae dansmuggen nonbiting midges IDSIM Insecta/Diptera - Simuliidae kriebelmuggen black flies IDREM Insecta/Diptera - remaining overige vliegen remaining flies INCOL Insecta - Coleoptera kevers beetles INEPH Insecta - Ephemeroptera haften mayflies INHET Insecta - Heteroptera wantsen true/typical bugs INLEP Insecta - Lepidoptera motten moths INODO Insecta - Odonata libellen dragonflies/damselflies INTRI Insecta - Trichoptera schietmotten/kokerjuffers caddisflies INREM Insecta - remaining overige insecten remaining insects

68 69 al .:Ctgrzto fwtrtpsa sdi hssuyi eaint h rgnlwater- original map the Topographic to Dutch relation the in as study well this as locations. in (WFD) of number used Directive denote Framework as Numbers Water types (TOP10NL). the water of of categorization Categorization S.2: Table

TABLE S.2 Categorization of water types as used in this study in relation to the original watercatego- rization of the Water Framework Directive (WFD) as well as the Dutch Topographic map (TOP10NL). Numbers denote number of locations.

Original Source Ditch Slow Fast Canals Ponds Small Large Brackish Ditch Waterway Waterway River Small variable running running shallow lakes water non-WFD 3-6m 6-12m non-WFD lakes water water lakes non-WFD non-WFD non-WFD non-WFD M01 Water 136 M02 framework 2 M03 directive 161 M06 74 M07 4 M08 22 M10 28 M11 25 M12 57 M13 18 M14 3 M20 14 M25 9 M26 8 M30 32

99 M31 7 R01 3 R02 10 R03 29 R04 229 R05 270 R06 77 R07 1 R08 3 R11 4 R13 26 R14 15 R15 5 R17 50 R18 15 Ditch Top 10 8 Waterway <3m 115 Waterway 3-6m 58 Waterway 6-12m 128 Waterway 12-50m 45 Waterway >50m 2 Small lake 14

70 Table S.3: Estimates of spatial and temporal semivariance parameters for each substance/variable, and per dimension of interpolation. Serial Spatial TABLE S.3 Variable model parameter Estimate(se) model parameter Estimate(se) NH4 Exp nugget 0.38(0.018) Sph nugget 0.943(0.016) Estimates of spatial and temporal semivariance parameters for each substance/variable, Exp part. sill 0.373(0.016) Sph part. sill 0.068(0.016) Exp range 41.09(3.517) Sph range 10.817(1.364) BOD Exp nugget 0.18(0.008) Exp nugget 0.282(0.003) and per dimension of interpolation. Exp part. sill 0.134(0.007) Exp part. sill 0.096(0.003) Exp range 53.909(6.725) Exp range 4.727(0.224) Cd Exp nugget 0.286(0.014) Sph nugget 0.319(0.016) Exp part. sill 0.17(0.014) Sph part. sill 0.111(0.016) Exp range 88.179(25.174) Sph range 18.404(1.904) Ca Exp nugget 0.006(0.001) Sph nugget 0.108(0.007) Exp part. sill 0.022(0.001) Sph part. sill 0.011(0.006) Exp range 47.513(7.059) Sph range 22.997(29.071) Cl Sph nugget 0.064(0.001) Sph nugget 0.213(0.004) Sph part. sill 0.051(0.001) Sph part. sill 0.039(0.005) Sph range 173.752(7.81) Sph range 19.659(4.372) Chloro. alpha Exp nugget 0.417(0.07) Sph nugget 1.245(0.014) Exp part. sill 0.846(0.067) Sph part. sill 0.229(0.116) Exp range 38.607(6.544) Sph range 37.344(25.581) Cr Exp nugget 0.218(0.021) Sph nugget 0.239(0.016) Exp part. sill 0.131(0.018) Sph part. sill 0.078(0.016) Exp range 52.933(17.081) Sph range 14.568(1.718) Combustion Exp nugget 0.005(0.002) Exp nugget 0.002(0.002) Exp part. sill 0.001(0.002) Exp part. sill 0.004(0.002) Exp range 1.024(5.861) Exp range 2.092(2.004) PO4 Exp nugget 0.237(0.016) Sph nugget 0.845(0.01) Exp part. sill 0.382(0.015) Sph part. sill 0.068(0.008) Exp range 58.184(6.256) Sph range 22.624(5.724) Sum P Sph nugget 0.161(0.003) Sph nugget 0.399(0.005) Sph part. sill 0.127(0.003) Sph part. sill 0.055(0.043) Sph range 160.503(6.582) Sph range 39.031(40.267) Industrial Sph nugget 0.002(0.001) Exp nugget 0.006(0) Sph part. sill 0.001(0.001) Exp part. sill 0.002(0.001) Sph range 3.13(5.208) Exp range 10.127(10.965) Cu Exp nugget 0.19(0.008) Sph nugget 0.249(0.006) Exp part. sill 0.098(0.007) Sph part. sill 0.055(0.006) Exp range 70.375(15.339) Sph range 20.648(2.87) Pb Sph nugget 0.431(0.016) Sph nugget 0.421(0.014) Sph part. sill 0.105(0.02) Sph part. sill 0.111(0.013) Sph range 226.584(81.492) Sph range 19.741(2.063) Mg Exp nugget 0.007(0.001) Exp nugget 0.057(0.007) Exp part. sill 0.014(0.001) Exp part. sill 0.003(0.007) Exp range 57.394(9.104) Exp range 2.292(6.215) Na Sph nugget 0.022(0.002) Sph nugget 0.05(0.042) Sph part. sill 0.042(0.002) Sph part. sill 0.059(0.042) Sph range 152.756(10.061) Sph range 0.855(0.702) Ni Exp nugget 0.034(0.01) Sph nugget 0.159(0.011) Exp part. sill 0.118(0.01) Sph part. sill 0.032(0.011) Exp range 30.038(5.341) Sph range 14.411(2.842) Suspended matter Exp nugget 0.276(0.011) Sph nugget 0.466(0.008) Exp part. sill 0.251(0.01) Sph part. sill 0.105(0.007) Exp range 62.38(6.909) Sph range 21.297(2.159) Pesticide Sph nugget 0.004(0) Sph nugget 0(0.013) Sph part. sill 0(0.001) Sph part. sill 0.008(0.013) Sph range 3.877(12.422) Sph range 0.791(1.289) NO2+NO3 Exp nugget 0.005(0.005) Sph nugget 1.179(0.366) Exp part. sill 0.894(0.022) Sph part. sill 0.347(0.366) Exp range 29.867(2.583) Sph range 2.71(1.399) Sum N Exp nugget 0.04(0.006) Sph nugget 0.248(0.003) Exp part. sill 0.143(0.005) Sph part. sill 0.025(0.003) Exp range 49.578(5.073) Sph range 18.285(1.983) Water temperature Exp nugget 1.555(0.147) Exp nugget 1.184(0.041) Exp part. sill 1.038(0.134) Exp part. sill 1.05(0.037) Exp range 55.063(18.327) Exp range 4.786(0.314) HCO3 Sph nugget 0.019(0.002) Sph nugget 0.176(0.014) Sph part. sill 0.034(0.002) Sph part. sill 0.047(0.014) Sph range 143.066(13.151) Sph range 17.534(5.234) Zn Exp nugget 0.144(0.013) Sph nugget 0.332(0.012) Exp part. sill 0.211(0.012) Sph part. sill 0.04(0.012) Exp range 59.648(9.746) Sph range 16.31(3.424) pH Sph nugget 0.06(0.003) Sph nugget 0.131(0.003) Sph part. sill 0.037(0.003) Sph part. sill 0.027(0.003) Sph range 102.509(9.997) Sph range 24.402(5.31) O Exp nugget 0.037(0.004) Exp nugget 0.098(0.011) Exp part. sill 0.055(0.003) Exp part. sill 0.053(0.01) Exp range 30.254(3.578) Exp range 1.171(0.258)

70 71

100 Table S.4: Estimated scale parameters for a range of land use categories used in weighting land cover in taxonomic group models. For each group and parameter, the fitted kernel (γ =1: exponential,γ = 2: gaussian) is given, along with estimated scale (or radius) parameter σ (in meters), and the sign of the effect of the weighted covariate on the response. Group Variable γσEffect sign INODO Buildings 1 800 -1 INODO Greenhouses 2 145 -1 INODO Agricultural land 2 190 -1 INODO Forest 2 763 1 INODO Open wet natural area 1 433 1 INODO Open dry natural area 1 87 1 INODO Crop field 1 800 -1 INODO Grassland 1 800 1 INREM Buildings 1 196 -1 INREM Greenhouses 2 44 -1 INREM Agricultural land 2 230 -1 INREM Forest 1 113 1 INREM Open wet natural area 2 508 1 INREM Open dry natural area 1 800 1 INREM Crop field 1 800 -1 INREM Grassland 2 800 1 INTRI Buildings 1 675 -1 INTRI Greenhouses 1 70 -1 INTRI Agricultural land 1 800 -1 INTRI Forest 1 225 1 INTRI Open wet natural area 2 26 -1 INTRI Open dry natural area 2 142 -1 INTRI Crop field 1 800 -1 INTRI Grassland 2 260 -1 TABLE S.4 IDCHI Buildings 2 220 1 IDCHI Greenhouses 2 202 -1 Estimated scale parameters for a range of land use categories used in weighting land cover IDCHI Agricultural land 2 557 -1 IDCHI Forest 2 22 -1 in taxonomic group models. For each group and parameter, the fitted kernel (γ =1: expo- IDCHI Open wet natural area 2 113 1 nential, γ = 2: gaussian) is given, along with estimated scale (or radius) parameter σ (in IDCHI Open dry natural area 2 252 -1 Table S.4: Estimated scale parameters for aTable range S.4: of land Estimated use categories scale parameters usedIDCHI in weighting Crop for field a range land of cover land1 800use categories1 used in weighting land cover meters), and the sign of the effect of the weighted covariate on the response. IDCHI Grassland 2 800 1 IDREM Buildings 1 800 -1 in taxonomic group models. For each groupin and taxonomic parameter, group the models. fitted kernel ForIDREM each (γ =1: group Greenhouses exponential, and parameter,γ 2= 219 the fitted-1 kernel (γ =1: exponential,γ = IDREM Agricultural land 2 67 -1 2: gaussian) is given, along with estimated scale2: gaussian) (or radius) is given, parameter alongσ with(inIDREM estimatedmeters), Forest and scale the (or sign radius)2 of 510 parameter1 σ (in meters), and the sign of IDREM Open wet natural area 1 60 1 IDREM Open dry natural area 2 46 1 the effect of the weighted covariate on the response.the effect of the weighted covariateIDREM on Crop the field response. 2 294 -1 Group Variable γσEffect sign IDREMGroup GrasslandVariable γσ1 800 Effect sign1 INODO Buildings 1 800 -1 IDSIMINODO Buildings 1 800 13 -11 INODO Greenhouses 2 145 -1 INODOIDSIM Greenhouses 2 145371 -1 INODO Agricultural land 2 190 -1 INODOIDSIM Agricultural land 21 190800 -1 INODO Forest 2 763 1 INODOIDSIM Forest 21 763800 -11 INODO Open wet natural area 1 433 1 INODOIDSIM Open wet natural area 12 433135 -11 INODO Open dry natural area 1 87 1 INODOIDSIM Open dry natural area 12 319 87 -11 INODO Crop field 1 800 -1 INODOIDSIM Crop field 1 800 -1 INODO Grassland 1 800 1 INODOIDSIM Grassland 12 800234 -11 INREM Buildings 1 196 -1 INCOLINREM Buildings 1 219196 -1 INREM Greenhouses 2 44 -1 INCOLINREM Greenhouses 21 332 44 -1 INREM Agricultural land 2 230 -1 INREMINCOL Agricultural land 12 148230 -11 INREM Forest 1 113 1 INREMINCOL Forest 1 113800 1 INREM Open wet natural area 2 508 1 INREMINCOL Open wet natural area 2 508585 1 INREM Open dry natural area 1 800 1 INREMINCOL Open dry natural area 1 800 17 1 INREM Crop field 1 800 -1 INREMINCOL Crop field 12 800107 -11 INREM Grassland 2 800 1 INREMINCOL Grassland 2 399800 1 INTRI Buildings 1 675 -1 INTRIINEPH Buildings 12 675 47 -11 INTRI Greenhouses 1 70 -1 INTRIINEPH Greenhouses 1 7092 -1 INTRI Agricultural land 1 800 -1 INTRIINEPH Agricultural land 12 800 30 -11 INTRI Forest 1 225 1 INTRIINEPH Forest 12 225 10 -11 INTRI Open wet natural area 2 26 -1 INEPHINTRI Open wet natural area 2 182 26 -1 INTRI Open dry natural area 2 142 -1 INTRIINEPH Open dry natural area 2 142164 -1 INTRI Crop field 1 800 -1 INTRIINEPH Crop field 1 800 39 -1 INTRI Grassland 2 260 -1 INTRIINEPH Grassland 2 260314 -1 IDCHI Buildings 2 220 1 IDCHIINHET Buildings 12 176220 -11 IDCHI Greenhouses 2 202 -1 INHETIDCHI Greenhouses 2 202 -1 IDCHI Agricultural land 2 557 -1 INHETIDCHI Agricultural land 12 800557 -11 IDCHI Forest 2 22 -1 INHETIDCHI Forest 12 800 22 -11 IDCHI Open wet natural area 2 113 1 INHETIDCHI Open wet natural area 12 217113 1 IDCHI Open dry natural area 2 252 -1 INHETIDCHI Open dry natural area 12 252 10 -11 IDCHI Crop field 1 800 1 INHETIDCHI Crop field 1 800 1 IDCHI Grassland 2 800 1 IDCHIINHET Grassland 2 800169 1 IDREM Buildings 1 800 -1 INLEPIDREM Buildings 21 216800 -1 IDREM Greenhouses 2 219 -1 INLEPIDREM Greenhouses 2 126219 -11 IDREM Agricultural land 2 67 -1 INLEPIDREM Agricultural land 12 6367 -11 IDREM Forest 2 510 1 INLEPIDREM Forest 12 510 14 -11 IDREM Open wet natural area 1 60 1 INLEPIDREM Open wet natural area 21 308 60 -11 IDREM Open dry natural area 2 46 1 INLEPIDREM Open dry natural area 12 162 46 -11 IDREM Crop field 2 294 -1 INLEPIDREM Crop field 12 162294 -1 IDREM Grassland 1 800 1 INLEPIDREM Grassland 21 800 95 -11 IDSIM Buildings 1 13 1 IDSIM Buildings 1 13 1 IDSIM Greenhouses 2 371 -1 IDSIM Greenhouses 2 371 -1 IDSIM Agricultural land 1 800 -1 IDSIM Agricultural land 1 800 -1 IDSIM Forest 1 800 -1 IDSIM Forest 1 800 -1 IDSIM Open wet natural area 2 135 -1 IDSIM Open wet natural area 2 135 -1 IDSIM Open dry natural area 2 319 -1 72 IDSIM Open dry natural area 2 319 -1 IDSIM Crop field 1 800 -1 IDSIM Crop field 1 800 -1 IDSIM Grassland 2 234 -1 IDSIM Grassland 2 234 -1 INCOL Buildings 1 219 -1 INCOL Buildings 1 219 -1 INCOL Greenhouses 1 332 -1 INCOL Greenhouses 1 332 -1 INCOL Agricultural land 1 148 1 INCOL Agricultural land 1 148 1 INCOL Forest 1 800 1 INCOL Forest 1 800 1 INCOL Open wet natural area 2 585 1 INCOL Open wet natural area 2 585 1 INCOL Open dry natural area 1 17 1 INCOL Open dry natural area 1 17 1 INCOL Crop field 2 107 1 INCOL Crop field 2 107 1 INCOL Grassland 2 399 1 INCOL Grassland 2 399 1 INEPH Buildings 2 47 1 INEPH Buildings 2 47 1 INEPH Greenhouses 1 92 -1 INEPH Greenhouses 101 1 92 -1 INEPH Agricultural land 2 30 1 INEPH Agricultural land 2 30 1 INEPH Forest 2 10 -1 INEPH Forest 2 10 -1 INEPH Open wet natural area 2 182 -1 INEPH Open wet natural area 2 182 -1 INEPH Open dry natural area 2 164 -1 INEPH Open dry natural area 2 164 -1 INEPH Crop field 1 39 -1 INEPH Crop field 1 39 -1 INEPH Grassland 2 314 -1 INEPH Grassland 2 314 -1 INHET Buildings 1 176 -1 INHET Buildings 1 176 -1 INHET Greenhouses 2 202 -1 INHET Greenhouses 2 202 -1 INHET Agricultural land 1 800 1 INHET Agricultural land 1 800 1 INHET Forest 1 800 1 INHET Forest 1 800 1 INHET Open wet natural area 1 217 1 INHET Open wet natural area 1 217 1 INHET Open dry natural area 1 10 1 INHET Open dry natural area 1 10 1 INHET Crop field 1 800 1 INHET Crop field 1 800 1 INHET Grassland 2 169 1 INHET Grassland 2 169 1 INLEP Buildings 2 216 -1 INLEP Buildings 2 216 -1 INLEP Greenhouses 2 126 1 INLEP Greenhouses 2 126 1 INLEP Agricultural land 1 63 1 INLEP Agricultural land 1 63 1 INLEP Forest 1 14 -1 INLEP Forest 1 14 -1 INLEP Open wet natural area 2 308 -1 INLEP Open wet natural area 2 308 -1 INLEP Open dry natural area 1 162 -1 INLEP Open dry natural area 1 162 -1 INLEP Crop field 1 162 -1 INLEP Crop field 1 162 -1 INLEP Grassland 2 95 -1 INLEP Grassland 2 95 -1

101 101 TABLE S.5 Coeffcients of full models per taxongroup.

Table S.5: Coeﬃcients of full models per taxongroup Table S.5: Continued Odonata Insects rem. Trichoptera Chironomidae Diptera rem. Simuliidae Coleoptera Ephemeroptera Heteroptera Lepidoptera Intercept -1.22(0.15) -2.91(0.26) 0.48(0.17) 3.12(0.11) -0.73(0.14) Intercept -90.94(3474765.26) -0.36(0.13) 0.26(0.16) 0.88(0.14) -6.44(0.36) Ditch 1.14(0.14) 1.11(0.23) 0.11(0.14) 0.4(0.09) 0.94(0.13) Ditch 86.16(3474765.26) 1.09(0.11) 1.41(0.15) 0.95(0.12) 2.4(0.34) Slow running water 1.14(0.13) 0.44(0.22) 0.34(0.14) 0.37(0.09) 0.74(0.12) Slow running water 86.3(3474765.26) 0.9(0.11) 1.16(0.15) 0.61(0.12) 2.18(0.34) Fast running water -0.32(0.16) -0.2(0.26) 0.52(0.17) 0.54(0.1) 0.84(0.15) Fast running water 87.19(3474765.26) 1.01(0.13) 1.25(0.18) -0.24(0.15) 0.25(0.46) Canals 1.15(0.12) 0.68(0.2) 0.27(0.13) 0.2(0.08) 0.73(0.11) Canals 83.74(3474765.26) 0.53(0.09) 0.97(0.13) 0.5(0.1) 2.02(0.32) Ponds 2.25(0.22) -2.35(0.35) -0.52(0.23) -0.28(0.14) 1.23(0.2) Ponds 78.11(3474765.26) 0.36(0.17) 1.34(0.25) 1.24(0.2) 3.08(0.48) Small shallow lakes 2.2(0.2) 0.46(0.31) 0.06(0.2) 0.35(0.13) 1.41(0.18) Small shallow lakes -8(6370069.75) 0.98(0.15) 1.44(0.22) 1.07(0.18) 2.44(0.42) Large lakes 0.37(0.23) -0.27(0.35) -0.98(0.23) 0.32(0.15) -0.49(0.22) Large lakes -2.15(8268554.23) -1.68(0.2) -0.64(0.25) -0.86(0.21) 1(0.5) Ditch non-WFD 0.69(0.13) 0.51(0.21) -0.17(0.13) 0.19(0.08) 1.13(0.12) Ditch non-WFD 88.49(3474765.26) 0.99(0.1) 0.69(0.14) 0.35(0.11) 2.05(0.33) Waterway 3-6m non-WFD 0.94(0.14) 0.81(0.22) 0.71(0.14) 0.02(0.09) 1.01(0.13) Waterway 3-6m non-WFD 85.01(3474765.26) 0.91(0.11) 1.29(0.15) 0.43(0.12) 2.35(0.34) Waterway 6-12m non-WFD 1.22(0.12) 0.66(0.2) 0.26(0.13) 0.17(0.08) 0.98(0.11) Waterway 6-12m non-WFD 86.36(3474765.26) 0.72(0.1) 1.33(0.14) 0.45(0.11) 2.12(0.32) River non-WFD 0.89(0.14) 0.38(0.23) 0.53(0.14) 0.08(0.09) 0.87(0.13) River non-WFD -2.47(4972379.15) 0.29(0.11) 1.13(0.15) 0.03(0.12) 1.82(0.34) Small lakes non-WFD 0.51(0.21) -1.17(0.46) 0.22(0.22) 0.16(0.14) 2.31(0.18) Small lakes non-WFD -3.07(7916832.49) -0.15(0.17) 1.23(0.23) 0.68(0.19) -0.49(0.61) Prox. sewage plant -0.01(0.02) -0.05(0.04) -0.08(0.03) 0.05(0.02) -0.12(0.03) Prox. sewage plant 0.51(0.08) -0.01(0.02) -0.04(0.03) -0.02(0.02) -0.06(0.05) Protected 0.04(0.04) 0.03(0.06) 0.13(0.04) -0.18(0.02) -0.17(0.03) Protected 0.85(0.11) -0.08(0.03) 0.07(0.04) -0.07(0.03) -0.05(0.07) Built-up -0.38(0.15) 0.69(0.24) -0.69(0.16) 0.08(0.07) -0.11(0.12) Built-up 1.96(0.31) -0.4(0.11) -0.03(0.1) -0.52(0.12) -0.12(0.24) Wet-natural 0.1(0.46) 1.98(0.66) -0.22(0.18) 0.93(0.15) 0.91(0.21) Wet-natural 3.06(0.62) 1.73(0.35) -0.39(0.3) 1.37(0.35) 0.4(0.67) Dry-natural 0.22(0.29) -3.2(0.72) -0.8(0.27) 0.4(0.2) 0.12(0.18) Dry-natural -0.25(1.29) 0.73(0.15) -1.88(0.3) 0.5(0.16) -2.41(0.79) Forest 0.44(0.17) 3.33(0.21) 0.53(0.15) -0.03(0.05) 0.3(0.13) Forest 0.75(0.38) -0.08(0.14) -0.73(0.08) -0.86(0.16) 0.02(0.18) Arable -0.23(0.14) -1.2(0.22) -0.62(0.16) -0.04(0.1) -0.26(0.1) Arable -0.81(0.52) 0.15(0.08) -0.12(0.11) 0.69(0.14) -0.29(0.23) Rem. agric. 0.06(0.09) 1.69(0.18) 0.07(0.13) -0.13(0.07) 0.15(0.06) Rem. agric. -0.92(0.33) 0.5(0.08) -0.25(0.08) -0.09(0.11) 0.88(0.2) Greenhouses -1.31(0.23) -2.85(0.77) -3(0.26) -0.21(0.14) -0.84(0.2) Greenhouses -6.55(3.24) -1.02(0.21) -4.12(0.28) -1.94(0.19) 0.81(0.41) Clay -0.52(0.17) 0.49(0.23) 0.4(0.15) 0.01(0.1) 0.89(0.12) Clay -0.41(0.3) 0.73(0.11) 0.48(0.16) 0.08(0.14) 0.3(0.55) Light clay 0.37(0.08) -0.15(0.13) 0.32(0.08) 0.2(0.05) 0.22(0.07) Light clay 0.15(0.48) 0.57(0.06) 0.53(0.09) 0.53(0.07) 0.7(0.15) Light loam 0.69(0.08) -0.02(0.12) 0.4(0.08) 0.3(0.05) 0.63(0.07) Light loam -0.35(0.23) 0.52(0.06) 0.63(0.08) 0.57(0.07) 0.98(0.16) Muddy on sand 0.79(0.12) 0.15(0.19) 0.74(0.13) 0.3(0.08) 0.45(0.11) Muddy on sand -0.06(0.31) 0.62(0.1) 1.05(0.14) 0.71(0.11) 0.52(0.27) Bog 0.59(0.1) 0.62(0.15) 0.68(0.1) 0.13(0.06) 0.3(0.09) Bog -0.5(0.29) 0.74(0.08) 0.42(0.1) 0.39(0.09) 1.14(0.19) Sand 0.52(0.08) -0.06(0.12) 0.31(0.08) 0.15(0.05) 0.4(0.07) Sand 0.21(0.21) 0.59(0.06) 0.65(0.08) 0.42(0.07) 0.63(0.16) Heavy clay 0.48(0.1) 0.66(0.15) 0.47(0.1) 0.31(0.06) 0.38(0.09) Heavy clay -0.89(0.87) 0.46(0.08) 0.44(0.1) 0.55(0.09) 0.85(0.17) Heavy loam 0.52(0.07) 0.21(0.12) 0.51(0.07) 0.29(0.05) 0.38(0.07) Heavy loam -0.23(0.26) 0.57(0.06) 0.7(0.08) 0.39(0.07) 0.6(0.15) PC1 0.24(0.02) -0.04(0.04) -0.11(0.02) 0.05(0.02) 0.02(0.02) PC1 -0.18(0.07) 0.07(0.02) 0.13(0.03) 0.15(0.02) 0.2(0.05) PC2 0.08(0.02) 0.44(0.03) 0.26(0.02) -0.01(0.01) 0.11(0.02) PC2 -0.24(0.05) 0.21(0.02) 0.06(0.02) -0.03(0.02) 0.15(0.04) PC3 0.05(0.02) 0.07(0.04) -0.04(0.02) 0.1(0.02) -0.03(0.02) PC3 -0.15(0.06) 0.06(0.02) -0.05(0.03) -0.01(0.02) -0.04(0.04) PC4 0.18(0.05) 0.2(0.07) 0.03(0.05) 0.07(0.03) 0.2(0.04) PC4 -0.37(0.12) 0.02(0.04) 0.31(0.05) 0.03(0.04) 0.21(0.09) PC5 0.01(0.04) 0.29(0.06) -0.08(0.04) -0.02(0.02) -0.02(0.03) PC5 0.22(0.1) -0.08(0.03) 0.18(0.04) -0.01(0.03) 0.13(0.07) PC6 -0.16(0.04) -0.12(0.06) -0.25(0.04) 0.1(0.03) -0.11(0.04) PC6 -0.27(0.1) 0.08(0.03) -0.45(0.05) 0.14(0.04) -0.18(0.07) PC7 -0.01(0.04) 0.15(0.06) 0.2(0.04) 0.02(0.03) -0.03(0.04) PC7 0.25(0.1) -0.11(0.03) 0.27(0.04) -0.05(0.04) -0.01(0.08) PC8 0.07(0.05) 0.18(0.07) -0.15(0.05) 0.12(0.03) 0.17(0.04) PC8 -0.33(0.11) -0.03(0.03) 0.22(0.05) 0.03(0.04) 0.29(0.08) PC9 0.07(0.03) 0.13(0.04) -0.08(0.03) 0.05(0.02) 0.06(0.02) PC9 -0.55(0.07) 0.05(0.02) 0.14(0.03) 0.03(0.02) 0.25(0.05) PC10 0(0.03) -0.06(0.05) 0.08(0.03) -0.01(0.02) 0.06(0.03) PC10 -0.07(0.08) 0.14(0.02) -0.11(0.03) 0.04(0.03) 0.08(0.06) PC11 -0.1(0.03) -0.08(0.05) -0.18(0.03) 0.07(0.02) -0.01(0.03) PC11 -0.08(0.09) 0.01(0.03) -0.09(0.04) -0.06(0.03) -0.22(0.06) PC12 -0.14(0.03) 0.09(0.05) 0.03(0.03) 0.07(0.02) 0.03(0.03) PC12 -0.31(0.08) 0.05(0.03) -0.21(0.04) -0.15(0.03) 0.12(0.07) PC13 0(0.02) -0.12(0.03) -0.05(0.02) 0.04(0.01) 0.01(0.02) PC13 -0.12(0.05) -0.02(0.02) 0.07(0.02) 0.03(0.02) -0.03(0.04) PC14 0(0.04) 0.15(0.06) 0.16(0.04) -0.11(0.03) 0.11(0.04) PC14 -0.06(0.12) 0.23(0.03) -0.04(0.05) -0.17(0.04) 0.18(0.08) PC15 -0.16(0.03) 0.23(0.05) -0.27(0.03) 0.02(0.02) -0.05(0.03) PC15 -0.18(0.09) 0(0.03) -0.13(0.04) 0.14(0.03) -0.05(0.06) PC16 -0.18(0.04) -0.06(0.05) 0.08(0.04) -0.07(0.02) 0.09(0.03) PC16 0.07(0.09) 0.02(0.03) 0(0.04) -0.24(0.03) -0.19(0.07) PC17 0.06(0.04) 0.2(0.06) 0.11(0.04) -0.06(0.03) 0.11(0.04) PC17 0.44(0.11) -0.01(0.03) 0.23(0.05) -0.11(0.04) -0.12(0.08) PC18 -0.08(0.03) -0.05(0.05) -0.19(0.03) -0.01(0.02) 0(0.03) PC18 -0.34(0.08) -0.01(0.03) 0.07(0.04) -0.12(0.03) -0.06(0.07) PC19 -0.02(0.03) 0.05(0.04) 0.09(0.03) -0.04(0.02) -0.04(0.03) PC19 -0.15(0.08) 0.02(0.02) 0.05(0.03) 0.13(0.03) 0.18(0.05) PC20 0.04(0.04) -0.07(0.06) -0.09(0.04) 0.02(0.03) -0.22(0.03) PC20 0.38(0.1) -0.17(0.03) -0.16(0.04) -0.04(0.04) -0.2(0.08) PC21 -0.11(0.04) -0.05(0.05) 0.02(0.04) -0.15(0.02) -0.21(0.03) PC21 0.39(0.09) -0.2(0.03) -0.33(0.04) -0.01(0.03) -0.22(0.07) PC22 0.05(0.04) -0.23(0.06) -0.04(0.04) -0.04(0.02) -0.01(0.03) PC22 -0.01(0.1) 0.12(0.03) 0.01(0.04) 0.1(0.03) -0.05(0.07) PC23 -0.2(0.04) -0.12(0.06) -0.34(0.04) 0.1(0.03) -0.05(0.04) PC23 -0.21(0.1) -0.01(0.03) -0.3(0.05) 0.02(0.04) -0.06(0.08) PC24 0.16(0.04) -0.06(0.06) 0.23(0.04) 0.02(0.02) -0.07(0.03) PC24 0.6(0.1) -0.21(0.03) 0.18(0.04) 0.13(0.03) 0.12(0.07) PC25 0.08(0.05) -0.15(0.07) -0.04(0.05) -0.03(0.03) -0.14(0.04) PC25 0.02(0.12) 0.11(0.04) -0.26(0.05) 0.11(0.04) -0.04(0.09) PC26 0.05(0.04) -0.28(0.07) 0.03(0.04) -0.03(0.03) -0.06(0.04) PC26 -0.04(0.14) -0.05(0.03) 0.03(0.05) 0.15(0.04) 0.03(0.07) PC27 0.16(0.05) 0.2(0.08) 0.08(0.05) 0.04(0.03) -0.07(0.04) PC27 -0.24(0.12) -0.12(0.04) 0.35(0.06) 0.24(0.04) 0.12(0.09) PC28 -0.06(0.05) 0.4(0.07) 0.5(0.05) -0.15(0.03) 0.22(0.04) PC28 -0.64(0.12) 0.16(0.04) 0.22(0.05) -0.26(0.04) 0.26(0.1) PC29 0.16(0.05) -0.07(0.07) -0.03(0.05) -0.02(0.03) -0.26(0.04) PC29 0.35(0.13) -0.03(0.04) -0.34(0.05) 0.22(0.04) -0.34(0.1) PC30 0.04(0.06) 0.2(0.09) -0.21(0.06) 0.01(0.04) -0.31(0.05) PC30 0.01(0.15) -0.18(0.04) -0.33(0.06) 0(0.05) -0.15(0.11) PC31 0.48(0.06) -0.42(0.09) 0.23(0.06) -0.11(0.04) 0.16(0.05) PC31 -0.14(0.14) -0.01(0.05) 0.07(0.07) 0.46(0.05) 0.3(0.13) PC32 0.15(0.06) -0.35(0.08) -0.15(0.06) 0.02(0.04) -0.24(0.05) PC32 0.06(0.15) -0.17(0.04) -0.16(0.06) 0.06(0.05) -0.09(0.11) PC33 -0.02(0.06) -0.15(0.09) -0.12(0.06) -0.04(0.04) 0.32(0.05) PC33 0.43(0.15) 0.15(0.05) -0.23(0.06) 0.05(0.05) -0.41(0.12) PC34 -0.08(0.06) -0.42(0.09) -0.15(0.06) -0.03(0.04) 0.12(0.05) PC34 0.3(0.15) -0.1(0.05) 0.07(0.07) -0.05(0.05) 0.08(0.12) PC35 -0.07(0.07) 0.02(0.1) -0.16(0.07) -0.02(0.04) 0.13(0.06) PC35 0.12(0.17) -0.06(0.05) 0.29(0.07) -0.07(0.06) 0.08(0.13) PC36 0.1(0.08) -0.12(0.12) -0.4(0.08) -0.17(0.05) -0.32(0.07) PC36 0.53(0.19) -0.12(0.06) -0.38(0.09) 0.2(0.07) -0.37(0.16) PC37 0.47(0.1) -0.22(0.15) 0.1(0.1) 0.25(0.06) -0.18(0.09) PC37 -0.05(0.24) -0.09(0.08) 0.53(0.11) 0.16(0.09) -0.06(0.19) PC38 -0.16(0.12) -0.25(0.18) 0.05(0.12) 0.01(0.08) -0.17(0.1) PC38 0.54(0.29) 0.14(0.09) 0.08(0.13) -0.1(0.1) -0.24(0.23)

72 73

102 103 SUPPLEMENTARY FIGURES

FIGURE S1 Taxonomic identiﬁcation depth of sampled individuals per year 1.0 pooled over the eight Water Authorities. The four taxonomic levels Regnum 1.0 Phylum Regnum that occurred most often are, from bottom to top: species, ’species Subphylum Phylum combi’, genus, and family. Most individuals (±65%) are identiﬁed to Infraclassis Subphylum 0.8 Classis species level or below, followed by genus (17%) and family levelInfraclassis (7%). Subclassis 0.8 Classis Infraordo Subclassis Ordo Infraordo 1.0 Subordo Regnum Ordo 0.6 Superfamilia 0.6 Phylum Subordo Subphylum Superfamilia Familia Infraclassis Familia Subfamilia 0.8 Tribus Classis Subfamilia 0.4 Subclassis Tribus Genus combi 1.0 0.4 Infraordo Genus combi Genus Regnum Ordo Genus Subgenus 0.6 Phylum Subordo Subgenus Proportion of individuals Species combi Proportion of individuals Subphylum Superfamilia Species combi 0.2 Species 0.2 Infraclassis Familia Species Subspecies 0.8 Classis Subfamilia Subspecies Varietas Tribus Forma 0.4 Subclassis Varietas Infraordo Genus combi Forma 0.0 Ordo Genus 0.6 0.0 Subordo Subgenus Proportion of individuals Superfamilia Species combi 1990 1994 1998 2002 2006 2010 2014 2018 0.2 1990 1994 1998 2002 2006Familia 2010 2014Species2018 Subfamilia Subspecies Tribus Varietas 0.4 Genus combi Forma 74 Genus 0.0 Subgenus

Proportion of individuals Species combi 0.2 1990 1994 1998 2002 2006 2010 2014Species2018 Subspecies Varietas Forma 0.0

1990 1994 1998 2002 2006 2010 2014 2018 FIGURE S2 a b c

Weather decomposition for temperature (a:c), precipi- −0.4 −0.4 3 8

−0.6 tation (d:f) and evapotranspiration (g:i), into a mean 6 0.2 2 spatial, a mean annual, and a mean seasonal trend. 4 −0.2 1 −0.4 2 0 0 0.6 0.2 −2 0 −1 Annual deviation Annual

0.4 0.2 Seasonal deviation −4 −2 0.2 −6 Spatial deviation −3 −8

d e f 0.8 0 0 0.4 0.6 −0.05 0.4 0 0.2 0 0.2 0.1 0.0 0.0

−0.05

−0.05 0.05 −0.2 −0.2 0.1 −0.1 deviation Annual

Seasonal deviation −0.4

−0.1 −0.6 0 −0.4

Spatial deviation −0.2 −0.8

g h i 0.4 1.5

−0.04 1.2 0.9 0.2 0.04 0.6 0 −0.06 0.06 0.3 −0.04 0.0 0.0

0.02 −0.3 0.1 −0.02 −0.6 Annual deviation Annual

0 −0.2 Seasonal deviation −0.9 0.08 −1.2 Spatial deviation 0 0.02 −0.4 −1.5

1980 1990 2000 2010 2020 0 100 200 300 Year Day−number (1 = Jan. 1)

Figure S2: Weather decomposition for temperature (a:c), precipitation (d:f) and evapotranspiration (g:i), into a mean spatial, a mean annual, and a mean seasonal trend. 74 75

105 FIGURE S3 Semivariance plots of residual spatial variation in temperature (a), precipitation (b) and evapotranspiration (c).

a b c 14 0.25 2.5 12 0.20 10 2.0 8 0.15 1.5 6 semivariance semivariance semivariance 0.10 1.0 4 0.5 0.05 2 0 0.0 0.00

0 50 150 250 0 50 150 250 0 50 150 250

spatial lag (km) spatial lag (km) spatial lag (km)

76 FIGURE S4 Example of weighting functions used in measuring land use covariates at certain distance form monitoring location. a: different values of scale (σ) while keeping γ = 2 and C = 1, and b: different values of shape (γ) while keeping σ = 200 and C = 1, in equation S.12.

a b

1.0 1.0 σ = 100 γ = 1 σ = 300 γ = 2 0.8 0.8 σ = 500 γ = 5 γ = 10 0.6 0.6

Weight 0.4 Weight 0.4

0.2 0.2

0.0 0.0

0 200 400 600 800 0 200 400 600 800

Distance Distance

76 77 evapotranspiration evap. weektemperatureevap. month 0.6 GDD GDD 0.6 BOD 0.2 Watertemp. temperatureweek prec. previous summ. temp. previous summ. temp. month prec. week O prec. previous summ. prec. month 0.2 Ni Cr 0.4 Mg Frost days Cl

0.4 BOD

0.0 Na Frost days Combustion precipitation temp. month Industrial precipitationGDD Chloro. alpha temp. month prec. month NH4 Cd Combustion Suspended matter Pb Zn Pb

Cd 0.2 SumPesticide P Industrial Chloro. alpha Combustion NH4 Cu PO4 prec. week Chloro. alphaSumIndustrial P NO2+NO3 Na prec. previousCr summ.prec. week 0.0 temp. week Suspended matter 0.2 temperature Pb Sum P Water temperature Chloro. alpha temp. previous summ. PO4 evapotranspiration HCO3 Zn Ni PO4 temp. week precipitation Mg Water temperature BOD pH prec.Cl previous summ. evapotranspiration evap. monthprec. month PesticideCa

NH4 Axis−4 Axis−6 evap. weekevap. month Axis−8 evap. week Pb Cl temperature Frost days NH4 pH Suspended matter Na Frost days Cd Zn −0.4 Cr prec. Cumonth Pesticide HCO3 0.0 O Cu Mg Sum N Cd Sum N HCO3 Ca Water temperaturepH Suspended matter Sum N evap. month Cl Zn NO2+NO3 PO4 Cr temperaturetemp. week −0.2 O

−0.2 BOD Pesticide temp.HCO3 previous summ. Ca O Cu Industrial temp. month pH NO2+NO3 Ni prec. week Na Ni evap. week Sum N MgNO2+NO3 temp. previous summ. Sum P GDD Ca precipitation evapotranspiration Combustion −0.8 −0.4 −0.4 −0.6 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 −0.4 −0.2 0.0 0.2 0.4 −0.4 −0.2 0.0 0.2 0.4 −0.4 −0.2 0.0 0.2 0.4

Sum N 0.3 prec. previous summ. precipitation Frost days Cr Cr 0.2

Axis−1 Mg Axis−3 0.3 Axis−5 PO4 Axis−7 Chloro. alpha Na Sum P NH4 Combustion WaterNi temperatureprec. month Pesticide Frost days NO2+NO3 0.2 Ni prec. week temperature evapotranspirationPO4 Mg Sum P

0.2 temp. previous Clsumm. Combustion precipitationCutemp. week prec.evap. week week Chloro. alphaNaMg temp. month evap. month 0.0 GDD Industrial Pb SuspendedO matter PesticideCdCr Zn O evap.evap. week month Suspended matter Sum N ZnWaterIndustrial temperature O prec. previous summ. Cd precipitation

Cl HCO3 0.1 Mg Pesticideprec.temp. month week BOD HCO3 0.1 Sum temperatureN pH Na CuCa evapotranspiration evap. week evapotranspirationtemp.Cd month prec. week 0.0 NO2+NO3 Ca Industrial pH NO2+NO3 NH4HCO3Cr Caevap. month PesticideChloro.GDD alpha pH Pb temp.Cu previous summ. temp.temperature week BOD −0.2 prec. month Waterprec. temperature previous summ. prec. monthWaterprecipitation temperaturetemp. month Chloro.temperature alpha pH prec. previous summ. PbSum P temp.ClZnNH4 week Frost days evapotranspirationIndustrial evap. week Suspended matter Axis−12 Axis−14 Axis−16 Sum N 0.0 prec. weekevap. monthCd GDD CombustionNO2+NO3temp. monthSumPO4 P Cu PO4 Ni −0.1 temp. previous summ. BOD −0.2 Na Cl −0.4 Frost days GDD Suspended matterNi O HCO3 Ca Combustion temp. previous summ. BOD Zn Pb NH4 −0.4 −0.6 −0.2 −0.3

FIGURE S5 −0.4 −0.2 0.0 0.2 0.4 0.6 −0.2 0.0 0.2 0.4 −0.4 −0.2 0.0 0.2 0.4 −0.2 −0.1 0.0 0.1 0.2 0.3 Principal component analysis of Explanatory covariates. 0.2 Ni Pesticide Pb Axis−9 Axis−11 Axis−13 O Axis−15 Na

0.2 prec. week

0.2 Sum N Cd

Cl 0.2 NH4 0.1 prec.Pesticide month Suspended matter NO2+NO3evapotranspirationO Cr Ca Cl precipitation Zn Pb temperature evapotranspirationSumPO4 PCd prec. week HCO3 IndustrialBOD evap. week Cr Frost days PO4Mg BOD temp. previous summ. 0.1 evap. month Combustion temperature NO2+NO3 evap.temp. month monthSum P temp. previous summ. pHHCO3evap.temp. month GDDweek Pb temp. weekprecipitation Industrial 0.0 Na Ni Cl Mgtemp. month Combustion O temperature prec. previous summ. Frost days 0.1 Industrial WaterNa temperatureprec. week temp. previousBOD summ. temp. month 0.0 Chloro. alpha prec. previous summ. CombustionNO2+NO3Sum N prec. previousSum N summ.PO4NH4 CaWater temperatureGDD evap.Cr week O pH Zn prec. week NH4 Chloro.evap. alpha weekWater temperature 0.0 evapotranspiration NH4 Frost days temp. weekSumGDD P Ni Frost daysZnprec. month pH Chloro. alpha precipitation Zn CaCu Water temperaturetemp.HCO3 week GDD

Axis−20 Axis−22 SuspendedAxis−24 matter evapotranspiration Mg temp.prec. previous previous summ. summ. Chloro. alpha Na BOD Cr SumCa P prec. month temperature Pesticide

−0.1 Ni Mg 0.0 −0.2 PO4 precipitation pH temp. month Cl Cd prec. monthNO2+NO3Pb evap. week Combustionevap.Cd month Cu HCO3 Suspended matter −0.2

Cu −0.2 Industrial Pesticide −0.4 Cu −0.1 SuspendedSum matter N

−0.2 −0.1 0.0 0.1 0.2 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 −0.3 −0.2 −0.1 0.0 0.1 0.2 −0.1 0.0 0.1 0.2 0.2 0.2 evapotranspiration 0.15 evap. weektemperatureevap. month 0.6 Zn GDD pH GDD Ca HCO3 0.6 BOD NO2+NO3 NO2+NO3 0.2 Watertemp. temperatureweek Axis−17 Industrial Axis−19 prec. previous summ. Axis−21 Axis−23 prec. week Pb PO4 temp. previous summ. temp. month prec. month O prec. previous summ. 0.1 Cl 0.2 Cr Cr Ni 0.1

0.4 Sum N NO2+NO3BOD Mg Frost days Chloro. alpha Cl Mg 0.4 BOD Industrial Ni

0.0 Na Frost days Combustion precipitation pHPb Ni Na Na prec.evap. month month 0.1 Industrial precipitationGDD Chloro. alpha temp. month precipitation temp.Water monthevap. temperature week Pesticide GDD temp. month 0.05 prec. month NH4 evapotranspiration CombustionCl SuspendedCombustion matter 0.0 Sum P Cd temperature Water temperature Combustion Suspended matter Pb prec. weekSuspended mattertemp. week Zn IndustrialNH4 Pb CuSuspended matter PbpH

Cd 0.2 Pesticide Industrial temperature Chloro. alphaPesticideCa HCO3Mg Combustion Cu Ni evap. monthCr NH4temperatureSum P Sum P Chloro. alpha Ni temperature NH4 PO4 Frost days PO4evapotranspirationprecipitationFrost days prec. week Cu PO4 O Chloro. alphaSumIndustrial P Pb evap. weektemp.prec. month month prec.Chloro.prec.temp. month previous alphaprevious summ. Cusumm. Frost days 0.0 temp. week temp.Industrial week NO2+NO3 Na prec. previousCr summ.prec. week 0.0 Cl Cr Suspended matter 0.2 Water temperature temperature temp.temp.prec. previous precipitationweek previous summ. summ. prec. week Pb Sum P temp.prec. previousNH4 summ. Water temperature O Mg temp.evap.evap.GDD month monthweek temp. previous summ. PO4 evapotranspirationSuspended matter HCO3 Pesticideprec.NH4 week BOD Cd O Zn Ni Chloro. alpha precipitation evapotranspiration PO4 Watertemp. temperature week prec. weekCombustionCaFrost days Combustion Mg Mg prec. previous summ. temp. week SumPO4Cu P Zn pH Cl −0.1 evapotranspiration temp. previousevap.NO2+NO3 month summ.prec. month Pesticide WaterSumGDD temperature P BOD 0.0 Ca NH4 Axis−4 Axis−6 evap.NH4 weekevap. month Axis−8 prec.Sumevap. previous N weekprec. summ.precipitation month pH HCO3 Zn Pb Cl temperature Frost days Axis−28 Chloro. alpha Axis−30 Axis−32 Suspended matter Na Frost days Cd Zn evap. week Cd −0.4 Cr prec. Cumonth Na GDDevapotranspiration Pesticide HCO3 Mg 0.0 O OSum N Na Cu Sum N Water temperatureCdpH evap. month −0.05 BOD Sum N HCO3 Ca Suspended matter temp.NO2+NO3 month Sum N evap. month Cl Zn Cd Cr −0.2 PO4 Cr temperaturetemp. week O −0.1

−0.2 BOD Pesticide temp.HCO3 previous summ. BOD Cl Ca O Cu Pesticide Industrial temp. month pH NO2+NO3 Ni prec. week Na Ni evap. week Sum N MgNO2+NO3 temp. previous summ. −0.1 Zn pH Sum P GDD Ca Cd precipitation Ca evapotranspiration HCO3 Sum N −0.3 Combustion −0.8 −0.15 −0.2 −0.4 −0.4 −0.6 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 −0.4 −0.2 0.0 0.2 0.4 −0.3−0.4 −0.2−0.2 −0.10.0 0.20.0 0.40.1 −0.4−0.2 −0.1−0.2 0.00.0 0.1 0.2 −0.15 −0.05 0.05 0.15 −0.2 −0.1 0.0 0.1 0.15 0.4 0.20 Sum N 0.3 prec. previous summ. evap. week evap. month precipitation Cr Sum P Cr Frost days 0.10 0.2 temp. week Axis−1 Mg Axis−3 0.3 Axis−5Axis−25 PO4 Axis−7Axis−27 Axis−29 Axis−31 Chloro. alpha Na Sum P NH4 Combustion WaterNi temperatureprec. month Pesticide Frost days NO2+NO3 0.2 temp. week Ni prec. week temperature evapotranspirationPO4 GDDprec.Mg week Sum P 0.2 temp. previous Clsumm. Combustion precipitationCutemp. week prec.evap. week week Cl Chloro. alphaNaMg temp. month evap. month

0.0 Industrial GDD Pb 0.05 O PesticideZn Water temperature Suspended matter evap. month SuspendedCdCr matter 0.10 O Sum N evap. week 0.05 ZnWaterIndustrial temperature O prec. previous summ. Cd precipitation GDD

Cl HCO3 0.1 Mg Pesticideprec.temp. month week pHBOD HCO3 0.1 Sum temperatureN evapotranspirationevap.Na weekIndustrial prec. month CuCa evapotranspiration PO4 pH evapotranspiration evapotranspirationtemp.Cd month prec. week Chloro. alpha HCO3 NO2+NO3 Sum P Chloro.pHprecipitationNa alpha 0.0 Ca NO2+NO3 Frost PesticidedaysNi NO2+NO3 prec.IndustrialPesticide CupreviousFrostNO2+NO3NH4PbMgOCr daysCd summ. NO2+NO3 NH4Cr SuspendedCombustionCd matterCaZnIndustrialPbevap.Mg monthSum N pHCa temp.Suspended CombustionpreviousNiHCO3BODSumZnClCa summ.matterNPO4 HCO3 Pb temp.prec. previouspreviousprecipitationCrCu summ.summ.NH4 Water temperature PesticideChloro.GDDtemperature alpha temperature pH Ca temp.Cu previous summ. temp. week 0.00 BOD −0.2 prec. month BOD Waterprec.Sum temperature previous PO summ. Mgprec. monthWaterprecipitation temperaturetemp. monthGDD prec. week Sum P Chloro.temperature alpha pH prec. previous summ.Cl temperature evap. week Pb temp.ClZnNH4 week Frost days temp.evapotranspirationIndustrialprec. previousFrostCombustionPbCrCdevap. Cudays summ. weekprec.evap. week monthSuspended matter Axis−36 Axis−38 Axis−12 Axis−14 Axis−16 0.0 evapotranspirationprecipitationprec.prec. weekevap.BOD Omonth monthCd Sumprec. N month PesticideZnChloro.Ni alpha Water temperature

0.00 IndustrialHCO3 evap. week GDD temp. monthSumPO4 P temp. monthSuspendedNH4 matter Na CombustionNO2+NO3 temp. week Cu pH PO4 Ni −0.1 temp. previous summ. Na BOD −0.2 Sum N −0.05 Na Cl −0.4 Frost days GDD Suspended matterNi temp. month O temperature HCO3 Ca Combustion temp. previous summ. evap. month temp. month BOD Zn Pb PO4 NH4 −0.4 −0.6 −0.10 −0.10 −0.2 −0.3 −0.15 −0.4 −0.2 0.0 0.2 0.4 0.6 −0.2 0.0 0.2 0.4 −0.4 −0.2 0.0 0.2 0.4 −0.2 −0.1 0.0 0.1 0.2 0.3 Ni 0.2 Pesticide Pb Axis−9 Axis−11 Axis−13 O Axis−15 Na

0.2 prec. week

0.2 Sum N Cd

Cl 0.2 NH4 0.1 prec.Pesticide month Suspended matter NO2+NO3evapotranspirationO Cr Ca Cl precipitation Zn Pb temperature evapotranspirationSumPO4 PCd prec. week 78 HCO3 IndustrialBOD evap. week Cr Frost days PO4Mg BOD temp. previous summ. 0.1 evap. month Combustion temperature NO2+NO3 evap.temp. month monthSum P temp. previous summ. pHHCO3evap.temp. month GDDweek Pb temp. weekprecipitation Industrial 0.0 Na Ni Cl Mgtemp. month Combustion O temperature prec. previous summ. Frost days 0.1 Industrial WaterNa temperatureprec. week temp. previousBOD summ. temp. month 0.0 Chloro. alpha prec. previous summ. CombustionNO2+NO3Sum N prec. previousSum N summ.PO4NH4 CaWater temperatureGDD evap.Cr week O pH Zn prec. week NH4 Chloro.evap. alpha weekWater temperature 0.0 evapotranspiration NH4 Frost days temp. weekSumGDD P Ni Frost daysZnprec. month pH Chloro. alpha precipitation Zn CaCu Water temperaturetemp.HCO3 week GDD

Axis−20 Axis−22 SuspendedAxis−24 matter evapotranspiration Mg temp.prec. previous previous summ. summ. Chloro. alpha Na BOD Cr SumCa P prec. month temperature Pesticide

−0.1 Ni Mg 0.0 −0.2 PO4 precipitation pH temp. month Cl Cd prec. monthNO2+NO3Pb evap. week Combustionevap.Cd month Cu HCO3 Suspended matter −0.2

Cu −0.2 Industrial Pesticide −0.4 Cu −0.1 SuspendedSum matter N

−0.2 −0.1 0.0 0.1 0.2 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 −0.3 −0.2 −0.1 0.0 0.1 0.2 −0.1 0.0 0.1 0.2 0.2 0.2 0.15 Zn Ca HCO3 pH NO2+NO3 NO2+NO3 Axis−17 Industrial Axis−19 Axis−21 Axis−23 Pb PO4

0.1 Cl

Sum N Cr 0.1 NO2+NO3BOD Chloro. alpha Mg Industrial pHPb Ni Na Ni

Na prec.evap. month month 0.1 precipitation temp.Water monthevap. temperature week Pesticide GDD 0.05 evapotranspiration CombustionCl SuspendedCombustion matter 0.0 Sum P temperature WaterPb temperature Chloro.prec. alpha weekSuspended mattertemp. week IndustrialNH4 CuSuspended matter pH temperature PesticideCa HCO3Mg temperature Ni evap. monthCr NH4temperatureSum P Ni evap. weekFrostprec. days month PO4evapotranspirationChloro.precipitationFrost daysalpha Cu PO4Frost daysO Pb temp. month prec.temp.prec.Industrialtemp. month weekprevious previous summ. Cusumm.

0.0 Cl Cr temp. previous summ. Water temperature O temp.temp.prec. previous precipitationweek previous summ. summ. temp. monthprec. week prec. previousNH4 summ. Suspended matter Mg prec.NH4 week Cdevap.evap.GDD monthweek Pesticideevapotranspiration BOD O prec. weekCombustionCaFrost days Combustion Mg temp. week SumPO4Cu P Zn −0.1 temp. previousNO2+NO3 summ. WaterSumGDD temperature P 0.0 prec.Sum previous N prec. summ.precipitation month HCO3 Zn Axis−28 Chloro. alpha Axis−30 Axis−32 evap. week Cd Na O GDDevapotranspiration Na

evap. month −0.05 BOD temp. month Sum N Cd Cr −0.1 BOD Cl Pesticide

−0.1 Zn pH Cd Ca HCO3 Sum N −0.3 −0.15 −0.2

−0.3 −0.2 −0.1 0.0 0.1 −0.2 −0.1 0.0 0.1 −0.15 −0.05 0.05 0.15 −0.2 −0.1 0.0 0.1 0.15

evap. week 0.20 evap. month Sum P temp. Axis−25week Axis−27 0.10 Axis−29 Axis−31

temp. week GDDprec. week Cl

Water temperature 0.05 0.10 0.05 GDD evapotranspirationIndustrial evapotranspirationprec. month Chloro.PO4 alpha pH HCO3 NO2+NO3 Sum P Chloro.pH Na alpha NO2+NO3 Frost PesticidedaysNi prec.precipitationIndustrialPesticide CupreviousFrostNO2+NO3NH4PbMgOCr daysCd summ. SuspendedCombustionCd matterZnPbMg Sum N Ca temp.Suspended CombustionpreviousNiHCO3BODSumZnClCa summ.matterNPO4 temp.prec. previouspreviousprecipitationCrCu summ.summ.NH4 Water temperature temperature Ca 0.00 BOD Sum PO Mg GDD prec. week Cl evap. week temp.prec. previousFrostCombustionPbCrCd Cudays summ.prec.evap. week month temperature Axis−36 evapotranspirationprecipitationprec.BOD Omonth Axis−38 prec. month PesticideZnChloro.Ni alpha Water temperature 0.00 temp. monthSuspendedIndustrialHCO3NH4 matter evap. week Na temp. week pH Sum N Na −0.05

temp. month temperature evap. month temp. month PO4 −0.10 −0.10 −0.15 evapotranspiration evap. weektemperatureevap. month 0.6 GDD GDD 0.6 BOD 0.2 Watertemp. temperatureweek prec. previous summ. temp. previous summ. temp. month prec. week O prec. previous summ. prec. month 0.2 Ni Cr 0.4 Mg Frost days Cl

0.4 BOD

0.0 Na Frost days Combustion precipitation temp. month Industrial precipitationGDD Chloro. alpha temp. month prec. month NH4 Cd Combustion Suspended matter Pb Zn Pb

Sum N 0.3 prec. previous summ. precipitation Frost days Cr Cr 0.2

0.2 prec. week

0.2 Sum N Cd

Axis−20 Axis−22 SuspendedAxis−24 matter evapotranspiration Mg temp.prec. previous previous summ. summ. Chloro. alpha Na BOD Cr SumCa P prec. month temperature Pesticide

−0.1 Ni Mg 0.0 −0.2 PO4 precipitation pH temp. month Cl Cd prec. monthNO2+NO3Pb evap. week Combustionevap.Cd month Cu HCO3 Suspended matter −0.2

Cu −0.2 Industrial Pesticide −0.4 Cu −0.1 SuspendedSum matter N

0.1 Cl

Sum N Cr 0.1 NO2+NO3BOD Chloro. alpha Mg Industrial pHPb Ni Na Ni

evap. month −0.05 BOD temp. month Sum N Cd Cr −0.1 BOD Cl Pesticide

−0.1 Zn pH Cd Ca HCO3 Sum N −0.3 −0.15 −0.2

−0.3 −0.2 −0.1 0.0 0.1 −0.2 −0.1 0.0 0.1 −0.15 −0.05 0.05 0.15 −0.2 −0.1 0.0 0.1 0.15

evap. week 0.20 evap. month Sum P temp. Axis−25week Axis−27 0.10 Axis−29 Axis−31

temp. week GDDprec. week Cl

Water temperature 0.05 0.10 0.05 GDD evapotranspirationIndustrial evapotranspirationprec. month Chloro.PO4 alpha pH HCO3 NO2+NO3 Sum P Chloro.pH Na alpha NO2+NO3 Frost PesticidedaysNi prec.precipitationIndustrialPesticide CupreviousFrostNO2+NO3NH4PbMgOCr daysCd summ. SuspendedCombustionCd matterZnPbMg Sum N Ca temp.Suspended CombustionpreviousNiHCO3BODSumZnClCa summ.matterNPO4 temp.prec. previouspreviousprecipitationCrCu summ.summ.NH4 Water temperature temperature Ca 0.00 BOD Sum PO Mg GDD prec. week Cl temperature evap. week temp.prec. previousFrostCombustionPbCrCd Cudays summ.prec.evap. week month Axis−36 evapotranspirationprecipitationprec.BOD Omonth Axis−38 prec. month PesticideZnChloro.Ni alpha Water temperature 0.00 temp. monthSuspendedIndustrialHCO3NH4 matter evap. week Na temp. week pH Sum N Na −0.05

temp. month temperature evap. month temp. month PO4 −0.10 −0.10 −0.15

78 79 FIGURE S6 NH4 Correlation matrix between 38 explanatory variables BOD Cd 1.0 used in PCA analysis and subsequent modelling. Green Ca sub-boxes denote groups of covariates (Weather, Toxicity Cl and physical/chemical water-properties). Chloro. alpha Cr PO4 Sum P Cu Pb 0.5 Mg Na Ni Suspended matter NO2+NO3 Sum N Water temperature HCO3 0.0 Zn pH O Combustion Industrial Pesticide temperature temp. week temp. month −0.5 GDD Frost days precipitation prec. week prec. month evapotranspiration evap. week evap. month −1.0 temp. previous summ. prec. previous summ. O Cl Ni Cr Zn Pb Cd Ca Cu Na pH Mg NH4 PO4 BOD GDD HCO3 Sum P Sum N Pesticide Industrial Frost days NO2+NO3 prec. week evap. week evap. temp. week temp. prec. month Combustion temperature precipitation evap. month evap. temp. month temp. Chloro. alpha Chloro. evapotranspiration Suspended matter Water temperature Water prec. previous summ. prec. previous temp. previous summ. previous temp.

80 HHD WAM 200 100 80 100 60 50 40

10 20

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

WD WHD 120 100 200 80

100 60

40 FIGURE S7 50 Total insect-macrofauna abundance (pooled over all taxonomic groups) per WA. 20 20

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

HHD WAM WPM WRD 200 100 80

80 70 500 100 60 60

50 50 200 40 40 20 100

10 20 30 50

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

WD WHD WRIJ WRO 120 500 140 100 200 120 80 100 100 60 200 80 40 50 100 60

20 20 50 40

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

WPM WRD 80

70 500 60

50 200

40 100

30 50

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

WRIJ WRO 500 140 120 100 200 80 80 81 100 60

50 40

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 WRO Odonata WRO Insects rem. WRO Trichoptera

WRIJ WRIJ WRIJ

WRD WRD WRD

WPM WPM WPM

WHD WHD WHD

WD WD WD

WAM WAM WAM

Odonata Insects rem. Trichoptera WRO WRO WRO HHD HHD HHD

WRIJ WRIJ WRIJ

WRD WRD WRD WRO Odonata WRO Insects rem. WRO Trichoptera WPM WPM WPM −20 0 10 −20 0 10 −20 0 10 WRO OdonataOdonata WRO InsectsInsects rem.rem. WROWRIJ TrichopteraTrichoptera WRIJ WRIJ WROWRO WHDOdonata WROWRO WHD Insects rem. WHDWROWRO Trichoptera WRO Chironomidae WRO Diptera rem. WRO Simuliidae

WRD WRD WRD WRIJWRIJWRIJ WD WRIJWRIJWRIJ WD WRIJWDWRIJWRIJ WRIJ WRIJ WRIJ

WAM WAM WAMWPM WPM WPM WRDWRDWRD WRDWRDWRD WRDWRDWRD WRD WRD WRD HHD HHD HHD WHD WHD WHD WPMWPMWPM WPMWPMWPM WPMWPMWPM WPM WPM WPM

WD WD WD FIGURE S8 WHDWHDWHD WHDWHDWHD WHDWHDWHD WHD WHD WHD −20 0 10 −20 0 10 −20 0 10 WAM WAM WAM WDWDWD WDWDWD WDWDWD WD WD WD Average insect macrofauna trends for each of the taxonomicWRO groupsChironomidae and each of the WAs.WRO Diptera rem. WRO Simuliidae WAM WAM WAMHHD HHD HHD WAMWAM WRIJ WAMWAM WRIJ WRIJWAMWAM WAM WAM WAM

HHDHHDHHD WRD HHDHHDHHD WRD WRDHHDHHDHHD HHD HHD HHD WPM WPM WPM −20 0 10 −20 0 10 −20 0 10 WHD WHD WHD WRO Odonata WRO −20−20 Insects rem.00 1010 WRO −20−20 Trichoptera00 1010 WRO −20−20 Chironomidae00 1010 WRO Diptera rem. WRO Simuliidae −20 WD 0 10 WD−20 0 10 WD −20 0 10 −20 0 10 −20 0 10 −20 0 10 WRO Odonata WRO Insects rem. WRIJWRO Trichoptera WRIJWRO ChironomidaeChironomidae WRIJWRO DipteraDiptera rem.rem. WROWRIJ SimuliidaeSimuliidae WRIJ WRIJ WROWRO WAMChironomidae WROWRO WAM Diptera rem. WAMWROWRO Simuliidae WRO Coleoptera WRO Ephemeroptera WRO Heteroptera

WRIJ WRIJ WRIJWRD WRD WRD WRD WRD WRD WRIJWRIJWRIJ HHD WRIJWRIJWRIJ HHD HHDWRIJWRIJWRIJ WRIJ WRIJ WRIJ

WRD WRD WPMWRD WPM WPM WPM WPM WPM WRDWRDWRD WRDWRDWRD WRDWRDWRD WRD WRD WRD

WPM WPM WPMWHD WHD −20 0 10 WHD −20 0 10 WHD −20 0 10 WHD WHD WPMWPMWPM WPMWPMWPM WPMWPMWPM WPM WPM WPM WRO Coleoptera WRO Ephemeroptera WRO Heteroptera WHD WHD WHDWD WD WD WD WD WD WHDWHDWHD WHDWHDWHD WHDWHDWHD WHD WHD WHD WRIJ WRIJ WRIJ WD WD WAMWD WAM WAM WAM WAM WAM WDWDWD WDWDWD WDWDWD WD WD WD WRD WRD WRD WAM WAM WAMHHD WAMHHD WAMHHD WAMHHD HHD HHD WAMWAM WPM WAMWAM WPM WPMWAMWAM WAM WAM WAM

HHD HHD HHD HHDHHDHHD WHD HHDHHDHHD WHD WHDHHDHHDHHD HHD HHD HHD −20 0 10 −20 WD 0 10 −20WD 0 10 WD −20 0 10 −20 0 10 −20 0 10 WAM WAM WAM −20 0 10 −20 0 10 WRO −20 Chironomidae0 10 WRO −20−20 Diptera rem.00 1010 WRO −20−20 Simuliidae00 1010 WRO −20−20 Coleoptera00 1010 WRO Ephemeroptera WRO Heteroptera −20 HHD 0 10 HHD−20 0 10 HHD −20 0 10 −20 0 10 −20 0 10 −20 0 10 WRO Chironomidae WRO Diptera rem. WRIJWRO Simuliidae WRIJ Coleoptera WRIJ Ephemeroptera WRIJ Heteroptera WRIJ WRIJ WROWROWRO ColeopteraColeoptera WROWROWRO EphemeropteraEphemeroptera WROWROWRO HeteropteraHeteroptera WRO Lepidoptera

WRIJ WRIJ WRIJWRD WRD WRD WRD WRD WRD WRIJWRIJWRIJ −20 0 10 WRIJWRIJWRIJ −20 0 10 WRIJWRIJWRIJ −20 0 10 WRIJ WRD WRD WPMWRD WPM WPM WPM WPM WPM WRDWRDWRD WRO Lepidoptera WRDWRDWRD WRDWRDWRD WRD

WPM WPM WPMWHD WHD WRIJ WHD WHD WHD WHD WPMWPMWPM WPMWPMWPM WPMWPMWPM WPM WRD WHD WHD WHDWD WD WD WD WD WD WHDWHDWHD WHDWHDWHD WHDWHDWHD WHD WPM WD WD WAMWD WAM WAM WAM WAM WAM WDWDWD WDWDWD WDWDWD WD WHD WAM WAM WAMHHD WAMHHD WAMHHD WAMHHD HHD HHD WAMWAM WD WAMWAM WAMWAM WAM

HHD HHD HHD HHDHHDHHD WAM HHDHHDHHD HHDHHDHHD HHD −20 0 10 −20 HHD 0 10 −20 0 10 −20 0 10 −20 0 10 −20 0 10 −20 0 10 −20 0 10 WRO −20 Coleoptera0 10 WRO −20−20−20 Ephemeroptera000 101010 WRO −20−20−20 Heteroptera000 101010 WRO −20−20−20 Lepidoptera000 101010 −20 0 10 −20 0 10 Coleoptera Ephemeroptera Heteroptera WRO WRO WRIJWRO WRIJWROWROWRO LepidopteraLepidopteraLepidoptera WRIJ WRIJ

WRIJ WRIJ WRIJWRD WRIJWRDWRIJWRIJ WRD WRD Percentual annual change Percentual annual change WRD WRD WPMWRD WPMWRDWRDWRD WPM WPM

WPM WPM WPMWHD WPMWHDWPMWPM WHD WHD

WHD WHD WHDWD WHDWHDWDWHD WD WD

WD WD WAMWD WAMWDWDWD WAM WAM

WAM WAM WAMHHD WAMWAMHHDWAM HHD HHD

HHD HHD HHD HHDHHDHHD −20 0 10 −20 0 10 −20 0 10 −20 0 10 −20 0 10 −20 0 10 WRO −20 Lepidoptera0 10 −20−20 00 1010 −20 0 10 82 WRO Lepidoptera WRIJ Percentual annual change WRIJ WRD PercentualPercentual annualannual changechange WRD WPM Percentual annual change

WPM WHD

WHD WD

WD WAM

WAM HHD

HHD −20 0 10 −20 0 10

Percentual annual change Percentual annual change Odonata Insects rem. Trichoptera Chironomidae 5.0 5.0 200 10 2.0 100 5 2.0 1.0 50 0.5 2 1.0 0.2 20 1 0.5 0.1 10

1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010

Diptera rem. Simuliidae Coleoptera Ephemeroptera 8 50 1e−01 6 20.0 Odonata Insects rem. Trichoptera Chironomidae 5 5.0 5.0 20 4 5.0 200 10 10 3

2.0 Odonata Insects rem. Trichoptera Chironomidae 2.0 1e−03 5.0 5.0 5 100 5 2 200 2.0 10 1.0 0.5 2 50 2.0 0.2 0.5 100 5 2 1e−05 1.0 2.0 FIGURE S9 1990 2000 2010 1990 1.0 2000 2010 1990 2000 2010 1990 2000 2010 50 0.2 Heteroptera Lepidoptera 20 0.5

Trends among ten insect-macrofauna groups for each of the three WA groups. West (W): 1 2 0.5 1.0 20 0.1

HHD & WHD, South (S): WAM, WD, WPM & WRO, East (E): WRD & WRIJ. 0.10 10 0.2 20 1 0.06

10 1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 0.5 0.1 10 Odonata Insects rem. Trichoptera Chironomidae 0.04 Diptera rem. E Simuliidae Coleoptera Ephemeroptera 8 5 S 5.0 5.0

1990 2000 2010 1990 2000 2010 1990W 2000 2010 1990 2000 2010 50 200 10 1e−01 6 20.0 0.02

Odonata Insects rem. Trichoptera Chironomidae Diptera rem. Simuliidae Coleoptera 5 Ephemeroptera 2.0 8 5.0 5.0 20 100 5 50 4 200 2.0 5.0

10 1990 2000 2010 1990 2000 2010 1.0 1e−01 6 20.0 10 3 5 50 2.0 2.0 0.5 20 100 5 1e−03 4 2 1.0 2.0 5 5.0 1.0 2 10 3 50 0.2 20 0.5 2.0 0.5 1 1e−03 2 2 0.5 1.0 5 0.1 2 0.2 10 0.2 20 1e−05 0.5 1

1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 2 1990 2000 2010 1990 2000 2010 0.5 0.1 0.2 10

Diptera rem. Simuliidae Coleoptera Ephemeroptera 1e−05 Heteroptera Lepidoptera 8

1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 50 1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 20 0.10 1e−01 6 20.0

Diptera rem. Simuliidae Coleoptera 5 Ephemeroptera Heteroptera Lepidoptera 8 20 50 4 0.06 20 5.0 10 0.10 1e−01 6 20.0 10 3 5 2.0 0.04 20 E 1e−03 4 0.06 5 5.0 5 10 S 2

10 W 3 0.5 2.0 0.04 E 1e−03 0.02 2 5 5 S 2 0.2 W 1e−05 0.5 0.02 1990 2000 2010 1990 2000 2010 2 1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 0.2 Heteroptera Lepidoptera 1e−05 1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 2000 2010 20 0.10 Heteroptera Lepidoptera 0.06 20 10

82 0.10 83

0.04 E 0.06 5 10 S W

0.04 E 0.02

5 S W

0.02 1990 2000 2010 1990 2000 2010

1990 2000 2010 1990 2000 2010 FIGURE S10 FIGURE S11 Trends in species richness for each of the Water Authorities (WA) in 1990-2017 Trends in Shannon index of diversity for each of the Water Authorities in 1990-2017

HHD WAM HHD WAM 50 50 4 4

3 3 20 20 2 2

10 10 1 1

5 5 0 0

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 WD WHD WD WHD 50 50 4 4

3 3 20 20 2 2

10 10 1 1

5 5 0 0

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 WPM WRD WPM WRD 50 50 4 4

3 3 20 20 Shannon's Index Shannon's Species richness 2 2

10 10 1 1

5 5 0 0

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 WRIJ WRO WRIJ WRO 50 50 4 4

3 3 20 20 2 2

10 10 1 1

5 5 0 0

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

84 FIGURE S12 FIGURE S13 Trends in Simpson’s index of diversity for each of the Water Authorities in 1990-2017 Trends in Shannon’s index of evenness for each of the Water Authorities in 1990-2017

HHD WAM HHD WAM 0.9 0.9 0.9 0.9

0.8 0.8 0.8 0.8

0.7 0.7 0.7 0.7

0.6 0.6 0.6 0.6

0.5 0.5 0.5 0.5

0.4 0.4 0.4 0.4

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 WD WHD WD WHD 0.9 0.9 0.9 0.9

0.8 0.8 0.8 0.8

0.7 0.7 0.7 0.7

0.6 0.6 0.6 0.6

0.5 0.5 0.5 0.5

0.4 0.4 0.4 0.4

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 WPM WRD WPM WRD 0.9 0.9 0.9 0.9

0.8 0.8 Evenness 0.8 0.8

0.7 0.7 0.7 0.7 Simpson's Index Simpson's 0.6 0.6 0.6 0.6

0.5 0.5 0.5 0.5

0.4 0.4 0.4 0.4

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 WRIJ WRO WRIJ WRO 0.9 0.9 0.9 0.9

0.8 0.8 0.8 0.8

0.7 0.7 0.7 0.7

0.6 0.6 0.6 0.6

0.5 0.5 0.5 0.5

0.4 0.4 0.4 0.4

1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

84 85 FIGURE S14 Back-transformed covariate effects (z-scores) for each of the ten taxonomic insect groups. Black bars indicate signiﬁcant effects. COD = chemical oxygen demand, BOD = biochemical oxygen demand, maxHU = maximum hazard unit, msPAF = multiple-substance Potentially Affected Fraction.

Odonata Insects rem. Odonata Insects rem.

Weather prec. previous summ. Weather prec. previous summ. Weather prec. previous summ. Weather prec. previous summ. temp. previous summ. temp. previous summ. temp. previous summ. temp. previous summ. evap. month evap. month evap. month evap. month evap. week evap. week evap. week evap. week evapotranspiration evapotranspiration evapotranspiration evapotranspiration prec. month prec. month prec. month prec. month prec. week prec. week prec. week prec. week precipitation precipitation precipitation precipitation Frost days Frost days Frost days Frost days GDD GDD GDD GDD temp. month temp. month temp. month temp. month temp. week temp. week temp. week temp. week temperature temperature temperature temperature Toxicity Pesticide Toxicity Pesticide Toxicity Pesticide Toxicity Pesticide Industrial Industrial Industrial Industrial Combustion Combustion Combustion Combustion Psysical/Chemical properties O Psysical/Chemical properties O Psysical/Chemical properties O Psysical/Chemical properties O pH pH pH pH Zn Zn Zn Zn HCO3 HCO3 HCO3 HCO3 Water temperature Water temperature Water temperature Water temperature Sum N Sum N Sum N Sum N NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 Suspended matter Suspended matter Suspended matter Suspended matter Ni Ni Ni Ni Na Na Na Na Mg Mg Mg Mg Pb Pb Pb Pb Cu Cu Cu Cu Sum P Sum P Sum P Sum P PO4 PO4 PO4 PO4 Cr Cr Cr Cr Chloro. alpha Chloro. alpha Chloro. alpha Chloro. alpha Cl Cl Cl Cl Ca Ca Ca Ca Cd Cd Cd Cd BOD BOD BOD BOD NH4 NH4 NH4 NH4 Land use prox. sewage plant Land use prox. sewage plant Land use prox. sewage plant Land use prox. sewage plant Protected Protected Protected Protected wet−natural wet−natural wet−natural wet−natural dry−natural dry−natural dry−natural dry−natural forest forest forest forest built−up built−up built−up built−up rem. agric. rem. agric. rem. agric. rem. agric. Greenhouses Greenhouses Greenhouses Greenhouses Arable Arable Arable Arable

−6 −2 2 4 6 8 −5 0 5 10 15 −6 −2 2 4 6 8 −5 0 5 10 15

Trichoptera Chironomidae Trichoptera Chironomidae

Weather prec. previous summ. Weather prec. previous summ. Weather prec. previous summ. Weather prec. previous summ. temp. previous summ. temp. previous summ. 86 temp. previous summ. temp. previous summ. evap. month evap. month evap. month evap. month evap. week evap. week evap. week evap. week evapotranspiration evapotranspiration evapotranspiration evapotranspiration prec. month prec. month prec. month prec. month prec. week prec. week prec. week prec. week precipitation precipitation precipitation precipitation Frost days Frost days Frost days Frost days GDD GDD GDD GDD temp. month temp. month temp. month temp. month temp. week temp. week temp. week temp. week temperature temperature temperature temperature Toxicity Pesticide Toxicity Pesticide Toxicity Pesticide Toxicity Pesticide Industrial Industrial Industrial Industrial Combustion Combustion Combustion Combustion Psysical/Chemical properties O Psysical/Chemical properties O Psysical/Chemical properties O Psysical/Chemical properties O pH pH pH pH Zn Zn Zn Zn HCO3 HCO3 HCO3 HCO3 Water temperature Water temperature Water temperature Water temperature Sum N Sum N Sum N Sum N NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 Suspended matter Suspended matter Suspended matter Suspended matter Ni Ni Ni Ni Na Na Na Na Mg Mg Mg Mg Pb Pb Pb Pb Cu Cu Cu Cu Sum P Sum P Sum P Sum P PO4 PO4 PO4 PO4 Cr Cr Cr Cr Chloro. alpha Chloro. alpha Chloro. alpha Chloro. alpha Cl Cl Cl Cl Ca Ca Ca Ca Cd Cd Cd Cd BOD BOD BOD BOD NH4 NH4 NH4 NH4 Land use prox. sewage plant Land use prox. sewage plant Land use prox. sewage plant Land use prox. sewage plant Protected Protected Protected Protected wet−natural wet−natural wet−natural wet−natural dry−natural dry−natural dry−natural dry−natural forest forest forest forest built−up built−up built−up built−up rem. agric. rem. agric. rem. agric. rem. agric. Greenhouses Greenhouses Greenhouses Greenhouses Arable Arable Arable Arable

−15 −5 0 5 −5 0 5 −15 −5 0 5 −5 0 5 Odonata Insects rem.

−6 −2 2 4 6 8 −5 0 5 10 15

Trichoptera Chironomidae Diptera rem. Simuliidae

−15 −5 0 5 −5 0 5 −10 −5 0 5 −4 0 2 4 6 8

Coleoptera Ephemeroptera

Weather prec. previous summ. Weather prec. previous summ. 86 temp. previous summ. 87 temp. previous summ. evap. month evap. month evap. week evap. week evapotranspiration evapotranspiration prec. month prec. month prec. week prec. week precipitation precipitation Frost days Frost days GDD GDD temp. month temp. month temp. week temp. week temperature temperature Toxicity Pesticide Toxicity Pesticide Industrial Industrial Combustion Combustion Psysical/Chemical properties O Psysical/Chemical properties O pH pH Zn Zn HCO3 HCO3 Water temperature Water temperature Sum N Sum N NO2+NO3 NO2+NO3 Suspended matter Suspended matter Ni Ni Na Na Mg Mg Pb Pb Cu Cu Sum P Sum P PO4 PO4 Cr Cr Chloro. alpha Chloro. alpha Cl Cl Ca Ca Cd Cd BOD BOD NH4 NH4 Land use prox. sewage plant Land use prox. sewage plant Protected Protected wet−natural wet−natural dry−natural dry−natural forest forest built−up built−up rem. agric. rem. agric. Greenhouses Greenhouses Arable Arable

−8 −4 0 2 4 6 −10 0 5 10 15 DipteraDiptera rem. rem. SimuliidaeSimuliidae

Weather Weatherprec. previousprec. summ. previous summ. Weather Weatherprec. previousprec. summ. previous summ. temp. previoustemp. summ. previous summ. temp. previoustemp. summ. previous summ. evap. monthevap. month evap. monthevap. month evap. weekevap. week evap. weekevap. week evapotranspirationevapotranspiration evapotranspirationevapotranspiration prec. monthprec. month prec. monthprec. month prec. week prec. week prec. week prec. week precipitationprecipitation precipitationprecipitation Frost days Frost days Frost days Frost days GDD GDD GDD GDD temp. monthtemp. month temp. monthtemp. month temp. weektemp. week temp. weektemp. week temperaturetemperature temperaturetemperature Toxicity Toxicity Pesticide Pesticide Toxicity Toxicity Pesticide Pesticide Industrial Industrial Industrial Industrial CombustionCombustion CombustionCombustion Psysical/ChemicalPsysical/Chemical properties propertiesO O Psysical/ChemicalPsysical/Chemical properties propertiesO O pH pH pH pH Zn Zn Zn Zn HCO3 HCO3 HCO3 HCO3 Water temperatureWater temperature Water temperatureWater temperature Sum N Sum N Sum N Sum N NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 Suspended Suspendedmatter matter Suspended Suspendedmatter matter Ni Ni Ni Ni Na Na Na Na Mg Mg Mg Mg Pb Pb Pb Pb Cu Cu Cu Cu Sum P Sum P Sum P Sum P PO4 PO4 PO4 PO4 Cr Cr Cr Cr Chloro. alphaChloro. alpha Chloro. alphaChloro. alpha Cl Cl Cl Cl Ca Ca Ca Ca Cd Cd Cd Cd BOD BOD BOD BOD NH4 NH4 NH4 NH4 Land use Land prox.use sewageprox. plant sewage plant Land use Land prox.use sewageprox. plant sewage plant Protected Protected Protected Protected wet−naturalwet−natural wet−naturalwet−natural dry−naturaldry−natural dry−naturaldry−natural FIGURE S14 forest forest forest forest built−up built−up built−up built−up Continued from previousrem. agric. rem. page. agric. rem. agric. rem. agric. GreenhousesGreenhouses GreenhousesGreenhouses Arable Arable Arable Arable

−10 −10−5 −50 05 5 −4 −4 0 2 0 4 2 6 4 8 6 8

ColeopteraColeoptera EphemeropteraEphemeroptera Heteroptera Lepidoptera

Weather Weatherprec. previousprec. summ. previous summ. Weather Weatherprec. previousprec. summ. previous summ. prec.Weather previous summ. prec.Weather previous summ. temp. previoustemp. summ. previous summ. temp. previoustemp. summ. previous summ. temp. previous summ. temp. previous summ. evap. monthevap. month evap. monthevap. month evap. month evap. month evap. weekevap. week evap. weekevap. week evap. week evap. week evapotranspirationevapotranspiration evapotranspirationevapotranspiration evapotranspiration evapotranspiration prec. monthprec. month prec. monthprec. month prec. month prec. month prec. week prec. week prec. week prec. week prec. week prec. week precipitationprecipitation precipitationprecipitation precipitation precipitation Frost days Frost days Frost days Frost days Frost days Frost days GDD GDD GDD GDD GDD GDD temp. monthtemp. month temp. monthtemp. month temp. month temp. month temp. weektemp. week temp. weektemp. week temp. week temp. week temperaturetemperature temperaturetemperature temperature temperature Toxicity Toxicity Pesticide Pesticide Toxicity Toxicity Pesticide Pesticide Toxicity Pesticide Toxicity Pesticide Industrial Industrial Industrial Industrial Industrial Industrial CombustionCombustion CombustionCombustion Combustion Combustion Psysical/ChemicalPsysical/Chemical properties propertiesO O Psysical/ChemicalPsysical/Chemical properties propertiesO O Psysical/ChemicalO properties Psysical/ChemicalO properties pH pH pH pH pH pH Zn Zn Zn Zn Zn Zn HCO3 HCO3 HCO3 HCO3 HCO3 HCO3 Water temperatureWater temperature Water temperatureWater temperature Water temperature Water temperature Sum N Sum N Sum N Sum N Sum N Sum N NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 NO2+NO3 Suspended Suspendedmatter matter Suspended Suspendedmatter matter Suspended matter Suspended matter Ni Ni Ni Ni Ni Ni Na Na Na Na Na Na Mg Mg Mg Mg Mg Mg Pb Pb Pb Pb Pb Pb Cu Cu Cu Cu Cu Cu Sum P Sum P Sum P Sum P Sum P Sum P PO4 PO4 PO4 PO4 PO4 PO4 Cr Cr Cr Cr Cr Cr Chloro. alphaChloro. alpha Chloro. alphaChloro. alpha Chloro. alpha Chloro. alpha Cl Cl Cl Cl Cl Cl Ca Ca Ca Ca Ca Ca Cd Cd Cd Cd Cd Cd BOD BOD BOD BOD BOD BOD NH4 NH4 NH4 NH4 NH4 NH4 Land use Land prox.use sewageprox. plant sewage plant Land use Land prox.use sewageprox. plant sewage plant Landprox. use sewage plant Landprox. use sewage plant Protected Protected Protected Protected Protected Protected wet−naturalwet−natural wet−naturalwet−natural wet−natural wet−natural dry−naturaldry−natural dry−naturaldry−natural dry−natural dry−natural forest forest forest forest forest forest built−up built−up built−up built−up built−up built−up rem. agric. rem. agric. rem. agric. rem. agric. rem. agric. rem. agric. GreenhousesGreenhouses GreenhousesGreenhouses Greenhouses Greenhouses Arable Arable Arable Arable Arable Arable

−8 −8−4 −40 2 04 26 4 6 −10 −10 0 50 105 1510 15 −5 0 5 −4 0 2 4

88 FIGURE S14 Continued from previous page.

Heteroptera Lepidoptera prec.Weather previous summ. prec.Weather previous summ. temp. previous summ. temp. previous summ. evap. month evap. month evap. week evap. week evapotranspiration evapotranspiration prec. month prec. month prec. week prec. week precipitation precipitation Frost days Frost days GDD GDD temp. month temp. month temp. week temp. week temperature temperature Toxicity Pesticide Toxicity Pesticide Industrial Industrial Combustion Combustion Psysical/ChemicalO properties Psysical/ChemicalO properties pH pH Zn Zn HCO3 HCO3 Water temperature Water temperature Sum N Sum N NO2+NO3 NO2+NO3 Suspended matter Suspended matter Ni Ni Na Na Mg Mg Pb Pb Cu Cu Sum P Sum P PO4 PO4 Cr Cr Chloro. alpha Chloro. alpha Cl Cl Ca Ca Cd Cd BOD BOD NH4 NH4 Landprox. use sewage plant Landprox. use sewage plant Protected Protected wet−natural wet−natural dry−natural dry−natural forest forest built−up built−up rem. agric. rem. agric. Greenhouses Greenhouses Arable Arable

−5 0 5 −4 0 2 4

88 89 Larva of spring stonefly - Nemoura cinerea - beeksteenvlieg Photo: Bureau Biota ABOUT STOWA

90 Plecoptera Nemoura cinerea larvae Photo:Bureau Biota

STOWA (Acronym for Foundation for Applied Water Research) is the knowledge centre of STOWA is not only a link between the users of knowledge and knowledge providers, but the regional water managers (mostly the Dutch Water Authorities) in the Netherlands. also between the regional water managers. The collaboration of the water managers Its mission is to develop, collect, distribute and implement applied knowledge, which within STOWA ensures they are jointly responsible for the programming, that they set the water managers need in order to adequately carry out the tasks that their work the course, that several Water Authorities are involved with one and the same project supports. This expertise can cover applied technical, scientific, administrative-legal or and that the results quickly benefit all Water Boards. social science fields. MISSION STATEMENT STOWA is a highly demand-driven operation. We carefully take stock of the knowledge STOWA’s fundamental principles are set out in our mission: requirements of the Water Authorities and ensure that these are placed with the Defining the knowledge needs in the field of water management and developing, correct knowledge providers. The initiative for this mainly lies with the users of collecting, making available, sharing, strengthening and implementing the required this knowledge, the water managers, but sometimes also with knowledge institutes, knowledge or arranging for this together with regional water managers. business and industry. This two-way flow of knowledge promotes modernisation and innovation. STOWA P.O. Box 2180 Demand-driven operation also means that we are constantly looking for the ‘knowledge 3800 CD Amersfoort requirements of tomorrow’ - requirements that we dearly want to put on the agenda The Netherlands before they become an issue - in order to ensure that we are optimally prepared for the future. 033 460 32 00 [email protected] We ease the burden of the water managers by assuming the tasks of placing the www.stowa.nl invitation to tender and supervising the joint knowledge projects. STOWA ensures that water managers remain linked to these projects and also retain ‘ownership’ of them. In this way, we make sure that the correct knowledge requirements are met. The projects are supervised by committees, which also comprise regional water managers. The broad research lines are spread out per field of practice and accounted for by special programme committees. The water managers also have representatives on these committees.

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STICHTING TOEGEPAST ONDERZOEK WATERBEHEER LONG-TERM TRENDS AND DRIVERS OF AQUATIC INSECTS IN THE NETHERLANDS

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