Posted on Authorea 28 Sep 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160133650.04034920 — This a preprint and has not been peer reviewed. Data may be preliminary. Paavola Eastwood Niamh a in services ecosystem world and changing biodiversity of problem wicked The Brown t r ttecr ftes-ald‘ikdpolm ope,oe-ne n nrcal fecosystem of - intractable and open-ended complex, - problem’ ‘wicked so-called profitabil- economic the for economic of need and the (Jax core ecological and services the objectives the competing at Additionally, increasing resources, are 1). feedback, limited ity diverge; non-linear (Fig. can and consequences risks create complex these unpredictable ecosystem and of because impact and values an temporal, complicated risks the of is spatial, of and uncertainty components management different finite the Ecosystem the across is among unknown 2017). capital interactions and/or Nagendra natural interdependent uncertain & because (DeFries is challenging scales ecosystems is economic on ecosystems intervention human natural of of (Mace integrity including services the (ES), recreational) taining services and ecosystem aesthetic provide (e.g. that systems self-regulating provisioning and self-sustaining are Ecosystems to rationale their practitioners and and to Background services practice. tools ecosystem and accessible of management provides future environmental framework for the The solutions forecast practical accurately scenarios. into to research change then cutting-edge used climate are translate then different fingerprinting dynamics. and under biodiversity this hindcasting, and impact change from by environmental socio-economic between obtained tested environmental relations are dynamics cause-effect and identify models abiotic long-term biochemical to biodiversity, Predictive The pipeline of of learning integration machine dynamics centuries. a unprecedented long-term in spanning which an placed archives in using biological gap, reconstructed implementation services of are ecosystem the fingerprinting functions and closes collapse ecosystem that function and framework ecosystem paleo novel counter properties, across to to a makers has transfer propose policy economics knowledge We and and trans-disciplinary and managers loss. statistics, natural Truly Collaboration resources science, of scientists, computer use for services. dynamics. sciences, mustered increasing ecosystem water biodiversity be biogeography, an of evolution, affecting development of ecology, change sustainable global because climatology, environmental a rapidly biology, and for declining needed needs are are human provides solutions meet it services to ecosystem resources the (finite) and biodiversity Earth’s The Abstract 2020 28, September 6 5 4 3 2 1 Orsini Luisa and oteUiesttFakuta Main am Frankfurt Goethe-Universitat Laboratory National Berkeley Paris Lawrence of University American The Leeds Biosciences of of University School Cardiff University Cardiff Birmingham of University 5 aiUllah Sami , 3 atnDallimer Martin , (food), tal. et 1 2018). regulating 1 ila Stubbings William , 1 tfnKrause Stefan , eg climate), (e.g. 3 eeaPantel Jelena , 1 ai Hannah David , 1 supporting oae Abdallah Mohamed , tal. et 4 aulJohnson Samuel , 1 02.Assanddlvr fsrie hl main- while services of delivery sustained A 2012). ntin yln,piaypouto) and production), primary cycling, (nutrient 1 aa Crawford Sarah , 1 1 sbleDurance Isabelle , iriZhou Jiarui , 6 atnWidman Martin , 1,1 James , 2 Jouni , cultural 1 , Posted on Authorea 28 Sep 2020 — The copyright holder is the author/funder. 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However, of biodiversity. value intuitively People biodiversity with starts all restoration It the Strategic prevent positive constraints priorities. provide Diversity socio-economic and Biological because capital. 2018). capital on critical natural (WWF Convention all is natural 1970 the of actions restore since and conservation index to IPBES of planet interventions the prioritization living of addressing freshwater destruction prioritization global outcomes, of the the cultural threat in enables increasing reduction framework under 83% Our being an and with ecosystem recreation) loss, important and diversity delivering provision the value, conservation (Ruckelshaus from food dry- degradation high water, ranging and including of clean distributed, diverse, are ecosystems are widely (e.g. Freshwater ecosystems geographically services equator. these and the because ponds/lakes, to services and poles their streams/rivers and wetlands, ecosystems lands, freshwater on scenarios functions focus and ecosystem We forecasts to model feedback their of and lack (Nicholson largely properties the services interactions) by remains and (biotic hindered network services currently food-web ecosystem services, incorporate ecosystem and that restoring evolution (Rudman of between forecasts benefits relationship model and (Baert in the services omitted yet and and which for, unexplored functions functions accounted ecosystem ecosystem is associated of evolution affecting and and (Nicholson biodiversity al. changes efforts of et biodiversity loss modelling driving between in in space links included evolution through dynamics cause-effect not abiotic individual the typically to and quantify are species biotic between single to feedback of biodi- time the responses on understanding dose-effect and factors to ii) environmental limited multiple 2016); often of (Kanno are impact pollutants which associated synergistic measures issues the current addresses assessing unlike that directly i) versity framework approach novel by: trans-disciplinary a constraints proposed discipline Our propose We with interde- effort. services. necessary spatio-temporal this ecosystem the guide and of can functions, screening in-depth ecosystem comprehensive biodiversity, an a provide of to through pendencies natural combined, complexities (Kinzig be assets, ecosystem must loss capital of disciplines its across understanding of approaches upon forms methodological only and other recognized conceptual Novel to and Relative public the ecological exist. and weighting not al. businesses evidence governments, et do by objective simply undervalued on priorities is based capital socio-economic mechanisms as intervention (Ehrlich well of ecological, framework address as Roux prioritization same that the stakeholders the (e.g. for enable within tools questions or that issues into scales, cross-disciplinary translated social been disconnected address and rarely or to economic also bound- inappropriate has inadequate knowledge disciplinary at are Scientific by undertaken which 2017). constrained research tools is in discipline-specific ES result and use and (Barnosky interactions, inefficient biodiversity needed process been are on far neglect solutions research may so cross-disciplinary because have truly part capital whereas in natural aries, is restoring This and natural conserving of inadequate. at use aimed increasing interventions an (Ruckelshaus by Mitigation needs caused 2012 human found the services Diversity meet 2020 of associated to in Assessment Biological and resources biodiversity Global (IPBES) of Earth’s (finite) first Services Convention in the Ecosystem the decline efforts, and accelerating these Biodiversity (e.g. an despite on ES However, Platform and 2019). Science-Policy Cardinale Targets (Mace biodiversity Intergovernmental Biodiversity overused preserve (e.g. Aichi been to cause the or main targets and deteriorated its set have as have ES identified bodies of been 60% has years, loss biodiversity 50 last the In 2011). 06.Rcn okhshglgtdta aycnevto ol a emtol fterl of role the if only met be can goals conservation many that highlighted has work Recent 2016). tal. et 2019). tal. et 00.Fehae cssesaeas mn h otipce ybio- by impacted most the among also are ecosystems Freshwater 2020). 2 tal. et tal. et tal. et 07;i)poiigaraitcvlaino costs of valuation realistic a providing iv) 2017); 2020). tal. et tal. et 09;adii nesadn h oeof role the understanding iii) and 2019); 02.Teetovesaekonas known are views two These 2012). tal. et tal. et 02.Dcso-aigframeworks Decision-making 2012). tal. et 06.Dsilnr approaches Disciplinary 2016). 02.Itrainlgoverning International 2012). 02.Rpdadsevere and Rapid 2012). tal. et Posted on Authorea 28 Sep 2020 — The copyright holder is the author/funder. 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Data may be preliminary. ihhbttls,ussanbeueo eore,itouto fivsv pce n lmt hnehas change climate and species invasive of introduction resources, of (Bonebrake use affecting 1) unsustainable factors (Fig. loss, environmental threats habitat change) by change) with climate complicated agriculture/land-use (e.g., further pollution, ‘horizon’ of overexploitation, is (e.g., and understanding ES ‘traditional’ mechanistic including and a dynamics, biodiversity biodiversity gain between to links needed Establishing are (Stockwell change functions dynamics environmental ecosystem future span altered and that current to Studies shifts function. ecosystem community and from shifts, community biodiversity 2013); change, what (Cadotte environmental e.g. understand phylogenies better or to genes, al. traits, progressed in (Spaak have evenness, composition Ecologists and or structure richness services. - However, ecosystem biodiversity maintaining of 2002). effects may aspects for its Shiva they to effective water; and (Perkins compared be over ways beneficiary processes non-additive can the wars ecosystem in by (e.g. ecosystems individual defined of generations are multifunctionality on is and affect services can but communities These biodiversity cycling), nature. individuals, because nutrient considering cultural across when (decomposition, a convincing differ services of is regulating thus services This or with services. fish), obvious ecosystem of less and capture, provision function (resource (e.g. ecosystem function services of ecosystem provisioning levels overall complexity (Cardinale higher to inherent cycling) imply correlated the nutrient thus positively decomposition, of is production, because traits) biomass functional challenging species, is 2017). genes, service Johnson Durance in model & and to (e.g. Klaise function that biodiversity (e.g. apparent ecosystem including, of coherence become trophic to 2017), therefore, into the biodiversity Johnson has, replicate fall Linking It to circular & network) 2017). or needs (Klaise directed direct Jones one less any & properties complexity along web (Johnson in food-web than which feedback food rather to nodes other of predators, degree extent the many apex the the to directionality: (or underlies crucially, overall species web of coherence basal measure from Trophic food web, a paths. as a food regarded the (Johnson in up be coherence” flows also species biomass can “trophic the It of levels. which trophic degree to complexity well-defined and sufficient extent size increasing a the with stable have captures more complexity networks become can and the interval models size ecosystem the if that increasing shown within with been recently cannot species unstable has they It all more yet Dom´ınguez-Garc´ıa become consumes proposed; interactions; of webs been it interval of subsequently food continuous that density have artificial a model such (i.e. since, niche along point, the paradox, position (Williams lower of May’s a model refinements chosen solve acyclic to Several niche randomly species the outside. (e.g. a assign recently, none models at to and More various axis centred with 1985). one-dimensional axis, webs Newman a the uses & food 2000) (Cohen of Martinez model features & cascade essential the the through capture feature networks some to nature. be attempted extinctions must in the have biodiversity to there effect Scientists in that (i.e. leading this argued complexity fluctuations counters processes he and which any size feedback Hence, structure then runaway that communities. graphs, food-web cause destabilise assumed random of to were to is was more tend webs likely nature and it should food connectivity) less if larger in – and that, be the observe is showed would May that we it species assumed 1966). as stable (Paine given widely biodiversity more a been such the of had of ecosystem, biomass it existence an then very Until interconnected the densely that 1972). and/or showed (May paradoxical charismatic May underpinning seventies mathematically of in biodiversity 1). early subset biodiversity for (Fig. the a ES of hold In to on role people links focused the its that often and latter, values biodiversity efforts the the of conservation In reflect (Smith and not species processes. ignored, threatened does ecosystem is but in processes implicit role ecosystem is functional (Mace role respectively its functional perspective’, beyond ‘conservation a the former, and the perspective’ services ‘ecosystem the 06 eetydsrbdteiprac feprclmnplto fevrnetldiest etrlink better to drivers environmental of manipulation empirical of importance the described recently 2016) tal. et tal. et 02.Nihro hs esetvscpue h opeiyaddynamics and complexity the captures perspectives these of Neither 2012). tal. et 06.Goigeiec hw htbooia iest tevariation (the diversity biological that shows evidence Growing 2016). 07 otiuet csse ucin.D ane D Laender (De Laender De functions. ecosystem to contribute - 2017) tal. et tal. et 2020). tal. et 05,peevto ficesdvlmso biodiversity of volumes increased of preservation 2015), 3 2019). tal. et tal. et 09.Ceia olto,together pollution, Chemical 2019). 02.Hge eeso biodiversity of levels Higher 2012). tal. et 04.Tohccoherence Trophic 2014). tal. et 02.In 2012). et Posted on Authorea 28 Sep 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160133650.04034920 — This a preprint and has not been peer reviewed. Data may be preliminary. oia n aedmgahcdnmc fpriua pce,adifrso pce xicin (Hambrecht extinctions species ( on DNA informs and ancient species, Sedimentary particular 2017). of dynamics paleodemographic and organisms, logical living to (Rossel ( applied material be DNA input only ‘Ancient of can quantity it and and match, (Ashfaq quality percent seawater high on based as requiring species such investigate matrices to identities environmental assign (White in 2018). dust biodiversity Arbizu settling & microbial and 2019) advanced (Rossel on studies the pollutants fingerprinting, across applied chemical proteome comparable have of on spectra studies mass recent based resolution example, specimens high For identify of advantage ionisation-time can desorption the (MALDI-TOF-MS) laser providing matrix-assisted (e.g. spectrometry The matrices environmental 2014). flight-mass fossil Marchant in by of & remains explained well-preserved (Gillson with be bones) changes, groups exoskeletons, may taxonomic use pollen, which to limited land understood and poorly of throughput low are impacts ecosystems the aquatic ( sediment analysis in examine remains contaminants dated that of in (Korosi effects reconstructions contaminants profiles past environmental other contaminant improve and historical Worm stressors to & and environmental (Lotze cores, loads, data nutrient ice archeo records, and and paleo pollen and and 2019). assemblages, documents paleolimnology (Damgaard species historical ignored in are are Studies data level longitudinal site 2009). studied of the the of source at properties valuable dynamics requires equilibrium A environments, temporal the as static time- of the short realizations assumes same that different over clines) it and the to occur (e.g. because due process, are studied are problematic succession ecological time dynamics patterns is same the in spatial it observed when turnover the However, the and biological 2013). that of 2018), Trexler control Wang stages & that & different (Wogan (Banet drivers space at frames the in be when operate to useful that between drivers assumed is relationships are This the 2019). that scar- studying (Damgaard sites for space-for-time-substitutions, at compensate use To variables often lifespans. ecological human researchers beyond understand data, often better longitudinal and to ce projects, (Nogues-Bravo impact research patterns environmental beyond current commitments major requires to predating observations leading which (Balint baseline at dynamics decades) scales to multiple long-term the access least between and provide year) (at relations 1 also place most take cause-effect ta (at observations processes model empirical biological of to underlying timescale the the and longi- between gap patterns Therefore, the bridging present-day time. mics (Baert over explain change to environmental occur and change paramount biodiversity environmental are and data dynamics, tudinal biodiversity data processes, longitudinal continuous Ecological traveling: time of promise The (Nogues-Bravo years (Bonebrake expe- many evolutionary over scales the operating temporal outside processes are and from threats spatial when (Brook different adaptation occurs outpace on commonly or most species extinction (Doherty of e.g. predators rience invasive - of adaptation nutrient impacts local increases the (Alexander invertebrates of exacerbate pressure freshwater can grazing on modification reducing changes habitat impact by community e.g. insecticides blooms or effects algal e.g. species (toxic) synergistic – of by interactions that 68% pollution biotic to knowing affect up considered, and they be 50 cause least to of at has impact for (Jackson threats responsible detrimental Cardinale are multiple rain; the threats acid of understand environmental (e.g. among effect to scenarios combined efforts (Backhaus pollution simple the triggered loss beyond Ultimately, biodiversity has biodiversity techniques, of on causes analytical pollutants chemical main in advances the with of ned one as identified been tal. et 06.Hwvr nesadn idvriyrsosst utpetrasi hlegn be- challenging is threats multiple to responses biodiversity understanding However, 2016). aDNA morphotaxonomy o ) rvdsdee eeec aeie,ealstercntuto fpaleoeco- of reconstruction the enables baselines, reference deeper provides 1)’ Box , tal. et seda 09.Hwvr hstcnqerle nicmlt eeec irre to libraries reference incomplete on relies technique this However, 2019). paleoecotoxicology eurn pcaitsil eg ih irsoyadtxnm) being taxonomy), and microscopy light (e.g. skills specialist requiring ) DNA tal. et tal. et o )hsbe sda neegn olt reconstruct to tool emerging an as used been has 1) Box ; 06.Te r aubefrsuyn idvriydyna- biodiversity studying for valuable are They 2016). 08.Mroe,cue fboiest oscnoperate can loss biodiversity of causes Moreover, 2008). 4 tal. et MALDI-TOF-MS tal. et tal. et 09 n r l otx-eedn outcomes context-dependent all are and 2019) aeue utpelnso vdne uhas such evidence, of lines multiple used have tal. et 07.Yt ersetv hrceiainof characterization retrospective Yet, 2017). tal. et 2018). 2019). 08.Hwvr olcigteedata these collecting However, 2018). tal. et tal. et ehiu osuyteimpact the study to technique 02.Ti vdnecombi- evidence This 2012). 03;eooia rd-ff – trade-offs ecological 2013); 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All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160133650.04034920 — This a preprint and has not been peer reviewed. Data may be preliminary. faitceoytmcnrl,sc spitsuc olto n xrm yr eerlgcleet (Bern- events meteorological hydro extreme and moments pollution hot source and point hotspots as in- environmentalhardt including such the controls, of and dynamics, with ecosystem and distributions of, abiotic behaviour scales, non-linear distribution of ecosystem heterogeneous the temporal non-linear strongly resulting and and includes and connectivity properties spatial This the across controlling species. between, related properties teractions landscape inherently physical are of resilience heterogeneity ecosystem and Biodiversity constraints analytical and matters Scale Moing pesticides; (e.g. enables pollution pollutants environmental environmental by to With sediment disrupted (Chang 2020). pathways exposed dated possible functional species 2019). on the now keystone Raine of profiling are of investigations & metabolomics dynamic metabolomics (Franklin by pollutants’ and loss complemented with of spectrometry biodiversity approach, Together reconstructions of mass predictors. driver longitudinal resolution as main samples, high variables the in climate is developments large-scale recent pollution with chemical change, models climate impact resolutions biodiversity with of regions formulation many for Cornes covered al. are (e.g. et decades records few station last gridded (Meyer- gridded Navarro the 1901 with Global However, after km areas data. 1-20 2013). land these of (IPCC for by available provided kilometres are not hundred 50km is about few of most a resolution For of spatial Christoffer 20 a scales data. the with of climatic spatial records beginning are on precipitation the environ- data observations since and environmental temperature station loss of long-term gridded evolution biodiversity complete temporal between the most relationships areas, the land cause-effect Possibly, change the environmental Pedersen variables. establish of (e.g. mental to assessment years continuous potential of the retrieval enormous thousands with the offers and data for biodiversity hundreds limits longitudinal to continuous tens temporal Coupling from the ranging ( back scales matrices pushed environments at environmental have undisturbed out different DNA, or carried from input past ‘dirty-DNA’ low recent so-called and the of quality to low technology (e.g. this with skills of satisfactorily (Willerslev taxonomic use permafrost the non-specialist in limiting with preserved biodiversity, and the costs; of affordable tion at real-time Traditionally, near morphotaxonomy). in species (Domaizon endangered (Pace time and through identified interactions be species-species to and networks have management Furthermore, ecosystem 2018). for Hebert critical ‘regime-shifts’ al. of et warning early feasible, not (Balint (e.g. biodiversity scales in (Ashfaq time decline longer ( biodiversity on DNA and occurring Environmental pesticides) trends (e.g. of pollutants one specific the between from relationship change. blooms) biodiversity (Jiguet algal temperature) long-term toxic average (Orsini waves, of evolution heat drivers and droughts, structure the (e.g. ecosystem (Hofman concerning migration, continuity, scales hypotheses al. population et or change test understanding environmental seeds, help explicitly with data bones, to coupled These as needed times such evolutionary are retrospective remains, spanning records for data persistent systems longitudinal leave lake biodiversity of that Continuous communities groups algal taxonomic and microbial of pollen, (Tse of assessment trends environmental and occurrence past the 03 Nogues-Bravo 2013; 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All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160133650.04034920 — This a preprint and has not been peer reviewed. Data may be preliminary. rmpitn odgae csses hoigsdmnayacie htsa tlatacnuyallow century a least at span that archives flows sedimentary matter Choosing archives and biological energy ecosystems. food-web studying degraded and space complexity to system and pristine between time relationship from the through explains changes that abiotic framework and processes biotic natural Tracking oversimplify (Orsini impact change change environmental dynamics. environmental of and and measures biodiversity state-of-the-art between However, interactions context-dependent needed. is the of understanding deep A fingerprinting Biochemical (Fig. 1. scenarios Step climate of future understanding under our services revolutionize ecosystem we to and following, biodiversity potential the of the In forecasts provide 2). has Targets. the and Biodiversity that and sites, Aichi framework dynamics between the past and cross-disciplinary of by within novel ambition flows hindered a the and is prioritization propose addressing stocks for climates dynamics revitalise outcomes, future complex capital, cultural of and natural positive understanding present restore deep the to a provide interventions under that of services approaches system-level ecosystem of and lack the services biodiversity ecosystem of and resilience biodiversity The resilient project for to framework variability or cross-disciplinary 2018). climate A Widmann 20km, & regional-scale around Maraun and of see large-scale review resolutions past a spatial (for between which with climate of links in methods, large-scale (RCMs) use downscaling effects changes future models by that radiative regional alleviated climate models partly the uncertainties, regional be statistical atmospheric by small simulated physically-based can the challenges relatively dominated either in These with is biases use 2010). to simulated Woollings temperature due uncertainties be (e.g. mean substantial circulation climate thus carry global resolved can future can the precipitation in generate and 100 on and change temperature changes to gases climatic (approx. the used regional greenhouse resolution are Whilst projecting the spatial in that difficulties limited (GCMs) scales. the the Models and spatial are 2018), Circulation (IPCC. projections General projections biodiversity state-of-the-art change for the challenges of major km) the of Some forecasts. function (Shyamalecosystem management Laurin farm-specific (Vaglio for monitoring (Bengio abstraction, data pollution higher-level low-level and a observed are biodiversity in the applications information in RL feature hidden of the factors amples summarise explanatory can underlying and the data unveiling raw richness. from species detection and feature factors for environmental between relations cause-effect learning establish resentation (Schuwirth to outputs used environ- be and and can features they biological between as relationships such quantitative data, establishing non-replicated and (Willcock a data heterogeneous in within mental (DDM), complex, data modelling interrogate Data-driven heterogeneous to constraints. these capabilities disciplinary of of fingerprinting because analyses biochemical applied the particular continuous not for often in are advances limitations framework the methodological single of matrices, scales. many environmental temporal function, lifted of and and have spatial structure advances different ecosystem technological at monitoring While for disturbance potential ecosystem the of of have impact consequences pollution), the air (longer-term) revealing an larger-scale flooding (e.g. the (Krause humans interventions reflect predictive fact, and adequately in (Mao networks or to (LCSNs) monitoring disturbances ability research moment) and (hot the operational hotspot hampering for challenge often a models, been has behaviour ecosystem tal. et ut-iwlearning multi-view 03 Nogues-Bravo 2013; tal. et 09,wihlreyfcso eetn n iiaigeet ihdrc maton impact direct with events mitigating and detecting on focus largely which 2019), tal. et R)culdwt ahn erig(o )dtrie h ersnain needed representations the determines 2) (Box learning machine with coupled (RL) 08.Teeapoce dniyadrn nu aibe ae ncuaiy by causality, on based variables input rank and identify approaches These 2018). admforest random and tal. et nepeal ahn learning machine interpretable tal. et 08 nbe st a h onainfraknowledge-based a for foundation the lay to us enables 2018) and 00.A motn etse st ikboiest and biodiversity link to is step next important An 2020). eplearning deep 6 tal. et 04,adi eoeysne aaapplications data sensed remotely in and 2014), nhprpcrliaigrcgiinfor recognition imaging hyperspectral in tal. et Bx2 aeepne analytical expanded have 2) (Box 05.Lwcs esrnetworks sensor Low-cost 2015). tal. et 09.Frexample, For 2019). tal. et 03.Ex- 2013). Rep- Posted on Authorea 28 Sep 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160133650.04034920 — This a preprint and has not been peer reviewed. Data may be preliminary. h edakbtenboi n boi neatosadseiseouinr oeta r o accounted not are potential evolutionary species and interactions abiotic and scenarios biotic climate future between feedback under services The ecosystem the and of biodiversity Forecasting 2014). function Fattorini a 3. & be Step (Strona might network properties relationships the other symbiotic of interactions of layer of existence other mutualism kinds in the the occupy other instance, of For species For such nestedness ecosystems. levels account. expected trophic similar into mean of taken probability the coherence be the against trophic weighed can example, the be For can and associations. 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Posted on Authorea 28 Sep 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160133650.04034920 — This a preprint and has not been peer reviewed. Data may be preliminary. eplann od rmssfrpeitv oeatn;w rps h s fBysa irrhclmixed hierarchical Bayesian of use the propose we forecasting; ecosystems. and sediments/aquatic predictive polluted model for with effects promises associated will holds trajectories environment changing risk learning a of Deep in understanding exposure our pollutants’ further to to web adaptation help food in patterns on from micro-evolutionary ‘learn’ information with (Fordham to trends multidecadal forecasts guide dynamics model threats, for and to sets or assemblages training changes resource powerful biodiversity a environmental provide unprecedented feedbacks future eco-evolutionary (Urban an by and architecture (Pace caused resilience are biodiversity that biodiversity in archives from trends of biological differ and patterns estimates from future inaccurate about collected predictions to data leading longitudinal models, Continuous forecasting in for eaddt h bevdlcltmeaue dlacag prah aan&Wdan21) The importance, 2018). feature Widmann local and & the confidence Maraun local in of approach; do measures change to can using signal straightforward (delta assessed change be is temperatures climate can which simulated local models the the observed biases, of the model performance to local Forests potential simulations added Random reduce climate be To the regional in scenarios. state-of-the-art used climate from different be projections can feedbacks temperature Jacob data eco-evolutionary (EURO-CORDEX; with and longitudinal together measures fluxes empirical include (iRF) energy that framework using architecture, dynamics from (Nogues-Bravo web biodiversity-ecosystem hindcasting, dynamics past in food by present-day data, of explained empirical predict validated against as to validated be and constructed archives then optimized be biological can should of from screening predictions models collected the Predictive forecasts these from model 2). changes; captured optimized iteratively ecosystems past (Fig. which the natural validated against scaling c) of be data the state and longitudinal can on degraded robustness; empirical dynamics to provides ecosystem coherence multidecadal archives, pristine for by trophic biological provided from groups food-web Information transition functional stability. of the of ecosystem information during effect on importance with feedbacks the the archives eco-evolutionary a) of b) biological effects biodiversity; the including: with from causality, stability obtained Granger of data as biodiversity such in from Fallani interactions. 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Data may be preliminary. ilpoieipoe rdciecpct o sesn h ieiodo o-iereoytmrsossto responses ecosystem non-linear of behaviour likelihood ecosystem the (long-term) scale assessing scale large for on large capacity dynamics and predictive process trends improved term) long-term long-term provide (short of scale and will importance small scale (Krause the of conditions large ecosystem assess importance onto impacts local the to duration the and as short regional predictions reflecting well onto of reliable changes adequately as moments system making biodiversity for hot for and opportunities and capacity stability spa- new hotspots our ecosystem unprecedented small-scale provides in of at This changes (network) implications conditions step sensor and behaviour. environmental causes global ecosystem resolution heterogeneous the non-linear and observe by of scales (Mao to released globally temporal ability being and experiencing our opportunities are tial the we in that strategies. utilizing change mitigation technologies of resulting sig- and sensing scope interventions can environmental patterns of is in and prioritisation there revolution dynamics to dif- analyse respect, comes we in ecosystems. this it which ecosystems resilient at when In achieve of scale making to the resilience decision interventions and change abiotic the units nificantly and isolated forecasting biotic not in are target understanding and ecosystems enables in However, interactions This state-of-the-art abiotic the scenarios. advancing and climate significantly ferent biotic controls of ecosystem between potential of feedback the dynamics the has and framework scale developed of The importance the systems: Interconnected 5. Step recreational and provisioning of loss economic with associated the loss using assess economic communities to to quantified. the up of example, be used projections For can livelihood IPCC be using future. services the can scenarios, the future and models in under years communities ecology ecological-economic 100 local by ecosystem Coupled on driven degradation the biodiversity system service. of altered simplified impact provisioning have a natural a with may in as assemblage species complexity fish fishing invasive native restore using an a to by replacing gained action whether e.g. can are proactive assessing insights we by specific of Important communities framework, on as so. fish archives our doing well biological for from in as arguments collected valuation inaction non-monetary data alongside non-monetary of ES, benefits and and and ecosystems monetary costs with such the modelling tools, quantify valuation ecological and of coupling making 2014). importance By decision Kelman the participatory & of (Shreve requires recognition analysis also multi-criteria values recent as (Kenter broader rather assessments these service the ecosystem of handling reflects into consideration and value which that conventional of multi-dimensionality studies, suggest of the valuation We combination incorporating of (Kenter a 2019). proportion approaches requires participatory Primmer modest ecosystems service ecosystem and of & an value (Paavola deliberative of multi-dimensional novel ecosystems use the restoring future understanding (Dallimer of and insurance chance and studies such conserving the ecosystems valuation having intact for insurance: in of of natural support considered values value non-use providing quasi-option full been as thus and the seldom such and option capture have stresses goods as not environmental and well may value as of shocks has Monetary values, against it facets as often 2001). resilience all ES Bergh values maintaining for of den non- in appropriate social valuation van and be shared monetary & Therefore, not (Nunes and monetary values may models. ecosystems combining practiced economic of approach conventional traditionally perceptions of An been suggestions people’s non-monetary the and because ecosystems. from monetary needed differ both of of is restoration use arguments making and monetary by conservation challenges states; these unknown for address (Turner and arguments can changes timescales new propose into non-marginal with we systems of framework is align the kinds The tipping challenge not these future, handle Another do the cannot which in valuation ecosystems 2006). economic timescales, of conventional Faulkner unspecified resilience (Dallimer & the or decision-making undermine (Ullah short, economic also years over and 13 estimated political after often of removal are nitrate values for that denitrification of level oeua prtoa aooi units taxonomic operational molecular 9 tal. et tal. et 00.Ciaeadevrnetlcag can change environmental and Climate 2020). tal. et 05.Telte prahssilfr a form still approaches latter The 2015). 00.Yt hycudpoiefurther provide could they Yet, 2020). tal. et 05.Utmtl,understanding Ultimately, 2015). tal. et tal. et tal. et MTs e.g. (MOTUs) 09.The 2019). 05.The 2015). 2003). 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Data may be preliminary. imnhmfrteatoki iue1adWlimSaoe ete tdo S o h rwr nFigure in of artwork the University for the USA at Studio, Kestrel Sciences Scavone, Environmental William and and 1 Earth Figure Geography, in artwork of the School for Birmingham Jackson, Chantal thank We nutrient mitigation, climate (e.g. Acknowledgments services regulating effect and cascading water) positive clean a food, with functions (e.g. targeted integrity ecosystem cycling). the provisioning ecosystem of improves and e.g., delivery biodiversity impact, ES sustained on ecosystem to enables on effects of tools and Resilience adverse magnifiers and ecosystems severe MOTUs, resilience. with knowledge entire of and factors provides loss benefit environmental the framework which of Mitigating The interventions regulation provide. bottom-up provision). they through services food sustainably the (e.g. capital (e.g. change environmental ES natural by data. driven alter manage MOTUs) finds climate that of it and loss because pollution) bio-chemical conditions, both (e.g. warming, environmental long-term dynamics of and community on ecosystems time recapitulate all forecasts that through to model signatures applicable dynamics novel is complex framework ‘train’ predictive machine the novel to combines for This time, interactions account first abiotic the that Decision- and for networks weighting biotic framework, evidence functional practice. proposed environmental objective with The on for algorithms priorities. based learning tools socio-economic mechanisms outdated as intervention well in prioritize hinders as results tools to ecological state-of-the-art and needed and on are practice, academia practitioners frameworks between into and making divide stakeholders science for The of training ES. translation of and lack the biodiversity the of scales as problem spatio-temporal well wicked different as and so-called practitioners interactions constrained the process tools address capture and to to Approaches unable necessary with well-being. been mismanagement human have to and boundaries led economy discipline often the by has environment, services the ecosystem for underpin dis-benefits that clear processes the of understanding of Lack remarks Concluding manage- and practice environmental of for accessibility solutions The practical into non-specialists. research to ment. cutting-edge the accessible translate and made to dynamics be together past should more of resilience identifying dis- the interpretation ES help of both for and will number framework biodiversity ecosystems representative analytical of undisturbed a the forecast and of Similarly, baselines e.g. properties pristine approaches bioindicators. biochemical to monitoring informative of compared traditional as screening complement ecosystems initial likely throughput turbed will the high Ideally, a toolkit the into This microscopy. for packaged light protocols screening. are that evidence- routine require whole-system ecosystems in to will natural implement 1995) framework of novel Whitton & the fingerprinting 2014) Kelly Marchant of biochemical (TDI; & uptake Index (Gillson The Diatom remains interventions. Trophic fossil based the of as taxonomic ecosys- such (morpho bioindicators, natural bioindicator-based that or from using of interventions shift restoration approach mitigation a ecological and system-level of practice the accelerating favour This prioritization feedback, will units the interventions and taxonomic These tems. complexity enable of stability. natural will ecosystem abundance of for approach relative responsible re-establishment This and units taxonomic shifts operational name. compositional preserve Linnaean of a analysis the without information, allowing by phylogenetic changes, units, Using biotic core taxonomic and changes. of abiotic abiotic and abundance) between functions links relative tem cause-effect and the (presence uncover dynamics to enable quantifying will framework proposed management The and practice environmental for Implications (Dakos behaviour threshold and points tipping including interactions multi-stressor wetlab n the and rdcietoolkit predictive a nyb civdi cdmc n rciinr okclosely work practitioners and academics if achieved be only can MOTUs 10 eurstemdriaino urn environmental current of modernization the requires MOTUs MOTUs elbtoolkit wetlab htaeascae ihecosys- with associated are that a epae nooperational into placed be can tal. et htpattoescan practitioners that 2019). Posted on Authorea 28 Sep 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160133650.04034920 — This a preprint and has not been peer reviewed. Data may be preliminary. ro,BW,Sdi ..&Basa,CJA 20) yege mn xicindiesudrglobal under drivers extinction among Synergies Integrating (2008). (2019). C.J.A. L.A. Bradshaw, Ashton, & & N.S. change. R.L. Sodhi, Kitching, B.W., Conservation. D.M., for Brook, Biodiversity Baker, to C., Threats Dingle, Horizon and F., Proximal Guo, T.C., M. monitoring. Knapp, Bonebrake, biodiversity S., and Control Creer, biology G.R., (2017). wildlife Carvalho, E.C. M.T., for Concept. Moment , DNA Gilbert, Hot A., Seybold Evans, Spot & K., Hot K.E. Bohmann, the Kaiser, Beyond M.L., Moving Fork, Ecosystems: C.D., reference in Ficken, Defining Points J.R., Blaszczak, (2011). synthesis. E.S., a T.A. 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All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160133650.04034920 — This a preprint and has not been peer reviewed. Data may be preliminary. hti hyrqielresml sizes. sample and large metabarcoding, require DNA they via is biomonitoring that prediction, data feature (Cios the genomics genomic on discovery, population biomarker assumptions for parametric biology limited and in with clustering, features Primary and under- input tion 2015). from to Mitchell ”learn” need & to statistics the (Jordan manages traditional and it to samples) challenges because ‘imperfect’ heterogeneous significant data, of pose and volumes dynamics values, Large Conversely, biological missing 2019). approaches. underpinning Jackson replication, mechanisms & low the (Xu stand research (e.g. biology current data approaches in analytical empirical whereas bottleneck limiting, the longer no become is have generation data biochemical advances, technological With biology in learning Machine 2 Box (Bohmann the patterns enable biodiversity spans temporal time and centennial spatial or predict and detecting multidecadal to al. of processes on ecosystems et opportunities ecosystem-level entire studies novel of Longitudinal explore and survey to stability unprecedented taxa. ecosystem ability difficult-to-sample longitudinal of improved or to points rare applied an tipping eDNA including identifying markers limita- diversity, potential, multiple analytical community exist, enormous and databases degradation has reference eDNA samples sequencing with incomplete assigned associated with incorrectly challenges in or associated genes technical organisms unassigned tions and Although of to taxa lead and 2013). ecosystems, can skills some (Kvist assignment of taxonomic reads reconstruct taxonomic representation specialist for to poor used of the choice databases However, need of reference the 2014). method Marchant without & the communities (Gillson present approaches becomes remains design. and eDNA-based eDNA past probe As approaches, of detection multi-marker (metagenomics). dynamics using for to sequencing by interest single-marker fashion eDNA of throughput from total high species move a or the in (metabarcoding) obtained of markers be genetics multiple can the data can biodiversity of community species knowledge whole single Semi-quantitative, requires of it However, identification Quantitative cation. time. and ( (Garlap- over aeDNA PCR time quantitative aDNA, biodiversity real targeted in in using scale changes achieved global be assess a to on species/taxa and of baselines census the enabling by ati (Hofman science biodiversity biodiversity on and impact 2019) human throughput estimating high al. by and genomics biology et with conservation combined informs macrofossils technologies and sequencing matrices (Tse environmental assessment from environmental extracted retrospective DNA for waters inland of al. to communities et microbial potential of ( (Hofman the occurrence DNA scales) has past ancient seeds, eDNA Sedimentary (bones, sites 2014). 2018). archaeological ( Marchant Hebert in or DNA & ( traces ancient DNA’ DNA last (Cristescu visible to palaeoenvironmental ‘Environmental behind 2000s the opposed as leave the In as that to of isms data referred beginning outcome. temporal is the continuous throughput matrices at provide (Balint low environmental momentum emerged the gained from term have and extracted this monitoring techniques biodiversity DNA large-scale survey non-standardized for 2018). some expertise, methods taxonomic DNA-based of specialist years, nature limited few stages, invasive life the juvenile iden- or sampling, correct with species associated (cryptic) difficulties of to due tification problematic are and techniques monitoring aeDNA, biodiversity Traditional eDNA aDNA, maze: DNA The tal. et 2014). 2018). 05,ivso ilg yietfigtmn n eeiyo le pce nain(Ruppert invasion species alien of severity and timing identifying by biology invasion 2015), 09.I diin DApoie otnostmoa aata r e oietfigtemporal identifying to key are that data temporal continuous provides eDNA addition, In 2019). sed DA ti o-ot/ihacrc prah utbefrsnl pce identifi- species single for suitable approach, accuracy low-costs/high a is It aDNA. 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