Population  modeling  using  harpacticoid  copepods  

Bridging  the  gap  between  individual-‐level  effects  and  protection  goals  of  environmental  risk  assessment  

   

 Elin  Lundström  Belleza  

           

         

Doctoral  thesis  in  Applied  Environmental  Science  Department  of  Applied  Environmental  Science  (ITM)  

Stockholm  University  2014  

                 

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Doctoral  Thesis,  2014  Elin  Lundström  Belleza  Department  of  Applied  Environmental  Science  (ITM)  Stockholm  University  SE-‐106  91  Stockholm,  Sweden  

                                                   ©Elin  Lundström  Belleza,  Stockholm  2014  ISBN,  978-‐91-‐7447-‐894-‐5  pp.  1-‐36  Printed  in  Sweden  by  Universitetsservice  US-‐AB,  Stockholm,  2014  Distributor:  Department  of  Applied  Environmental  Science  (ITM)  Cover  by  Gian  Carlo  Belleza,  including  artwork  by  Göte  Göransson.  

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 To  Carlo,  Wellington  and  Winter  Alba  

                                           

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Abstract    

To  protect  the  environment  from  contaminants,  environmental  risk  assessment  (ERA)  evaluates  the   risk   of   adverse   effects   to   populations,   communities   and   ecosystems.   Environmental  management   decisions   rely   on   ERAs,   which   commonly   are   based   on   a   few   endpoints   at   the  individual  organism  level.  To  bridge  the  gap  between  what  is  measured  and  what  is  intended  for  protection,  individual-‐level  effects  can  be  integrated  in  population  models,  and  translated  to  the  population   level.   The   general   aim   of   this   doctoral   thesis   was   to   extrapolate   individual-‐level  effects   of   harpacticoid   copepods   to   the   population   level   by   developing   and   using   population  models.  Matrix  models  and  individual  based  models  were  developed  and  applied  to  life-‐history  data  of  Nitocra  spinipes  and  Amphiascus  tenuiremis,  and  demographic  equations  were  used  to  calculate   population-‐level   effects   in   low-‐   and   high-‐density   populations.   As   a   basis   for   the  population  models,  individual-‐level  processes  were  studied.  Development  was  found  to  be  more  sensitive   compared   to   reproduction   in   standard   ecotoxicity   tests   measuring   life-‐history   data.  Additional   experimental   animals   would   improve   statistical   power   for   reproductive   endpoints,  but  at  high  labor  and  cost.  Therefore,  a  new  test-‐design  was  developed  in  this  thesis.  Exposing  animals  in  groups  included  a  higher  number  of  animals  without  increased  workload.  The  number  of  reproducing  females  was  increased,  and  the  statistical  power  of  reproduction  was  improved.  Individual-‐level  effects  were  more  or  equally  sensitive  compared  to  population-‐level  effects,  and  individual-‐level  effects  were  translated  to  the  population  level  to  various  degrees  by  population  models   of   different   complexities.   More   complex   models   showed   stronger   effects   at   the  population   level   compared   to   the   simpler   models.   Density   dependence   affected   N.   spinipes  populations  negatively  so  that  toxicant  effects  were  stronger  at  higher  population  densities.  The  tools  presented  here   can  be  used   to  assess   the   toxicity  of  environmental   contaminants  at   the  individual   and   population   level,   improve   ERA,   and   thereby   the   basis   for   environmental  management.    

                                       

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Svensk  sammanfattning    För   att   skydda   miljön   från   föroreningar   utvärderar   miljöriskbedömningar   risken   för   negativa  effekter   på   populationer,   samhällen   och   ekosystem.   Riskhantering   är   beroende   av  miljöriskbedömningar,  vilka  ofta  är  baserade  på  ett  fåtal  mätvärden  på  individorganismnivå.  För  att  överbrygga  klyftan  mellan  det  som  mäts  och  vad  som  är  avsett  att  skyddas,  kan  effekter  på  individnivå   integreras   i   populationsmodeller,   och   översättas   till   populationseffekter.   Det  övergripande   syftet   med   denna   avhandling   var   att   extrapolera   effekter   på   individnivå   från  harpacticoida   copepoder   till   populationsnivå   genom   att   utveckla   och   använda  populationsmodeller.  Matrismodeller  och  individbaserade  modeller  utvecklades  och  tillämpades  på  livshistoriedata  för  Nitocra  spinipes  och  Amphiascus  tenuiremis,  och  demografiska  ekvationer  användes  för  att  beräkna  effekter  på  populationsnivå  i  låg  -‐  och  högdensitetspopulationer.  Som  underlag   för   populationsmodellerna   studerades  processer  på   individnivå.  Utveckling   visade   sig  vara  känsligare  än  reproduktion  i  standardiserade  ekotoxicitetstester  som  mäter  livshistoriedata.  Ytterligare   försöksdjur   skulle   förbättra  den   statistiska  känsligheten   för   reproduktion,  men  med  ökad   arbetsinsats   och   kostnad   som   följd.   Därför   utvecklades   en   ny   testdesign   i   denna  avhandling.  Exponering  av  försöksdjur  i  grupper  gjorde  det  möjligt  att  inkludera  ett  större  antal  djur   utan   ökad   arbetsbörda,   och   en   statistiska   känsligheten   för   reproduktion   förbättrades.  Effekter  på   individnivå  var  mer  eller   lika  känsliga   i   jämförelse  med  effekter  på  populationsnivå,  och  översattes  till  populationsnivå  i  olika  grad  av  populationsmodeller  av  olika  komplexitet.  Mer  komplexa   modeller   visade   starkare   effekter   på   populationsnivå   jämfört   med   de   enklare  modellerna.   Densitetsberoende   påverkade   populationer   av   N.   spinipes,   så   att   de   toxiska  effekterna   var   starkare   vid   högre   populationsdensitet.   De   verktyg   som   presenteras   i   denna  avhandling   kan   användas   för   att   bedöma   toxiciteten   av  miljöföroreningar   på   populationsnivå,  förbättra  miljöriskbedömningar,  och  därmed  grunden  för  riskhantering.  

                                 

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List  of  papers    Paper  I  Lundström,   E.;   Björlenius,   B.;   Brinkmann,   M.;   Hollert,   H.;   Persson,   J-‐O.;   Breitholtz,   M.  Comparison   of   six   sewage   effluents   treated  with   different   treatment   technologies-‐   Population  level   responses   in   the  harpacticoid  copepod  Nitocra  spinipes.  Aquatic  Toxicology.  2010,  96   (4),  298-‐307;  DOI  10.1016/j.aquatox.2009.11.011    Paper  II  Preuss,  T.G.;  Brinkmann,  M.;   Lundström,  E.;  Bengtsson,  B-‐E.;  Breitholtz,  M.  An  individual-‐based  modeling  approach  for  evaluation  of  endpoint  sensitivity  in  harpacticoid  copepod  life-‐cycle  tests  and  optimization  of   test  design.  Environmental  Toxicology  and  Chemistry.  2011,  30   (10),  2353-‐2362;  DOI  10.1002/etc.614    Paper  III  Lundström   Belleza,   E.;   Brinkmann,   M.;   Preuss,   T.G.;   Breitholtz,   M.   Population-‐level   effects   in  Amphiascus   tenuiremis:   Contrasting   simple   and   complex   population   models.   Submitted   to  Aquatic  Toxicology.      Paper  IV  Lundström  Belleza,  E.;  Breitholtz,  M.  Density-‐toxicant  interactions  and  reproductive  responses  in  Nitocra  spinipes.  Manuscript.  

Statement    I  made  the  following  contributions  to  the  papers  presented  here:    Paper  I  I  took  the  lead  role  in  planning  and  carrying  out  the  ecotoxicity  tests.  Experiments  were  carried  out   together  with  one  of   the   co-‐authors   and   technicians.   I   took   the   lead   role   in   analyzing   the  data  and  constructing  the  matrix  model,  and  I  took  a  large  part  in  simulating  in  the  model.  I  took  the  lead  role  in  writing  the  paper.    Paper  II  I   took  a  major  part   in  data  synthesis   for  the  model.  Co-‐  authors  programmed  and  simulated   in  the  individual  based  model.  I  took  a  minor  part  of  writing  the  paper.    Paper  III  I   took   the   lead  role   in  data  synthesis   for   the  models,  and  also   took   the   lead  role   in   the  matrix  model  simulations.  Co-‐authors  programmed  and  simulated  in  the  individual  based  model.  I  took  the  lead  role  in  writing  the  paper.    Paper  IV  I   took  the   lead  role   in  planning  and  performing  the  ecotoxicity  tests.  Experiments  were  carried  out  together  with  technicians.  I  took  the  lead  role  in  analyzing  the  data  and  writing  the  paper.  

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Contents  Abstract  ...............................................................................................................................................  5  Svensk  sammanfattning  ................................................................................................................  6  List  of  papers  .....................................................................................................................................  7  Statement  ...........................................................................................................................................  7  Abbreviations  ...................................................................................................................................  9  1.  Introduction  ...............................................................................................................................  10  2.  Aim  and  hypotheses  of  the  thesis  .......................................................................................  11  3.  Background  ................................................................................................................................  11  3.1  Environmental  risk  assessment  (ERA)  .....................................................................................  11  3.2  Test  methods  and  organisms  .......................................................................................................  12  3.2.1  Ecotoxicity  tests  ...........................................................................................................................................  12  3.2.2  Individual-‐level  endpoints  ......................................................................................................................  12  3.2.3  Harpacticoid  copepods  .............................................................................................................................  13  

3.3  Population  models  ..........................................................................................................................  14  3.3.1  Unstructured  models  .................................................................................................................................  15  3.3.2  Biologically  structured  models  .............................................................................................................  15  3.3.3  Individual  based  models  (IBMs)  ...........................................................................................................  15  3.3.4  Population-‐level  endpoints  .....................................................................................................................  16  

3.4  ERA  and  density  dependence  .......................................................................................................  17  4.  Material  and  Methods  .............................................................................................................  19  4.1  Test  organisms  .................................................................................................................................  19  4.2  Test  substances  ................................................................................................................................  19  4.3  Test  methods  .....................................................................................................................................  19  4.3.1  Cohort  experiments  ...................................................................................................................................  19  4.3.2  Time-‐series  experiments  .........................................................................................................................  20  4.3.3  Population  models  ......................................................................................................................................  20  4.3.4  Measure  of  adverse  effects  ......................................................................................................................  22  

5.  Results  and  Discussion  ...........................................................................................................  23  5.1  Model  development  ........................................................................................................................  23  5.2  Contrasting  individual-‐  and  population-‐level  effects  ..........................................................  24  5.3  Statistical  power  and  replicates  for  reproductive  endpoints  ...........................................  25  5.4  Contrasting  simple  and  complex  modeling  approaches  ....................................................  26  5.5  Contrasting  effects  in  low-‐  and  high-‐density  populations  .................................................  27  

6.  Conclusions  ................................................................................................................................  29  7.  Future  perspectives  ................................................................................................................  29  Acknowledgement  –  Tack!  ........................................................................................................  30  References  ......................................................................................................................................  32          

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Abbreviations    λ   Lambda,  finite  rate  of  increase,  population  growth  rate  

A   Adult  stage  

CI   Copepodite  stage  one  

CV   Copepodite  stage  five  

EFSA   European  Food  Safety  Authority  

EC10   Effect  Concentration  at  10  %  

ERA   Environmental  (ecological)  Risk  Assessment  

NI   Naupliar  stage  one  

NOEC   No  Observed  Effect  Concentration  

NVI   Naupliar  stage  six  

IBM   Individual  Based  Model  

LOEC   Lowest  Observed  Effect  Concentration  

LTRE   Life-‐Table  Response  Experiment  

MM   Matrix  Model  

PCB   PolyChlorinated  Biphenyls  

PEC   Predicted  Environmental  Concentration  

PNEC   Predicted  No  Effect  Concentration  

r   Intrinsic/instantaneous  rate  of  increase,  population  growth  rate  

               

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1.  Introduction    A  vast  number  of  anthropogenic  substances  are  used  in  society  today.  As  an  example,  there  are  more   than   143   000   industrial   chemicals   pre-‐registered   for   commercial   use   in   the   European  Union   (ECHA,   2014).   To   protect   the   environment   from   adverse   effects   of   environmental  pollutants,   environmental   risk   assessment   (ERA)   is   used   as   a   tool   for   protecting   populations,  communities  and  ecosystems  (e.g.  European  Commission,  2003;  EMEA,  2006;  van  Leeuwen  and  Vermeire,   2007).   The   effects   of   environmental   pollutants   are   commonly   estimated   from   the  results   of   standard   laboratory   (eco)toxicity   tests.   In   order   to   detect   adverse   effects   in   these  tests,  it  is  important  that  the  measured  endpoints  have  high  statistical  power  and  that  endpoints  are  sensitive.  Using  many  replicates  or  test  concentrations  commonly  increases  statistical  power,  but   at   increased   labor  and   cost.   Ecotoxicity   tests   are  often  performed  on   individually   exposed  animals,  and  the  effects  are  measured  on  for  example  survival,  development  and  reproduction.  In  ERA,  it   is  therefore  assumed  that  data  from  simple  ecotoxicity  tests  can  be  used  to  estimate  risk  for  the  ecological  entities   intended  for  protection  (e.g.  Forbes  et  al.,  2001).   In  this  context,  population  models  are  useful  since  they  can  bridge  the  gap  between  what  is  measured  and  what  is  intended  for  protection  (e.g.  Barnthouse  et  al.,  2008;  Forbes  et  al.,  2008),  and  can  be  used  to  reduce   uncertainty   in   extrapolation   of   (standard)   test   results   to   ecologically   relevant   effects  (Forbes  et  al.,  2011).  There   is  a  range  of  population  models  available   in  the  scientific   literature  that  has  been  used  to  assess  the  risk  of  chemicals  to  many  different  organisms  (e.g.  Pastorok  et  al.,  2002;  Akcakaya  et  al.,  2008).  Even  though  the  most  sensitive   individual-‐level  endpoints  are  likely  to  be  equally  or  more  sensitive  to  stressors,  such  as  environmental  pollutants,  than  effects  on  the  population  level  (Forbes  and  Calow,  2002),  the  relationship  is  sometime  reversed  (Forbes  and   Calow,   1999).   Moreover,   effects   that   are   measured   on   isolated   individuals,   at   low  population   density,   ignore   density   dependence,   which   in   natural   populations   may   affect   the  responses  (e.g.  Forbes  et  al.,  2001).  Experiments  carried  out  at  low  densities  may  underestimate  population  stress  responses  compared  to  high-‐density  populations  due  to  the  lack  of  interaction  between  density  and  toxicity  (e.g.  Sibly,  1999),  or  overestimate  the  effects  due  to  compensation  in   high-‐density   populations   (Forbes   et   al.,   2001).   Models   for   calculating   concentrations   of  pollutants   in   the  environment  have   long  been  used   in  directives   relating   to   risk  assessment  of  chemicals   (e.g.   Hommen   et   al.,   2010).   Ecological   models,   including   population   models,   are  however  lagging  behind,  and  stakeholders  name  e.g.  the  lack  of  guidance  on  how  to  choose  and  use  population  models  as  a  reason  why  they  are  not  put  to  practice  in  ERA  (Hunka  et  al.,  2013).  Contrasting  population  models  of  differing  complexity  may  aid   risk  assessors   in   choosing  what  population   model   to   use   (Meli   et   al.,   2014).   In   the   last   years,   population   models   have   been  included   in   several   directives   and   their   related   guidance   documents   (e.g.   EFSA,   2009,   2010;  SCENIHR  2012;  EFSA,  2013,  2014),  bringing  on  the  new  era  in  ERA.                    

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2.  Aim  and  hypotheses  of  the  thesis    The  overall  aim  of  this  doctoral  thesis  was  to  extrapolate  individual-‐level  ecotoxicological  effects  to  the  population  level  by  developing  and  using  population  models  for  harpacticoid  copepods.      The  hypotheses  were  that:    

• Individual-‐   and  population-‐level   effects   are   found   in   the   same   concentration   range   for  copepods  exposed  to  single  substances  and  mixtures  (papers,  I,  III  and  IV).  

 • The  number  of  replicate  animals  can  be  increased  without  a  higher  workload  by  grouping  

of   animals,   which   will   in   turn   increase   fertilization   success   and   statistical   power   of  reproductive  endpoints  (papers  II  and  IV).    

 • Simple  stage-‐based  matrix  population  models  do  not  translate  individual-‐level  effects  on  

development   time   to   the   population   level,   to   the   same   degree   as   individual   based  population  models  (paper  III).    

 • Toxic   effects   in   harpacticoid   copepods   are   negatively   influenced   by   population   density  

(paper  IV).      

 

3.  Background    

3.1  Environmental  risk  assessment  (ERA)  To  protect  the  environment,  risk  management  decisions  are  based  on  environmental  (ecological)  risk   assessment   (ERA).   ERA   is   the  process   for   evaluating   the   risk   that   the   environment  will   be  impacted  as  a  result  of  exposure  to  environmental  pollutants.  ERA  is  normally  a  tiered  process  that  in  lower  tiers  focuses  on  “worst-‐case”  scenarios,  and,  for  substances  that  initially  resulted  in  unacceptable   adverse   effects,   proceeds   to   more   realistic   assessments   at   higher   tiers   (e.g.  European   Commission,   2002;   van   Leeuwen   and   Vermeire,   2007).   Environmental   fate   and  exposure  of  environmental  pollutants  are  often  estimated  using  models,  or  when  available,  on  environmental   measurements   (e.g.   European   Commission,   2003).   Effects   of   environmental  pollutants,  on  the  other  hand,  are  estimated  from  the  results  of  laboratory  (eco)toxicity  tests  or  sometimes   on   mesocosm   or   field   studies.   Standard   test   data   from   the   laboratory   are   still  preferred  and  recommended  for  ERA  (e.g.  European  Commission  2002;  2003),  even  though  non-‐standard   data   could   improve   the   scientific   basis   by   providing   relevant   and   more   sensitive  endpoints   (Ågerstrand   et   al.,   2013).   Standard   tests   are   performed   using   established   and  validated   protocols,   which   is   why   they   are   considered  more   reliable   than   non-‐standard   data.  However,   different   evaluation   protocols   for   peer-‐reviewed   data   exist,   and   reporting   data   in   a  sufficiently  detailed  manner  would  facilitate  the  use  of  non-‐standard  data  for  ERA  (Ågerstrand  et  al.,  2013).  To  estimate  the  risk  for  the  environment,  risk-‐quotients  are  used.  They  consist  of  the  predicted  environmental  concentration  (PEC)  of  a  substance,  divided  by  the  predicted  no-‐effect  concentration   (PNEC),   which   is   based   on   ecotoxicological   tests.   To   reflect   uncertainties   (e.g.  intra-‐   and   inter-‐species   variations,   the   extrapolation   from   short-‐term   toxicity   to   long-‐term  toxicity  and  the  extrapolation  of  laboratory  test  results  towards  the  field),  PNECs  are  combined  

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with  uncertainty   factors   (OECD,  2011a;   ECHA,  2012).   Commonly,   the  most   sensitive  endpoints  derived  from  ecotoxicological  testing  are  used  for  the  assessment  (European  Commission,  2002;  2003).   In  ERA  it   is  therefore  assumed  that  data  on  direct  effects  (on  e.g.  survival,  development  and  reproduction)  in  simple  toxicity  tests  (combined  with  uncertainty  factors)  reflect  effects  on  the  population  level  and  can  be  used  to  protect  populations,  communities  and  ecosystems  (e.g.  Forbes   et   al.,   2001).   To   bridge   the   gap   between   test-‐endpoints   performed   on   individual  organisms  and  the  ecological  entities  intended  to  be  protected  by  ERA,  population  models  have  an   important   role   to   play   (Forbes   et   al.,   2008;   EFSA,   2010).   Population   models   integrate  potentially   complex   interactions   among   life-‐history   traits,   such   as  mortality,   development   and  reproduction.   In   this  way,   they   include   ecological   complexity,   and   can   reduce   uncertainties   in  extrapolation   of   individual-‐level   test   endpoints   to   ecologically   relevant   impacts   (Forbes   et   al.,  2011).  Ignoring  endpoints  above  the  individual-‐level  often  leads  to  an  overestimation  of  risk,  but  sometimes  to  underestimations  (Forbes  and  Calow,  1999).  Using  population  models  in  ERA  could  therefore   lead   to   distributing   resources   better   and   more   efficiently   in   environmental   risk  management   (e.g.   Pastorok   et   al.,   2002;   Barnthouse   et   al.,   2008).   There   is   currently   no  regulatory   framework   for   ERA  based  on   ecological  modeling,   but   suggestions   on  how   such   an  approach  could  be  structured  are  given  in  e.g.  Pastorok  et  al.  (2002)  and  Wentzel  et  al.  (2008).    Population  models  are  however  more  and  more  mentioned  in  European  directives  and  guidance  documents   on   ERA   (e.g.   EFSA   2009,   2010;   SCENIHR   2012;   EFSA   2013),   and   there   is   also   a  guidance  document  on  good  modeling  practice  for  risk  assessment  of  plant  protection  products  (EFSA,  2014).        

3.2  Test  methods  and  organisms    

3.2.1  Ecotoxicity  tests  Ecotoxicity  tests  study  the  effects  of  single  toxicants  or  mixtures  (stressors)  on  organisms.  There  are   a   large   variety   of   test   methods,   and   tests   can   be   performed   on   different   levels   of  organization,   from  subcellular  through   individual  organisms  to  populations.  Acute  toxicity  tests  are  short  tests  (hours  or  days)  that  generally  measure  lethality  as  a  response.  Concentrations  of  test  substance  are  usually  higher  in  acute  tests  compared  to  chronic  tests.  Chronic  (or  long-‐term  tests)   generally   cover   a   significant   fraction   of   the   life   cycle   (weeks,   months   or   years),   and  concentrations  of  test  substance  are  lower  in  order  to  measure  sub-‐lethal  endpoints.  Life-‐table  response  experiments   (LTREs)  are  often  used   in   chronic   testing,  and  commonly   follow  animals  from   newborn   until   they   reproduce   (Caswell,   2001).   Ecotoxicity   tests   can   also   be   performed  directly  on  populations  (e.g.  Sibly,  1999),  or  communities  in  e.g.  mesocosm  studies  or  in  the  field  (European  Commission,  2002).      

3.2.2  Individual-‐level  endpoints  Organism  attributes  commonly  used  as  endpoints  in  ERA  include  life-‐history  rates,  which  are  the  rates   of   birth,   growth,   development,   fertility   and   mortality,   and   describe   the   movement   of  individuals   through   the   life   cycle   (Caswell,   2001).   LTREs   measure   how   single   toxicants   or  mixtures   affect   life-‐history   rates.   Other   individual-‐level   endpoints   are   e.g.   body   size   and  physiological   characteristics   such  as   respiration,   food   intake  and  metabolic   rate   (Menzie  et  al.,  2008).    

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3.2.3  Harpacticoid  copepods  Invertebrates   account   for   approximately   95   %   of   all   known   species   on   Earth   (Wilson,   1999).  Crustaceans  are  the  second  largest  invertebrate  subphylum  after  insects,  including  some  35.000  classified   species.   Harpacticoid   copepods   are   a   subclass   of   crustaceans,   comprising   3.000  species.   They  usually  make  up   the   second  most   abundant   group  of   animals   in  marine  benthic  communities  (Huys  et  al.,  1996),  and  are  a  primary  food  source  for  juvenile  fish  (Hicks  and  Coull,  1983).  N.   spinipes   is   a   harpacticoid   copepod,  widely   distributed   in   shallow  waters   around   the  world  (Lang,  1948).  It  acclimatizes  to  fluctuations  in  salinity  (0-‐30  %0)  and  temperature  (0-‐26  0C)  (Noodt,   1970;   Wulff,   1972),   and   can   therefore   be   used   for   testing   of   various   environmental  conditions.  N.  spinipes   is  well   suited   for   long-‐term  (chronic)  ecotoxicity   testing  since   it   is   small  (adults  <  1  mm   long,  Abraham  and  Gopalan,  1975),   reaches  sexual  maturity   in  10-‐12  days  and  completes   a   life   cycle   in   16-‐18   days   at   20   0C   (Dahl,   2008).   N.   spinipes   molts   and   sheds   an  exoskeleton   between   each   developmental   stage.   It   has   six   naupliar   stages   (NI   to  NVI)   and   six  copepodite  stages  (CI  to  CV  +  A;  the  reproducing  adult  stage)  (Figure  1).  Between  stages  NVI  and  CI   the   animals   complete   a   metamorphosis   with   profound   changes   to   body   shape   and  segmentation.   Amphiascus   tenuiremis.   (e.g.   Chandler   et   al.,   2004)   is   another   harpacticoid  copepod,  closely  related  to  N.  spinipes.  As  opposed  to  the  more  commonly  used  test  organism,  the   water   flea   Daphnia   magna   that   reproduces   asexually,   N.   spinipes   and   A.   tenuiremis   are  sexually   reproducing  species.  This   introduces  ecological   relevance   into  ecotoxicity   testing  since  both  sexes  are  present,  and  introduced  stressors  can  potentially  affect  reproductive  behavior.      

   Figure   1:  N.   spinipes,   six  naupliar   stages  and  ovigerous   female.   Illustrations  by  Göte  Göransson,  modified  by  Gian  Carlo  Belleza.  

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3.3  Population  models  The  overall  purpose  of  population  models  is  to  evaluate  the  ecological  significance  of  observed  or  estimated  effects  on  individual  organisms  (e.g.  Pastorok  et  al.,  2002).  There  is  a  broad  range  of   population   models   that   have   been   applied   to   address   ecotoxicological   problems   (e.g.  Schmolke  et  al.,  2010),  even  though  they  are  not  commonly  used  in  ERA.  Population  models  are  however   used   for   decision-‐making   in   conservation   ecology   and   fisheries-‐management   (EFSA,  2010;  SCENIHR,  2012).  Figure  2  (modified  from  Munns  et  al.,  2008)  describes  the  three  classes  of  models   dealt   with   in   this   thesis;   unstructured,   biologically   structured   and   individual   based  models   (IBMs).   Several   other   types   of   population  models   also   exist,   including  metapopulation  models  that  consider  many  subpopulations  of  a  species  that  interact  through  migrations  (Munns  et  al.,  2008).  Spatially  explicit  models  focus  on  the  environment  that  a  species  inhabits,  and  the  variability  therein  (Munns  et  al.,  2008).  These  models  are  demographic  and  study  e.g.  the  size,  structure   and   distribution   of   populations.   Dynamic   energy   budget   models   describe   how  individuals  in  different  classes  assimilate  energy  from  food  and  use  it  for  maintenance,  growth,  reproduction,  and  development  (Nisbet  et  al.,  2000).        

 Figure  2:  Taxonomy  of  population  models  for  population-‐level  ERA.  Modified  from  Munns  et  al.  (2008).      Models  are  simplifications  of  real  systems,  and  it  is  important  to  understand  their  limitations  and  applicability.  Specificity  and  testability  of  predictions  from  simple  models  is   low  (Topping  et  al.,  2005),  which  makes  it  difficult  to  define  how  well  they  describe  the  system  they  are  supposed  to  simulate.  Depending  on  the  research  question,  and  the  available  data,  simple  models  can  still  be  useful   (Topping   et   al.,   2005).   Predictions   from   complex   models   have   higher   specificity   and  testability,   and   can   often   be   tested   to   make   sure   they   are   realistic   enough   to   meet   their  intended  purpose  (Augusiak  et  al.,  2014;  EFSA,  2014).      

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3.3.1  Unstructured  models  The  simplest  form  for  assessing  population-‐level  risk  is  by  using  unstructured  models.  Individuals  within  the  population  are  treated  identically  in  terms  of  their  life  history  rates  which  usually  only  consists   of   births   and   deaths,   measured   as   population   size   (Munns   et   al.,   2008).   Data   for  unstructured  models  can  be  obtained  from  time-‐series  experiments   in  which  population  size   is  sampled   over   time   (Sibly,   1999;   Moe,   2008).   These   models   can   be   either   deterministic   (no  randomness)   or   include   stochasticity.   Stochastic   models   are   founded   on   the   properties   of  probability   so   that   given   input   produces   a   range   of   possible   outcomes   due   to   random  effects  (Pastorok  et  al.,  2002).  Unstructured  models  can  also  either  assume  exponential  growth,  which  is   defined   as   density-‐independent   growth   under   no   limitation   of   resources,   or   include  environmental   carrying  capacity.  The  concept  of  carrying  capacity  describes   the  maximum  size  (density)  of  a  population  that  the  environment  they  live  in  can  sustain  (Pastorok  et  al.,  2002).  At  the  environmental  carrying  capacity,  density-‐dependent  processes  will  affect  births  and  deaths  in   the  population   (Moe  et  al.,   2008).  Unstructured  models   can  also  be   continuous  or  discrete,  where   time   is   treated   incrementally.   These   kinds   of   models   have   been   used   to   investigate  adverse   effects   of   chemicals.   As   an   example,   Hendriks   and   Enserink   (1996)   investigated   the  change  in  abundance  of  D.  magna  populations  in  response  to  polychlorinated  biphenyls  (PCBs).  These  models  are  very  generalized  and  have  low  data  requirements,  why  they  are  useful  mostly  in  lower  tiers  (screening)  of  ERA  (Munns  et  al.,  2008).    

3.3.2  Biologically  structured  models  Biologically  structured  models  divide  individuals  within  the  population  into  distinct  classes,  and  incorporate   biological   structure   by   assigning   those   classes   with   life-‐history   rates   of   mortality,  development   and   reproduction   (Munns   et   al.,   2008).   Classes   can  be   identified  on   the  basis   of  age,   developmental   stage   or   size.   Biologically   structured   models   are   commonly   matriarchal,  meaning  that  once  in  the  reproductive  state,  only  females  are   included  since  they  are  the  only  ones  contributing  to  population  growth  in  future  generations  (Caswell  et  al.,  2001).  Life-‐history  rates  from  different  age-‐  or  stage  classes  are  commonly  obtained  from  LTREs  where  animals  are  exposed  to  control   treatments  and   to  stressors   (e.g.  chemicals).  Biologically   structured  models  are   often   density-‐independent   and   deterministic,   but   can   include   density   dependence   (e.g.  Grant  1998)  as  well   as  environmental   (e.g.  Hamda  et  al.,   2014)  and  demographic   stochasticity  Munns  et  al.,  2008).  One  of  the  most  commonly  used  biologically  structured  models  is  the  Euler-‐Lotka   equation   (e.g.   Calow   and   Sibly,   1990;   Sibly,   1999),   which   has   been   used   to   investigate  contaminant  effects  on  population  growth   rate.  A   few  examples   include  effects  on  population  growth  rate  in  D.  magna  from  titanium  oxide  nanoparticles  (Jacobasch  et  al.,  2014)  and  effects  of  synthetic  musks  for  N.  spinipes  (Breitholtz  et  al.,  2003).  Matrix  models  (MMs)  have  been  used  for   studying   the   effects   of   increasing   environmental   copper   concentrations   to   the   earthworm  Lumbricus   rubellus   (Klok   and   de   Roos,   1996),   and   in   A.   tenuiremis   for   studying   population  consequences   of   the   insecticide   fipronil   (Chandler   et   al.,   2004)   and   crude   oil   (Bejarano   et   al.,  2006).  Biologically  structured  models  can  be  almost  as  general  as  unstructured  models,  or  more  specific   depending   on   the   research   question   addressed   and   relevant   detailing   regarding   e.g.  environmental   and   demographic   stochasticity   and   density   dependence.   These   models   can  therefore  be  used  for  screening  as  well  as  higher-‐tiers  of  ERA  (Munns  et  al.,  2008).    

3.3.3  Individual  based  models  (IBMs)  IBMs,   also   called   agent   based   models,   focus   on   the   individual   as   the   basic   element   of  populations.   These   models   track   the   characteristics   of   each   individual   (all   sexes   and   stages)  through   time   and   assume   that   individuals   can   differ   with   respect   to   their   behavioral   and  physiological   responses   to   the   environment   (Munns   et   al.,   2008;   SCENIHR,   2012).   Individual  

16    

variability   is   key   in   IBMs,   and   different   individuals   have   different   probabilities   of   e.g.   survival,  growth   and   reproduction   (Munns   et   al.,   2008).   IBMs   are   mechanistic   in   their   nature   and  implement  simple  behavioral  rules  that  give  rise  to  complex  behavior.   In  this  way,  effects  from  e.g.   toxicant   stress   on   physiological   processes   and   individual   behavior   are   modeled.   In   more  aggregated  models,   such  as  unstructured  and  biologically   structured  models,   these  effects  are  indirectly  measured  as  effects  on  e.g.  survival  and  reproduction  (Munns  et  al.,  2008).  Individual  variability   is   often  modeled  as  probability  distributions   from  which   individual   events   and   their  realizations  are  drawn  (Munns  et  al.,  2008).   IBMs  can  have  specific  assumptions  related  to  the  life  cycle  of  the  species  being  modeled,  which  can  result  in  high  specificity  and  realism  (Munns  et  al.,  2008).   IBMs  have  been  produced  for  a   large  variety  of  organisms,  both  animals  and  plants,  and  Grimm   (1999)   reviews   some   50   IBMs   for   animal   populations   alone.   IBMs   can   range   from  spatially  uniform  such  as  the  IBM  for  D.  magna  (Preuss  et  al.,  2009)  or  to  spatially  explicit  such  as  the   IBM   for   Skylark   (Topping   et   al.,   2005).   IBMs   have   a   broad   range   of   applications   and   have  been   used   to   study   e.g.   how   soil   contamination   of   different   spatial   heterogeneity   affects  population  dynamics  of  soil  invertebrates  (Meli  et  al.,  2013),  population-‐level  effects  of  PCBs  on  largemouth  bass  (Micropterus  sulmoides)  (Jaworska  et  al.,  1997),  and  to  predict  the  population  capacity   and   extinction   probability   of   D.   magna   exposed   to   3.4-‐dichloroaniline   at   laboratory  conditions   (Preuss  et   al.,   2010).   IBMs  are  well   suited   for  higher-‐tier   ERA  because  of   their  high  level  of  ecological   realism  and   their   flexibility   to   include  e.g.   various  environmental   conditions  (Forbes  et  al.,  2011).  

3.3.4  Population-‐level  endpoints  There  are  several  important  population  attributes  that  can  be  measured  and  used  as  endpoints  in  ERA   (Menzie  et  al.,  2008).  Population  abundance   is   the   size  of   the  population,  measured  as  the  number  of  individuals  or  the  biomass  of  the  population.  Population  density  is  a  related  term,  which   describes   the   size   of   the   population   per   unit   of   habitat   (area   or   volume).   Population  growth  rate  is  generally  thought  of  as  the  key  intervening  variable  linking  individual  level  effects  to   effects   on   populations   (e.g.   Calow   et   al.,   1997;   Caswell,   2001),   and   integrates   effects   on  survival,   development   and   reproduction,   (Forbes   and   Calow,   1999).   Population   growth   rate   is  best  expressed  on  a  per  capita  basis,  and  there  are  two  ways   in  which  population  growth  rate  can  be  reported.  Expressed  as  the  finite  rate  of  increase  (λ)  the  population  growth  rate  describes  how  much   the  population  has  potential   to  grow  or   shrink   in   the  next   time   step.  Multiplying  λ  with   the   population   size   projects   the   population   size   in   the   next   time   step.   In   practice   λ   >   1  indicates   a   growing   population,   λ   <   1   a   shrinking   population   and   λ   =   1   a   stable   population.  Expressed  as  the  intrinsic  or  instantaneous  rate  of  increase  (r)  population  growth  rate  describes  the  potential  of  the  population,  in  each  instant,  to  contribute  to  how  much  the  population  grows  or  shrinks.  In  practice  r  >  0  indicates  a  growing  population,  r  <  0  a  shrinking  population  and  r  =  0  a   stable  population.  These   two  measures  of  population  growth  are   related  so   that   the  natural  logarithm  of  λ  is  r  (!"# = !; !! =  !)  (Sibly,  1999;  Menzie  et  al.,  2008).    In  this  thesis,  λ  and  r  are  both   called   the   population   growth   rate,   and   distinguished   by   their   symbols   when   necessary.  Population   structure   is   commonly   the   distribution   of   individuals   with   respect   to   age   or  developmental   stages,   sex,   reproductive   status  and   so  on.  Population  dynamics   describes  how  population   structure   varies   over   time.   Population   structure   can   both   influence   and   be   an  indicator   of   the   dynamics   of   the   population   since   life-‐history   rates   such   as   survival   and  reproduction  often  vary  across   the   life  cycle   (Menzie  et  al.,  2008).  Other  population  attributes  could   be   related   to,   for   example,   extinction   and   recovery   of   populations   or   their   spatial  distribution.      

17    

3.4  ERA  and  density  dependence  Standard   tests   used   for   ERA   are   commonly   performed   on   isolated   individuals   in   low-‐density  populations   (Sibly,   1999),   and   in   ERA,   the   concept   of   density   dependence   offers   some  challenges.   Density   dependence   is   a   fundamental   concept   in   population   biology,   affecting   the  responses  of  most  animals  and  plant  species  (Moe,  2008).  Crowding  is  a  concept  that  can  create  density-‐dependent   effects   in   populations   (e.g.   Gergs   et   al.,   2014),   especially   at   laboratory  conditions.  Crowding  can  lead  to  decreased  feeding  rate  at  higher  animal  density,  due  to  inter-‐individual  interactions.  Most  natural  populations  are  likely  to  be  in  steady  state  (i.e.  not  growing  or  declining)  whereas  populations  used  in  toxicity  testing  often  grow  exponentially  (Forbes  et  al.,  2001).  An  important  problem  for  ERA  is,  therefore,  that  populations  under  density  dependence  (high-‐density   populations)   may   respond   differently   to   toxicants   than   those   growing  exponentially   (low-‐density   populations)   (Forbes   et   al.,   2001;   Forbes   and   Calow,   2002).   Sibly  (1999)   suggests   that  high-‐density  populations  are   likely   to  be  more   sensitive   to   toxicants   than  low-‐density  populations,  because  of  generally   lower  fitness,  due  to   increased  competition  over  resources.   Forbes   et   al.   (2001),   on   the  other   hand,   suggest   that   compensation   in   high-‐density  populations   could  make   the  populations   less   sensitive   to   toxicants.  Compensation   is   a  process  where  density  reductions  caused  by   increasing  toxicant  concentrations  would  be  compensated  by  an  amelioration  of  density-‐dependent  effects.  Interactions  between  density  dependence  and  toxic  stress  can  be  broadly  categorized  in  antagonistic  (effects  of  the  toxicant  is  weaker  at  higher  population   densities),   additive   (toxicant   effects   are   not   affected   by   density)   and   synergistic  (effects  of  the  toxicant  are  stronger  at  higher  densities)  (Forbes  et  al.,  2001;  Moe,  2008)  (Figure  3).    

18    

 Figure  3:  Possible  interactions  between  density-‐dependent  effects  and  toxicant  exposure  on  population  growth  rate.  Low  and  High  refers  to  populations  of  low  and  high  densities,  respectively.  The  slopes  of  the  “low”  curves  are  held  straight  since  they  represent  no  density  dependence  but  only  toxicant  effect.  Modified  from  Forbes  et  al.  (2001).    The  interactions  between  density  dependence  and  toxicity  are  not  straightforward,  and  may  be  affected  by,  for  example,  the  initial  age-‐or  stage  structure  of  the  population  (Stark  and  Banken,  1999),  and  whether  populations  are  growing  or  declining  (Forbes  and  Calow,  1999).  Since  there  are   so   many   factors   affecting   the   density-‐toxicant   interactions,   they   are   difficult   to   foresee,  which  is  why  experimental  approaches  have  to  be  taken  (Forbes  et  al.,  2001).    

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19    

4.  Material  and  Methods    

4.1  Test  organisms  Harpacticoid  copepods  are  well  suited  for  long-‐term  testing  due  to  their  small  size  and  relatively  short  life  cycle  (Dahl,  2008).  They  are  also  relevant  test-‐species  since  they  are  abundant  in  many  different  ecosystems  around  the  world  (Lang,  1948).  In  all  studies,  the  harpacticoid  copepods  N.  spinipes  (ecotoxicity  tests  in  papers  I  and  IV,  modeling  in  paper  II)  and  A.  tenuiremis  (modeling  in  paper   III)  were  used.  Culturing  conditions  and  handling  of  N.  spinipes  as  laboratory  animals  has  been   published   elsewhere   (e.g.   Breitholtz   and   Bengtsson,   2001;   Breitholtz   et   al.,   2003).   A.  tenuiremis  as  a  test  species  has  been  described  in  e.g.  Coull  and  Chandler  (1992)  and  Chandler  et  al.   (2004).   In   all   laboratory   tests   performed   for   this   thesis,   the   animals  were   fed  with   the   red  microalga  Rhodomonas   salina.   Test  medium  was  natural   brackish  water   filtered   through  0.03-‐mm,  pre-‐heated  to  80  °C  and  GF/C  (glass  microfiber)-‐filtered.      

4.2  Test  substances  In  paper  I  municipal  sewage  effluent  from  Henriksdal  sewage  treatment  plant  in  Stockholm  was  tested  on  N.  spinipes.  The  sewage  was   treated  with  conventional  and  novel  sewage  treatment  technologies  aimed  at  removing  pharmaceuticals.    The   IBM   for  N.   spinipes   in  paper   II   was   developed   using   control   data   from  paper   I,   an  OECD  validation  report  (OECD,  2007),  and  Dahl  and  Breitholtz  (2008).      The   IBM  for  A.  tenuiremis   in  paper   III  was  developed  using  control  data  on  A.  tenuiremis   from  Chandler  et  al.  (2004)  and  an  OECD  validation  report  (OECD,  2011b).  Effects  from  lindane  (OECD,  2011b)  were  simulated  in  the  model.  Lindane  is  a  gamma-‐hexachlorocyclohexane  used  mainly  as  an  insecticide.      In  paper   IV,   lindane  was  used  as  model  substance  and  tested  on  N.  spinipes  since  it  previously  showed  clear  effects  in  harpacticoid  copepods  (e.g.  Dahl  and  Breitholtz,  2008).    

 

4.3  Test  methods  Two   main   types   of   experiments   were   performed   for   this   thesis;   cohort   and   time-‐series  experiments.    

4.3.1  Cohort  experiments  Cohort  data   is  obtained   from  experiments   that   follow  even-‐aged  groups  of  organisms   through  (parts   of)   their   life   cycle   (Moe,   2008).   LTREs   and   life-‐cycle   tests   are   two   terms   often   used   to  describe   experiments   that   produce   cohort   data,   and   they   are   performed   on   low-‐density  populations   (commonly   isolated   individuals)   (Sibly,   1999).   Two   different   kinds   of   cohort  experiments  were  performed  in  this  thesis:        In   paper   I,   newborn   nauplii   (NI)   were   isolated   in   wells   in   96-‐well   micro   plates,   and   their  development  to  the  first  copepodite  stage  (CI),  and  adulthood  (A)  was  recorded  as  the  number  of  days  the  development  took.  Force-‐mating  pairs  of  one  male  and  one  female  were  constructed  in  24-‐well  micro  plates,  and  the  number  of  offspring  and  the  fertilization  success  of  mating  pairs  

20    

were  recorded.  Observations  were  performed  on  a  daily  basis  and  also  endpoints  such  as  time  to  mating  and  time  between  clutches  of  viable  offspring  were  recorded.  Mortality  was  pooled  from  the   life  stages  NI   to  CI,   from  CI   to  A  and  for  parent  animals,  as  well  as  over  all   life-‐stages.  The  same  test  method,  based  on  the  “Harpacticoid  Copepod  Development  and  Reproduction  Test  for  Amphiascus  tenuiremis”  (OECD,  2013)  was  also  used  to  obtain  the  data  modeled  in  papers  II  and  III.  Here,  this  test  is  termed  LTRE.      In  paper  IV,  a  low-‐density  LTRE  was  started  by  rearing  N.  spinipes  in  groups  of  6  on  24-‐well  micro  plates.   Population   density   was   32   mm2/animal   and   given   as   per   area   since   N.   spinipes   are  bottom-‐dwellers.   Development   and   survival   from   NI   to   CI   was   closely   monitored,   and   when  animals   reached   the   first   copepodite   stage,   they   were   isolated   on   96-‐well   micro   plates,   and  development  and  survival  was  observed  until  animals  reached  the  adult  stage.  Reproduction  was  studied   separately:   Newborn   nauplii   were   reared   in   groups   of   24   on   6-‐well   micro   plates  (population  density  was  40  mm2/animal)  until  ovigerous  females  (females  with  egg-‐sacks)  were  discovered.  Ovigerous  females  were  then  isolated  on  24-‐well  micro  plates  and  the  time  to  first  reproduction,  number  of  offspring,  fertilization  success  and  survival  was  recorded.  Here,  this  test  is  termed  separated  LTRE.    

4.3.2  Time-‐series  experiments  Time-‐series   data   is   obtained   from   experiments   on   whole   populations   that   are   followed  (preferably)   through   several   generations   (Moe,   2008).   Time-‐series   experiments   are   commonly  performed  at  high  population-‐density  (Sibly,  1999).  In  paper  IV,  populations  were  started  with  3  individuals  from  each  developmental  stage  (nauplii,  copepodites,  males,  females),  12  individuals  in   total.   Populations   were   kept   in   20   ml   glass   vials     with   an   initial   population   density   of   42  mm2/animal.  As   the  number  of   individuals   in   each  experimental   unit  was   growing,   population  density   increased,  and   the  mean  population  density  over   the   test-‐period   for  all   replicates  was  9.3  mm2/animal.  The  number  of  animals  in  each  life  stage  was  recorded  once  a  week  for  seven  weeks.  Here,  this  test  is  termed  population  test.    

 4.3.3  Population  models  Four  types  of  population  models,   ranging  from  simple  to  complex,  were  used   in  this   thesis.  All  models  used  assumed  exponential  growth,  i.e.  included  no  density  dependence,  or  limitation  of  resources  such  as  food.    Unstructured  model  From  the  population   test   (paper   IV),  population  size  was  sampled  over   time  and  deterministic  population  growth  rate  was  calculated  by  applying  an  equation  for  exponential  growth  (1),    

! ! = ! 0 !!"       (1)    where   r   =   population   growth   rate,  N   =   population   size,   t   =   time.   The  natural   logarithm  of   the  population  size  was  plotted  against  time.  The  slope  of  the  regression  was  r,  and  λ  was  calculated  using:  r  =  lnλ.  Endpoints  obtained  from  the  equation  only  included  population  growth  rate.            

21    

Biologically  structured  models    Equations  In  paper  IV,  life-‐history  rates  of  survival,  development  and  reproductive  output  were  used  in  an  equation  for  calculating  deterministic  relative  finite  rate  of  increase  (population  growth  rate)  (2),      

! = 1/(!!!!)!/!!       (2)    where  λ  =  relative  population  growth  rate,   lt  =  survival  probability  from  NI  to  A  (mean  for  each  population),  bt  =  reproductive  output,  which  is  the  product  of  sr  (sex  ratio,  50  %),  fs  (fertilization  success,   mean   for   each   population)   and   n   (mean   number   of   nauplii   over   two   clutches,   per  female),  t  =  time  to  first  reproduction  in  days  (mean  for  each  population).  In  the  traditional  Euler  Lotka  model  (3),      

1 = Σ!!!!!!!             (3)    lx   is   the   probability   of   surviving   to   age   x,  mx   is   the   age-‐specific   fecundity,   and   x   is   the   time  between  reproductive  events.  Population  growth  rate  was  termed  “relative”  in  equation  2  since  x  was  exchanged  for  time  to  first  reproduction  t.  Using  the  Euler-‐Lotka  equation  routinely  in  ERA  for   a   sexually   reproducing   species   such   as   N.   spinipes   would   be   very   expensive   and   time-‐consuming  (Breitholtz  et  al.,  2003).  Endpoints  obtained  from  the  equation  only  included  relative  population  growth  rate.  

 MMs  Stage-‐based  MMs   (Lefkovitch  MM)  were   used   for   the   copepods   in  papers   I   and   III.   The  MMs  include   life  stage  transitions   (the  proportions  of  animals  at   the  start  of   the  test   that  survive  to  and  reach,  each  development  stage)  and  fecundity.  Stochastic  matrixes  were  generated  from  the  distributions  defined  by   the   test  data.  Population-‐level  endpoints  were  calculated  using  matrix  algebra  by  multiplying  a  vector  with  the  MM  (4),      

!!!!!!!!! !

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                                                                                       (4)  

       matrix                                  vector    where   N   represents   the   number   of   individuals   in   a   certain   stage   class   and   P   the   life   stage  transition  rate.  Indices  represent  the  different  stage  classes:  n,  nauplius;  c,  copepodite;  f,  female;  fo,  ovigerous  female,  F   represents  the  number  of  offspring  per  female.  Population  growth  rate  was  obtained  as  λ.  The  MMs  were  matriarchal,  meaning  that  once  sexually  mature,  males  were  excluded  from  the  calculations.  Time  in  the  MM  was  treated  as  discrete  time-‐steps,  which  were  not   equidistant,   meaning   that   different   time-‐steps   correspond   to   different   lengths   of   time.  Endpoints  from  the  MMs  included  population  growth  rate  and  population  dynamics.              

22    

IBMs  IBMs   for   the   copepods   (Figure   4)   were   developed   and   used   in   papers   II   and   III,   and   were  parameterized  using   control   data   for   the   copepods.  Population-‐level   endpoints  were  obtained  using   stochastic   simulation   techniques   of   individual-‐level   effects.   The   IBMs   included   7   input  variables;   stage  specific  mortality,  development   time  to   reach   the  copepodite  and  adult   stage,  sex   ratios,   interclutch   time   (time  between  consecutive  clutches),   latency   (time   from  mating   to  first   clutch,   minus   interclutch   time),   clutch   size   and   fertilization   success.   The   IBM   models  included  both  sexes  during  the  simulations  (assuming  a  1:1  sex  ratio),  and  the  instantaneous  rate  of   increase   (population   growth   rate)   was   obtained   as   r.   Endpoints   from   the   IBMs   included  population  growth  rate  and  population  dynamics.      

 Figure  4:  Conceptual  diagram  of  the  harpacticoid  copepod  life  cycle  implemented  in  the  individual  based  models.   Rectangles   indicate   processes   on   the   individual   level   and   queries   are   expressed   in   rhombs,  whereby  “Dev?”  indicates  if  development  is  finished.  y  =  yes,  n  =  no.  

 4.3.4  Measure  of  adverse  effects    Individual-‐level  effects  At   the   individual   level,   effect   values  were   given   as   the   Lowest  Observed   Effect   Concentration  (LOEC)   in  paper   I.   LOEC   is   the   lowest   concentration   tested   that   is   statistically   different   to   the  control.  The  No  Observed  Effect  Concentration  (NOEC)  is  the  highest  concentration  tested  that  is  not   statistically  different   from  the  control,   i.e.   the  next   lower  concentration  after   the  LOEC.   In  papers   III  and   IV,   the  effect  values  were  given  as   the  Effect  Concentration  at  10  %  (EC10).  EC10  values   are   estimated   by   fitting   a   curve   to   the   test   data   points   over   the   tested   concentration  interval.  The  value  at  which  there  is  a  10  %  effect  compared  to  the  control  treatment  is  termed  EC10.  NOEC  and  EC10  are  considered  equivalent  to  each  other  (European  Commission,  2003).   In  ERA,   NOECs   or   EC10   values   are   considered   the   PNEC   value,   which   is   first   combined   with  

Die? Nauplii

Development

Copepodite

Adult Mate Initiate brood

Offspring Development

Die?

Dev?

Die?

Development Dev?

Dev?

Die

Die

Die

y

n n

y

y

n

n

y

y n

n

y

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uncertainty  factors,  before  the  PEC  for  the  environment  is  divided  by  the  PNEC  to  obtain  a  risk-‐quotient.      Statistical   power   (or   sensitivity)   is   a   concept   that   describes  how   likely   it   is   that   a   type   II   error  occurs.  A  type  II  error  is  to  obtain  a  false  negative  response,  meaning  that  the  statistical  test  says  there  is  no  effect,  when  there  actually  is  an  effect.  High  sensitivity  makes  it  less  likely  to  make  a  type   II   error.   The   use   of   NOECs   promotes   the   use   of  many   replicates   in   order   to   obtain   high  sensitivity   in   hypothesis   testing   (Landis   and   Chapman,   2011).   For   curve   fitting/regression  analysis,  the  use  of  many  test  concentrations  allows  for  better  curve  fitting  and  therefore  more  reliable   estimates   of   EC10   (Landis   and   Chapman,   2011).   The   number   of   replicates   and   test-‐concentrations  are  however  often  limited  due  to  logistic-‐  or  economic  reasons.    Population-‐level  effects  A   commonly   used  measure   of   adverse   effects   at   the   population   level   is   the   concentration   at  which  population  density  is  stable,  i.e.  when  λ  =  1  and  r  =  0.  Declining  populations  are  defined  by  λ  values  <  1  and  r  values  <  0  (e.g.  Sibly,  1999),  and  this  approach  was  used  in  papers  I,  III  and  IV.  Other  methods   for   assessing   population-‐level   effects   are   by   calculating  NOECs   (e.g.   Lin   et   al.,  2005)  or  EC10  (e.g.  Beaudouin  and  Péry,  2013)  from  population-‐level  endpoints.  In  papers  III  and  IV,  EC10  were  calculated  from  λ.  In  paper  I,  the  95  %  confidence  limits  for  population  growth  rate  were  used  in  a  way  that  non-‐overlapping  confidence  limits  between  control  and  treatment  were  interpreted   as   a   statistically   significant   effect   (Environment   Canada,   2005).   To   determine   the  type  of  interaction  between  density  and  toxicant  from  the  low-‐and  high-‐density  population  tests  in  paper  IV,  linear  regressions  were  used.      

5.  Results  and  Discussion    This  thesis:  

• Developed   and   used   four   different   kinds   of   population   models   for   harpacticoid  copepods,  ranging  from  simple  equations,  through  MMs  and  complex  IBMs.    

• Compared  toxic  effects  at  the  individual-‐  and  population  level.    • Developed  a  new   test-‐design  where  animals  were  grouped,   to   increase   the  number  of  

replicate   animals   without   a   higher   workload,   and   to   increase   fertilization   success   and  statistical  power  of  reproductive  endpoints.    

• Contrasted  population  models  of  different  complexity  for  how  they  translate  individual-‐level  effects  to  the  population  level.  

• Compared  toxic  effects  at  different  population  densities.        

5.1  Model  development  Four  different   types  of  population  models  were  developed  and  used   in   this   thesis.  MMs  were  applied   to  N.   spinipes   (paper   I)   and  A.   tenuiremis   (paper   III).   IBMs   were   developed   for   both  species  of  harpacticoid  copepods,   in  paper   II   for  N.  spinipes   and   in  paper   III   for  A.   tenuiremis.  Unstructured  and  biologically  structured  demographic  equations  were  applied  to  N.  spinipes   in  paper   IV.   The   MM   in   paper   I   was   used   to   project   long-‐term   effects   of   sewage   treatment  technologies   aimed   at   removing   pharmaceuticals.   The   IBM   in   paper   II   was   used   to   study  endpoint   sensitivity   and   test-‐design   of   a   draft   OECD   guideline   for   harpacticoid   copepods.   The  MM   and   the   IBM   in   paper   III   were   used   to   project   individual-‐level   effects   of   lindane   to   the  

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population   level.   In   paper   IV,   the   unstructured   equation   was   used   to   calculate   population  growth   rate   from   population   sizes   of   populations   exposed   to   lindane   over   time.   Finally,   the  biologically  structured  equation  in  paper   IV  was  used  to  calculate  population  growth  rate  from  life-‐history   rates  measured   in   a   LTRE   (separated   LTRE).   The  A.   tenuiremis   MM  was   tested   by  plotting  the  projected  abundance  of  females  in  the  different  test  concentrations  after  four  time  steps,  against  the  measured  abundance  of  females  in  the  experimental  data  (paper  III,  Figure  1,  appendix  A).  The  projected  abundances  correlate  well  with  the  measured  abundances.  The  IBMs  were   tested   against   the   data   used   to   parameterize   them   (N.   spinipes:   paper   II,   Figure   3;   A.  tenuiremis:  paper  III,  Figure  2,  appendix  A).  The  model  structures  were  concluded  appropriate  to  simulate  the  experiments  and  the  models  were  well  implemented.      

5.2  Contrasting  individual-‐  and  population-‐level  effects    ERA   of   today   is   commonly   based   on   individual-‐level   endpoints.   The  most   sensitive   individual-‐level   endpoints   are   likely   to   be   equally   or   more   sensitive   to   stressors   than   effects   on   the  population  level  (Forbes  and  Calow,  2002).  Analyzing  effects  by  integrating  key  life-‐history  rates  in   population   models   is   however   a   more   robust   approach   for   assessing   ecological   risk   of  stressors   (Forbes   and   Calow,   2002).   In   this   thesis,   comparisons   of   individual-‐   and   population-‐level   endpoints   were   therefore   compared.   As   an   example,   development   time,   which   was   the  most  sensitive  individual-‐level  endpoint,  was  significantly  affected  already  at  3  %  conventionally  treated  effluent  (paper  I).  At  the  population  level,  however,  population  growth  rate  indicated  a  significant  population  decline  (λ    <  1)  only  at  75  %  effluent  (Table  1).  The  EC10  values  of  the  most  sensitive  individual-‐level  endpoints  were  in  the  same  range  (paper  III)  or  more  sensitive  (paper  I  and   IV)   than   the   population-‐level   endpoints   (Table   1).   The   results   from   these   studies   were  therefore   in   agreement   with   the   view   of   Forbes   and   Calow   (2002),   and   the   most   sensitive  individual-‐level  endpoint  would  hence  in  most  cases  be  protective  of  population-‐level  effects.    Table  1:  Examples  of  effect  concentrations  at  the  individual-‐  and  population  level.      

  Test  substance   Individual-‐level    endpoint  

Population-‐level  endpoint  

paper  I    N.  spinipes  

Conventionally  treated  effluent  (%)  

 

Development  time  (NI-‐A)    effect  at  3  %  

 

λ  effect  at  75  %  

paper  III    A.  tenuiremis  

Lindane  (μgL-‐1)  

 

Brood  size    EC10  of  2.8  

 

EC10  5.6  λ,  MM  2.8  r,  IBM  

paper  IV  N.  spinipes  

Lindane  (μgL-‐1)  

Brood  size    EC10  of  2.6  

EC10  94.7  λ,  LTRE    13.7  λ,  POP  

   

NI  =  naupliar  stage  I,  A  =  adult  stage,  LTRE  =separated  LTRE,  POP=  population  test    The   importance  of  population-‐level   data   is   however  not   that   it   should  be  more   sensitive   than  individual-‐level   data.   Instead,   population-‐level   data   can   be   used   to   reduce   uncertainty   in  extrapolation  of  (standard)  test  results  to  ecologically  relevant  effects  (Forbes  et  al.,  2011).  For  example,  in  paper  IV,  effect  on  brood  size  was  46.9  %  in  the  highest  lindane  concentration  in  the  separated  LTRE  (low  population-‐density  test)  (Table  1,  paper  IV),  whereas  the  effect  on  λ  for  the  same   lindane  concentration  was  only  6.2  %   (Figure  2,  paper   IV).   This   indicates   that  effects  on  brood   size   were   much   stronger   than   effects   on   population   growth   rate,   which   is   the   more  ecologically   relevant   endpoint.   Effects   on   λ   in   the   high-‐density   population   test  were   however  

25    

larger   (47.5  %,  Figure  2,  paper   IV),   indicating   that  population  density   influenced   the  effects  of  lindane.    In  paper   I,   combining   individual-‐level  effects  with  population-‐level  effects   resulted   in  different  conclusions  than  conclusions  that  would  be  reached  using  either  of  the  measures  of  effect  on  its  own.   In   this   case,   juvenile   development   and   survival   allowed   for   a   closer   monitoring   of   the  molting  process.  Novel   treatment   technologies  were  evaluated,  and   the  ecotoxicity   tests  were  used  to  observe  effects  at  the   individual   level,  as  a  way  of  discriminating  between  the  animals  exposed   to   different   treatments.   The   population   modeling   was   useful   for   studying   potential  long-‐term  effects  from  the  effluents  at  the  population  level.  Life-‐cycle  test  or  LTREs  are  the  basis  for   many   models   used   in   effect   modeling   (Caswell,   2001),   where   individual-‐level   effects   are  analyzed  in  order  to  parameterize  the  models.  Meli  et  al.  (2014)  conclude  that  “two  pairs  of  eyes  are  better  than  one”,  and  by  that  they  mean  using  both  simple  and  complex  population  models  to   assess   toxicant-‐   induced   effects.   This   is   also   true   for   combining   individual-‐   and   population-‐level  effects  to  assess  the  risk  of  a  toxicant.  Effects  on  the  individual  level  that  are  not  translated  to   population-‐level   effects   are   still   important   for   understanding   the  mechanisms   involved,   to  design  testing  procedures  and  build  alternative  models.      

5.3  Statistical  power  and  replicates  for  reproductive  endpoints  The   IBM   for  N.   spinipes  was   in  paper   II   used  as   a   virtual   laboratory,  where  experiments  were  carried   out   to   evaluate   endpoint   sensitivity   and   to   optimize   test   design   in   the   draft   guideline  “Harpacticoid  Copepod  Development  and  Reproduction  Test  for  Amphiascus  tenuiremis“  (OECD,  2013).   The   guideline   test-‐design  was   used   in  paper   I   (experiment,   using   72   replicates)   and   in  paper   III   (data  collection).  The  test-‐design   in  the  draft  guideline   is  work-‐intensive,  which   limits  the   number   of   replicates   (or   test   concentrations)   possible   to   include.   As   an   example,  paper   I  included  10  different   treatments   in  72  replicates,  which  required  two  person’s  attention  every  day  for  46  days,  aided  by  a  third  person  when  sex-‐determinations  and  counting  of  offspring  was  performed.   At   least   five   test   concentrations   and   60-‐120   replicates   are   suggested   in   the   draft  guideline   (OECD,   2013).   The   impact   of   the   number   of   replicates   on   the   statistical   power   of  different   endpoints   in   the   guideline  was   investigated   (paper   II).   As   an   example,   using   only   25  instead   of   72   replicates   resulted   in   no   reliable   detection   of   adverse   effects.   Increasing   the  number   of   replicates   from   72   to   144   did   surprisingly   not   make   it   easier   to   detect   effects   on  developmental  endpoints.  To  statistically  detect  effects  on   reproductive  endpoints  when  using  72  replicates,  the  effect  had  to  be  a  minimum  of  40-‐50  %,  whereas  developmental  effects  were  detected   at   20   %   effect.   Increasing   the   number   of   virtual   replicates   to   144   only   increased  sensitivity   of   reproductive   effects   by   10   %,   meaning   that   effects   on   reproduction   could   be  detected   at   30-‐40   %   effect.   Developmental   endpoints   therefore   had   higher   statistical   power  compared  to  reproductive  endpoints  in  the  guideline  test  design.  Also  the  inspection  interval  of  the  draft  guideline,  which   is  daily  observations   (OECD,  2013),  was   investigated   in  paper   II.  The  results  from  the  virtual  experiments  concluded  that  it  is  possible  to  reduce  inspection  to  every  3  days   without   losing   statistical   power.   Using   IBMs   to   evaluate   endpoint   sensitivity   and   to  optimize  test  design  for  guidelines  under  development  could  greatly  speed  up  the  process  and  be   of   good   cost-‐benefit.   For   instance,   the   number   of   replicates   and   the   inspection   interval  required   to   obtain   reliable   data   can   be   investigated   before   validation   of   the   test   method   is  initiated.      The   results   from   paper   II   resulted   in   the   development   of   a   new   test-‐design   with   a   revised  inspection   regime   in   the   next   study   (paper   IV).   There  were   two  main   differences   in   the   test-‐design  between  the  guideline   (used   in   the  experiments   in  paper   I   -‐  72  replicates,  and   for  data  

26    

collection  in  paper  III  –  60  replicates)  and  paper  IV:  The  first  difference  was  that  the  number  of  animals   used   for   reproductive   endpoints  was   doubled   from  72   to   144,   and   that   animals  were  grouped   (24   animals   were   grouped   in   each   of   6   replicates).   The   second   difference   was   that  males  and  females  could  mate  freely  since  they  were  grouped,  instead  of  using  the  force-‐mating  pairs   suggested   in   the   guideline.   The   aim   of   the   study   was   to   increase   statistical   power   for  reproductive  endpoints  by  increasing  the  number  of  replicates,  without  increasing  workload,  as  compared  to  the  draft  guideline.  Sensitivity  of  brood  size  was  in  paper   IV   increased  by  10  %  by  the   use   of   144   replicates,   as   predicted   in   paper   II   (Table   2).   The   number   of   replicates   for  reproductive   endpoints   in   paper   IV   was   also   increased   due   to   higher   fertilization   success,  compared   to   fertilization   success   for   N.   spinipes   in   paper   I   (Table   2).   Another   study,   which  allowed   for   free   mating   of   10-‐15   animals,   yielded   fertilization   success   of   70-‐99   %   in   three  separate   controls   (Breitholtz   and   Bengtsson,   2001).   It   seems   that   fertilization   success   varies  substantially   for   N.   spinipes,   and   that   force-‐mating   males   and   females   may   result   in   lower  fertilization  success  than  when  they  are  allowed  to  mate  freely.  A.  tenuiremis  do  not  seem  to  be  affected  in  the  same  way,  but  are  more  of  “love  the  one  your  with”  kind  of  animals  (Table  2).  In  treatments  where  endpoints  are  affected  also  by  a  toxicant,  low  fertilization  success  can  further  reduce   the   number   of   replicates   for   reproductive   endpoints   substantially.   In  paper   I   (Table   5,  paper  I)  the  proportion  of  the  force-‐mating  pairs  producing  two  viable  clutches  in  effluent  C2  (75  %)   was   only   0.10.   In   reality,   this  means   that   there   were   only   two   replicates   for   reproductive  endpoints   in   this   treatment.   High   statistical   power     of   reproductive   endpoints   is   important   in  traditional   ERA   so   that   false   negatives,   or   type   II   errors,   are   avoided.   Higher   numbers   of  replicates   reduces   uncertainty   in   measurements,   also   when   individual-‐level   effects   are  extrapolated  to  the  population  level.  Uncertainty  of  population  model  output  was  mentioned  as  one  important  problem  relating  to  the  use  of  population  modeling  for  ERA  (Hunka  et  al.,  2013).      Table  2:  Fertilization  success  of  controls  and  %  effect  on  brood  size  statistically  detected.    

Statistically  detected  effect,  brood  size   Fertilization  success    paper  II  N.  spinipes  

 40  %  (virtual  experiment)  

 paper  I  N.  spinipes      

 63  and  54  %;  force-‐mating  pairs  

    paper  III  A.  tenuiremis    

90  %;  force-‐mating  pairs  

paper  IV  N.  spinipes    

30  %    

paper  IV  N.  spinipes    

96%;  free  mating  

 

5.4  Contrasting  simple  and  complex  modeling  approaches  Contrasting  population  models  of  differing  complexities  may  aid  risk  assessors  in  choosing  what  population  model  to  use  (Meli  et  al.,  2014).  In  paper   III,  an  IBM  and  a  MM  were  contrasted  for  their  ability  to  translate  individual-‐level  effects  to  the  population  level.  The  MM  was  very  simple  and  included  only  life  stage  transitions  and  brood  size  as  input  variables,  whereas  the  IBM  used  7  (including  time-‐dependent)  parameters  (Table  1  paper  III).  The  number  of  parameters  needed  to  run  the  model  is  lower  in  the  MM  compared  to  the  IBM,  but  the  experimental  work  needed  to  derive   these  values  was   similar.   For  A.   tenuiremis   exposed   to   lindane,   IBM-‐derived  population  growth  rate  showed  stronger  effects  compared  to  the  MM  (Figure  5).  Individual-‐level  effects  in  this   data   set   included   time-‐dependent   effects,   such   as   shifts   in   development   time   (Figure   2,  paper  III).  These  effects  were  not  translated  to  the  population  level  response  to  the  same  degree  

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in   the  MM   output,   which,   therefore,   showed   lower   population-‐level   effects   compared   to   the  IBM,  especially  at  the  highest  lindane  concentration.  Development  time  was  strongly  affected  at  the  individual  level  for  A.  tenuiremis  exposed  to  lindane  (Figure  2,  paper  III),  and  since  the  MM  did  not  account  for  delay  in  development,  this  was  the  probable  reason  behind  the  differences  in  effects  at  the  population  level.      

     Figure  5:  Population  growth  rates  relative  to  control  (mean  values)  from  a  MM  and  an  IBM  for  A.  tenuiremis  exposed  to  lindane.  Error  bars  represent  95  %  confidence  intervals.        Other   studies   have   compared   simple   and   more   complex   population   models.   Topping   et   al.  (2005),   used   population   growth   rate   to   contrast   a   life-‐history   model   (MM)   and   an   individual  based   landscape  model   for  Skylark  populations  exposed   to  pesticide.  They   found   that   the   two  models  gave   largely   the   same   results.  Meli  et  al.   (2014),  on   the  other  hand,   found   that  a  MM  was  less  sensitive  compared  to  an  IBM  for  detecting  different  spatial  patterns  of  exposure  of  F.  candida   to  copper   sulfate.  The  conclusion   from  paper   III  was   that   the   IBM  should  be  used   for  analyzing  datasets  where  time-‐dependent  effects  are  included.  The  simpler  MM  is  in  its  current  form  sufficient  for  analyzing  datasets  including  effects  on  mortality  and/or  reproduction.  Effects  of   toxicants   measured   at   the   individual   level   can   with   these   models   be   projected   to   the  population   level,   and   provide   information   of   population-‐level   consequences,   which   are  important  in  ERA.    

5.5  Contrasting  effects  in  low-‐  and  high-‐density  populations  Due   to   density   dependence,   high-‐density   populations   may   respond   differently   to   stressors  compared   to   low-‐density   populations.