Modelling Spatial Distribution of fish, by Benjamin Planque
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Modelling Spatial Distribution of fish, by Benjamin Planque

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Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)

Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)

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Modelling Spatial Distribution of fish, by Benjamin Planque Modelling Spatial Distribution of fish, by Benjamin Planque Presentation Transcript

  • EURO-­‐BASIN,  www.euro-­‐basin.eu   Introduc)on  to  Sta)s)cal  Modelling  Tools  for  Habitat  Models  Development,  26-­‐28th  Oct  2011  
  • projec1ng  spa1al  distribu1ons   niche-­‐based  models   climate     predicted   forecast/scenario   spa1al   distribu1on  biological  response   +   environment   EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • Why  model  the  spa1al  distribu1on  of  fish?  Interpolate between observationsProject distributions under scenariosUnderstand processes that control distributions…multiple objectivesEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • A  general  view  of  the  modelling  method   adapted from Anderson, 2010EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • uncertain1es  in  observa1ons  sampling  design:     sampling  intensity,  spa1al/temporal  scales,   aggregated  distribu1ons    sampling  gear  (trawl)  or  observa1on  (acous1cs):   accessibility  to  observa1on,  sensi1vity,  bias  and   precision   EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • uncertain1es  in  conceptual  models   environmental conditions density geographical dependent attachment habitat selection a spatial Proportional Constant Basin distribution Local density spatial Persistence dependency spatial location b high Habitat suitability medium species demographic interactions structure low high Local density medium lowEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application. spatial location
  • uncertainty  in  numerical  formula1on   func1onal  rela1onships   linear,  polynomial,  piecewise,  etc...     model  complexity   number  of  parameters,  non-­‐linearity     interac1ons   addi1ve,  mul1plica1ve,  other     sta1s1cal  distribu1ons   Normal,  Poisson,  Log-­‐Normal,  Gamma,  Binomial,...  EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • uncertainty  in  parameter  es1mates  and  model  fiKng   sta1s1cal  distribu1on  of  parameters   confidence  intervals,  sta1s1cal  significance   correlated  parameters   are  parameters  independent,  and  how  is  this  handled  by   the  modeling  method?   overparametrisa1on  and  overfiKng   number  of  parameters  vs.  number  of  independent   observa1ons   autocorrelated  observa1ons   spa1al/temporal  autocorrela1on  reduces  the  true   number  of  independent  observa1ons   metric  for  model  fiKng  performance   variance,  deviance,  likelihood,  AIC,  AUC,  GCV,...    EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • uncertainty  in  model  evalua1on   metric  for  model  predic1ve  performance   variance,  deviance,  likelihood,  AIC,  AUC,...     true  independence  of  the  valida1on  data   are  the  valida1on  data  correlated  with  fiKng  data?  EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • Addi1onal  considera1ons   Spa1al  scale   is  spa1al  scale  considered?   are  the  scales  of  observa1on  and  modelling  consistent?     adaptability  of  living  systems   complex  adap1ve  systems,  these  may  modify  their   behaviour  in  the  future,  surprise  is  to  be  expected    EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • Evalua1ng  uncertain1es   future world adaptation Scale(s)EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • Conceptual  models  EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • Null hypothesis / Geography H0: no control•  Random spatial distribution,•  Not really plausible, but often use (implicitly) to test other hypotheses H1: geography•  Spatial distribution is determined by fixed geographical coordinates (site attachment)•  This is usually not used unless no other hypotheses are available•  It can be used as a null hypothesis for habitat control by other factors EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • H2:environment•  The habitat can be defined as the geographic manifestation of the realized niche•  It may or may not be occupied by the species Species abundance Environmental gradient habitat EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • H3: density-dependent habitat selectionEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • spatial location H3: density-dependent habitat selection b high Habitat suitability medium low The basin model high Local density medium low spatial locationEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • H3: density-dependent habitat selection•  Recent evidence from Lake Windermere (UK) show that pike fish moves between two basins as predicted by DDHS EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application. Haugen et al. 2006.
  • H4: Spatial dependency"… everything is related to everything else, but near things are more related than distant things” Tobler, 1970 Sea Surface Temperature Chlorophyll Plaice EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • H4: Spatial dependency•  Patterns of spatial distribution are explained by spatial interactions between (groups of) individuals such as attraction or repulsion•  It can be driven by many processes: spawning or feeding aggregations, swimming capabilities, gamete dispersal/retention•  It is often referred to as patchinessEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • H4: Spatial dependencyEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • H5: Demographic structure•  Spatial distributions of individuals vary depending on their size, age, sex or other individual traits Anchovy length Lesser spotted dogfish distribution Circles diameter show mean body size of anchovy Females Males Planque et al. (2005) EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • H6: species interactions Balanus alone Balanusgrowth fundamental rate niche Chthamalus alone Chthamalus fundamental niche low middle high Location in intertidal zone EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • H7: memory•  The spatial distribution does not solely results from instantaneous conditions but also from the population’s history –  Imprinting (e.g. natal homing) –  Social learning (tradition) –  Individual memory (habit formation)EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • the  conceptual  models  summarised   environmental conditions density geographical dependent attachment habitat selection spatial distribution spatial Persistence dependency species demographic interactions structureEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • Environmental  gradients,  niches  and  models   Gradients Niches •  Resource •  Fundamental •  Direct •  Realised •  Indirect Models •  Mechanistic •  Statistical •  MixedEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • Observa1ons,  data,  distribu1ons?   Observation process, scale & support •  How is the data collected? •  Trawl samples •  Sub-sampling •  Hydroacoustics •  Sampling design •  Random, stratified, transects,… •  Observation scale •  Distance between observations •  Observation support •  Volume/area sampledEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • Observa1ons,  data,  distribu1ons?   Observation process, scale & support What statistical distribution to choose? •  Continuous: •  Normal •  Log-Normal •  Gamma •  Discrete: •  Binomial, multinomial •  Poisson •  Negative binomialEuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • Count  observa1ons  versus  latent  density  distribu1on   Survey area Sampling unit Trawl haulNumber of fish in haul= draw from a binomial distribution with mu= underlying density at the scale of the sampling unit and ‘size’= underlying dispersion at the scale of the sampling unit EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • An  example:  TrawlCatchModels.R  Context and data•  9y of trawl sampling. Stratified random sampling. Bottom trawl. Same location each year. 30’ at 3 knots. 25 x 5m opening. Number of individual fish.•  Additional data on temperature and chlorophyll (surface) and bottom topographyHypotheses•  Fish spatial distribution is controlled by 1) temperature, 2) chla, 3) bathymetry, 4) past distribution, or any combination of the above EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • An  example:  TrawlCatchModels.R  Analysis•  Plot your data: spatial & statistical distributions, scatterplots•  Explore your data: spatial structures, temporal structures, appropriate statistical distributions, sampling effects•  Model the spatial distributions: write model equations for various hypotheses, fit models on a subset of the data and predict the otherInterpretation•  Which models fit best? predict best? Which predictor should be retained? How complex should the model be?Projections•  Projections of future spatial distribution of fish density•  Projections of future survey results EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • What  you  need  to  run  the  exercise  •  R (2.13.2) •  Data files:•  Librairies: •  TrawlCatches_9199.txt •  fields (6.6.1) •  depth.Rdata •  mgcv (1.7-8) •  R code •  spatial (7.3-3) •  gstat (1.0-6) •  gamlss (4.1-0) •  gamlss.dist (4.0-5) •  gamlss.mx (4.0-4) •  plotrix (3.2-6) EuroBasin training workshop – 26-28 October 2011 - Modelling the spatial distribution of fish: some key concepts and an application.
  • EURO-­‐BASIN,  www.euro-­‐basin.eu   Introduc)on  to  Sta)s)cal  Modelling  Tools  for  Habitat  Models  Development,  26-­‐28th  Oct  2011