Predicting Life H istory - PowerPoint Presentation

Slide1

Predicting Life History Traits for All Fishes: Proof of Concept and Next Steps

Rainer Froese

GEOMAR

Presentation at the

FishBase

Symposium

1 September 2015, Los

Baños

, Philippines

FishBase Data Content

Predicting LWR for All Fishes

Bayesian Inference on LWRFishBase contains Phylogeny and body shape for all speciesFor species without published LWR estimates, LWRs of close relatives with same body shape are used for Bayesian predictionsThis results in LWR predictions for all species of fish, with indication of uncertainty Whenever new LWRs are entered, these predictions are updated (confidence limits become narrower)

FishBase Data Content

Proof of Concept228 LWR published in JAI 31 were compared against Bayesian predictions:69% point estimates fell within predicted range85% were not significantly differentMost of the significantly different LWRs were actually questionable (small number of specimens, narrow length range, inclusion of early juveniles, other problems)

Thanks to Rudy Reyes for compilation of data and provision of example

Change in Body Shape between Juveniles and Adults

b

= 2.6

Alectis

indica

17 cm

> 1 m

Solution: Estimate separate LWR

f

or juveniles and adults!

Improving ScienceLWR studies submitted to JAI or ACTA are now routinely compared against predictions in FishBaseSignificantly different LWR estimates are subjected to extra scrutinyFor example, if indeed body shape changes substantially during adult life (b > 3.5 or < 2.5), that should be visible when comparing photos of juveniles and adults

Bayesian Prediction of GrowthGrowth is essentially described by two parameters, asymptotic length (L∞), and how fast that length is approached (K)Phylogeny, maximum length, body shape, environmental temperature and activity level will be used to predict growth parameters for all species

Asymptotic length and maximum length are highly correlated

1:1

Influence of Temperature

Influence of Habitat or Life Style

Understanding Growth Space

t

ropical

p

elagiceel-like

bony fishcold water

d

emersal

h

agfish

s

ubtropical

p

elagic

f

usiform

b

ony fish

Next stepsPublish the “proof of concept” for LWRFinalize and publish Bayesian growthPredict mortalityPredict generation timePredict resilience (intrinsic rate of population growth)

Big Question?If we know growth for all fish, what “big question” shall be answered:Do bony fish grow faster than sharks?Do freshwater fish grow faster than marine fish?Other big questions????

Thank You

AbstractPredicting life history traits for all fishes: proof of concept and next stepsFor 25 years we have compiled in FishBase key life history traits such as maximum size, growth, longevity, mortality, maturity, fecundity, or diet composition. The compilation reflects what is known, such as maximum length for most species, but mortality for only a few hundred species. Users of these data face three questions: 1) If many estimates of a trait are available for a species, which one shall be used or how shall all estimates be summarized? 2) If only one estimate is available, how representative is it? 3) If no estimate is available, what is the best guess? A rigorous statistical procedure to answer these questions is Bayesian inference, which has recently become practical through new software and fast computers. This technique allows the inclusion of related information, such as correlated traits or estimates from close relatives or previous general knowledge about the trait. We have already applied it to length-weight relationships (LWR), where body shape and LWR of close relatives was used to predict LWR for species without estimates. A meta-analysis showed that these predictions were, in most cases, not significantly different from subsequently published LWR estimates. We are now applying the technique to the prediction of growth parameters, based on maximum size, body shape, temperature, activity level, and estimates for close relatives. First results look very promising. Over the next three years, we plan to apply the approach also to maturity and mortality, aiming at the prediction of the intrinsic rate of population increase (= resilience) for all species of fishes.