Factors affecting the health and fitness of Juvenile winter flounder - - PowerPoint PPT Presentation
Factors affecting the health and fitness of Juvenile winter flounder ( Pseudopleuronectes americanus ): Associations among data from the cellular to population level Anne McElroy and colleagues 10th International Flatfish Symposium St.
10th International Flatfish Symposium – St. Malo France, November 11-16 2017
both funded by NOAA Fisheries through the Saltonstall-Kennedy Program 2010-2013 & 2016-2018 This work lead by Mike Frisk and I has been done collaboratively with a large team over time
Robert Cerrato (SBU), Demian Chapman (Florida Atlantic U.) Mark Fast (UPEI,), Kevin Feldheim (Mus. Nat. Hist. Chicago), Karin Limberg (SUNY ESF), Nolwenn Dheilly (SBU) Lyndie Hice (Ecol &Environ. Inc.) Shannon O’Leary (TX A&M), Brian Gallagher (UMD – CBL) and as you’ve heard Tara Dolan and Matt Siskey (SBU)
Recruitment into fishery appears to be limited by young of the year (YOY) survival
Investigate factors influencing the survival of YOY winter flounder in Long Island bays ØBiological- predation, individual condition, population structure ØEnvironmental - temperature, oxygen stress, urban gradient
Jamaica Bay Moriches Bay Shinnecock Bay Cold Spring Pond Napeague Harbor Hempstead Bays
Degraded “Cleaner”
– Dissolved oxygen, salinity, temperature
– Abundance, size
– Cage study- predator free survival and growth
– Condition- Fulton’s K, HSI – Muscle RNA:DNA-proxy for recent growth – Otolith analysis- settlement date, daily growth increment – Fin clip- population genetics (microsatellites) – Liver RNA- gene expression analysis
Comprehensive model & multivariate statistics
week from May-October
– 10 random stations per day, 1-2m depth – 2010 and 2011
YOY mortality relatively high, 3-6% per day – higher than in previous studies in nearby areas
Cage study to assess predator-free mortality
– Four 1m x 1m x 0.5m cages at each site – 6-9 YOY per cage
Continuous environmental monitoring
– Temperature, salinity, dissolved oxygen – sonde attached to cage at each site taking measurements every 30 min
Hypoxia seemed to be most closely linked to mortality in free and caged fish – and in some bays predation also a factor
Jamaica Bay
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5/17/2011 6/6/2011 6/26/2011 7/16/2011 8/5/2011 8/25/2011 LN(N) Date
Jamaica
Cage Field
1 2 3 4 5 6 7 8 9 10 2 4 6 8 10 12
Incremental growth (µm) Month
2010 2011
Growth Trend in RNA:DNA best matched growth
Jamaica Hempstead Moriches Napeague Shinnecock Cold Spring 0.0104 0.0036* 0.0075 0.0142** 0.0046 Jamaica 0.0116 0.0046 0.0134 0.0133* Hempstead 0.0079 0.0153* 0.0087* Moriches 0.0108** 0.0022** Napeague 0.0128**
* P < 0.05 ** p<0.001
O’Leary et al. 2013: PloS ONE 8(6):e66126.
Data indicates effective population size is between 60-260 per bay !!!
Jamaica Bay Moriches Bay Shinnecock Bay Cold Spring Pond Napeague Harbor Hempstead Bays
from adjacent sites with divergent condition (Shinnecock vs. Moriches)
genome available
– de novo assembly using SOAPdenovo-trans program
– >100 bp, Mean = 579bp
– reads mapped back to assembly using Tophat program
– Moriches > Shinnecock : 180 – Shinnecock > Moriches : 73 – Glucose and glycogen metabolism major responder
Contaminant Exposure: Cytochrome P4501A (CYP1A) Vitellogenin (VTG) Immune Response: Hepcidin II (HEP) Complement C3 (C3) Pleurocidin (PLEUR) Phospholipase A2 (PLA2) Glycolosis and Glucose Metabolism: Glycerol 3 phosphate dehydrogenase (GPDH) Glucokinase (GCK) Glutamate decarboxylase (GAD)
Almost all showed significant differences among sites, but most not clearly associated with stressed degradation
McElroy et al.2015: Comp. Biochem.Physiol.Genomics
Hierarchical linear models used to see which variables were statistically associated with growth
Incremental growth was significantly affected by: settlement date, age at capture, condition index (Fulton’s K and HSI), genes associated with immune response (PLEUR) contaminant exposure (CYP1A), and energy metabolism (GPDH). Both Shinnecock and Jamaica Bay were significant as well.
Gallagher et al. 2016: Mar. & Coast. Fish.
Ø High YOY mortality rates in wild Ø Extremely low spawning stock Ø Survival negatively associated with both hypoxia and predation in some cases Ø A complex mix of individual responses at many levels Ø HLM as useful tool to identify associations among which of the wide diversity of parameters were associated with growth
Ø Stock structure using both otolith microchemistry (Siskey) and genetics (Dolan – in progress) Ø Predator exclusion as a restoration tool (Dolan) Ø Cellular markers of growth and condition (Dolan – in progress)
Otolith microchemistry in YOY and adults already has identified resident inshore, migratory, and offshore fish, may identify bay fingerprints and with genetic analysis will indicate what contingents are contributing to recruitment
Adult YOY
Ø Caging studies have demonstrated influence of predation and we are examining influence of environmental factors Ø Genetic analysis will further evaluate and identify stocks and help further differentiate contingent structure Ø Multiple cohorts indicating wide diversity of spawning times
ü We conducted more extensive sampling to obtain more similar size/age groups to compare among sites ü More extensive gene analysis underway:
more at contaminant exposure, and general stress response: pi-GST, Vegf, HSP70, and Cortisol in 2016 individuals as well as some reanalysis of earlier samples for new transcripts
Value of holistic multidisciplinary approach
contaminant exposure hypoxia, temperature
community – predation population – stock structure, interrelatedness, individual – survival and growth cellular – response patterns Hierarchical linear models as a promising tool to find statistical associations among diverse parameters and space and time
Relative Contribution of Expression Changes
Fold change of each transcript used to weight relative contribution to expression change 57% have functional annotations from teleost genomes (50% flatfish)