What makes some parts of the ocean sticky to fish? Ocean Observing for marine habitat science & ecosystem management

(The following is a short white paper we drafted for publication in a NOAA Technical Memorandum that will serve as the proceedings of a workshop that was hosted by the Mid-Atlantic Fisheries Management Council in December, 2010 on Habitat and Ecosystem Approaches to Management in the Mid-Atlantic Bight)

We often use our experiences as terrestrial organisms

inhabiting landscapes to draw inferences about the ways

marine organisms use and are constrained by seascapes. 

 In the sea the properties and dynamics of the fluid are

critical habitat features that control the vital rates of most

organisms while most terrestrial animals have evolved

mechanisms to at least partially decouple many of their

 vital rates from the dynamics and properties of the 

atmospheres fluid.

Marine organisms have evolved in an aqueous environment, with a high viscosity, high heat capacity, and solute concentrations similar to those in the spaces of their living cells.  The organisms are exposed to motions and environmental conditions in the sea that are dramatically slower and less variable than similar motions and conditions in the atmosphere. Furthermore, since the density of seawater is only slightly less than the density of living tissues, drag rather than gravity is the dominant force controlling movements in the sea.  The oceans are inhabited by nearly neutrally buoyant organisms that grow in direct contact with the “hydrosphere” throughout life cycles that usually include egg and larval stages a few millimeters long and adults with body sizes that can range from 10's of centimeters to meters.  Rates of metabolism, growth, survival, dispersal, and reproduction in marine organisms are tightly coupled to many scales of variability (millimeters to 1000s of kilometers, seconds to decades) in the water column as well as the seabed as the organisms make the dramatic habitat transitions usually required to complete their life cycles. In contrast, early development in most terrestrial animals is internal (or external, but aquatic in amphibians and some insects); while juveniles and adults are exposed to the atmosphere over a range of body sizes an order of magnitude smaller than marine organisms. Terrestrial organisms are largely constrained to two spatial dimensions by gravity and have evolved elaborate mechanisms to decouple metabolism and other physiological rates from the short-term variability of the atmosphere.  Despite these profound differences, we often use terrestrial frameworks to think about and investigate the ways marine organism use and are affected by their habitats.  We treat seascapes as analogues of landscapes; as two-dimensional matrices of habitat patches with slow spatial dynamics.  We use our own experiences as terrestrial organisms inhabiting landscapes to draw inferences about the constraints seascapes impose on the forms and ecologies of marine organisms, often overlooking the dynamic water column processes that define habitats, even for organisms strongly associated with the seabed.  Further, even when we do recognize that the vital rates of marine organisms and dynamics of their populations are strongly regulated by the ocean's “hydrosphere”, the absence of data describing the dynamics and structure of the water column at ecologically relevant space-time scales has made it difficult to consider the ocean's fluid explicitly in the design and analyses of relationships between species and their habitats in the sea.

MARACOOS remotely sensed ocean data and several

assimilation circulation models.

Index of divergence derived from vertical current velocities

 measured with HF radar and frontal boundaries indentified

using classification of satellite data.  The plots on the right

show abundance responses of squid and butterfish to 

divergences and fronts.

However, the state of the art, Integrated Ocean Observing Systems (IOOS), now monitor and model the physical and primary production dynamics of the ocean at the broad spatial but fine time scales required to understand the ways water column processes affect the vital rates of marine organisms and dynamics of their populations.  IOOS is an intergovernmental/inter-agency effort focused on the development of ocean observing and forecasting systems.  IOOS themes range from public health and safety to marine operations and natural resource conservation.  As part of the US IOOS program, partners in the Mid-Atlantic region along the US East Coast have developed a regional scale ocean observing network. The footprint of the Mid Atlantic Regional Ocean Observing System (MARACOOS=MARCOOS=MACOORA) stretches along 1000 km of coastline from Cape Hatteras, North Carolina to Cape Cod, Massachusetts and offshore to the continental shelf break.  MARACOOS uses a multi-platform approach to characterize the fine scale structure and dynamics of the coastal ocean. The platforms include US and foreign satellites in space, a network of high-frequency (HF) radars deployed along the shore, and a fleet of robotic gliders flying beneath the ocean's surface (for more data see here).  Satellites provide time series maps of surface temperature, chlorophyll A, and other ocean color products describing light absorption and backscatter.  Ensemble clustering is applied to the satellite information to objectively identify and visualize water masses and the surface fronts between them. The HF radar network provides hourly surface current measurements from the edge of the continental shelf into estuaries.  These current measurements can be processed to show near-real time and statistical forecasts of horizontal surface flows, upwelling and downwelling dynamics, and the evolution of surface fronts.  Robot gliders that carry sensors measuring temperature, salinity, chlorophyll-A, and particle backscatter describe seasonal to inter-annual changes in the vertical structure of the ocean.  Satellite, HF radar, and glider data are assimilated into an ensemble of numerical circulation models (UMD-HOPS, NYHOPS, ROMS) that are evaluated by comparing model realizations to field measurements. MARACOOS data and model forecasts provide spatially and temporally explicit descriptions of the physical forcing, flows of materials, and primary productivity that structures and regulates the mid-Atlantic Bight ecosystem. In addition to an extensive data archive, MARACOOS makes these data freely available in real time via Internet portals managed by trained operational oceanographers. Developments in high speed wireless communications and internet infrastructure now permit real time virtual collaboration between marine habitat and ecosystem ecologists in the field and operational oceanographers with expertise in IOOS data streams and forecasts.  Access to IOOS data and expertise allows ecologists to easily consider processes in the water column as well as on the seabed in studies of the life history processes that ultimately determine recruitment and the dynamics of populations of ecologically and economically organisms in the mid-Atlantic Bight ecosystem.

Analysis of deviance derived from Generalized Additive

Modeling describing the proportions of variation in the 

abundance of four species explained by IOOS ocean data, water 

column (pelagic) and bottom data (benthic) data measured 

on surface ships (=insitu), as well as abundance of prey for 

two predators strongly associated with the seabed. 

Over the past six years we have been developing an approach to integrate IOOS remotely sensed data and short-term model forecasts into regional scale habitat studies.  Our approach has included the development of distribution based habitat models for resource species that are also ecologically important in the mid-Atlantic Ecosystem, as well as adaptive surveys designed to measure habitat specific distributions and life history processes rates for these species.  We are nearing completion of a NOAA Fisheries and the Environment Funded project in which we have used multivariate and single species modeling to evaluate the power of IOOS data to describe distributions of organisms with different vertical habitat preferences in the mid-Atlantic Region using abundance data collected on North East Fisheries Science center bottom trawl surveys 1,2.  In analyses targeted at species important in the Mid-Atlantic Bight food web, we have found that our models, built using remotely sensed surface measurements, explain more of the abundance variation for pelagic species (longfin inshore squid and butterfish ~73%) than demersal species (spiny dogfish and summer flounder ~50%).  However, bottom habitat variables (e.g. rugosity & depth) and surface pelagic features measured by IOOS remote sensing (e.g. surface fronts, vertical & horizontal current velocities) were equally important for all species, while in-situ shipboard measurements of water column stability and structure were more useful for modeling pelagic species.  All species were associated with specific surface current flows, regions of upwelling, and/or surface fronts identified with IOOS remote sensing, indicating that pelagic processes affecting energy costs of movement, prey production and prey aggregation influenced distributions of the animals regardless of their vertical habitat preference.  We found that most of our IOOS informed habitat models had greater explanatory power and out of sample prediction capabilities than previously published models built using the same analytical technique, but without the benefit of access to IOOS data streams.  

Industry and scientific collaborators involved in finding a
solution for the bycatch mortality of butterfish in the longfin
inshore squid fishery.

We have begun to extend IOOS our informed habitat studies in two directions.  In a project recently funded by the NOAA/NEFSC Cooperative Network, we are collaborating directly with members of the Garden State Seafood Association to use the ecological knowledge of fishers to refine our habitat models in an effort to develop tools to reduce the bycatch of butterfish in the longfin inshore squid fishery.  The goodwill required for this close collaboration between the fishing industry, government and academic scientists was developed in IOOS regional association meetings that serve as “neutral ground” for many stakeholders with diverse and sometimes competing interests in the services of the ecosystem.  

Ongoing analysis of adult summer flounder abundance
responses during autumn migration and spawning to
features measured with IOOS remote sensing.

Spatial part of summer flounder egg habitat model, tracks
of simulated particles released in surface currents measured
during the early fall 2009.  Circle shows locations of
simulated particles near the mouth of New York harbor.
Plot on the bottom right shows the relative abundances of
summer flounder larvae collected in adaptive plankton
surveys off the mouth of New York Harbor.

In another project, we are using archived IOOS data along with NEFSC bottom trawl survey data of adults and egg collections from NEFSC MARMAP surveys made during the 1970s and 1980s to identify the characteristics of summer flounder spawning grounds in the mid-Atlantic region.  Our preliminary analyses indicate that autumn spawning may be concentrated outside the mouths of several large estuaries where processes of nutrient enrichment from estuarine outflows and coastal upwelling, high phytoplankton productivity, and processes of particle concentration along water mass convergences may create pelagic habitats promoting the survivorship and growth of summer flounder larvae.  Furthermore, we have been using MARACOOS assimilative circulation model nowcast and short-term forecasts to adaptively route surveys investigating habitat quality for fish larvae.  On these cruises we have collected large numbers of summer flounder larvae that appear, based on estimates of larval age and particle tracking in surface currents measured with HF radar, to be derived from a specific spawning ground identified in the analysis of summer flounder spawning grounds described above.  While this study is still in its infancy, we believe our IOOS informed approach that combines regional scale habitat analysis and modeling with adaptive process based field studies will allow us to develop broad scale habitat models that couple ontogenic habitats and important life history processes for this and other species in the mid-Atlantic region.  This is just the kind of approach required for effective space based ecosystem management.

Habitat science in the service of ecosystem management
could focus on processes that affect many species rather
than just a few.  Andrew Bakun described the triad physical
of physical processes in his book "Patterns in the Ocean".
We are finding that distributions of summer flounder adults
during autumn migration and spawning and eggs appear to
be associated with these processes and they are likely to be
important to many species.

We believe our IOOS informed approach to habitat science will be most useful for the development of tactical tools for ecosystem assessment and management.  There are several pathways toward the development of habitat science in the service of ecosystem management in the region.  The first of these is to develop single species models focused on ecosystem keystone species indentified in ecosystem modeling efforts in the Northwest Atlantic 3,4.   The rational behind this an approach is that the identification and conservation of habitats maintaining the resilience of ecosystem keystone populations should be translated across a level of ecological organization to promote the resilience of the ecosystem as a whole.  By resilience we mean the tendency of populations and ecosystems to return relatively rapidly to healthy states following significant perturbations 5.  One potential flaw with this approach is that rapid changes in climate are producing rapid changes in the distributions of animals, particularly in regions of faunal transition like the mid-Atlantic Bight 6,7.  If this is the case, the identity of ecosystem keystones may also be changing and thus targeting at a few individual species could fail to meet the goal of promoting ecosystem resilience. What is most intriguing about our study of summer flounder spawning grounds is that the hydrographic processes and structures we have identified that may promote nutrient enrichment, concentration, and larval delivery are the same “triad” of processes that appear to define important spawning grounds for pelagic species in the eastern Pacific Ocean and Mediterranean Sea 8,9.  Thus, we may be able to shift focus from habitat studies of individual keystone species, toward investigations of “keystone habitats” where physical and biological processes in the water column and on the seabed promote the survival of critical life history stages of many species rather than just a few.  This approach focused on habitat processes will be essential if the ecosystem is changing rapidly with climate change.  No matter what approach we take, habitat science in support of ecosystem assessment and management will require close, honest and open collaboration between physical and chemical oceanographers, habitat ecologists and ecosystem scientists, as well fisherman who arguable have the most intimate and practical understanding of the ecosystem as a whole.

1 Manderson J, L Palamara, J Kohut, MJ Oliver.  (in review) Using an ocean observatory to identify habitat associations of species with different vertical habitat preferences in the coastal ocean.

2 Palamara L, Manderson J, J Kohut, Oliver MJ & J Goff (in review) Improving Models of Covariation Between Marine Communities and their Habitats by Incorporating Pelagic Features Captured by Coastal Ocean Observatories

3 Link J, Overholtz W, O'Reilly J, Green J, Dow D, Palka D, Legault C, Vitaliano J, Guida V, Fogarty M, Brodziak J, Methratta L, Stockhausen W, Col L, Griswold C (2008) The Northeast U.S. continental shelf Energy Modeling and Analysis exercise (EMAX): Ecological network model development and basic ecosystem metrics. Journal of Marine Systems 74:453-474

4 Link JS, EA Fulton, RJ Gamble (2010) The northeast US application of ATLANTIS: A full system model exploring marine ecosystem dynamics in a living marine resource management context. Progress In Oceanography 87: 214-234

5 Levin SA & J Lubchenco. (2008) Resilience, Robustness, and Marine Ecosystem-based Management. BioScience 2008 58 (1), 27-32

6 Sorte CJB, SL Williams, JT Carlton (2010). Marine range shifts and species introductions: comparative spread rates and community impacts. Global Ecology and Biogeography

19(3): 303–316.

7 Nye JA, Link JS, Hare JA, Overholtz WJ (2009) Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf. Marine Ecology Progress Series 393:111-129

8 Bakun A.(1996) Patterns in the ocean: Ocean processes and marine population dynamics.  California Seagrant System

9 Agostini VN & Bakun A. (2002) ‘Ocean triads’ in the Mediterranean Sea: physical mechanisms potentially structuring reproductive habitat suitability (with example application to European anchovy, Engraulis encrasicolus).  Fisheries Oceanography 11(2): 129-142

“Now where are all those kids I left by the side of the road?” I. Summer flounder life history & some observations of broad scale patterns.

Figure 1. Summer flounder range from Cape Canaveral,

 Florida to George's Bank off the Coast of Massachusetts. 

Subpopulations North and South of Cape Hattaras, North 

Carolina have different ecologies which are summarized 

in detail by Packer et al., 1999. Mapped data is available 

from Fishbase.

One of the things we have been trying to do is to link our discoveries in the adaptive plankton and bottom surveys of the seascapes with our understanding of the dynamics of fish and invertebrates important in the Mid-Atlantic Bight Ecosystem. The birth and death rates that underlie population dynamics occur to individuals with specific traits in habitats defined by sets of environmental factors affecting those rates (e.g. growth is dependent on temperatures; predation mortality is affected by the availability of hidey holes as well as other things such as body size). I am sure this is blindingly obvious to anyone who is not a marine biologist. But often we seem to ignore this because making the connection between local habitat effects on individuals and the emergent dynamics of populations at regional scales in a quantitative way is so hard. Somehow “information” is translated across scales and levels of biological organization in both directions, from local to regional scales (e.g. effects of individual births and deaths related to habitat quality within subpopulations), and from regional to local scales (e.g. connectivity among subpopulations provided by dispersing larvae and migrating adults that can supplement, rescue, or even found new subpopulations). Sometimes science is thinking out loud, and I am going try to amble through some data on summer flounder (AKA “fluke”) to try to understand these connections better.

Figure 2. Summer flounder life cycle in the mid-Atlantic Bight.

Adults spawn as they migrate in the autumn from nearshore 

coastal feeding grounds to deep overwintering habitats on 

the edge of the continental shelf. Fertilized eggs hatch larvae 

that live in the water column in the coastal ocean for a month

 or more before moving into estuaries and transforming into 

small juveniles. The juveniles overwinter in the estuaries.

  

Why choose summer flounder or “fluke” as a model species? One reason is that fluke integrate a particularly broad spectrum of habitats throughout the mid-Atlantic Bight (Fig. 2). During their complex life history which includes eggs and larvae that live in the water column as well as adults and juveniles strongly associated with the seabed, they use habitats ranging from marsh creeks in estuaries, to pelagic & seabed habitats in the coastal ocean, to deep habitats on the seabed at the edge of the continental shelf. From late spring through the summer, adults and juveniles are important predators in shallow estuaries and the near-shore coastal ocean. In early autumn they begin to migrate from these nearshore feeding habitats to deep overwintering grounds near the edge of the continental shelf. Adults two or more years old spawn as they migrate across the inner continental shelf. Each ripe female carries between ½ million to over 4 million eggs depending on her body size. The females release these eggs directly into the sea where they are externally fertilized by males. The fertilized eggs hatch in less than about 4 days. The larvae which are shaped like a regular fish (fusiform or laterally compressed) develop in the water column for 1 to 3 months depending on the temperature, availability of food, and their "ability" to avoid predators. Late stage larvae then move into estuaries during the winter. As they do, they metamorphose into small juvenile flatfish. Metamorphosis involves the “migration” of their eyes to the left side of their heads, and the twisting of their spinal cord into loop as they flatten into a flatfish. They are 10-20 millimeters long when they “settle” out of the water column to become more strongly associated with the bottom habitats in estuaries.

With the exception of fishing, we believe most of the important processes affecting birth and death rates occur from spawning through the early juvenile phase. Unfortunately for field ecologists working in the northern part of summer flounders range, the interesting habitat specific processes influencing summer flounder population dynamics happen during the autumn, winter, and spring when it's cold. Who named this fish? Why would a fish spawn in the autumn and float around in the plankton to settle in estuaries in the winter when its so cold? Our guess is that larvae and juveniles are probably exposed to fewer predators in the ocean and estuaries during the fall and winter than they would during the spring and summer. By growing slowly over the winter they achieve a size advantage when spring rolls around that makes them less vulnerable to predators but capable of eating larvae and early juveniles of other animals colonizing estuaries later in the spring. This is the expression of an important rarely violated ecological theorem: “You can't eat anything bigger than your head”.  However, the tradeoff for overwintering in the coastal ocean and estuaries for this subtropical flatfish is that it exposes larvae and small juveniles to cold winter temperatures in the northern part of the species range. Small juveniles die when exposed to temperatures below about 2 or 3 degrees Celsius in the laboratory (Keefe & Able 1993, Szedlemeyer et al., Malloy & Target, 1991, 1994 ).

Figure 3. Estimated trends in summer flounder population

size and age class diversity in the mid-Atlantic Bight. 

The population size was low and dominated by a few 

young age classes in the late 1980s early 1990 when a 

Fisheries Management Plan (FMP) was implemented. 

This plan was amended (Amend 2) in the early 1990s. In the
1990s and 2000s the population increased in size and older
fish became more abundant increasing the age class diversity. 

Data from the 47th Northeast Regional Stock Assessment 

Another reason “fluke” are an interesting fish is that the mid-Atlantic bight population has been getting healthier over the past two decades. The estimated size of the population decreased from a peak in the early 1980s to a low about 1990 (Fig.3). During this time it was dominated by young fish and had a low age diversity. In response to this decline a fisheries management plan was established in late 1980s and amended in 1990s to restrict the amount of summer flounder that could be caught and the gear used to catch them. In the early 1990s the population began to rebound and older larger individuals became more common. The increased survival of older age classes is particularly important because higher age class diversity makes populations more resilient to environmental shocks. In part this is just simple bet hedging. But there is also evidence that older females produce more, higher quality eggs over a longer spawning periods than younger fish.  They are thought to produce kids more likely to survive the dangers of early life. This is the BOFFF hypothesis (Big, Old, Fat, Fecund, Females) . The summer flounder population is doing well enough that the current precautionary fishing regulations have become pretty contentious.

Figure 4. Estimated centers of summer flounder biomass 

during the autumn shifted toward the north east nearly
250 kilometers between 1973 and 2006. Centers of biomass
were calculated by fitting sample coordinates to year in a
generalized additive model that weighted the observations
by the standardized biomass of summer flounder collected
in North East Fisheries Center Bottom trawl surveys.  

But was the rebound of the summer flounder population entirely due to fisheries management and the reduction in predation by human predators? As the population increased the distribution of the animals also changed. The center of distribution of summer flounder in the Autumn seems to have shifted from an area offshore of Cape May, New Jersey in the 1970s, to one off the eastern end of Long Island, New York by the mid-2000s (Fig. 4). The velocity of this shift  was approximately 11 km per year from 1991 to 2005 (also see Janet Nye's work). These patterns suggest that changes in climate along with fishing may have affected the summer flounder population. Nineteen ninety one has been identified as the year in which the ecological dynamics of a number of marine populations changed. The summer flounder species range shift could have occurred because warmer temperatures allowed summer flounder to remain inshore on northern feeding grounds later in the autumn. However such a change in the timing of fall migration wouldn't necessarily result in an increase in population size. Alternatively, more early juvenile summer flounder may have survived more winters in northern estuarine nurseries because winter temperatures have been warmer in recent years. If this is the case summer flounder may be extending their species range to the north by coupling their life cycle and affecting affect the entire suite of habitats/ecosystems they use during their life history (Fig. 2). There is some evidence that the survival of young juveniles has increased in northern estuaries. Indices of Age-0 summer flounder abundance are have been higher in Massachusetts, Rhode Island and Connecticut (Fig. 5) than they have been in the past.  Furthermore winters have been warmer with fewer subfreezing degree days since the early 1990s (Fig. 5).  This is interesting since the survival of winter flounder juveniles on mid-Atlantic Bight estuarine nursery grounds has been poor since the 1990s apparently as a result of the high frequency of warm springs (Manderson, 2008).  Unlike summer flounder which are subtropical, winter flounder are a cold temperate flatfish.   

Figure 5. Standardized trends in age-0 summer flounder abundance estimated in surveys conducted in states ranging from Massachusetts (MA) to North Carolina (NC).  Abundance in Massachusetts, Rhode Island (RI), and Connecticut (CT) has been relatively high during the last decade (data from 47th SAW). This may have resulted from higher overwintering survival. Winters have been mild with fewer subfreezing degree days in the mid-Atlantic regions since the late 1980s based on analysis of daily temperature records compiled by the Academy of Natural Sciences in Philadelphia. New Jersey (NJ), Delaware (DE), Maryland(MD), Virginia (VA).

The effects of fishing regulations, and climate on migratory patterns and/or the survival of early juveniles summer flounder are alternative hypothesis of mechanisms that have could have different impacts on the mid-Atlantic ecosystem and implications for ecosystem and fisheries management. They are not necessary mutually exclusive, but may effect the population dynamics of summer flounder and the other animals they interact with simultaneously.   How can we find a method to estimate the relative contribution of fishing and changing climate on the habitat specific processes affecting the summer flounder population in the Mid-Atlantic Bight?

Keefe, M & KW Able, (1993) Patterns of metamorphosis in summer flounder, Paralichthys dentatus. Journal of Fish Biology 42(5): 1095-8649

Keefe M. & KW. Able (1994) Contributions of Abiotic and Biotic Factors to Settlement in Summer Flounder, Paralichthys dentatus. Copeia 1994 (2) 458-465 

Malloy, K. D., and T. E. Targett (1991) Feeding, growth and survival of juvenile summer flounder Paralichthys dentatus: experimental analysis of the effects of temperature and salinity. Mar. Ecol. Prog. Ser. 72:213-223.

Malloy, KD, and TE Targett. (1994) Effects of ration limitation and low temperature on growth, biochemical condition, and survival of juvenile summer flounder from two Atlantic Coast nurseries.
Trans. Am. Fish. Soc. 123:182–193.

Manderson. JP  (2008) The spatial scale of phase synchrony in winter flounder (Pseudopleuronectes americanus) production increased among southern New England nurseries in the 1990's. Canadian Journal of Fisheries and Aquatic Sciences 65:340-351.

Nye JA, Link JS, Hare JA, Overholtz WJ (2009) Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf. Mar Ecol Prog Ser 393:111-129

Szedlmayer, S. T., K. W. Able, and R. A. Roun-tree. 1992. Growth and ,temperature-induced mortality of young-of-the-year summer flounder (Paralichthys dentatus) in southern New Jersey. Copeia 1:120-128.

Scales of variation in the coastal ocean fish and invertebrate community: An analysis in progress

Figure 1. Spawning longfin inshore squid.  We caught many 
small juveniles in trawls and saw their egg mops in 
underwater video in the seascapes.  Age-0 Juveniles labeled 
as LOLPEA_0 in figures 3 & 4 below. 

The design of our bottom sampling which we described in detail earlier was pretty simplistic.  We used depth and sediment characteristics to divide up the New York and New Jersey seascapes into bottom habitat patch types. Using a patch model to classify habitat based on bottom features is usually flawed in the sea particularly in the temperate coastal ocean where dynamic water column features like temperature and currents affect everything from the physiology to the movements of cold blooded and often nearly neutrally buoyant animals. But you have to start sampling systematically based on the questions you want to ask and what little you know about the system in the beginning. The information we had to design our field study with was sonar measurements of depth and sediment type and an intensive study of oceanography (Chant et al.2008, Schofield et al., 2008,  Moline et al.2008).

Figure 2. Estimates of the percent variance (total 

inertia =5.18) in the fish & invertebrate community

 associated with beam trawl survey design factors 

made using partial redundancy analysis (pRDA). 

The design factors explained ~38% of the species 

variance while 62% remained unexplained. About 

15% occurred over time and much of this was 

seasonal changes in species dominating the 

community (see fig 4). Most of the spatial 

variability (23%) occurred at the finest scale among 

patches (within depth strata within seascapes). 

 Two percent of the species variance occurred 

simultaneously in space and time and these spatial 

dynamics also contributed to the variance ascribed 

to patches. All of the variance components that could

 be tested were significant in permutation tests at a 

P<0.01 level (see Borchard and Legendre 1994 

for basic method)

Our big questions in the ECOS research program are: What are the dominant physical and biological processes controlling the abundance and health of animals and their assembly into communities in the coastal ocean? What are the relative importances and scales of operation of those dominant processes? And finally, how can we use information about those processes to identify sweet spots in the ocean likely to sustain healthy marine communities which should therefor be conserved?

Figure 3. Ordination of species (blue) and temporal 

factors (year and season in red) along the first two 

axes from non-metric multidimensional scaling of 

the ecological distance between beam trawl samples

collected in the seascapes in 2008 & 2009. Changes 

in community structure over time were captured on 

NMDS axis 1 while spatial differences were captured 

on NMDS 2 (see fig. 5). Species abundances often 

increase in direction of the arrows (see fig. 4 below).

Species diversity (simpson's index) and evenness 

were higher at stations with high scores on both 

NMDS 1 &2. Many of these samples were collected 

at deep sites during the Fall, particularly in 2009. 

 Species codes: SCOAQU = windowpane flounder, 

PLEAME= winter flounder, PARDEN=summer 

flounder, ETRMIC =Smallmouth flounder, CITARC 

= Gulf stream flounder, PAROBL =Fourspot flounder,

LEUOCE= Winter skate, LEUERI=Little skate, 

MERBIL=Silver hake, PRICAR =Northern searobin, 

PRIEVO=striped searobin, AMMAME=american sand 

lance, STECHR= scup, PEPTRI =Butterfish, ANCMIT

=Bay anchovy, CENSTR= black seabass, LOLPEA = 

lonfin inshore squid, CANIRR=Rock crab age, 

CRASEP= sand shrimp, DICLEP=Bristled longbeak

shrimp, ASTSPP=Seastar asterias, ECHPAR=Sand 

dollar, PLAMAG=sea scallop. Codes followed by _0 

are age-0 individuals, _1 older animals.

In our inshore surveys we sampled at two time scales (year and season) and three spatial scales (seascapes [10s kms] , depth strata within seascapes [3-5 kms], patches of sediment within depth strata within seascapes [100s of m]). We assume that scales of community variation match the scales of operation of the important physical and biological processes causing the variation ¹. So we can use the nested survey design to identify scales of community variation falling within the limits of the study resolution and get clues about the operational scales some of the driving processes. (Our study resolution doesn't include variation on time scales of hours to weeks or decades, or over spatial scales more than a few10s of kilometers. We can identify meter to sub-meter scale spatial changes in the distributions and habitats of some animals visible in imagery we collected with the underwater video sled. There are other longer and larger scale surveys we can turn to to define the context for our study). Once we identify the dominant scales of community variation we can use our scale matching assumption and measurements of water column and bottom features made with satellites and radar by MARCOOS and with water quality sensors, acoustics, and underwater video by us to winnow down the likely candidate processes.

More specifically we can use our nested beam trawl survey to ask:

-Are dissimilarities in the fish and invertebrate community bigger in time or in space?

-Is the species turnover bigger between years or between seasons?

-How dissimilar are the communities in the two seascapes?

-Are differences in the communities in the two seascapes bigger than those in depth strata within the seascapes or sediment patches within depth strata within seascapes.

-Does the community vary simultaneously in space and time and at what scales do those spatial dynamics occur?

Figure 4. Abundance trends of selected species along 

the first NMDS axis which primarily captured changes 

in the biological community sampled with beam trawls 

in the two seascapes over time (see Fig 2). Much of 

this temporal variation was seasonal. Age 1+ northern 

searobin, sand shrimp and butterfish were most 

abundant during the spring surveys and had low scores

on NMDS1, while early juvenile (age-0) northern 

searobin, age-0 squid and scup were more common in 

the seascapes in the Fall and had high scores on NMDS1.

The curves are generalized additive model (GAM) 

smoothing spline fits of proportions of maximum 

abundance for each species to station scores on NMDS1

(Fig. 5 below). Two standard error confidence bands 

are shown in blue. Rare species and those that did not 

show significant trends on the axis are not shown. Titles 

above plots are species common names while y-axes are

labeled with the species codes used in Figure 3

Answering the last question should give us clues about the nature of dynamic water column features strongly affecting the distributions and abundances of the animals.

ANALYTICAL METHODS

To answer these questions I used the vegan library in R software to calculate ecological distances between samples and to visualize the relationships between community structure and the survey design factors (year, season, seascape, depth strata, patches of fine or medium sand) using non metric multidimensional scaling (metaMDS; e.g. Figs 3 & 4). I then used partial redundancy analysis (pRDA) to estimate proportions of the variation in the fish and invertebrate community to the nested factors we used to design the study (Fig. 2).

Before running these analyses I removed species occurring in less than 3 samples from the data and standardized abundances by dividing numbers of the remaining fish and invertebrates by the number of meters of bottom trawled to collect each sample. I then double square root transformed these standardized abundances to shrink the range and make the analyses sensitive to the rare as well as common species. Finally I used the bray-curtis index of similarity (%) in species composition as the index of ecological distance between the samples for the multidimensional scaling.

Before running these analyses I removed species occurring in less than 3 samples from the data and standardized abundances by dividing numbers of the remaining fish and invertebrates by the number of meters of bottom trawled to collect each sample. I then double square root transformed these standardized abundances to shrink the range and make the analyses sensitive to the rare as well as common species. Finally I used the bray-curtis index of similarity (%) in species composition as the index of ecological distance between the samples.

Figure 5. Ordination of samples along the first two axes
derived from the multidimensional scaling of ecological 
distances. Distances between points approximate 
differences in species composition and abundance between 
the samples. Samples collected in the New Jersey 
seascape are represented by circles while those collected 
in New York are squares. Open symbols represent shallow 
sites 10-20 meters deep whole deep sites (20-30m) are 
indicated by closed symbols. Red symbols are samples 
collected in patches of fine sand while blue symbols 
represent the samples from patches of medium to coarse 
sand. (still under construction).

  

¹ Is the scale matching assumption always valid? Just as the variability of the atmosphere is buffered  as it it translated across the oceans surface, don't the organisms use their physiologies and behaviors to lower “pitch” of the ocean?

Borcard, D. & P. Legendre. 1994. Environmental control and spatial structure in ecological communities: an example using Oribatid mites (Acari, Oribatei). Environmental and Ecological Statistics 1: 37-61.

Seascapes, Landscapes & Marine Habitat Dynamics

Because the density and viscosity of water are higher than air, the structures in the ocean span shorter distances and last longer than similar structures in the atmosphere. The space-time diagram above shows that the speed of the ocean is about 100 times slower than the atmosphere. This difference in the speed of the environment on land and in the sea has allowed marine animals to remain more tightly coupled to the oceans “weather” than land organisms are to the atmospheres weather. In the diagram V1-V5 are lines of equal velocity at 10³, 30, 0.3 , 3x10^-3 & 3x10^-5 centimeters per second. The red lines and blue lines are characteristic velocities of the atmospheric and ocean structures that are labeled in the same colors. The approximate space-time scales of forests on land and phytoplankton in the sea are also in the plot. Phytoplankton live and die fast like cavalier poets. The darker dotted line at the bottom is the threshold where laminar flow becomes turbulent flow. Life happens in turbulent flows. The diagram combines information from those in Mamayev (1996) and Steele & Henderson (1994)

One of the really interesting things to think about as a marine scientist is how different from landscapes, seascapes must be from an organisms perspective. Wrestling with this is central to our research program which is concerned with understanding why some parts of the ocean are essential to the survival of marine organisms and the resilience of marine ecosystems. Understanding the nature of the seascape is not easy because we come at the problem with terrestrial bias's; biological and ecological bias's that are even harder to overcome than cultural biases. Understanding differences between marine and terrestrial animals and the environments they occupy is also difficult because it requires a deep understanding of differences in the rates of change in space and time of the external environmental characteristics that affect survival and which therefor have guided the evolution of the animals in the sea. This is a problem of scaling. The space-time diagram above tries to identify the differences in the length-time scales of important dynamic features of the ocean and atmosphere.

Variations in wind, temperature, and precipitation associated with atmospheric storms, fronts, cyclones and long fronts are translated across the surface of the sea to create the waves, fronts, eddies, gyres, and deep ocean circulation which are the “weather” of the ocean. The high density and viscosity of water, its capacity to retain heat and dissolve salts, and its huge volume in the sea, causes the variability in atmosphere to be dampened and slowed down as it is translated across the sea surface. In the plot above, the turbulent structures of the oceans (in blue) are connected by the dotted blue line that is parallel to, but shifted to the right of the structures making the weather of the atmosphere. That rightward shift indicates that it takes much longer for similar structures move over a given distance in the sea. The take away message for me is that while life happens everywhere in turbulent flows and the speed of the ocean and its habitats is about 100 times slower than the speed of the atmosphere.

Because the variability of the atmosphere is slowed down and dampened in the sea, most marine organisms have not evolved the elaborate mechanisms of metabolic and physiological regulation required of terrestrial organisms to maintain homeostasis while in contact with a fast, extremely variable atmosphere. This means that most marine animals are more tightly coupled to the dynamics of the ocean's weather than terrestrial organisms are to the atmosphere's. The properties and dynamics of the water in the ocean are therefor critical to defining the habitats of marine organisms; even those strongly associated with the bottom. There are all sorts of interesting ramifications to this for marine animals who usually start out floating about in the ocean as fertilized eggs a few millimeters long, but grow in spatially dynamic universe made of structured water with 3 spatial dimensions over a huge range of body sizes. The habitat of a baby fish is probably not even perceived by a juvenile or adult. This is not the case for terrestrial organisms.

However there may be some bad ramifications to all this too. Below is a figure made from Sorte et al. (2010) who demonstrated that recent poleward shifts in distributions of marine organisms have occurred at 10 times the speed of the poleward shifts of land animals. These fast species range shifts in the sea may have to do with the tight physiological coupling of marine animals with oceans "weather:. They might also be related to the fact that the dominant force controlling movements in the sea is viscosity instead of gravity which controls the movements of organisms on land. The flows of materials and thus the connections between distant parts of seascapes are greater than for landscapes. Its generally easier to disperse faster over longer distances in the sea. The range shifts of marine and land animals depicted in the graph are probably the result of global climate change.

Steele JH & EW Henderson (1994) Coupling between physical and biological scales. Phil. Trans. R. Soc. Lond. B. 343: 55-9

Mamayev OI (1996) On space time scales of oceanic and atmospheric processes. Oceanology 35(6) 731-734


Sorte et al. (2010)  Marine range shifts and specie introductions: comparative spread rates and community impacts.  Global Ecology and Biogeography.  19: 303-316