The Seascapes

The Seascapes

Sunday, September 19, 2010

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.

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?

Friday, September 3, 2010

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