Excavating the Bare Bones of Community Structure Using Loop AnalysisBy Patricia LaneDalhousie University

Goal: to illustrate how Dick Levins’ loop analysis is useful for analyzing complex systems using a marine ecosystem example

• Rationale: we have one ocean, which is constantly under threat; we need to understand how perturbations affect ecological networks through a myriad of pathways and feedbacks

• Although this is a marine example, a similar use of loop analysis could be helpful to understand any complex system with a detailed data set.

Traditional DescriptiveFood Webs

• This diagram is essentially a summary of every feeding relationship known to the author

• Certainly not stable-excessive connections

• Poor sense of functional groups and links that are ecologically relevant

• Cannot locate parameter inputs

• Not useful for making predictions about perturbations

Objectives1. To introduce the field and

laboratory studies at the Marine Ecosystem Research Laboratory (MERL) (NOAA Grant to Levins/Lane)

2. To illustrate ecological skeletons and some of their properties

3. To acknowledge Dick’s considerable influence on this and other work

1. Methods: Field Studies • 10 cruises in Narragansett Bay

• Temperature/light

• Nutrients & water quality parameters

• Phytoplankton

• Zooplankton

1. Methods: Laboratory Studies Nutrient Enrichment ‘Press’ Experiment at MERL

Tanks: 1.8 m diameter

5.5 m deepwith sediments

3 controls6 treatments1X, 2X, 4X, 8X, 16X, 32X~sewage effluents = 1X12.8N, 1.0P, 0.9Si-inorganicSimulate Bay mixing, light, temperature, residence time, benthic-pelagic coupling

1. Methods: Loop Analysis Models

Loop Analysis Data Preparation• Prepare a qualitative

correlation matrix

• Determine functional groups and loop variables

• Calculate directed changes in data sets by loop variable

• Prepare models

• Fit loop predictions to data –try to achieve 90-100% accuracy

• Created by Dick Levins in 1973 -NY Acad. Science paper

• Signed digraphs, qualitative analysis that predicts changes in standing stocks of variables and their turnover rates following a parameter change

• Also gives measures of network structure and stability

• Used theoretically to explore complex systems as well as with data sets

Loop Variables (~300 taxonic units)

• Si=silica

• N=nitrogen/phosphorus

• N2 =organic nitrogen

• A1=diatoms

• A2=dinoflagellates

• A3=luxury consuming diatoms

• A4=silica flagellates

• A5=microflagellates/monads

• Z1 & Z3 = copepods

• Z2=copepodids and nauplii

• Z4 = cladocerans

• M=mollusc larvae

• C=cirriped larvae

• P=polychaete larvae

• G=gammarids

• R=rotifers

• S = Sagitta elegans

• D = decapods

• MD=medusae

2. Results: 9 Field Loop Models (Annual Cycle: No 2 models identical)

Ecological Skeleton (ES) of the Field Loops[- +] with 9 link types

• Variables in a minimum of 3 models

• Solid links 2/3 of networks

• Dashed Links ≥ 1/3 of networks

• 63 feedback loops & 4400 pathways

• ES does not appear in nature-too connected, but it is a framework for the community

• ~ Chemical isomers

Core Loops = Ecological Skeleton (ES)• LA provides basic structure of an

ecosystem (or any complex system) as the prevalent variables, links and parameter inputs that explain the pattern of changes in standing stocks

• Thus, the Ecological Skeleton (ES) is a form of minimal description (bare bones) used to capture the main aspects of community structure & how parts and whole interact

• Most ‘nibbling’ in traditional webs is irrelevant – ES helps get rid of the ‘noisy’ detail

Some Loop Analysis Benefits• Marine food webs have a 3 tier structure, largely composed of L1

and L2 feedbacks, short negative feedback loops that are stabilizing, but several links have 2 or more link types = volatile links (often nonlinearities can be identified)

• Can identify missing variables (N3 and Ax) and link types (silica-diatoms) that are counter-intuitive, but later explained by biology

• Can identify where parameter inputs enter-not always the manipulated one

• Can observe how the variables and links change through an annual cycle and how one community prefigures another

• Loop analysis helps identify what is necessary to measure with great economy of resources (10-15%)

• Can compare over ecosystems to measure ES similarities

• IN SUMMARY, LA PROVIDES NEW INFORMATION AND INSIGHTS

Comparison of the Field and Lab ESs

Narragansett Bay(9 cruise dates)

MERL Tanks(135 dates x tanks)

Some Results

Field• 3 large pelagic predators out-

competed in MERL tanks

• Predictions 145 (3 wrong or 2% and 6 zeroes or 4%) 94% correct

• System of small plankton less discrete than in tanks

• Connectance: 16%-in line with theoretical predictions of Gardner and Ashby of 13 +/-2 % dividing stable and unstable networks

MERL Lab (X-32X nutrient additions)• Benthos absorbed most of

increased productivity-strong coupling

• 1046 predictions (12 wrong or 1% and 102 zeroes or 10%) 88% correct

• Little response of small algae and copepods up trophic gradient

• Connectance: higher (19%)-perhaps because of confined space

• Limits to enrichment after 4X

Structural Complexity Calculations• MACRO-UNIVERSE: A 20

variable loop model has 3N² or 3400 or 7.06 x 10190 mathematically possible configurations-but most are not biologically reasonable

• Lab: 15 variables = 3225 or 2.25 x 10107

• MICRO-UNIVERSE: 6 x 107 to 1.3 x 1012 for the tanks-biologically reasonable

• THE TRUTH “truly” IS THE WHOLE in the structural and functional complexity of all of these feedback relationships in ecosystems

Stable Unstable

Ecological Complexity• Dick has called complexity, “the central intellectual problem of our time.”

• A complex system cannot be completely described: has at least on incomputable model. (Robert Rosen)

• Both structural and functional definitions

• To advance ecological theory, we need to understand the level of complexity in ecological skeletons with their rich feedbacks, only studying pairwise interactions in natural communities is not enough.

• Need to develop ecological skeletons for all major kinds of ecosystems

Emergence• Core models or ecological skeletons (ES) have been computed as summaries of a set of loop models and therefore are calculated as collective properties

• Over a number of marine ecosystems, the ES are robust and very similar, transcending space, time, taxonomy, field & lab methods and investigator intuition: EMERGENT???

• ESs for freshwater ecosystems are different and less robust and have more structural change with nutrient enrichment

• Attempts to ‘break out’ of the marine ES have been unsuccessful

• Can’t prove one ES is the best or most optimum descriptor

4. Thank You Rosario

• When the MERL work first started, Dick had a massive heart attack and many of us wondered if he would reach his 55th birthday let alone his 85th.

• Although Rosario is not here today, she has to be given a lot of credit.

• She oversaw Dick’s change of lifestyle, which gave him another 30+ years of great accomplishments, and he gave us innumerable academic gifts, friendships, and life-altering influences.

4. Thank You Dick1. Teaching

-Communities and Ecosystems

-Political Ecology

-Sustainability & Global Change

2. Research

-Tropical fieldwork & theoretical ecology/Ph.D.

-Ecological networks/loop analysis

Political Work

-International Development-with a political conscience

-Cuban Model-an alternative to neoliberal globalization

-How Cuba could be the First Sustainable Society?

Happy 85th Birthday to an amazing complex system with delightful emergent properties & unique self-organization always exuding creativity and humanity in equal portions.