Ecological software: Ecopath with Ecosim (EwE)

EwE is windows based ecological simulation software with development centred on the University of British Columbia’s Fishery Centre. EwE has three main components: Ecopath – a static, mass-balanced snapshot of the system; Ecosim – a time dynamic simulation module for policy exploration; and Ecospace – a spatial and temporal dynamic module primarily designed for exploring impact and placement of protected areas. The Ecopath software package can be used to:

• Address ecological questions;


• Evaluate ecosystem effects of fishing;


• Explore management policy options;


• Evaluate impact and placement of marine protected areas;


• Evaluate effect of environmental changes.

EwE together with papers and published models are available at the EwE website.

The foundation of the EwE suite is an Ecopath model (Christensen and Pauly 1992, Pauly et al. 2000), which creates a static mass-balanced snapshot of the resources in an ecosystem and their interactions, represented by trophically linked biomass ‘pools’. The biomass pools consist of a single species, or species groups representing ecological guilds. Pools may be further split into ontogenetic (juvenile/adult) groups that can then be linked together in Ecosim.

Initial impressions – beginning the journey

For the non-expert user I recommend starting with the default models installed with EwE. Note that EwE is now available in version 6.x.x. Although most of the features available in the previous version 5.x.x some elements have not been able to be carried forward (notably Ecoranger) so it may be worthwile downloading and using both versions in order to explore the full functionality of the software suite. It is likely that over time the later version will totally supercede V5 and earlier and personally I prefer the latest version of Ewe 6. The models should be thoroughly explored before advancing to other data sets.

Predator/prey niche overlap screen in EwE 6.04

Data requirements for the model are not overly onerous and can be obtained from stock assessment, ecological studies, or the literature: biomass estimates, total mortality estimates, consumption estimates, diet compositions, and fishery catches. As an enthusiast user it did take me a little time to track down data sources relevant to certain Australian marine environments however the fact that I could do this without too much effort shows how readily available the data can be if a little effort is expended.

I am exploring published models available on the Ewe website and online at other sources. I am also completing models for which a published paper exists but the model itself is not available for download. I am using two models by Gribble (Gribble, N. A. (2003) GBR-prawn: modelling ecosystem impacts of changes in fisheries management of the commercial prawn (shrimp) trawl fishery in the far northern Great Barrier Reef and Gribble, N. A. (2005) Ecosystem Modelling Of The Great Barrier Reef: A Balanced Trophic Biomass Approach) as a base to fill in the gaps to complete a model based on a masters thesis by Paul Tudman (Tudman, P. D. (2001) Modelling the trophic effects of fishing on a mid-shelf coral reef of the central Great Barrier Reef ) for which limited data was available. The results have been useful and interesting to date although more work fine tuning and getting the relationships between clades correct needs to be done by me before my modelling of the Rib Reef ecology is “completed”.

Biomass screen in EwE 6.0.4

Habitat base map of Rib Reef (GBR) in EwE 6.0.4

The most important conclusion from running my adapted Rib Reef model thus far is that maximum yield fishery (MYF) management is not the way to proceed as it is not a sound scientific basis for management of the entire fishery. Fisheries management of a species based on MYF does not allow for the often dramatic consequences for other clades that either feed upon, directly or indirectly, or are preyed upon, again directly or indirectly, the species which is being managed. This is fascinating stuff for the enthusiast and no doubt even more so for the cutting edge expert scientists!

I advise reviewing the literature and modifying already well documented habitat and ecological data where prime sourced empirical data of your own is not available. I have used Gribble who in the Australian Great Barrier Reef context is an authority. Where Gribble’s data is not sufficient I have utilised data from similar reef habitats located elsewhere in other ecosystems. Obviously empirical data from the actual locality of interest is preferable although for a non funded party costly, time consuming and let’s face it almost impossible to obtain. Even the scientists have difficulties obtaining the data so do not let this discourage you. With careful data selection and analysis important relationships can be analysed and explored and the need for exact data is not required. Basic knowledge of modelling in any context after all tells us the utility of a model is not dependant upon its realism but rather its value lies in its predictive ability and in this context I am referring to realism or exactness of data specific to a particular locality. Generalised data fine tuned to specific systems can be used as the basis for later field research to obtain specific data for the system and focus further research required. Validation through empirical research is another topic for more research by scientists.

Ecosim plot of Rib Reef (GBR)

From the EwE authors

This section of the about file (quoted below) on the EwE site gives an excellent snapshot overview of the software and how it works. The literature (available on the site) gives much deeper insight and analysis.

“The parameterization of an Ecopath model is based on satisfying two ‘master’ equations. The first equation describes the how the production term for each group can be divided:

Production = catch + predation + net migration + biomass accumulation + other mortality

It is the aim with the Ecopath model to describe all mortality factors; hence the ‘other mortality’ should only include generally minor factors as mortality due to old age, diseases, etc. The second ‘master’ equation is based on the principle of conservation of matter within a group:

Consumption = production + respiration + unassimilated food

In general, an Ecopath model requires input of three of the following four parameters: biomass, production/biomass ratio (or total mortality), consumption/biomass ratio, and ecotrophic efficiency for each of the functional groups in a model. Here, the ecotrophic efficiency expresses the proportion of the production that is used in the system, (i.e. it incorporates all production terms apart from the ‘other mortality’). If all four basic parameters are available for a group the program can instead estimate either biomass accumulation or net migration. Ecopath sets up a series of linear equations to solve for unknown values establishing mass-balance in the same operation. The approach, its methods, capabilities and pitfalls are described in detail by Christensen and Walters (2000).

The process of constructing an Ecopath model provides a valuable end product in itself through explicit synthesis of work from many researchers. Several EwE models illustrate this, e.g., for the Prince William Sound (Okey and Pauly 1999), the Strait of Georgia (Pauly et al. 1998), the Hecate Strait (Haggan and Beattie 1999) and several North Atlantic models being created by the Sea Around Us project at the UBC Fisheries Centre. The model construction process has brought together scientists, researchers and data from state and federal levels of government, international research organizations, universities, public interest groups and private contractors. Key results include the identification of data gaps as well as common goals between collaborating parties that previously were hidden or less obvious. We find the process especially important for enabling the interest groups to take ownership of the model that is derived; this is especially required when operating at the ecosystem level, where multi-faceted policy goals have to be discussed widely as part of the management process. This is facilitated by the policy exploration methods included in the Ecosim model discussed further…”.

Although EwE has a primary focus on modelling marine systems it is possible to develop other models. In the future I will be exploring this functionality. I will be posting more on the software (after all it is the namesake of this blog) including details of my modelling so check back and see where it is taking me.

Modelling of Life History Changes

A study published on 10 November 2008 (Kuparinen A, O'Hara RB, Merilä J (2008) Probabilistic Models for Continuous Ontogenetic Transition Processes. PLoS ONE 3(11): e3677. doi:10.1371/journal.pone.0003677) by PLuS One introduces probabilistic reaction norm models of continuous ontogenetic transitions such as animal life history changes or invertebrate metamorphosis.

Reaction norms have traditionally been formulated deterministically, so as an organism's developmental status is assumed to change exactly at the time it hits the reaction norm.

Two alternative survival analyses based modeling approaches for describing ontogenetic transition processes in continuous time are proposed by the authors. The models are simplified compared to previous models but are easier to use in terms of modelling effort and data requirements. Therefore, they provide user-friendly and broadly applicable tools for continuous time analyses for typical ontogenic transition data sets, as well as for predicting transitions at very fine time scales. The authors examine performance of the models using empirical data on timing of metamorphosis in the common frog, Rana temporaria.

All equations reproduced here have the same reference numbers as in the original article for ease of use. A brief summary of the models is presented below and taken largely from the paper the original of which is provided in a link at the end of this post. Any errors in the summary are mine and the orignal should be referred to by those interested in this topic.

Survival analysis based approaches for ontogenetic life-history transitions

Ontogenetic transition processes are of a type where an individual ages, and at some point in time experiences an event that can only occur once for each individual. In medicine, this kind of process is investigated using survival analyses, the name deriving from the fact that the considered event is often death. The starting point for survival analysis is to assume that the probability that an event will occur is governed by a rate h(t), which is usually called the hazard. If the mathematical form for how this changes with t is known, then the probability that the event (e.g. maturation or death) will not occur before time t, denoted as S(t) and called the survivor function, can be calculated by

(2)

Or, if time is discrete, the probability that nothing happens before time t is the product of the probabilities of nothing happening in each time step before t. Typically, survival analyses use data on the times to events (T) to ask how the hazard function or, equivalently, the survivor function is affected by different covariates.

The paper proposes two alternative modelling approaches that relax the effort of composing the exact analytical form of the hazard, by directly making assumptions about the survivor function, and how it is affected by the covariates.

1. A Parametric Survival Analysis

The model used in the paper reduces modelling effort to a parametric survival regression in which the distribution of an individual's survival time T (i.e. the time it takes until an individual faces the transition event) is modelled directly by

(3)

where α is an intercept parameter, β is a vector of free model parameters, x is a vector of optional covariates, σ is a scale parameter (also a free model parameter) describing variance in the data, ε is a random variable, following some distribution, and f is a link function appropriate for the distribution of given ε.

The estimated parameters of eqn (2) can be used directly for assessing how much variation is induced to the timing of the transition event by the covariates.

Once the parameters of the survivor function have been estimated, the paper explains how model can be used to predict the occurrence of transitions.



Figure 1. Timing of metamorphoses of common frog (Rana temporaria) reared in a common garden experiment.

Individuals are exposed to three temperature and two food level (ad libitum or restricted) treatments. In both panels, growth temperatures are indicated with colours (see colour legend), and restricted food is indicated with open circles/dashed line, and ad libitum feeding with filled bullets/solid line. Individual observations of ages and weights at metamorphosis are shown in panel A. Cumulative probabilities for the timing of metamorphoses calculated from the raw data are shown in panel B.

2 Semi-parametric Survival Analysis.

A method developed by Cox performed an analysis of survival data by splitting the model into two parts: 1) the survival function, which only depends on time, and 2) a term for the ratio of the hazards (i.e. rates of the events) for different classes (the proportional hazards model). This modelling approach is particularly convenient if interest lies on the proportional effects of the covariates on T, rather than the distribution of T itself. The Cox proportional hazards model assumes that the effects of the covariates are multiplicative, so that the survival probability S(t) = P(T>t) is given by

(4)

where β and x are as above, t is any freely chosen time point, and P0 (t) is a baseline hazard function that gives the probability P(T≤t x = 0). The authors state that the Cox regression is very convenient in the sense that no underlying distribution for the transition time needs to be assumed. Similarly, when assessing the proportional effects of the explanatory variables on S(t), P0 does not have to be known either. It is only required for estimating the actual survival probability S(t) = P(T>t). From these, predictions for transition probabilities can be derived for any time interval similarly as above.



Figure 2. Cumulative probabilities for the timing of metamorphoses predicted by the parametric survival model (eqn. 3).

Different growth temperatures are indicated with different colours, and different food level treatments with different line types (solid line = ad libitum, dashed line = restricted food). Gray lines beneath the estimated cumulative probabilities are the observed cumulative distributions for the timing of metamorphosis.

The models presented in the paper make the concept of probabilistic modelling of ontogenetic life-history transitions in continuous time more easily accessible. Transition data can be analysed which is typically available from the wild with little prior knowledge of mechanisms underlying transitions. As random effects can be incorporated into the model survival based models can be used to estimate genetic variability in transition probabilities by using data obtained from breeding under controlled conditions or through the use of genetic markers. The aquaculture industry is an obvious possible user of these models.

Of particular interest to me is that survival based models would be useful in studies investigating and predicting patterns of metamorphosis in insects and amphibians. The authors conclude that more generally, the models should aid the concept of probabilistic reaction norm becoming as general and applicable tool in the studies of life-history variation as the deterministic reaction norms are today.

The article can be found here at PloS One.