Ecological Modeling in Risk Assessment - Chapter 13 pdf

1982, Iwasa 1998, Tanaka 1998, Hakoyama and Iwasa 1999 Stressor: Any stressor whose impacts can be summarized as a change in the mean or variance of the population growth rate or density

Trang 1

Profiles of Selected Models Robert A Pastorok

Table 13.1 summarizes the models selected for further development and use in ecological risk assessment in the near future These models were rated relatively high in the evaluation within model type Profiles of models selected for further development and use are shown in Tables 13.2 through 13.7

Toxicity extrapolation models are intended mainly for use in screening-level risk assessments and for developing generic environmental criteria (e.g., EPA’s ambient water quality criteria) They are not recommended for use alone in a detailed (e.g., baseline) risk assessment Nevertheless, they may be useful in supporting population, ecosystem, or landscape models as part of a detailed assessment.

Of the population models, stochastic scalar abundance models (either discrete or continuous-time) and deterministic life-history matrix models are most appropriate for screening-level ecolog-ical risk assessments (Table 13.1) Some simple food-web models, founded on RAMAS Ecosystem

or Populus, for example, may be appropriate for screening-level assessments at larger, complex sites, especially where disruption of food-web structure may be an issue More complex ecosystem models and landscape models are not recommended for screening-level assessments because of the relatively high level of effort and expense involved in developing and running these models For detailed assessments, stochastic life-history matrix models and metapopulation models (e.g., RAMAS GIS and VORTEX) are recommended These models, as well as aquatic ecosystem models like AQUATOX, CASM, and IFEM, aquatic landscape models like ATLSS, AQUATOX, and CASM, and terrestrial landscape models like LANDIS, JABOWA, and the disturbance bioge-ography model, are suitable for application in detailed ecological risk assessments Several model categories lack specific examples of available models for detailed assessments Further development

of such models could include integration of metapopulation models with food-web models and other ecosystem models and with landscape models (Table 13.1)

The selection of specific models for addressing an ecological risk issue depends on the balance between model complexity and the availability of data, the degree of site-specificity of available models, and the issue, ecosystem, endpoints, and chemicals of interest The models must be appropriate for the context, whether for the evaluation of risks associated with new chemicals and their uses, of ecological impacts and risks associated with past uses, or of clean-up and restoration issues Because the selection of models is specific to the issue, chemical, and site of interest, we have provided only general guidance for selecting ecological models (see Chapter 1, Introduction,

1574CH13.fm Page 195 Tuesday, November 26, 2002 6:23 PM

Trang 2

Table 13.1 Ecological-Effects Models Selected for Further Development and Use in Chemical Risk Assessment

Toxicity-Extrapolation Level of

General

Screening Stochastic scalar

abundance

interspecies Life-history matrix

(deterministic)

RAMAS Ecosystem

Species- sensitivity distribution Detailed Life-history matrix

(stochastic)

Integration of RAMAS Ecosystem with spatially explicit metapopulation models

AQUATOX CASM IFEM

Integration of spatially explicit metapopu-lation and food-web models

Integration of spatially explicit metapopulation and landscape models

ATLSS AQUATOX CASM IFEM

LANDIS JABOWA Island disturbance biogeo-graphic

N/Aa

Metapopulation

RAMAS GIS

VORTEX

Note: N/A - not applicable.

aRelationships for use with ecological models

Trang 3

The Process of Ecological Modeling for Chemical Risk Assessment) Moreover, the models that

we selected for further development and use are not necessarily the only models that would be useful for ecological risk assessments in the near future.

Many of the models reviewed here are applicable for answering ecological risk questions We urge the reader to consider his/her needs, select a general category of models according to the endpoint

Table 13.2 Profiles of Selected Population Models — Scalar Abundance Models

Model: Stochastic Continuous-Time Models

References: Poole (1974), Goel and Richter-Dyn (1974), May (1974), Ginzburg et al (1982),

Iwasa (1998), Tanaka (1998), Hakoyama and Iwasa (1999) Stressor: Any stressor whose impacts can be summarized as a change in the mean or

variance of the population growth rate or density-dependence parameters Type of ecosystem: Various (terrestrial, freshwater, estuarine, marine)

Temporal resolution: Determined by resolution of available census data and generation time of the

species; time step is usually between 1 week and 1 decade Time scale: Determined by the modeler according to the question addressed, the quality

and amount of available population data (e.g., period during which the data were collected), and generation length of the species; time period is usually between several weeks and a century

Spatial resolution: Not spatially resolved

Geographic scale: Implicitly identified with the geographical scale of modeled population

Biological scale: Population

Model type: Simple scalar abundance model

Level of detail: Screening

Endpoints: Expected future population abundance; statistical distribution of abundance;

risk of a population decline of a specified size; time to a population decline

of a specified size Comments: Has minimum data requirements for any population-level, risk-analytic model;

can include effects of density dependence or omit them in a conservative assessment; can integrate effects of chemical and non-chemical stressors through time; methodology can be used now but would benefit from further development of allometric relationships to inform data parameterizations when empirical information is sparse

Model: Stochastic Discrete-Time Models

References: Poole (1974), Capocelli and Ricciardi (1974), Goel and Richter-Dyn (1974),

May (1974), Ginzburg et al (1982), Ferson (1999) Stressor: Any stressor whose impacts can be summarized as a change in the mean or

variance of the population growth rate or density-dependence parameters Type of ecosystem: Various (terrestrial, freshwater, estuarine, marine)

Temporal resolution: Determined by resolution of available census data and generation time of the

species; timestep is usually between 1 week and 1 decade Time scale: Determined by the modeler according to the question addressed, the quality

and amount of available population data (e.g., period during which the data were collected), and generation length of the species; time period is usually between several weeks and a century

Spatial resolution: Not spatially resolved

Geographic scale: Implicitly identified with the geographical scale of the modeled population Biological scale: Population

Model type: Simple scalar abundance model

Level of detail: Screening

Endpoints: Expected future population abundance; statistical distribution of abundance;

risk of a population decline of a specified size; time to a population decline

of a specified size Comments: Has minimum data requirements for any population-level, risk-analytic model;

can include effects of density dependence or omit them in a conservative assessment; can integrate effects of chemical and non-chemical stressors through time; methodology can be used now but would benefit from further development of allometric relationships to inform data parameterizations when empirical information is sparse; easier both to implement in software and to parameterize than continuous stochastic models

Trang 4

desired, and review the ratings of individual models For specific needs, the ratings may be differen-tially weighted, and the best models may differ from the ones selected by us for further development.

Table 13.3 Profiles of Selected Population Models — Life-History Models

Model: General Matrix Models

Stressor: Any (including chemical and thermal pollution, harvest, entrainment,

impingement, habitat loss, habitat fragmentation); depends on implementation Type of ecosystem: Various (terrestrial, freshwater, estuarine, marine)

Temporal resolution: Theoretically any temporal resolution is allowed; practically, temporal resolution

of the model depends on temporal resolution of the data; generally ranges from 1 day to 10 years

Spatial resolution: Not spatially explicit, but can be embedded within a metapopulation model Geographic scale: Not spatially explicit

Time scale: Theoretically any; practically, depends on temporal resolution and question

being asked (endpoint); generally ranges from several weeks to several decades

Model type: Structured population

Level of detail: Tier 2

Endpoints: Depends on implementation (e.g., which software package is being used);

possibilities include expected future abundance, risk of decline in abundance, risk of extinction, time to extinction, time to decline, harvested biomass, as well as measures of uncertainty about these endpoints

Comments: Highly recommended as a general class of models; assumptions are realistic

and endpoints are comprehensive; implementation generally requires the use

of a software package or programming; matrix models have been embedded

in metapopulation models in which each subpopulation is modeled with a matrix; new theory that will add to the utility of matrix models is the development of risk-based sensitivity analysis

Model: RAMAS Age, Stage, Metapop, or Ecotoxicology

Reference: Applied Biomathematics (2000)

Stressor: Any stressor, including chemical and thermal pollution, harvest, entrainment,

impingement, habitat loss, habitat fragmentation Type of ecosystem: Various (terrestrial, freshwater, estuarine, marine)

Temporal resolution: Theoretically any temporal resolution is allowed; practically, temporal resolution

of the model depends on temporal resolution of the data; generally ranges from 1 day to 10 years

Spatial resolution: Not spatially explicit (but see comments)

Geographic scale: Not spatially explicit (but see comments)

Time scale: Theoretically any; practically, depends on temporal resolution and question

being asked (endpoint); generally ranges from several weeks to several decades

Model type: Structured population

Endpoints: Expected future abundance, risk of decline in abundance, risk of extinction,

time to extinction, time to decline, harvested biomass, and measures of uncertainty around each endpoint

Comments: These programs comprehensively apply matrix models; Age and Stage are

only available in DOS, whereas Metapop and Ecotoxicology are Windows® -based; only Ecotoxicology explicitly models the effects of toxic chemicals; the other programs can be manipulated to model toxicant effects by running several simulations (e.g., with and without toxic chemicals)

Trang 5

Table 13.4 Profiles of Selected Population Models — Metapopulation Models

Model: RAMAS GIS (and Metapop)

Reference: Akçakaya (1998a,b); Applied Biomathematics (2000)

impingement, habitat loss, habitat fragmentation Type of ecosystem: Various (terrestrial, freshwater, estuarine, marine)

Temporal resolution: Variable; determined by resolution of available demographic data (e.g., census

frequency) and generation length of the species; typically between 1 week and 1 decade

Spatial resolution: Variable; determined by resolution of available spatial data (e.g., size of raster

map cells) and spatial behavior of the species (e.g., home range size, daily movement distance, etc.); typically between one meter and several kilometers Geographic scale: Variable; determined by the range of the species, the question addressed, and

the availability of geographic data; typically between one hectare and thousands of square kilometers

Biological scale: Population or metapopulation

Time scale: Variable; determined by the question addressed, the quality and amount of

available demographic data (e.g., period during which the data were collected), and generation length of the species; typically between several weeks and several centuries

Model type: Metapopulation

time to extinction, time to decline, harvested biomass Comments: Has high realism, relevance, and flexibility and can be used for risk assessment

without additional development; however, further development that will likely make it more suitable includes linking it to a GIS-based hydrodynamic model, adding sex structure, genetics, and different types of density-dependence; GIS/Metapop enables one to model several structured subpopulations that together comprise a metapopulation

Model: VORTEX

Reference: Lacy (1993, 1999)

impingement, habitat loss Type of ecosystem: Various, but mostly terrestrial

Temporal resolution: Variable; determined by resolution of available demographic data (e.g., census

frequency) and generation length of the species; typically between 1 week and 1 decade

Spatial resolution: Not spatially explicit; spatial structure based on predefined populations Geographic scale: Variable; determined by the range of the populations modeled and the question

addressed Biological scale: Population or metapopulation

Time scale: Variable; determined by the question addressed, the quality and amount of

available demographic data (e.g., period during which the data were collected), and generation length of the species; typically between several weeks and several centuries

Model type: Individual-based metapopulation

Endpoints: Expected future abundance, risk of extinction, time to extinction

Comments: Has medium level of realism (in comparison with other individual-based models

reviewed), high relevance, and high resource efficiency; parameters in the model can be estimated with relative ease; may not be suitable for plants, highly fecund species, and very abundant species; does not incorporate habitat relationships

Trang 6

Table 13.5 Profiles of Selected Ecosystem Models — Food-Web Models

Model: RAMAS Ecosystem

Reference: Spencer and Ferson (1997a); Applied Biomathematics (2000)

Type of ecosystem: Various (terrestrial, freshwater, estuarine, marine)

Temporal resolution: Any temporal resolution is allowed by the program, but generally ranges from

weeks to years Spatial resolution: Not spatially explicit

Geographic scale: Not spatially explicit

Biological scale: Food web, food chain, or simple predator–prey interaction

Time scale: Any time frame is allowed, but the general range is between several months

and several decades

time to extinction, time to decline, toxicant concentration in the environment, toxicant concentration in the organisms

Comments: This program is quite flexible and easy to use; it explicitly models the effects

of toxic chemicals, and does not assume that populations are at equilibrium (which is in its favor); several relevant endpoints are available; a generalization

to allow an intermediate between prey-dependent and ratio-dependent functional responses would increase its utility

Model: Populus

Reference: Alstad et al (1994a,b); Alstad (2001)

Type of ecosystem: Various (terrestrial, freshwater, estuarine, marine)

Temporal resolution: Any temporal resolution is allowed by the program, but generally ranges from

weeks to years Spatial resolution: Not spatially explicit

Geographic scale: Not spatially explicit

Biological scale: Food web, food chain, or simple predator–prey interaction

Time scale: Any time frame is allowed, but the general range is between several months

and several decades

Endpoints: Expected future abundance

Comments: Endpoints are limited, but the program is flexible in that any kind of

predator–prey model can be implemented, as equations can be defined by the user; also offers several predefined predator–prey models; however, Populus does not explicitly model the effects of toxic chemicals and has no treatment of uncertainty

Trang 7

Table 13.6 Profiles of Selected Ecosystem Models — Aquatic Ecosystem Models

Model: AQUATOX

Reference: Park et al (1974); Park (1998); U.S EPA (2000a,b,c)

Stressor: User-defined toxic chemicals

Type of ecosystem: Freshwater, estuarine

Temporal resolution: Days

Spatial resolution: Not applicablea

Geographic scale: Not applicablea

Biological scale: Guild

Model type: Multicompartment; linear additive integral

Endpoints: Guild biomass, body burdens of contaminants; specific fish type abundance Comments: AQUATOX was specifically designed to model the ecological impact of chemical

contaminants on aquatic ecosystems; it not only models changes in productivity and standing stock abundance but also the transfer of contaminants between media and biota as well as through various trophic levels; AQUATOX is supported by EPA and is available on a software platform that is still undergoing revision and development

Model: CASM (Modified SWACOM)

Reference: DeAngelis et al (1989); Bartell et al (1992, 1999)

Stressor: Nutrients and user-defined toxic chemicals

Temporal resolution: User-defined periodicity

Spatial resolution: Not applicablea

Geographic scale: User-defined; area treated as a homogeneous unita

Model type: Multicompartment; linear additive integral

Endpoints: Nutrient concentrations, productivity, abundance, and diversity of fish Comments: This model is very similar to AQUATOX; unlike AQUATOX, SWACOM was

initially developed to model nutrient impacts

Model: IFEM

Reference: Bartell et al (1988)

Stressor: User-defined; established for naphthalene

Temporal resolution: Days

Spatial resolution: Not spatially explicita

Geographic scale: Not applicablea

Model type: Compartment dynamic; integrated linear additive

Endpoints: Guild productivity, toxic impact, guild-specific body burdens of contaminants Comments: This model is an iterative simulation that describes food-web transfer for both

carbon and contaminants and estimates both primary and secondary impacts

of toxicity

a Spatially aggregated model May also be applied as a landscape model

Trang 8

Table 13.7 Profiles of Selected Landscape Models

Model: ATLSS

Reference: DeAngelis (1996)

Stressor: Developed for mercury, but may be modified for other contaminants

Type of ecosystem: Large-scale wetlands (Florida Everglades)

Temporal resolution: Variable; executed on user-defined cycles

Spatial resolution: Currently based on 100-m2 grids

Geographic scale: Variable; based on multiple grids

Biological scale: Variable; guild at lower trophic levels and species at upper trophic levels Time scale: Open

Model type: Spatially explicit; low trophic levels are process models, middle trophic levels

are structured cohort models, and high trophic levels are individual-based models

Endpoints: Abundance of guild or species; intertrophic carbon flow and populations

(abundance and diversity) of keystone species; mercury bioaccumulation Comments: ATLSS is one of the most comprehensive landscape models available; its

ambition is balanced by its flexibility in that the creators recognized that different trophic levels require different types of model formats; this allows the simulations to be tailored not only based on the life histories of a particular species but also on the available data and understanding of that receptor’s behavior; although this model was originally developed for application to the Florida Everglades, it could be used in other biomes where there is a terrestrial food web reliant on the productivity of a large-scale aquatic habitat

Model: LANDIS

Reference: Mladenoff et al (1996); Mladenoff and He (1999)

Stressor: Wind and fire disturbances; harvesting; any physical disturbance

Type of ecosystem: Northern temperate zone forests

Temporal resolution: Hundreds of years

Spatial resolution: 10 × 10 m

Geographic scale: Thousands of hectares

Biological scale: Tree species as the presence or absence of 10-year age cohorts (not as

individual stems)

Model type: Stochastic, spatially explicit model of forest landscape processes; raster-based Level of detail: Tier 2

Endpoints: Tree species presence/absence, stand age structure and species richness,

potential impact of toxic chemicals on species-specific life-history traits (e.g., mortality, seed dispersal, etc.)

Comments: LANDIS is an elaboration of the VAFS/LANDSIM model; however, LANDIS

operates in raster mode, and the spatial interactions are based on distances (instead of polygon neighborhoods); operation can be at different scales of resolution and is programmed in C++ by using hierarchical, object-oriented data structures; LANDIS uses a free-standing spatial analysis package, reads and writes ERDAS raster files, and incorporates wind-throw and fire disturbance, which are absent from other models

Trang 9

Table 13.7 (cont.)

Model: JABOWA

Reference: Botkin et al (1972); West et al (1981); Bodkin (1993a,b)

Stressor: Physical disturbance, primarily harvesting

Type of ecosystem: Multispecies forests throughout the world

Temporal resolution: Annual time step

Spatial resolution: 10-m × 10-m grid sections (default value, which is user-adjustable in the early

versions of the model) Geographic scale: User-defined landscape

Biological scale: Individual trees of various species

Time scale: User-defined

Model type: Spatially explicit model of forest landscape with stochastic functions for

mortality and reproduction Level of detail: Tier 2

Endpoints: Tree biomass, stand biomass, stand age structure, forest productivity, and

species richness based on growth, reproduction, and mortality of individual trees; species and biomass distribution in space

Comments: JABOWA was developed as a general model of forest dynamics; it was among

the first successful multispecies computer simulations of terrestrial ecosystems and has gone through extensive modification and use during the

30 years since it originated; one of the best-documented and most flexible models of its type

Model: Island Disturbance Biogeographic Model

Reference: Villa et al (1992)

Stressor: Variable; manifest as impacts to populations

Type of ecosystem: Various; model must be tailored by the user

Temporal resolution: Periodicity is user defined

Spatial resolution: Grid size and resolution are user defined

Geographic scale: Grid size and resolution are user defined

Biological scale: Communities across a landscape

Time scale: Periodicity is user defined

Model type: Spatially explicit; quantitative rule-based model based on iterative

Lotka-Volterra kinetics Level of detail: Tier 2

Endpoints: Biodiversity and temporal species sensitivity

Comments: The Villa model uses the same principles as most island biogeographic models;

however, this implementation (1) is spatially explicit, (2) has implicit integration

of the disturbance function, (3) has multiple population interactions, and (4) calculates immigration relative to island carrying capacities; this model,

as presented, is highly theoretical; this increases its flexibility but would require explicit modifications to tailor it to a specific situation; it does not currently have functions to explicitly model toxicity

Định dạng
Số trang	9
Dung lượng	428,98 KB