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TECHNICAL REFERENCE DOCUMENT

DEVELOP THE CAPACITY TO MAKE ECOLOGICAL FORECASTS

1. Introduction

Ecological forecasts offer decision makers estimates of ecological outcomes given specific natural 

events, and/or management or policy options. In ecological forecasts, projections of the future 

state of ecosystems, and ecosystem services and/or natural capital are derived from specific 

scenarios of future land and human resource use, natural events, climate change, and policy 

actions. Society benefits from and depends on a wide range of ecological services including 

purification of air and water, mitigation of droughts and floods, generation and preservation of 

soils and renewal of their fertility, detoxification and decomposition of wastes, pollination of crops 

and natural vegetation, dispersal of seeds, cycling and movement of nutrients, control of many 

agricultural pests, maintenance of biological diversity, protection of coastal shores from erosion, 

and provision of aesthetic beauty and opportunities for recreation (ESA 1997). Alterations of 

ecosystem processes can dramatically affect important ecological services and the quality and 

standard of life, including local and regional economies (Loucks and Gorman 2004). Ecological 

forecasting is critical in understanding potential changes in ecological services, before they 

happen, and are critical in developing strategies to off-set or avoid catastrophic losses of services. 

Ecological forecasting often involves the actual prediction of ecological outcomes based on a 

combination of biophysical observation data and models (Clark et. al. 2001). The primary goal of 

ecological forecasting is to predict the effects of biological, chemical, physical, and human 

induced pressures on ecosystems and their components at community, landscape, watershed, 

regional, and national spatial scales and over a range of temporal scales, given a certain set of 

assumptions (CENR 2001). Examples of such pressures include extreme natural events, climate 

change, land and resource use, pollution, invasive species, and human/wildlife diseases. Once 

certain cause-effect relationships are established, the goal then is to develop management 

strategies and options to reverse declining trends, reduce risks, and to protect important ecological 

resources and associated processes (Baker et. al. 2004, Bradley and Smith 2004, Fitz et. al. 

2004). Such an approach is critical to the concept of sustainable development (Reid et. al. 2002, 

Valette-Silver and Scavia 2003, NASA 2004).

Ecological forecasting is an integral part of many of the goals of other societal themes as described 

in this chapter, but especially those related to: (1) protecting and managing terrestrial, coastal, and 

marine ecosystems, (2) understanding, assessing, and mitigating climate change impacts, (3) 

identifying options for sustainable agriculture and reversing and combating land degradation and 

desertification, (4) promoting human health and well being, (5) protecting water resources, and (6) 

understanding, monitoring, and preserving biological diversity. Forecasting is fundamental in 
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understanding what needs to be done to avoid human and environmental disasters and to promote 

sustainable development. In this regard, forecasting plays an important role in early warning and 

risk assessment. For example, NOAA integrates in situ and remotely sensed measurements with 

models to provide early warning and nowcasts for sea nettles, pink shrimp harvest, and coral 

bleaching (http://www.nos.noaa.gov/topics/coasts/ecoforecasting/welcome.html). Other examples 

from the physical sciences include observation and model-based short-term weather forecasts, 

which have been available for several decades, and the more recent and longer term predictions of 

climate phenomena, such as El Nino/Southern Oscillation (ENSO) events (McCade et. al. 2004).

Generally, managers, stakeholders, and decision makers require two types of ecological forecasts: 

(1) short-term forecasts (e.g., 3-24 months) and (2) longer-term forecasts (e.g., 5-50 years). Shortterm forecasts give stakeholders early warning of events and conditions that might affect key 

economic activities and human safety. For example, managers in the Gulf of Mexico use NOAA 

forecasts of harmful algal blooms to determine which shellfish habitats might be at greatest risk. 

These near-real time data are used by managers to help target areas that need more detailed data 

collection (http://www.csc.noaa.gov/crs/habf/bulletin.html). Similarly, integration of real- and 

near-real time climate and biophysical data with historical and current patterns of vegetation 

productivity (net primary production or NPP), are being used to target areas of greatest risk of 

crop failure (Reynolds et. al. 2000), as well as predicting patterns of greenness and departure of 

greenness from historical patterns (Running et. al. 2004). Early warning of the former risks helps 

farmers develop strategies to reduce crop loss and financial hardship (Kastens et. al. 2001, de 

Beurs et. al. 2004), whereas early warning of the later is being used to help nomadic farmers in 

Africa determine where to migrate (Hutchinson 2001, Herrmann and Hutchinson 2004). Other 

potential uses of NPP estimates and forecasts might include (1) when to move grazing animals to 

different pastures (to reduce impact of grazing on climate-stressed lands), (2) identification of 

streams at increasing risk to erosion and sediment and nutrient loading, (3) areas vulnerable to 

catastrophic wildfires, and (4) areas vulnerable to invasive species (Running et. al. 2004). 

Longer-term forecasting is more often used to address broad policy and management issues of 

Federal agencies, as well as to develop environmental planning options at local and watershed 

scales (Baker et. al. 2004).

Ecological forecasting can be lumped into two general approaches. The first general approach 

involves an attempt to predict future conditions of ecological goods (products) and services 

(processes and functions affecting environmental condition) with known levels of confidence 

(Clark et. al. 2001). This approach often uses spatially explicit models derived from historical 

change, which are then applied to the future, with the general assumption that future changes will 

approximate historical changes (Hall and Fagre 2003, Matheny and Endres 2003, Jackson et. al. 

2004). In some cases, these models are developed by evaluating changes in the ecological 

endpoint of interest across biophysical gradients. This is often referred to as trading space for time. 

For example, Galbraith et. al. (2003) developed habitat models for shorebirds based on current 

patterns of feeding habitats. They then used sea level change scenarios to forecast how shorebird 

habitat might change by the years 2050 and 2100. Stock sizes of shrimp can be forecast on the 

basis of estuarine water temperature (Hettler 1992). Menhaden recruitment is known to be 

correlated with river flow (Govoni 1997). 
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The second general approach involves the development and use of spatially distributed models, 

but unlike the first general approach, does not try to predict how the future will change but rather 

to identify a range of alternative environmental futures (White et. al. 1997, Monaco et. al. , 2003, 

Baker et. al. 2004, Hulse et. al. 2004, Kepner et. al. 2004) using a set of future scenarios derived 

from expected or plausible changes (Reid et. al. 2002, Martin 2003, Neale et. al. 2003, Scavia et. 

al. 2003, Aycrigg et. al. 2004), or through extensive interactions with the stakeholders. Similar 

to predictive approaches, spatially explicit landscape models used in scenario assessments are 

often derived by evaluating current patterns in spatial variability. For example, Stohlgren et. al. 

(2003) modeled vulnerability to invasive species spread by quantifying relationships between 

invasive species richness and spatial variation in certain biophysical parameters, including soil 

texture and chemistry, topographic position, elevation, slope, aspect, greenness indices, and land 

use data. From this empirical model it was then possible to evaluate potential vulnerability of 

invasive species establishment across the entire landscape. Peterson et. al. (2003) used a similar 

approach to model the spread of emerging diseases, plant and animal pests, invasive species of 

plants and animals and their effects on natural resources, and agricultural crops and human 

populations. Fonseca et. al. (2002) have quantified the connections between coastal morphology 

and wave regimes with the ability of seagrass meadows to recover from man-made or natural 

destruction. Stumpf et a.l (2003) have developed an operational forecast for landfalls of harmful 

algal blooms based on the remote sensing of chlorophyll. Coral reef bleaching has been found to 

be correlated with episodic peaks in sea surface temperature (Mumby et. al. , 2004). Empirical 

models are commonly used to extend estimates made from sparsely collected field samples to 

broader geographic regions (Jones et. al. 2001). 

Reid et. al. (2002) argue that forecasting differs from prediction in that “a forecast is the best 

estimate from a particular method, model, or individual. The public and decision makers 

understand that a forecast may or may not turn out to be true.” In fact, it is imperative for 

ecological forecasts to be associated with estimates of uncertainty or “error bars” so that decision 

makers using them have information as to the likelihood of a given forecast. 

Forecasting species and environmental changes represent a formidable challenge in science, in that 

the basic mathematics and modeling approaches for such forecasting are in their infancy. 

Ecosystem complexity and scaling issues increase error and uncertainty in ecological forecasting. 

However, ecosystem forecasting is critical if one is to reduce environmental threats and sustain a 

wide range of ecological services upon which humankind depends.

2. User Requirements

Ecological forecasting requires the acquisition of a wide range of environmental data, as well as 

development of models. However, the amount of environmental data and the number and 

complexity of models needed to conduct ecological forecasting varies tremendously, depending on 

the type of ecosystem and the set of specific forecasting questions being addressed (for example, 

short-term versus long-term forecasts). The types of questions being asked and the goals of the 

forecasting activity influence the number of spatial and temporal scales that need to be addressed; 

these in turn affect the magnitude and complexity of data and model needs (Costanza and Voinov 
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2004). For example, forecasting changes in run-off and sediment as a function of land cover 

change scenarios developed by stakeholders is a relatively straight forward process involving use 

of digital soil erosivity data, land cover, precipitation maps, and a spatially distributed hydrologic 

model (Kepner et al. 2004). In this case, the alternative future landscapes result in different soil 

exposure and land cover composition and patterns, which result in different sediment and run-off 

predictions from the hydrologic model. Alternatively, forecasting the responses of species to 

future environmental scenarios, or changes in ecological functions at relatively fine scales (e.g., 

within a 30 x 30 meter pixel), usually involve complex and dynamic models and data because of 

greater complexity in horizontal and vertical scaling functions (Martin 2003, Monaco and 

Livingston 2003, Rastetter et al. 2003, Costanza and Voinov 2004, Deal et al. 2004). Therefore, it 

is very important to identify a set of assessment questions in determining the specific requirements 

for ecological forecasting.

Table 1 provides examples of the types of questions that might be asked in association with 

ecological forecasting. The examples given are, in no way, a complete list of que stions that could 

be asked. These questions also could address current and future environmental threats associated 

with: (1) human population growth and demand for resources, (2) harmful invasive plants, 

animals, and diseases, (3) a wide range of contaminants, (4) altered disturbance regimes and other 

natural processes, and (5) other factors that could potentially affect fundamental ecological 

services. Assessment questions also reflect the need for short-term and long-term ecological 

forecasting.

Table 1. Assessment questions related to ecological forecasting

Short-term Forecasts (3-24 months)

Given current and near-term biophysical and climatic conditions, and information on 

historical patterns of invasions and occurrence, which areas are most vulnerable to 

invasive species spread? Which areas are not likely to experience invasive species 

establishment and spread over the next 2 years?

Given current and near-term projections of biophysical and climate conditions, and 

information on historical patterns of occurrence, which forests and woodlands are most 

likely to experience catastrophic fires over the next fire season? Which areas are likely to 

be the least vulnerable over the next 2 years?

Given current and near-term projections of biophysical and climate conditions, and 

information on historical patterns (spatial and temporal) of productivity, which areas in 

the mid-west are likely to experience significant levels of crop failures? Which areas are 

likely to produce normal or above average yields?

Given spatial patterns of ecosystem productivity (Net Primary Production or NPP), recent 

patterns of drought, air pollution, and other biophysical conditions, which forests are 

most vulnerable to disease outbreaks?

Given current and near-term projections of biophysical and climate conditions, which 

coastal waters are likely to experience harmful algal blooms? Once established, what is 

the spatial pattern of further spread?
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Which areas are likely to experience drought? What freshwater biota are most vulnerable 

to extended drought? What freshwater endangered species are threatened by extended 

drought?

Given measurements of salinity and water temperature, which areas of the Chesapeake 

Bay are likely to experience high occurrences of sea nettles? What is the spatial overlap 

between areas that are likely to have high abundances of sea nettles and beaches where 

people frequently swim?

How do broad-scale, seasonal to interannual climatic events (e.g., ENSO, NAO, etc) 

influence the productivity of selected coastal or pelagic fisheries

Given winter water temperatures, is pink shrimp harvest likely to be lower or higher than 

the average the following year?

Given spatial patterns of ecosystem productivity (NPP) which flyways are likely to 

experience higher mortality rates of neotropical migrant birds? Which areas are likely to 

experience reduced reproductive success? Which areas are likely to have normal or above 

average reproductive success?

Long-term Forecasts ( 5 – 50 years)

Under different scenarios of global climate change, which terrestrial ecosystems are 

likely to experience greater intensity and frequency of catastrophic fires? What is the 

likelihood that these ecosystems will convert to a less desirable state? Which areas will 

be at greatest risk to loss of property and human lives due to changes in key ecosystem 

attributes such as water infiltration capacity or fuel loading? Which coastal areas will 

receive more runoff and how will they respond?

Under difference scenarios of land use, what changes in biodiversity are likely? What 

species may have their range expanded > 25%? What species may have their range 

diminished >25%, or even put on a path of extinction

Under different climate change or land use scenarios, what areas are most vulnerable to 

increases in occurrence of invasive species?

Under different climate change or land use scenarios, what are the consequences and 

vulnerabilities on rare and endangered species?

What are the long-term effects of tropical deforestation on climate, wildlife, and human 

livability?

Under different climate change and urban sprawl scenarios, which forests are likely to 

experience significant declines in productivity? What will be the impact on the forest 

products economy? What are the spatial relationships between changes in forest 

productivity and species diversity?

Under different scenarios of urbanization and development (increases in impervious 

surfaces), which river basins and streams are likely to experience high rates of loss of 

aquatic and riparian biological diversity?

Which coral reefs are the more vulnerable to bleaching from spikes in sea surface 

temperature and will they recover?

Which marine, coastal, and estuarine ecosystems are at greatest risk given scenarios of 

future environmental change? Which changes in future environments will have the 

biggest negative impact on these resources? What policies and/or management scenarios 

would best reduce future risks to these 
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resources? If left unmitigated, what would be the economic consequences of future 

environmental change?

Under different exploitation scenarios, which stocks of living marine resources will be 

most vulnerable to over fishing, and how will marine ecosystems be affected?

Which coastal areas are the more vulnerable to sea level rise and how will different types 

of lowlands respond?

How will green technologies offset ecological and hydrologic changes associated with 

urbanization, desertification, and/or climate change?

What Best Management Practices (BMPs) need to be developed or implemented to offset climate- and urbanization-associated changes? What is the optimal spatial 

distribution of BMPs related to mitigating impacts of climate change and urbanization? 

How does this vary among communities in different biophysical settings? What BMPs 

are needed to protect aquatic resources? What BMPs are needed to protect terrestrial 

resources?

How effective are our land conservation programs likely to be in protecting biological 

diversity given specific scenarios for global climate, urbanization, and land degradation? 

What alternative land conservation strategies might better protect biological diversity in 

the face of scenarios of future environmental change? What management practices might 

be conducted in the matrix surrounding local ‘hot spots’ to promote biodiversity?

What management practices can best be utilized to reduce catastrophic forest fires, while 

maintaining the fire-dependent or fire-promoted communities over much of the nation’s 

forestlands? 

What policies and/or management practices can be adopted to reduce the loss of 

productive agricultural lands? Where are the agricultural lands that are at greatest risk of 

being converted to non-agricultural uses? Given different alternative future scenarios, 

what is the economic and social impact of agricultural lands converted to other uses?

Many organizations and agencies need Earth observation data and models to forecast changes in 

important ecological resources and processes. These include city, county, and watershed 

organizations that are trying to evaluate options for smart growth (Voinov et al. 2004, Berger and 

Bolte 2004), as well as initiatives to evaluate the vulnerability of ecological resources and 

processes to near-term environmental conditions, as well as longer-term degradation (ValetteSilver and Scavia 2003, Bradley and Smith 2004, Claggett et al. 2004). Other users will be 

managers of federal, state, and tribal lands and waters charged with maintaining the viability of 

these areas and complying with the mandates of relevant environmental legislation, such as the 

Clean Water Act, Endangered Species Act, and National Environmental Policy Act. 

A need for shorter-term forecasting (daily to monthly) in a direct or stochastic sense at the local 

level where many decisions are made or future scenarios are evaluated is also sorely needed. 

These forecasts should account for perturbations from large-scale atmospheric forcings (e.g. 

ENSO) which coupled ocean-climate models can now predict with greater accuracy. Current

weather generators, which are commonly used to provide input to management and ecological 

models could be improved by including teleconnection perturbations (Woolhiser, et al. 1993). 
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Incorporation of the ENSO signal using the Southern Oscillation Index (SOI) into daily 

precipitation models has significantly improved these models’ capacity to simulate sequences of 

daily precipitation in the southwest by increasing the variation of monthly and annual totals of 

precipitation occurrence and amount. It has been shown that a lead time exists between the 

Southern Oscillation Index and daily precipitation in this region. This information can be 

exploited to provide conditional simulations of near-future precipitation, valuable for land and 

water resource managers. These techniques, with long-term (~30 years) rain and temperature 

observations, can be used to geographically map the influence large-scale atmospheric forcing by 

statistically indicating whether the forcing as measured by some index or sea surface temperature:

1) significantly influences observed rainfall/temperature at the gauge and for which months; 

2) the lead-time between the index and the observations (see the NRCS GEM effort at 

http://www.wcc.nrcs.usda.gov/climate/gem.html).

Finally, a comprehensive forecasting system is needed to address the broader issue of 

bioinvasions, bioterrorism, and bioscecurity (Meyerson and Reaser 2003).

Because of the diverse needs for forecasting at a range of scales, there will be a comprehensive 

and wide range of Earth observation data requirements. However, these observations are likely to 

fall out into two general classes: spatially continuous biophysical data derived from remote 

sensing and other sources, and field or site data on specific ecological and hydrologic processes 

and/or state variables. Site data are needed to measure and estimate important ecological and 

hydrologic variables (across space and time) which then can be related empirically or via process, 

or mechanistic models, to spatially continuous biophysical data (Van Rompaey and Govers 2002, 

Kratz et al. 2003, Rastetter et al. 2003). Site data also are needed to calibrate ecological process 

models. Biophysical data provide an extrapolation framework to estimate conditions over broader 

areas, and to areas not sampled (Costanza and Voinov 2004). In addition, they allow for the 

detection, observation, and modeling of ecological phenomena occurring at landscape scales and 

beyond. Using concepts of ecological hierarchy theory (Allen and Starr 1982, O’Neill et al. 1986) 

biophysical and biogeochemical data can also be used to construct ecosystem classification 

approaches to reduce variability predictions in responses of ecosystems to perturbations. Table 2 

provides an initial list of the Earth observations that are required to conduct enhanced ecological 

forecasting in the United States. 
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Table 2. General list of Earth observation needs and capabilities

Digital national land cover at 30 meter resolution from the mid-1980s to the present day 

(to measure and estimate a number of important landscape pattern and process factors 

affecting ecological condition and changes and to improve forecasts based on future land 

cover change from urbanization, etc.) 

A national, consistent map of vegetation types, classified to the highest spatial and 

attribute level possible. (the FGDC vegetation classification, largely built by NatureServe 

and Ecological Society of America)

National soils database, including information on soil texture (SURRGO) (to estimate 

soil loss, erosion, and nutrient export at a range of spatial scales; higher resolution soil 

moisture information is a key observational parameter for future development)

A national, high resolution (<30m) map of long-term soil moisture availability (critical 

for vegetation mapping, moisture availability and storage, successional pathways 

development) (see Iverson et al. 1997 for an example: the Integrated Moisture Index)

10 meters digital elevation model data except in coastal regions where 0.5 meter data are 

needed (to estimate and model surface flow, run-off, and other critical processes related 

to water movement)

Precipitation/climate data at 1km resolution or less on hourly time-scales (to estimate 

surface flows, surface temperature, atmospheric temperature, evapotranspiration, soil 

erosion, and other important ecological functions that are linked to climate)

Enhanced stream hydrography data (to improve estimates of the spatial distribution of 

perennial stream flow and connectivity among aquatic habitats and biota

Increased number of continuous flow monitors for streams and other surface water (to 

improve models linking landscape condition, like impervious surface changes, to surface 

water chemistry, physical habitat, and aquatic biological condition)

Detailed locational information on water resources use (drinking water, industry, 

agriculture, power), extraction (irrigation, public, private), source (surface water, 

groundwater), management (permits, allocation, transfers, drought), and infrastructure 

(dam, impoundment, levee) to evaluate potential impacts and stress to hydrological 

processes and aquatic resources

Digital data on vegetation canopy structure and height (to improve modeling of suitable 

habitat for animal species; to improve estimates of biomass and leaf area indices that are 

important input variables in ecological processes models)

Data on physiological characteristics of vegetation (by species, mostly obtained via 

controlled, ground-based instrumentation), critical for modeling storage and allocation of 

carbon and nitrogen

Increased number of fixed and remote monitoring stations that measure fundamental 

ecological processes including carbon flux, energy flux, solar radiation, 

evapotranspiration, and nitrogen flux (to improve ecological process models and 

extrapolations to stand/patch and landscape scales)

Increased number of fixed monitoring stations that measure atmospheric deposition of 

sulfate, nitrogen, mercury, and other air toxics know to effect terrestrial and aquatic 
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ecosystem (to improve models and estimates of the impacts of future emissions policies 

and regulations on ecosystems)

An increased network of scientists collecting species- and genotype-level data related to 

population trends and environmental factors controlling the species. A fine-scale network 

of data collection so that range boundaries, by species, can be reliably detected and 

mapped. (for evaluation of range shifts under changing conditions, critical data for 

development and validation of models)

Increased number of fixed monitoring sites that measure both terrestrial and aquatic 

species richness, species diversity, genetic (molecular-based) diversity, and other 

measures of biological integrity (to improve habitat models of species over a range of 

spatial scales and to improve forecasts of how changes in biophysical properties affect 

spatial patterns of diversity, etc

Animal inventories (biodiversity and invasive species) on land, in river and lakes, and at 

sea

Direct measures, through remote sensing, of ecological variables related to key processes 

in ecological process models, including but not limited to greenness, net primary 

productivity, leaf area index, evapotranspiration, and phenology. Hyperspectral 

instruments may hold particular promise for some of these measures

Remote detection of a variety of key variables for marine ecosystems including: ocean 

color for the detection of chlorophyll-a concentrations at 1km resolution or better, sea 

surface temperature at 1km resolution or better, ocean wind speed and direction at 25km 

resolution or finer, ocean topography, and sea surface salinity (to estimate marine 

primary productivity, monitor sea surface circulation – a vital element in the dispersal of 

larvae and other propagules, track interannual climate events, improve understanding of 

marine food webs, locate ocean fronts rich in higher trophic level organisms, etc.) 

Higher spatial and temporal resolution data on stream and surface water temperatures via 

deployment of automated or remote monitoring devices (to improve estimates of stream 

temperature ranges that affect key aquatic species, including salmon)

Detailed locational information on human population demographics, road and 

improvement infrastructure, pesticide and other chemical application, emission and

effluent permits and inventories, resource uses (e.g., timber extraction, fishing, and 

agriculture), and other human related activities (to improve models relating a range of 

human related stresses to ecological resources and processes)

Detailed locational information on the type and date of Best Management Practices, 

habitat improvements, and other conservation actions (to improve our understanding 

about the effectiveness of improvements in maintaining and protecting ecological 

functions and biological diversity)

Maps of fuels at 30 m resolution. (needed for prediction potential catastrophic fires as 

well as habitat suitability modeling)

Data on the magnitude and distribution of disturbance agents (e.g., fires, hurricanes, 

tornadoes, ice storms, pest outbreaks, pathogen outbreaks) that might affect ecosystem 

structure and function over large areas (to improve evaluation of forecasting the impact 

of major disturbances on ecological resources and associated processes)

A consistent, fine-scale database on land-use history, predating the Landsat era. Air 
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photos from the 1930s to map and digitize. Before the 1930s there are maps and 

observations that can be used to put together these ecological legacy maps. The General 

Land Office records provide good data for a large section of the country and need to be 

consistently mapped as well.

A wide range of socio-economic data (e.g., cadastral data, average income levels, 

consumption preferences) to evaluate relationships between human behavior and 

fundamental ecological processes (to improve ecological forecasting based on alternative 

future economic and social conditions)

Finer-scale data of all biological, ecological, chemical, and physical data described above 

(to improve models and ecological forecasting at community and local scales)

Data on the spatial distribution of light pollution (intensity)

Bathymetry of coastal waters

3. Existing Capabilities and Commonalities

Remotely-Sensed Spatial Data

A number of currently available geospatial data sets are used for observations, monitoring, and 

ecological modeling that also have relevance to ecological forecasting (Kerr and Ostrovsky 2003, 

Turner et al. 2003).

Geodetically accurate global data sets of Landsat satellite imagery from 3 epochs (the 1970s at 80 

meter resolution, circa 1990 at 30 meter resolution, and circa 2000 at 30 meter resolution) are now 

becoming available from the Earth Resources Observation System (EROS) Data Center (EDC) 

(Tucker et al. 2004). These data sets constitute a unique record of land-surface state over the past 

30 years. National land cover (30 meter resolution) from the early 1990s is available for the lower 

48 United States through the EDC and these data have been used in a number of alternative futures 

assessments (see Wickham et al. 2002, Kepner et al. 2004, Claggett et al. 2004). Currently, a 

similar digital database is in development that is based on Landsat 7 data from the early 2000s, but 

it will also include digital coverages of impervious surface and tree canopy density. The addition 

of impervious surface estimates and tree canopy density will improve a number of ecological and 

hydrological models, which in turn should improve forecasting capability. The current estimate 

for the completion of these new digital databases is 2006. 

The Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging 

Spectroradiometer (MODIS) satellite sensors provide multi-spectral data at high temporal 

frequency (in many cases on a daily basis) and these data have been important in deriving 

important ecological variables linked to both terrestrial and marine ecological processes. These 

variables include: vegetation indices, land cover, and sea surface temperature from both AVHRR 

and MODIS and leaf area index and fractional photosynthetically active radiation (FPAR), 

evapotranspiration, net photosynthesis and primary productivity, land surface temperature and 

emissivity, fires and burning biomass, land cover change, vegetation cover conversion, snow 

cover, sea ice cover, ocean chlorophyll-a concentration, ocean chlorophyll fluorescence, ocean 
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primary productivity, coccolith concentration, marine organic matter concentration, and cloud 

products from MODIS (for a list of MODIS products please see 

(http://modis.gsfc.nasa.gov/data/dataproducts.html).

Because of their temporal resolution, these sensors are able to determine spectral patterns of 

greenness, which can then be used to help in the identification of plant species composition and 

stress. Additionally, MODIS provides a set of spectral vegetation bands at 250 meter and 500 

meter spatial resolutions, providing higher resolution imagery for several of the land feature 

products useful in ecological modeling. The SeaWiFS (Sea-viewing Wide Field of View Sensor) 

satellite sensor is used to evaluate phytoplankton and chlorophyll distributions in the world’s 

oceans and provides one of the key data elements needed to monitor harmful algal blooms (HAB). 

NOAA integrates these data with wind speed and direction data to produce its HAB Bulletin. The 

Bulletin, which provides near-real time information on HAB, provides timely information to the 

management community in the Gulf of Mexico during a bloom event 

(http://ccmaserver.nos.noaa.gov/rsd/products.html). 

There has been considerable use of data from satellite radars [e.g., RADARSAT (operational radar 

satellite that is owned and operated by Canada) and JERS-1 (Japanese Earth Resources Satellite)] 

and airborne radar systems to assess surface roughness, subsidence, three-dimensional aspects of 

vegetation canopies, biomass, and wetland extent, especially where these canopies prevent 

accurate estimates of some of these environmental variables from Landsat and other optical 

satellite sensors. Radars are sensors that transmit their own pulses of electromagnetic radiation to 

the surface and then record the radiation returning or “bouncing back” to the sensor. At certain 

wavelengths, these so-called “active” sensors have the added benefit of being able to view through 

clouds and even vegetation, both day and night. Many important aspects of habitat suitability 

relate to its structure and complexity, parameters that lend themselves particularly well to this type 

of remote sensing. In addition to habitat structure, radars are also quite proficient at detecting 

water (and topography) under a vegetation canopy and have proved useful at mapping wetlands, 

whether permanently or seasonally inundated. A SAR (Synthetic Aperture Radar) satellite-based 

sensor provides important information on marine environments, including oil-spills, ocean water 

masses, winds, and fronts. However, the U.S. currently does not have a SAR sensor on any of its 

satellite platforms.

Laser systems, known as LIDARS (Light Detection and Ranging), represent another active 

sensing technology that holds much promise for the remote detection of vegetation structure and 

complexity, as well as biomass. LIDAR data can provide fine-scale estimates of vegetation 

canopy structure and elevation profiles, which are important input variables in ecological and 

hydrological process models as applied to relatively small areas (e.g., within floodplains to 

evaluate surface flow and in coastal regions where finer-scale digital elevation data are needed). 

However, to date, vegetation LIDAR sensors have generally flown on airplanes, making the data 

relatively expensive to acquire for very large area applications (e.g., regional, multi-state, 

national). Nonetheless, they do provide data at the local scales for many ecological studies and 

can also serve as bridging data sets between such finer scales and broader landscape or regional 

scales. However, LIDAR data are beginning to be collected over entire states (e.g., Conneticut) 

and countries (e.g., Switzerland). In a coastal (and riverine) context, LIDARs provide high-
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resolution bathymetry data, as well as information on coral reefs. The integration of LIDAR (for 

elevation data) and spectral/hyperspectral sensors also shows great promise.

Although there is considerable research on the use of hyperspectral imagery to detect relatively 

fine-scale patterns of vegetation species distributions and structure, as well as the biochemical 

makeup of vegetation, soil, and surface waters, the availability of these data are limited. Early 

results from both airborne and satellite systems are promising and hyperspectral remote sensing is 

certainly an area for future work. Some see the possibility of developing systems capable of 

remotely fingerprinting biological phenomena, in terms of both taxonomy and condition or health, 

arising from this technology. Hyperspectral imagery is also very useful for evaluating conditions 

of coastal marine environments and coral reefs. For example, the CASI (Compact Airborne 

Spectral Imager) hyperspectral sensor is being used to monitor sea grass spatial distribution 

(http://www.esa.ssc.nasa.gov/). 

There are also a number of commercial satellites that provide relatively fine-scale spatial data of 

land and water features, including IKONOS (4 meter resolution multi-spectral imagery; 1 meter 

panchromatic imagery) and QuickBird (2.5 meter resolution multi-spectral imagery; 0.61 meter 

panchromatic imagery). However, these data are relatively expensive and are typically acquired 

over relatively small areas (e.g., counties, watersheds) due to narrow sensor swath widths. 

Nonetheless, they do provide data at the local scales of many ecological studies and can also serve 

as bridging data sets between such finer scales and broader landscape or regional scales. For 

example, IKONOS is being used in conjunction with historical aerial photography to determine 

changes in the extent of coral reefs (Palandro et al. 2003). SPOT satellite data provides various 

resolutions of spectral and panchromatic imagery (2.5m, 5m, 10m, and 20 m) and has for years 

been used to the map and monitor numerous Earth features and in associated applications. 

Imagery and data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer 

(ASTER) satellite sensor have been used to characterize geology and soils at relatively high spatial 

resolutions, especially in drier habitats. Nevertheless, better data for soil characterization are 

needed for many areas of the Earth, with systems capable of higher-resolution detection of soil 

types and levels of soil moisture being especially important. 

Airborne digital multi-spectral photography provides vital information on vegetation 

characteristics, stream morphology, coral reef extent, coastline characteristics, and many other 

Earth and marine features and biophysical variables at relatively high spectral and spatial 

resolutions. While satellite remote sensing of the Earth’s surface has only been in existence for 

just over 30 years, archives of photographic imagery (while not contiguous at the national level) 

extend much further back in time (e.g., early 1930s and 1940s). As such, they provide an 

invaluable time series for understanding landscape changes and associated phenomena. 

Preservation and digitization of these archives and the unique information they hold is of the 

utmost importance. 
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Field and Site Data

There are a number of monitoring programs that collect information on biological, physical, and 

chemical attributes of ecosystems, some of which are regional and national in scale. Data from 

these programs have been used to develop, calibrate, apply, and refine ecological models. 

Examples of national-scale ecological monitoring and assessment programs that produce 

ecological data on a large number of sites or areas include:

• The Long-term Ecological Research (LTER) network consists of 24 ecologically 

representative sites across the U.S. that collect data on a wide range of important 

ecological and biological processes; it is used to develop ecological process models at 

multiple scales. (NSF sponsored universities and institutes).

• Experimental Watershed Networks operated by the USDA Agricultural Research 

Service, U.S. Forest Service, and the U.S. Geological Survey, some of which are also 

LTER sites provide long-term spatially and temporally intensive hydro-climatic 

measurements and high-resolution watershed characterization with a focus on processbased research.

• Forest Inventory and Analysis (FIA) provides forest health and productivity indicators 

measured on an annual rotational basis on a range of probabilistic sample sites, mostly 

on private lands (U.S. Forest Service in collaboration with States).

• Natural Resources Inventory (NRI) supplies a set of ecological and environmental 

measures taken every five years on probabilistic area samples (but not on Federal 

Lands; U.S. Natural Resources Conservation Service).

• Breeding Bird Survey offers estimates of breeding bird abundance and diversity on 

approximately 3,700 25-mile road survey routes (USGS).

• Environmental Monitoring and Assessment Program (EMAP) provides a national 

survey involving several hundred sites with measurements of physical, chemical, and 

biological condition of estuaries (in collaboration with NOAA). Also, several hundred 

survey sites of streams involving chemical, physical habitat, and biological condition 

measurements. (U.S. EPA in collaboration with the states).

• National Agricultural Statistical Survey (NASS) performs probability based surveys to 

estimate county and national statistics on pesticide use, crop yield, and other important 

agricultural statistics (USDA).

• National Wetlands Inventory (NWI) produces national maps of wetland distributions 

as well as probability-based, area samples of wetland trends. (USFWS).

• The National Estuarine Research Reserve System (NERRS) network of 26 areas 

representing different biogeographic regions tracks short-term variability and longterm changes in estuarine waters and provides valuable long-term data on water 

quality and weather at frequent time intervals (NOAA).

• NOAA’s Fisheries’ Living Marine Resource Surveys provide ship-based surveys of 

commercial and non-commercial marine organisms.

• NOAA’s National Status and Trends Program has been providing data on the status 

and trends of the coastal environment since 1984. 
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Integration of Data

Many of the programs listed above have active research and development programs to integrate 

field data with larger, spatially continuous biophysical data with the aim of making estimates of 

ecological and biological conditions over broad geographic areas. These involve a wide range of 

in situ data, and empirical and process modeling approaches. Integration of data and models 

requires an “a priori” consideration of sampling designs and scaling functions, such as the 

framework developed by the CENR (CENR 1997, Figure 1). Examples of programs that integrate 

field and spatial data to conduct ecological forecasting include EPA’s Regional Vulnerability and 

Assessment (ReVA) program, the Invasive Species Science Program (USGS, Fort Collins, 

Colorado, in collaboration with NASA and Colorado State University), the USFS’s climate 

change atlas (Iverson et al. 1999), and several research initiatives in NOAA (see Valette-Silver 

and Scavia 2003). Extensive research is being conducted at universities and institutes to integrate 

field and remotely sensed data through development of dynamic, multi-scale process models (see 

Figure 2). Finally, tools now exist that assist in the development of models. For example, 

Lifemapper uses the Internet and leading-edge information technology to retrieve records of 

millions of plants and animals in the world's natural history museums. Lifemapper analyzes these 

data, computes the ecological profile of each species, maps where the species has been found and 

predicts where each species could potentially live (www.lifemapper.org). NOAA, the USGS, and 

the Smithsonian Institute are developing a similar system to map and project risks of invasive 

species spread into marine, estuarine, and coral reef systems 

(http://www.nccos.noaa.gov/documents/factsheet_invasivespecies.pdf).

Figure 1 Relationships among different levels of monitoring programs (from CENR 1997)
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Figure 2 An example of an integrated Earth Observation System framework 

to address ecological forecasting and risk assessment of land and 

freshwater ecosystems (Nemani et al. 2003, http://www.ntsg.umt.edu/tops/).

4. Major Gaps and Challenges

Although there are a number of deployed Earth observations systems across the U.S, including 

individual site monitoring stations and airborne and satellite imagery, lack of interoperability, 

coordination, and enforcement of Federal Geographic Data Committee (FGDC) data standards 
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within and among these programs prevent optimal use of resulting data and information for 

ecological forecasting. New approaches in data mining and networking should improve data 

integration and modeling to a certain extent, but there are a number of issues related to data 

collection and availability that will limit the success of such programs. Certain types and scales of

ecological forecasting are possible given current data and capabilities, as highlighted in sections 

Table 1 and Table 2, but many gaps still exist. These gaps are summarized in Table 3. There are 

six general categories of gaps: (1) field and site data (in situ), (2) remotely sensed and other spatial 

data, (3) models, (4) data and system interoperability and data standardization and management, 

(5) technology transfer, and (6) education gaps. These issues need to be resolved in order to 

improve and extend our ability to conduct ecological forecasting.

Table 3. Earth observation gaps and needs.

Field and Site Data

Some of the existing field surveys may be biased such that the data are not representative of 

the ecosystem, area, or ecological and biological processes that they are measuring. This 

might include biases associated with the proximity of sample sites to roads, or to a certain 

biophysical setting.

Some of the existing programs may have too few sample sites (sparsely dispersed) to 

capture spatial variability in ecological processes and conditions. Since many programs use 

spatial variability to model potential responses of ecological resources to future 

environmental change (for example, Stohlgren et al. 2003), samples must be representative 

of a gradient of ecological conditions, and/or stressor conditions.

Some programs do not reveal spatial locations that are necessary to link site conditions to 

broader landscape conditions, including sites protected by law related to property owner 

confidentiality. Relatively detailed spatial locations are needed to evaluate quantitative 

relationships between site measures and broader landscape conditions. In some cases, data 

on the locations of threatened and endangered species will require special protections.

New technologies are needed for biological and chemical sensors. Wireless networking of 

embedded sensor devices that can be deployed in remote areas (e.g. measure gas fluxes in 

soils, from whole ecosystems – AmeriFlux network, and over regions; acoustic sensors for 

assessing ecosystem health and population dynamics). Additionally, Autonomous 

Underwater Vehicles (AUV) and Remotely Operated Vehicles (ROV) are needed to 

measure biological, chemical, and physical variables in marine and coastal environments.

Many taxa and/or important issues related to certain taxa are missed by existing monitoring 

programs. For example, none of the programs provided information that would have helped 

forecast the spread of West Nile Virus. Monitoring networks have not been designed to 

detect species-level changes, and few detect changes in rare and uncommon species. Rare 

habitats are significantly under-represented in existing monitoring programs.

Outside of the LTER and NOAA National Status and Trends (NS&T) networks, which are 

relatively limited in its spatial extent and number of sites, there are very few long-term, 

fixed monitoring sites that can be used to evaluate seasonal and interannual influences, such 

as climate, on ecological resources and processes. Moreover, there are very few fixed sites 
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that have a range of chemical, physical, and biological measurements collected over long 

time periods. Such sites are important in evaluating how biological populations and 

communities respond to seasonal and interannaul variation in the physical environment. 

There is a need for a larger network of fixed, long-term ecological monitoring and research 

sites to address this issue. The National Ecological Observatory Network (NEON, 

Senkowsky 2003, NRC 2004), sponsored and supported by the National Science 

Foundation and American Institute of Biological Sciences, is a comprehensive proposal to 

establish geographically distributed infrastructure across the country, that will broaden the 

spatial representativeness of ecosystems.

There is a lack of historical data on ecological processes, trends, and disturbances to better 

inform models as to how ecological and biological resources might change in the future. 

New advances in molecular phylogenetics are just starting to provide a framework and 

dataset to evaluate historical relationships between environmental change and processes 

related to biological diversification and evolution. Yet another rationale for the retention of 

archival data on land cover and other ecosystem-level conditions is to allow for their use in 

providing a context for understanding the results of species-level phylogenies. 

There is a lack real-time and near-time monitors to evaluate how ecological and 

hydrological processes respond to varying environmental conditions. For example, we need 

in situ monitors to determine how nitrogen and sediment concentrations vary with flow 

among catchments in different biophysical settings.

We lack sufficient in situ observations of marine ecosystems to monitor changes due to 

climate variability and other anthropogenic activities such as fishing. In particular, data are 

lacking on secondary producers and non-exploited fishes that play keystone roles in marine 

ecosystems.

Remotely Sensed and Other Spatial Data

The spatial resolution of many climate data sets continue to be insufficient to model a set of 

ecological and hydrological processes occurring at finer spatial scales, especially in the 

western U.S. where spatial heterogeneity in precipitation is considerably higher than the 

eastern U.S..

There is a lack affordable remote sensing data that permit estimation of important 

ecological variables and vegetation classes at spatial scales relevant to many land 

management decisions.

We lack national databases on soils and geology of sufficient spatial resolution and 

information content (e.g., soil texture and hydraulic properties) that permit accurate 

modeling of ecological and physical processes at community and within-watershed scales.

We lack sufficient data on human uses of ecosystems.

There is a lack elevation data of sufficient resolution in coastal areas that permit an accurate 

assessment of the catchment area of individual water bodies and stream segments, as well 

as direction of flow. Delineating catchment areas of and flows into water bodies is critical 

in determining potential point and non-point source pollution contributions to observed 

ecological conditions.

There is a lack remote sensing technology to estimate the temperature of streams in a 

spatially and temporally continuous manner. Such estimates are critically important in 

evaluating fish habitat quality in many streams in the Pacific Northwest
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There is a need for technological developments in remote sensing, including: radar and 

LIDAR satellite systems that will enable the depiction, at suitable spatial resolutions, of the 

3

rd dimension in structurally complex terrestrial and coastal marine habitats; on-orbit 

hyperspectral sensors to improve our ability to detect ecological communities (and perhaps

species-level differences) and vegetation condition; and measures of soil surface moisture 

and sea surface salinity.

Modeling

Although many models have been developed that permit ecological modeling and 

forecasting, there is no central repository or web-based utility that allows a user to access 

and use these models. However, the EPA has developed a framework and set of standards 

for models supporting environmental regulation (CREM) and the NBII also is attempting to 

develop standards for models related to the prediction of species’ distributions.

There is no comprehensive approach or standards that have been developed to understand 

which models will work for a specific range of applications (for example, what model to 

use to estimate habitat suitability at a regional versus local scale).

Until global climate simulations accurate at the local decision making level are routine there 

is a need to understand and statistically characterize the influence of large-scale 

atmospheric forcing (ENSO, PDO, etc) on local climatic variable (precipitation, 

temperature, etc.).

There is a lack of integration of important biological, chemical, and physical parameters in 

models.

Many process models are too data and parameter intensive to apply over broader 

geographic regions (where the questions are regional in scope).

There are a number of ecological processes important to ecological forecasting, that 

because of scaling complexity and other factors, are difficult to model.

There is a lack of comprehensive methods to estimate and display uncertainty in model 

estimates, although Smith et al. (2001) and Wickham et al. (2002) have applied logistic 

regression and Bayesian approaches, respectively, to map the spatial distribution in 

modeling results.

There is a need for a wide range of in situ data for model validation.

There is a need for assessments and testing on appropriate methods to extrapolate point data 

into wall-to-wall maps.

Data and System Integration, Data Management, Technology Transfer, and 

Education

There is a lack of a comprehensive information management system that provides one-stop 

shopping for data, models, standards, tools, and training needed to conduct ecological 

forecasts.

Computational infrastructure and informatics are needed that provide efficient data 

querying/mining/analysis/integration of biological, chemical, and physical data for 

ecological forecasting. 

We lack an efficient system for disseminating data and models to those conducting and 

using ecological forecasts across the country.

There is a lack of comprehensive, web-based programs that would promote training and 

learning on ecological forecasting at the K-12 and college levels. Web-based programs, 
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like the EPA’s Surf-Your-Watershed (http://www.epa.gov/surf/), have had a dramatic 

positive impact on the awareness of students about environmental conditions in the 

watershed in which they reside.

There is a need for new education programs so that the public and the decision makers, as 

well as researchers, become aware of GIS/map accuracy issues and the subsequent validity 

of any information they use.
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