Proceedings of a workshop held July 27-31, 2003

Kihei, Maui, Hawaii

First NBII

Biodiversity Modeling

Workshop

Results and Recommendations
First NBII

Biodiversity Modeling

Workshop

Results and Recommendations

Proceedings of a workshop held July 27-31, 2003

Kihei, Maui, Hawaii

Sponsored by

The National Biological Information Infrastructure (NBII)

Convened by

The U.S. Geological Survey Center for Biological Informatics (CBI)

Hosted by

The NBII Pacific Basin Information Node (PBIN) 
ii 

Editors:

Bradley O. Parks, Director, NAC-BDEI, University of Colorado / Argonne National Laboratory

Mark D. Fornwall, Director, NBII Pacific Basin Information Node, U.S. Geological Survey

James F. Quinn, Director, Information Center for Environment, University of California-Davis and NBII California 

Information Node

Please cite this publication as follows:

Parks, Bradley O., Mark D. Fornwall, and James F. Quinn, eds. First NBII Biodiversity Modeling Workshop: Results and 

Recommendations. Proceedings of NBII Biodiversity Modeling Workshop, July 27-31, 2003, Maui, HI. Prepared by the 

North American Consortium for Biodiversity and Ecosystem Informatics (NAC-BDEI) at the University of Colorado 

at Boulder for the National Biological Information Infrastructure. Denver, CO: U. S. Geological Survey, Center for 

Biological Informatics, 2004.

Production and editorial assistance from the publication staff of the NBII National Program Office.
iii

Table of Contents

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

A. Problem statement – biodiversity modeling needs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

B. Position statement – NBII investments and interests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

C. Workshop description – terms of reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

II. Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

A. Modeling biodiversity – status and expectations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

 Recommendations

 1. Implement a clearinghouse for existing biodiversity models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

 Proposed constituent actions:

 a. Build classifications of critical ecological (functional) attributes of species . . . . . . . . . . . . . . . . 4

 b. Establish a decision support system for selecting and using biodiversity models . . . . . . . . . . . . 6

 c. Track reuse of biodiversity models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

 d. Enable and promote peer review of biodiversity models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

B. Data and information for biodiversity modeling - status and expectations . . . . . . . . . . . . . . . . . . . . . . . 9

 Recommendations

 2. Identify and catalog the relevant data universe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

 Constituent actions:

 a. Automate competent data discovery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 b. Assess information needs and data gaps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 c. Advocate and fund data digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 3. Improve data integration through schema element or ontology reuse . . . . . . . . . . . . . . . . . . . . . . . 11

 Proposed constituent actions:

 a. Develop and manage a federated data schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 b. Registry support and data services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

 4. Promote data quality, data exchange, and peer review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

 5. Provide education on biodiversity information management and modeling . . . . . . . . . . . . . . . . . . 14

 Proposed constituent actions:

 a. Use metadata to educate model users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

 b. Develop multi-scale consciousness and ecological perspective . . . . . . . . . . . . . . . . . . . . . . . . . 14

 c. Educate collaborators about biological and ecological informatics . . . . . . . . . . . . . . . . . . . . . . 15

 d. Train and expose users to biological and ecological informatics . . . . . . . . . . . . . . . . . . . . . . . . 15

 e. Develop a teaching text. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

 f. Found and sponsor an electronic journal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

 6. Provide a service outlet to develop models and maps operating through distributed 

 biodiversity modeling systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

 Proposed constituent actions:

 a. Provide scaled, remote computation service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

 b. Provide mapping and modeling services via the Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

 c. Build user interfaces using interoperable middleware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

 d. Implement distributed biodiversity modeling systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
iv 

III. Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

A. Adopt terms of engagement and proposed NBII Action Plan - status and expectations . . . . . . . . . . . 17

 Recommendations:

 7. Adopt a leadership role in improving biodiversity modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

 8. Proactively and systematically engage the biodiversity community . . . . . . . . . . . . . . . . . . . . . . . . 17

 9. Adapt NBII institutional interfaces to ensure successful leadership . . . . . . . . . . . . . . . . . . . . . . . . 17

 a. Designate leaders/managers/and ombudsmen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

 b. Make NBII’s contact persons apparent and accessible . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

 c. Define responsibilities and chain of authority. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

 d. Orient staff and management with ongoing internal education efforts . . . . . . . . . . . . . . . . . . . 17

 e. Establish, monitor, and implement strategic and tactical plans . . . . . . . . . . . . . . . . . . . . . . . . . 17

 10. Begin three-year start-up phase with proposed schedule of effort. . . . . . . . . . . . . . . . . . . . . . . . . . 17

B. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

IV. Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

A. Acronyms used. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

B. Workshop participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

 1. Contributors in attendance

 2. Contributors not in attendance

C. Workshop prospectus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

D. Workshop program. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
v

Foreword

The National Biological Information Infrastructure 

(NBII) is being implemented to serve national biodiversity 

management, research, and education needs. It began in 

the middle 1990’s as a World Wide Web-based information 

system developed through partnerships with key 

organizations. The U.S. Geological Survey (USGS) Center 

for Biological Informatics (CBI) serves as an infrastructure 

node and provides leadership for NBII implementation. The 

NBII seeks to provide a means to access the vast amounts 

of scientifically credible, biological resources data and 

information residing in virtually thousands of organizations 

ranging from federal, state, and local governments to 

universities, museums, and many non-governmental 

organizations. 

Over the past ten years the NBII has made considerable 

progress toward the goal as originally envisioned. In order 

to be responsive to needs of various users, the NBII has 

developed in accordance with the recommendations of 

the National Research Council.1 Guidance has also been 

provided by the Presidents Committee of Advisers on 

Science and Technology.2 Additional recommendations have 

emerged through collaboration with other national (BioEco, 

Biodiversity and Ecosystems Informatics Workgroup of 

the Committee on Environment and Natural Resources, 

National Science and Technology Council) and international 

bioinformatics organizations (Global Biodiversity 

Information Facility [GBIF] and the United Nations 

Convention on Biological Diversity [CBD] ClearingHouse Mechanism). The result is a distributed national 

information system built through regional and thematic 

focal areas (referred to as nodes). This approach allows 

the system to simultaneously address both geographic and 

issue/species-based biodiversity needs. However, as stated 

in the documents referred to above, the NBII must continue 

to evolve into an integral component of the biodiversity 

enterprise. 

Modeling biodiversity is a rapidly advancing method used to 

further our basic understanding of both species distributions 

and ecosystem functions. The discipline is data dependent 

and builds on our current knowledge through experimental 

hypothesis testing or by forecasting possible changes in 

landscapes and habitat as a result of human activity (both 

intentional and unintentional). As interest in these methods 

expands, there will be increasing need for improved data 

access and better biodiversity models to support both basic 

research and applied biodiversity management. Toward 

this end, the NBII has chosen to embark on a series of 

workshops to define its role and contributions in support of 

biodiversity modeling, a logical next step in its mission to 

support biodiversity enterprises. 

Specifically this workshop seeks to address two NBII 

objectives: 

• Developing a suitable framework to support 

knowledge discovery and creation for the nation’s 

biological and ecological resources; and 

• Leading the development, selection, and 

distribution of tools and standards necessary

to facilitate interoperability and allow meaningful 

interactions with scientific data and information. 

Both objectives require the NBII to provide access to key 

data sets for applications and to enable development and 

distribution of models and tools to apply those data to 

address needs of biodiversity enterprises. 

This workshop is the first of several planned by the NBII 

to investigate the range of possibilities in biodiversity 

modeling thought, practice, and collaboration. This first 

workshop was intended to lay the groundwork and we feel 

that mission was accomplished. It lays out a very broad 

vision, one that will require considerable advancement in 

both biodiversity modeling and informatics before it can 

be fully realized. Additional work is required to define 

a clear road map with discrete, attainable goals that will 

lead us toward our initial core vision. Early efforts will 

go into products showing proof of concept for distribution 

modeling as an attainable, highly visible, well-defined goal 

for the whole biodiversity informatics enterprise.

These are ambitious recommendations – the beginnings of 

a grand vision – that reach beyond the NBII, as currently 

constituted, and probably beyond currently available 

resources. In practice, these needs and recommendations 

are global ones, and progress will depend upon 

collaboration and priority-setting among a plethora 

1

National Research Council, A Biological Survey for the Nation

(Washington D.C.: National Academy Press, 1993).

2

President’s Committee of Advisers on Science and Technology 

(PCAST) Panel on Biodiversity and Ecosystems, Teaming With Life: 

Investing in Science to Understand and Use America’s Living Capital

(Washington, D.C.: Executive Office of the President of the United 

States, 1998). 
vi

of groups facilitating standardization and sharing of 

biodiversity data, including the GBIF, multiple initiatives 

in the museum community, a variety of international 

government-to-government efforts to achieve the goals 

of the CBD and subsequent treaties (for instance, the 

Inter-American Biodiversity Network [IABIN]), and 

some specific efforts focused on invasive species (Global 

Invasive Species Program [GISP] and various bilateral 

efforts).

It is also recognized that all viewpoints about biodiversity 

modeling were not included in this first workshop. This is 

partly a result of our initial decision to constrain ourselves 

to the issue of modeling species distributions, rather than 

considering much broader issues of “forecasting” for all 

of biodiversity and ecosystem informatics (BDEI). Within 

this narrower domain, and subject to time and resources 

available, we have tried to include as many view points as 

possible, both in the workshop and in subsequent review. 

We hope an inclusive approach is evident in this report and 

its “positioning” as only the opening discussion in what 

may be a series of workshops on this topic. The workshop 

was also, inevitably, limited by the availability of key 

participants. Due to time and travel considerations, some 

participants were unable to attend. One consequence 

of this, to name one compelling example, was that 

considerable attention was devoted to federated schemas, 

limiting what might have been said about ontology-related 

approaches (both approaches are evolving and we do not 

endorse either approach at the expense of the other).

Readers should note that, in the spirit of opening an 

inclusive discussion about biodiversity modeling, review 

comments by contributors have been preserved in the 

narrative of this report in order to maintain a transparent 

focus on important issues and productive differences 

of opinion, rather than pressing for premature closure. 

And, in that sense, even the recommendations (NBII 

recommendations noted in green text) provided in this 

document are intended as means, not ends.

This First Biodiversity Modeling Workshop was an 

important step for the NBII, one that depended upon 

support by those who attended workshop. We wish to 

thank all workshop participants and contributors (see 

Appendix B) for their valuable involvement with the 

workshop and their investment in the future direction of 

the NBII.
Biodiversity Modeling Workshop: Results and Recommendations

1

I. Introduction

A. Problem statement – biodiversity modeling 

needs

A recurring challenge to biodiversity analyses is that 

known occurrences of ecologically important biological 

elements (habitat types, species, or genetic types) typically 

represent only a fraction of the geographic range in 

which they are actually found. Furthermore, those known 

occurrences may also reflect substantial sampling artifacts, 

and they may be dynamic in space and time. 

In most cases, inferring the full present and future 

distribution of these elements is impractical or infeasible 

from field sampling alone, and must rely on a potentially 

complex set of modeling, mapping, visualization, and 

other tools. Important applications of tools for determining 

species distributions include setting geographic priorities 

for protecting rare species, genotypes, and habitats; 

identifying sites at particular risk for the establishment 

or spread of invasive species; aiding the discovery of new 

populations or genotypes of high conservation value; and 

inferring the impacts of future changes in environmental 

policy, land use, or climate.

Within a community representing overlapping interests 

of biological resource information providers (NBII, 

BDEI, CBI, World Data Center [WDC], National Invasive 

Species Council [NISC], and so forth), there has been 

some conceptual convergence in approaches to this 

class of analyses. However, these have not coalesced 

into a coordinated research agenda, despite some pilot 

collaborations. The purpose of convening a workshop was 

to assemble researchers and practitioners in biodiversity 

modeling, both to design such an agenda, and to identify 

and launch high priority demonstration or start-up projects. 

Following are suggested points of discussion and problems/

issues of common interest:

• Review models in general use, or under 

construction, for specific application to species 

distribution.

• Consider infrastructural actions that the NBII could 

undertake to aid adaptation or implementation of 

these modeling efforts.

• Identify and rate opportunities for useful support 

and collaboration.

• Identify and rate training, exchange, and possibly 

intern, postdoctoral, or sabbatical needs and 

opportunities.

• Identify and rate funding needs and opportunities.

• Recommend processes by which the NBII might

jumpstart distributional modeling as a core initiative.

B. Position statement – NBII investments and 

interests

As a national biodiversity information system, the NBII 

supports biodiversity science enterprises by both providing 

access to and enabling application of biodiversity data and 

information. At the same time the planet and its biota and 

ecosystems continue to rapidly change. This rapid change 

has put added pressure on scientists and resource managers 

to improve their knowledge of the current distribution of 

species, as well as to be able to forecast possible future 

changes in species distributions and the impacts of those 

changes on ecosystem functions. Thus, modeling of 

biodiversity is increasingly being used at local to national 

levels for conservation research and decision-making. 

With the complexity of questions confronting conservation 

communities and the early stage of this phase of the NBII’s 

evolution, the time has come to contemplate the ability of 

the NBII to support efforts of the biodiversity modeling 

community. 

The NBII has been directly involved in ongoing modeling 

efforts associated with state and regional gap analysis 

activities under the Gap Analysis Program (GAP) and 

related cooperative projects. Support to the broader 

biodiversity modeling community is a logical outgrowth 

of these efforts. Such involvement may include many 

elements, from community development, education and 

ad hoc meetings, through data needs assessment and 

standards promotion and development, to the possibility 

of supporting significant computational infrastructure. 

As an initial assessment of the NBII’s potential future 

involvement in biodiversity modeling, this report:

• Elaborates the range of problems challenging

contemporary biodiversity modeling, providing a 

framework for formulating the responses possible 

by the NBII; and

• Provides a first draft comprehensive strategy and 

schedule of efforts, as an aid to planning logistics, 

resource needs, priorities, and implementation.

C. Workshop description – terms of reference

A working meeting was convened by the CBI from 

July 27 – 31, 2003, hosted by the NBII Pacific Basin 

Information Node (PBIN) in Maui, Hawaii. The meeting 

Introduction
2

Biodiversity Modeling Workshop: Results and Recommendations 

assembled 21 persons (2 others could not attend), 

representing many agencies and interests, to evaluate 

the status and prospects for biodiversity modeling. This 

document reports the conclusions of participants and 

contributors. It is expected that participants in subsequent 

workshops will assess, modify, and build upon these 

findings using them as a point of departure, baseline, and 

early organizational framework.

Participants were selected to provide the broadest possible 

representation in terms of professional and scientific 

diversity of viewpoint, geographical and institutional 

variety and inclusiveness, subject to the maximum 

capacity of facilities, and budgetary, travel, and scheduling 

constraints. The meeting was designed as a forum for 

collaboration among qualified scientists and technologists 

specializing in modeling biological phenomena, taxonomic 

classification, biological data management (biological 

informatics), spatial data analysis (primarily using 

Geographic Information Systems [GIS]), information 

technology applied to biological informatics, and practical 

biodiversity preservation and conservation. Despite 

constraints on participation, basic criteria were met and 

distribution among these specialties was maintained. Plans 

to convene periodic workshops to continue to examine 

biodiversity modeling will allow further optimization by 

incrementally defeating these constraints. 

The working protocol for the meeting emphasized 

active discussion, reconciling competing viewpoints, 

and collaborative writing to prepare these consensus 

recommendations. The program defined a progressive, 

intensive, and goal-oriented meeting. All participants 

were required to work from objectives (see Problem 

Statement above) to agreement about useful outcomes 

(see Recommendations following), and priorities for clear 

action (see Schedule of Effort). Those hosting and those 

contributing deemed the meeting necessary and successful, 

demonstrated by the long hours worked. Participants 

expressed strong hope that the recommendations provided 

here will be adopted and/or adapted for implementation 

by the NBII and its institutional, policy, science, and 

management partners.

The following recommendations have been added to 

capture maximum benefit from the workshop, to provide 

for effective implementation, and to bridge to subsequent 

meetings. These have been prepared by the core group 

of meeting organizers and ratified by other meeting 

contributors and participants.

Those agencies and individuals accepting responsibility for 

the actions recommended in this report should:

• Actively solicit review of this document by 

additional contributors.

• Publish and disseminate findings (in whole or in 

part) in varied scientific and lay communications 

such as journals, newsletters, and press releases.

• Open comment mechanisms enabled via the Web, 

to assist dialog between workshops/meetings.

• Retain continuity by inviting a core group of 

participants to collaborate in future/subsequent 

meetings.

• Document deviating, competing, and alternative 

approaches and decisions as they accumulate.

Introduction
Biodiversity Modeling Workshop: Results and Recommendations

3

II. Findings

A. Modeling biodiversity - status and expectations

Need to understand the distribution of taxa

Species, community, and biome distributions are in a 

constant state of flux. Understanding this persistent change 

(particularly man-induced versus natural variations) at 

a variety of spatial and temporal scales is problematic 

but essential to our understanding and management of 

biodiversity. Our knowledge of what-lives-where is still 

incomplete and limited largely to sparsely distributed spatial 

samples. Descriptive, statistical models are necessary to 

fill in gaps between such sparse samples data, to estimate 

locations where sampling should be expanded, and to infer 

locations where species might be able to live, but currently 

are not found. Such modeling approaches are increasingly 

important for resource managers at local-to-regional, and 

even global, scales.

Complementing descriptive (statistical) models with 

mechanistic (process) models

Construction of such descriptive, statistical models can also 

provide some insights into understanding why species live 

where they do. However, they may lack critical processlevel (functional) information, such as CO2

 physiology, to 

accurately forecast how species might react to or migrate 

through a landscape or region under, for example, rapid 

climate change. Thus, the use of these kinds of descriptive 

models should be complemented by including physiological, 

phenological, and other life history information in order to 

understand the processes or mechanisms that allow a species 

to exist in a given region or locale, in the context of complex 

interactions with other species and disturbances, and how 

that species might move from one locale to another.

Distinguishing descriptive and mechanistic models 

This life form and physiological information can be used 

to inform mechanistic models of ecosystem processes and 

dynamics. Dynamic General Vegetation Models (DGVM) 

represent one class of such models. DGVMs combine the 

capability to simulate the distribution of vegetation and 

its temporal change through successional processes and 

disturbance regimes. Under conditions of rapid climate 

change, it is anticipated that different taxa will move 

across landscapes at very different rates. Rapid migrants 

often tend to be early successional species having suites 

of characteristics that adapt them for rapid dispersal, 

establishment, and early successional capability, following 

a disturbance. More sedentary species often tend to be late 

successional and/or locally endemic. Thus, rapid migrants 

will likely overrun species less adapted to migration, 

potentially placing narrowly distributed species in direct 

competition with “invaders” from neighboring domains. 

This also implies that there will be lags, or disequilibria, 

between the changes in climate and the geographic 

distributions of species. 

Descriptive models cannot capture such lags. Indeed, 

a statistical relationship developed between a species 

distribution at a time when, for example, that species 

migration lags behind changes in climate, may produce 

misleading results. [Counterpoint-reviewer’s comment: 

Purely statistical trend analyses are designed to estimate 

rates of change, and may do so better than poorly 

parameterized or unvalidated process models. Spatial/

migration lags can be estimated by using proximity 

as a predictive variable in a regression-like analysis.] 

Thus, planning for the eventual shift from descriptive 

to functionally mechanistic models will be important to 

accurately depict both current and potential future species 

distributions. In addition to the dynamics and lags between 

species and the environment, changes in CO2 concentration 

(expected as a consequence of climate change) can alter the 

relationship between species and environment, rendering 

statistical models that are accurate under current conditions, 

inaccurate under future conditions. For example, elevated 

CO2 can alter the photosynthetic characteristics of a species 

and its water use efficiency, and increased water use 

efficiency may allow a species to live in drier climes than it 

currently lives in.

Descriptive models generally simulate the “realized” niche 

of a species, which could be reduced in spatial extent from 

the “fundamental” niche space by competition, predation, 

herbivory, or other disturbances. Under a rapidly changing 

climate all of these interactions may change, such that the 

future “realized” niche might be different than that under 

current conditions. Also, the realized niche under dynamic 

change may be quite different from that under some possible 

future climate. Species will likely re-organize into different 

mixtures or communities, affecting the larger “emergent” 

biodiversity patterns and the resulting ecosystem processes. 

Mechanistic models have the potential to more accurately 

simulate these dynamics and possible future states. 

[Counterpoint-reviewer’s comment: But these are also 

interaction terms in a conventional descriptive analysis, 

and depending upon the quality of the data and validity of 

the reductionist model, could, in principle, work as well 

or better. It is usually an empirical matter of which kind of 

model produces a higher r2, if that is an appropriate way of 

measuring success.]

Findings
4

Biodiversity Modeling Workshop: Results and Recommendations 

Laying the conceptual foundation for future generations 

of biocomplexity-capable models

In the same sense that simpler statistical/descriptive 

models may eventually become complements to, or be 

replaced by, evolving mechanistic/functional models, 

those functional models too may subsequently be 

supplanted by other, still more capable and representative 

models. Likely those next steps in modeling will respond 

to challenges to address biological complexity, that is, the 

inherently complex behavior of living organisms operating 

as individual agents dynamically interacting in and with 

ecosystems. The need to address the related limits of 

mechanistic model projections may become most obvious 

where multiple simultaneous species interactions and 

real-time feedback from environmental (co-)modifications 

must be considered. These challenges will be more 

provocative where growing human involvements become 

implicit considerations in efforts to model complex 

interactions in and with living systems. Because these 

kinds of biologically complex system relationships are 

not strictly computable ones (within current paradigms), 

they will require other modeling approaches. One example 

is agent-based simulation which relies on behavior 

or decision rules applied through cellular automata 

representing individuals interacting dynamically and 

iteratively (ecologically) to produce a range of plausible, 

potential outcomes. 

To succeed, innovation will also be required in acquiring 

the additional dimensions of data and information needed 

to drive and qualify such models of complex behaviors 

in living systems. Just as purely taxonomic species data 

must now be augmented to include functional attributes 

in order to more fully realize mechanistic modeling 

capabilities, subsequent generations of models will also 

need to be enhanced. They will require learning how 

to structure supporting data in ways that capture and 

preserve revealing information about the ecological 

context (biotic and abiotic) in which observations and 

measurements about species and organism behaviors 

are made. If means can be found to capture adequate 

contextual or semantic, ecological information about the 

circumstances surrounding and accompanying core data 

observations, it will then become possible to recursively 

or iteratively re-examine those data. As modeling 

capability and knowledge expands, it will be possible 

to better understand their meaning and reinterpret their 

implications. Likely such future modeling capability 

will rely on projecting multiple possible outcomes in an 

attempt to bracket a range of potential future scenarios 

and patterns, rather than predicting singular forecasted 

results or consequences for complex biological systems. 

The implications of this challenge for data and information 

acquisition and handling will not be trivial.

As statistical models are further refined and adapted, 

and as mechanistic models continue to evolve with the 

functional data needed to properly implement them, 

thought will also need to be given to new generations of 

models that will better anticipate future states and trends 

involving biologically complex systems. Parallel effort will 

need to be devoted to the peculiar data and information 

requirements of these next generations of models.

Recommendations:

1. Implement a clearinghouse for existing biodiversity 

models 

There is currently an unmet need to develop a 

clearinghouse for biodiversity models to support both 

research and decision-making. Such a clearinghouse will 

provide a location to house, and capability to distribute, 

biodiversity models with guidance for their selection. 

The NBII should develop this capability in concert with 

both the biodiversity modeling and the management/

decision-making communities to ensure that the needs 

of both communities are designed into clearinghouse 

functions. The four actions below propose the functionality 

that should be incorporated into an effective clearinghouse 

implementation. 

Proposed constituent actions:

a. Build classifications of critical ecological 

(functional) attributes of species

Anticipating future states under changing 

circumstances

As noted earlier, rapid environmental change is now the 

rule rather than the exception and consequent impacts 

on plant and animal biota are ubiquitous. Modeling 

to simulate these changes in order to understand their 

resulting impacts on both current distributions and future 

dynamics of species and ecosystem processes suggests 

the need for comprehensive modeling at all scales from 

site to landscape, region, continent, and globe. But the 

number of species, even at small scales, is sufficiently 

large to preclude the possibility of modeling everything 

everywhere. At larger scales, the problem becomes 

even more difficult, if not intractable. With proper data 

and classification schemes, modeling can provide a 

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Biodiversity Modeling Workshop: Results and Recommendations

5

means to use limited knowledge about (existing) target 

systems to anticipate their future state(s) under changed 

circumstances.

Exploiting functional similarity to simplify challenging 

modeling tasks

Fortunately, it is in the character of biology that 

evolutionary processes have found similar (but nonidentical) solutions to the problems of survival in similar 

climates, but in very different geographical domains. A 

good example may be seen in the convergent evolutionary 

pathways and consequent similarity between Cacti in the 

Americas and Euphorbs in Africa, where each occupies 

an ecologically similar but geographical disparate domain. 

They are able to do this by exploiting different evolutionary 

“strategies,” drawing on different functional traits. Other 

examples abound and are borne out by studies showing 

the functional similarities between northern and southern 

hemisphere biota even though their genetic relatedness is 

very low. Modelers of vegetation dynamics at large spatial 

scales can take advantage of these functional similarities 

to simplify challenging modeling tasks by simulating 

distributional changes using aggregate Plant Functional 

Types (PFTs), rather than species. 

Functional indicators for invasion, dispersion, and 

migration

In earlier modeling efforts, PFTs were very highly 

aggregated, such as “Boreal Evergreen Needle-leaved 

Tree” or”Temperate Deciduous Broadleaved Tree”. These 

PFTs are then simulated in dynamic interaction with each 

other, including overstory and understory interactions 

between, for example, trees and grasses. The modeled 

ecosystem is then classified into a “physiognomic” 

community, such as “Temperate Deciduous Forest” or 

“Tropical Drought-deciduous Savanna.” This technique 

proved valuable in climate-change modeling, for example; 

but these early and rather simplified depictions of PFTs 

are no longer adequate for dynamic simulations of rapid 

climate change. There is now a need to be able to simulate 

the rates of migration of PFTs and their interactions with 

extant PFTs in the locales to which they are migrating. That 

is, circumstances in which exotic plants will be invading 

communities in neighboring domains (in a climate change 

scenario, for example). To accomplish this, existing 

PFT descriptions must now be subdivided into finer 

descriptions that capture variation in dispersal rates and 

other associated functional characteristics. It is known, for 

example, that pines can be arrayed along a gradient from 

rapid to slow dispersal capability. More fortunately, a host 

of other traits appear to co-vary with dispersal capability, 

such that whole “syndromes” can be defined within 

PFTs, ranking species from more to less invasive. These 

syndromes are similar in concept to “early successional” 

versus “late successional” plant classifications. 

Expanding from taxonomic to functional classification 

systems

Most current plant classification systems are based on 

genetic relatedness, that is, they are taxonomic (only). A 

new complementary system of classification or a revision of 

the current system based on functional attributes is needed 

for the further development of dynamic vegetation models. 

Such a classification system is beginning to emerge, 

but it is developing slowly and in an ad hoc manner. As 

noted earlier, newer DGVMs are based on measured 

physiological and other life history characteristics of plants. 

The advantage of this is that these models can now be 

implemented over small geographical domains with their 

more detailed PFTs specified (parameterized) as actual 

species allowing direct comparison of “simulated” and 

“observed” species distributions. If a species is simulated 

by itself, then a map of that species’ “fundamental” niche 

(distribution) should be possible to produce. However, 

when a species is simulated in its competitive milieu 

(shared context) with a number of other species or PFTs, its 

“realized” niche can be simulated. Such relationships can 

be used for improved model construction and validation. 

For application to larger spatial extents, PFTs can still 

be defined more generally to examine the dynamics 

and potential shifts of, or impacts on, ecosystems over 

continental and larger geographical domains. 

Construction of a more detailed, functional classification 

of PFTs will allow extension of the NBII’s existing 

physiognomic classification scheme to become a more 

useful functional description of ecosystems. The bottom 

of the classification system might culminate in small 

collections of species or ecotypes. Intermediate to higher 

levels of PFT classes might contain increasing numbers of 

species, but those species may or may not be genetically 

related. In this scheme, any given taxon of related species 

might recur (be classified into) several different PFTs. 

Although this discussion is focused on plant species 

and PFTs, it should also be possible to build a similar 

hierarchical PFT classification system for terrestrial and 

aquatic animals. 

[Reviewer’s comment: A new classification scheme to 

capture functional attributes of vegetation classes may 

be a very complex exercise and in any case would have 

to coexist with existing vegetation maps in most modeling 

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Biodiversity Modeling Workshop: Results and Recommendations 

environments. Function-based classifications would 

require the development of a multidimensional (structure, 

energetics, water relations, biogeochemistry...) theoretical 

construct (analogous to species concept and genetic 

linkages of taxonomic classification) on which to base the 

classification. While such schemes would undoubtedly 

be useful, an interim solution may lie in ability of exiting 

and new classification schemes to contain at a minimum 

descriptions of vegetation that enumerate functional/

mechanistic as well as taxonomic properties. The ability 

for the NBII to support functional modeling lies in its 

ability to capture important attributes of vegetation that 

help predict what species are present in the vegetation and 

the functional role of the species.] 

The NBII should provide leadership in a collaborative 

effort to extend the existing physiognomic classification 

system to include functional attributes, as it did when 

it helped lead the development of a national system for 

classifying vegetative communities and associations. 

Products of these collaborations should be delivered 

through the NBII’s biodiversity modeling clearinghouse.

Other classifications options – Life Histories

Another promising direction in classifications to support 

biodiversity modeling is in the parameter space of species’ 

life histories. Recent theoretical work by Hubbell and 

others suggests that some important properties of speciesabundance and species-area distributions can be captured 

by community ecology models characterized as “neutral” 

with respect to species ecological and demographic 

differences, but that do depend on the aggregate properties 

of reproduction, survival, and dispersal. Understanding 

the distributions of species’ life history parameters and the 

interaction of these parameters in models like Hubbell’s 

may provide a useful path to classifying organisms for 

assessments of biodiversity.

b. Establish a decision support system for selecting and 

using biodiversity models

Need for multi-criteria matching of problems to 

modeling requirements

Two very different and complementary types of models 

are distinguished here. The first type, statistical models, 

provides a descriptive analysis of biodiversity (species 

distribution for our immediate purposes) over the 

landscape. This type of model summarizes or estimates 

where species may occur in landscapes based upon 

purely statistical means. These models do little to address 

functional questions. The second type, mechanistic models, 

attempts to directly address ecological function. It attempts 

to ascertain why species may be present or to understand 

their role in the landscape or ecosystem. In practice, most 

working models include aspects of each approach. Within 

each of these two classes of models, however, there are 

model differences based upon assumptions, data, and 

a variety of parameters. To reduce resulting confusion 

and to better inform prospective users of such models, a 

decision support (selection) tree may be necessary to aid 

users in deciding what models may be most appropriate 

to investigate or address a particular problem. A 

decision support tree for selecting and using biodiversity 

models needs to incorporate information in a number of 

dimensions. By determining the position (requirements) of 

a proposed study among these various dimensions, a choice 

of models can be made that is most appropriate to the 

problem under study.

Model output needed (questions to be answered)

Most important among these dimensions (considerations) 

is the nature of the question that the model must address. 

One way to specify such questions is through classification 

of model outputs. Statistical models provide outputs with 

organismic content. These include spatial distributions 

of individual species; distributions of assemblages; 

distributions of diversity measures such as species 

richness, evenness, or any of the many other measures of 

diversity; distributions of population parameters such as 

individual species abundances; and distributions of species 

habitat suitability indices. Functional models provide 

outputs that represent functional properties of organisms 

such as physiognomic, trophic, or life history classes. 

Other types of output include energetic or other material 

properties of ecosystems such as productivity, biomass, 

or components of system biogeochemistry. A different 

type of output is that used by geographic targeting models 

for selecting conservation, restoration, or other sites for 

management or research purposes.

Spatial framework

Another dimension of model selection is the spatial 

framework needed for the study. The extent of the study 

area is the first defining property of the spatial framework. 

This extent may range from global to continental, regional, 

or down to a specific management unit, or it may require 

multiple scales. The spatial resolution of the study is also 

important in defining model selection. Some models use 

regular, geometric, cellular divisions of space and others 

use arbitrary divisions defined by political, ecological, or 

other physical criteria. Although it is relatively easy to 

convert (translate) between different spatial frameworks, 

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Biodiversity Modeling Workshop: Results and Recommendations

7

some models may not have this capability themselves, and 

may thus require spatial data preparation (pre-structuring) 

prior to modeling. Some models may incorporate or require 

a hierarchical spatial structure. Many of these issues 

are at least partially addressed in emerging Open GIS 

technologies, and current model developers should build 

architectures for spatial data that fit the NBII’s strategy for 

migrating to Open GIS tools and standards.

Temporal framework

The temporal framework is another important model 

specification. Many models output a single point-in-time 

estimation for change over time. This type of model would 

be used to do estimates for the boundaries of the time 

extent under study, and any intermediate points in time. 

The time extent could include any combination of past, 

present, and future points. Time dynamic models could 

include continuous time domain models such as ordinary 

differential equations (ODE) models, or discrete simulation 

models with either regular time steps or event-driven 

time steps. Some models may incorporate or require a 

hierarchical temporal structure.

Input data / information

Another dimension of model selection is input data/

information requirements. A wide variety of parameters 

are needed for models ranging from spatially or temporally 

explicit data to system-wide properties of organisms or 

ecosystems. Some models require initial distributions 

of, and/or other information about organisms from 

which they then build estimates of more comprehensive 

distributions (see “Expanding from taxonomic to 

functional classification systems” above). Many models 

require non-organismic, environmental data such as 

climate, topography, soils, hydrology, and others. All such 

data are now often required in a spatially explicit format in 

regular or irregular spatial frameworks. Some models may 

provide capabilities to convert input data to model formats, 

but many may not. A related issue is whether model inputs 

need to, or should, conform to prescribed data standards, 

or conventions or common practices in the relevant content 

community, and whether such conformity is, or should be, 

documented in model metadata. In practice, it is unlikely 

that data contributors can or will convert data into common 

schemas or formats, because many data sets are gathered 

for purposes different from biodiversity applications, or 

are constrained by long-term investments in incompatible 

legacy methods and protocols. An alternative and perhaps 

more realistic goal is to promote wide use of “Semantic 

Web” technologies under which schemas or ontologies for 

heterogeneous data are declared and expressed in opensource formats (such as Extensible Markup Language 

[XML], Resource Description Framework [RDF], or Web 

Ontology Language [OWL]) in ways that unambiguously 

define the meaning of the data and permit machine 

access and interpretation across multiple applications and 

platforms.

Computing resources

Although appropriate computing resources continue 

to become more widely accessible in the marketplace, 

variation in computing capabilities will certainly persist 

among potential modeling users. Modeling requirements 

for storage and compute cycles will be important for 

some users with limited resources. One dimension of a 

decision support system needs to balance these resources 

and related constraints. Included in this dimension could 

be the possibility of a hybrid solution through distributed 

implementation of models. This will, in turn, require 

consideration of whether the model can be downloaded 

to a user’s computer, the type of programming languages 

used, and other system requirements for models that can 

be so distributed. A decision support system could also 

include results of the performance of models solving 

similar problems (not to be confused with data benchmarks 

referred to in “Accreditation” below).

Accreditation

Another aspect of model selection is the accreditation 

status (confidence in and acceptance) of models. By this 

we mean any certifications or indications of the proved or 

approved status of the model within the content community 

that it serves. Accreditation could take such forms as 

peer-review reports by the NBII or other panels (see 

“Enable and promote peer review of biodiversity models” 

below), accuracy performance measures (benchmarks) on 

standardized data sets, or anecdotal evidence documenting 

author or user experience with the model (satisfaction 

or dissatisfaction). Researchers in the computer science 

community are experimenting with automated methods for 

measuring “trust” in Web (or other) services. Google rank 

is a simple example. It is likely that the NBII can adopt 

methods using the nature of calling links and services to 

automatically generate ways to help users determine the 

reliability of, and appropriate uses for, competing models.

Uncertainty assessment

Because no model can be perfect, assessments of model 

output uncertainty may be an important feature for 

some users. For complex space-time dynamic models 

with stochastic components, such assessments may be 

impossible in any comprehensive way. However, Monte 

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Biodiversity Modeling Workshop: Results and Recommendations 

Carlo simulation of the effects of variation or error in input 

parameters may be possible. This kind of uncertainty 

or model sensitivity analysis could be applied to lessdynamic models as well. A dimension of model selection, 

then, could be capabilities or amenabilities of models 

for such uncertainty assessment. Note that expressing 

and visualizing uncertainty in a complex spatial setting 

continues to be a research challenge.

Method and approach

Of course, an important component of model selection 

is the modeling method to be used. Part of the goal of 

a decision support tree is to suggest methods that are 

appropriate for studies with given output, spatial, temporal, 

computing, or accreditation requirements. This sifting 

through the dimensions of a modeling problem should 

help narrow and defend the choices of potential model 

users who have little familiarity with the universe of 

models that may be available. Even so, there may be a 

number of methods that can meet their requirements. A 

useful first step in distinguishing models is to discriminate 

between those that use assumptions about critical model 

parameters in their structure (parametric) and those that 

do not. Models that could be included in the parametric 

approach are those based on geostatistics, or other 

regression techniques such as multiple regression or 

decision trees. Non-parametric approaches include those 

using such modeling methods as genetic algorithms, 

neural nets, or simulated annealing. One example would 

be the Genetic Algorithm for Rule Production (GARP) 

modeling environment. Another approach is to choose 

models that perform space-time dynamic simulation of 

ecological processes or mechanisms in order to estimate 

their outputs. These often complex models may include 

some distributional assumptions about their inputs, but the 

relation of the outputs to the inputs is usually highly nonlinear and possibly chaotic. For some kinds of modeling 

requirements, space-time dynamic simulation models may 

be the only possible method. 

Advancing from less informed to more informed model 

selection and use 

The NBII should organize a collaborative effort to develop 

a decision support system to guide the selection and use 

of biodiversity models. This system can be used as an 

(educational) organizing element for models contained 

in the clearinghouse, will facilitate access to necessary 

metadata to characterize models, and will enable more 

effective and reliable search and retrieval of relevant 

models by users. Decision support tools should help 

modeling users identify what class of models are best 

suited to address their problem and to facilitate screening 

for those specific models whose requirements (dimensions 

above) can be best met. [Reviewer’s comment: The best 

fit of competing models to particular applications must 

consider simultaneous constraints of data availability, 

scale, time-scale, computer power, need to address policy 

or legal constraints, and so forth. It is really smarter 

metadata. These might start by developing ontologies for 

models, other services, and data sets. Software “agents” 

could then automatically explore available data and 

services for fit to a particular query of class of questions.]

c. Track reuse of biodiversity models

Even a simplistic clearinghouse implementation might, 

with modest effort, track by whom and when a particular 

model format or version was accessed or downloaded, but 

closer tracking would likely be advantageous. One would 

want such a clearinghouse to monitor and communicate 

who is applying an NBII-hosted model, for what purpose 

they are using it (objectives or questions to be answered), 

and the model’s status relative to its intended uses and the 

author’s maintenance of it. Note that if such applications 

are on the Web, emerging “Semantic Web” tools may both 

do some of this automatically, and use the results to create 

and update information on appropriate uses and trust, to 

better guide new users.

Fitness for use

There is a need for the exchange of more systematic 

information about model specifications and requirements, 

about the life cycle of models offered/available, and 

better information about users and their purposes and 

experiences. Adequate tracking information will help 

to create an understanding of the variety of model uses 

that the NBII is supporting, and would address concerns 

about whether a model was being applied for uses other 

than those intended by its author. Models can sometimes 

legitimately serve applications for which they were not 

originally intended, or they can be fairly adapted to do so. 

While some of these uses may be considered “extensions” 

of their value, others might be considered misuse or 

inappropriate use of a purposive modeling approach. 

Equally important, tracking data would also help to 

determine what models are most widely used and why. This 

information could then be used to improve NBII services, 

and could be fed back to the modeling community to 

guide the improvement of models and to inform modeling 

research planning.

Revision and expiration

Tracking data can also help by properly accounting for 

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Biodiversity Modeling Workshop: Results and Recommendations

9

the life cycles of models. Biodiversity models may be 

relevant and appropriate for use within a finite term, 

but they can quickly become out-of-date if they are not 

maintained or updated as new applications and approaches 

are developed. When submitting a model to the NBII 

Clearinghouse, a provider may stipulate, for example, that 

a model was posted on a given date, that the expected life 

span of the model expires after a specific period of time, 

and what may or may not be expected for the model’s 

further development. After a model’s life cycle expires, 

the model may be removed from the service (access at the 

clearinghouse), or the provider may offer a more current 

replacement model or extend the expected life of the 

current model by revision and reposting.

Updating models through revision must be facilitated by 

versioning, that is, by providing a version identity code 

or number for each rendition of a developing model. 

By enabling (or allowing) multiple versions of a single 

model, it is expected that researchers may be induced to 

make available works-in-progress. This flexible approach 

to version tracking is important because biodiversity 

modeling is a rapidly evolving field and many models 

remain in draft form without ever reaching a point that 

would be identified by the author as a final version. Note 

that to the extent that models are used to develop data 

that are subsequently used in downstream analyses or 

applications, it is important that the model version used be 

archived, so that dependent results can be documented and 

replicated.

The NBII should track specifications, development, and use 

of biodiversity models contributed to its clearinghouse. As 

proposed here, tracking will foster the creation of new and 

improved biodiversity models that effectively address the 

key issues for which clients seek models. Also, by tracking 

requests for models, as well as provision of models, the 

NBII can better understand unmet and evolving needs of 

biodiversity modeling applications and their users.

d. Enable and promote peer review of biodiversity 

models

Models, like publications, are representations of new 

knowledge (experience) gained from application of basic 

scientific principles. And like publications, models can 

have a large impact on scientific advancement and they 

may serve as tools to inform biodiversity management 

decisions. Accordingly, models require the same care 

and treatment as other scientific tools and products. In 

current practice, models receive a peer review only when 

submitted as part of a broader paper for publication in a 

scientific journal. There are no separate review processes 

that deal specifically with models, or more particularly, 

with biodiversity models. 

The NBII should proactively work with the biodiversity 

modeling community to develop a process in which the 

work of model authors is reviewed by one’s peers. Such 

reviews should be accessible at the clearinghouse and 

perhaps in an electronic journal, also implemented by the 

NBII (see “Provide education on information management 

and modeling” below). 

This review process could be the first step in posting 

models to the NBII biodiversity modeling clearinghouse 

and would be an effective way to encourage submission of 

models for use by others. Delivery of a peer review process 

would also be an excellent product for the new World Data 

Center for Biodiversity and Terrestrial Ecosystems, colocated with the USGS-CBI. Useful subjects of such peer 

reviews might include metadata, input data, model output, 

descriptions of independent variables, modeling methods 

and versions, model validation techniques (if any), data 

on model accuracy (especially errors of commission and 

omission), and data and version stamps.

B. Data and information for biodiversity modeling 

- status and expectations 

Data are required, variously, to invent, adjust, verify, refine 

and evaluate models. Thus observations and measurements 

(data) are necessarily linked closely with “predictions” and 

estimations (models). Biodiversity modelers begin with 

current scientific understanding of a particular species 

distribution pattern and biological processes, and couple 

those with known elements in the environment to test 

basic understanding of phenomena or to “forecast” future 

species distributions. Normally future-ward modeling may 

also be done in (or may require) a retrospective operation 

called a “hindcast.” Thus reliable modeling requires ready 

access to good quality data and information that accurately 

represents what is already known about problems, qualified 

by what was expected or learned from prior modeling 

efforts. The importance of adequate, credible, accessible, 

well-documented data and information cannot be overemphasized if robust results are required from biodiversity 

models.

Recommendations:

2. Identify and catalog the relevant data universe

The NBII should directly address the immediate data and 

information challenge to better serve biodiversity modeling 

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Biodiversity Modeling Workshop: Results and Recommendations 

by making biological and environmental data accessible 

to modeling tools, and by making the results of modeling 

processes available to a variety of users, including 

scientists, decision-makers and the public. 

The first step in achieving this goal is to identify the scope 

and nature of the pertinent data universe, particularly in 

regard to biological data resources. General categories 

of data needed for modeling purposes include organism 

occurrence (such as specimen and observation data), 

organism and population abundance (such as survey 

and population monitoring data), and environmental 

characteristics (such as climate, land-use, elevation, 

vegetation/habitat). Although some models require a 

broad array of data elements, in many cases only a few 

parameters are needed. Minimally, biological data must 

reference a taxonomic context (via taxon names and 

concepts), and then link to environmental data via a spatial 

(geographic) coordinate system. Model output itself should 

also be considered to be data, and may represent input data 

for use in other and/or subsequent models.

Constituent actions:

a. Automate competent data discovery

Enumeration and description of data

Simple enumeration and description of data resources 

enables potential users to discover those resources 

and evaluate their fitness for a particular use. The 

NBII’s Biological Extension of the Federal Geographic 

Data Committee (FGDC) Metadata Standard and its 

Clearinghouse Mechanism have made significant progress 

toward this end. The NBII metadata catalog should now 

be analyzed to identify gaps in taxonomic and geographic 

coverage, and in types of data available. While it is 

certainly a necessary step to provide intelligence about 

existing data sources, putting those data to use may be 

hampered by the often laborious task of transforming them 

into a more useful structure. 

Beyond simple human-readable metadata

To allow more efficient access to distributed data, 

electronic documentation of data must go beyond simple 

human-readable, descriptive metadata. Ideally, metadata 

for each data source should be documented with sufficient 

detail and consistency that data element cross-walking 

can be achieved more or less automatically. Without 

reliable automated data element cross-walking to relate 

disparate data sources, data users (such as members of 

the modeling community) are burdened with the manual 

(and possibly duplicative) implementation of this task. The 

panel encourages the NBII’s involvement with existing 

efforts to achieve this level of automation, such as that 

undertaken by the Science Environment for Ecological 

Knowledge (SEEK <http://seek.ecoinformatics.org/>) 

and the Semantic Prototypes in Research Ecoinfomatics 

(SPIRE <http://spire.umbc.edu/>) projects. SEEK tools 

include Ecological Metadata Language (EML <http://

knb.ecoinformatics.org/software/eml/>) and applications to 

the Long Term Ecological Research (LTER) network (e.g., 

see <http://www2.bren.ucsb.edu/~rbose/pubs/200005_

LTER/Metadata_ppt.pdf>. 

b. Assess information needs and data gaps

Appropriate and credible data

Common wisdom holds that models are only as good as the 

data used to generate them. Biodiversity modeling requires 

biological, environmental, and ecological input data that 

are reliable and specifically suited to meet related research 

or management objectives. Properly satisfying the need for 

such data means that one must assess the requirements of the 

modeling community to identify critical data gaps. Filling 

critical biological and environmental data gaps will prove 

invaluable to the development of better biodiversity models.

Profiling biological data gaps

Biological data are most commonly available in the form 

of species collection and observation records. Although 

their availability is increasing, these kinds of data are still 

lacking for some taxa and some geographic regions. A 

concerted effort is needed to evaluate the availability of 

collection records and other biological data sets nationally 

and internationally. Results should identify biological 

data gaps, and at a minimum, characterize them by 

major taxonomic group, spatial grain of data capture 

or storage (such as area covered by collection event or 

method of georeferencing), and data type (presence vs. 

presence/absence vs. abundance). Priorities should then 

be established for the order in which these gaps should be 

completed (filled) through expedited cooperative activity.

Profiling ecological and environmental data gaps

The relative distribution of plants and animals (one 

indicator of the diversity of biota) is determined by a 

wide array of environmental factors operating along a 

continuum of spatio-temporal (space-time) scales. Over 

the past two decades, there has been a great increase in the 

development and availability of geographically or spatiallyreferenced abiotic environmental data (such as climate and 

hydrologic parameters, elevation, and topography). This 

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Biodiversity Modeling Workshop: Results and Recommendations

11

trend complements the increase in collection and availability 

of biological data. Notably, however, the development of 

environmental data has been influenced by a variety of user 

needs, and not necessarily the needs of the biodiversity 

modeling community. Surveying available geospatial data 

and other data required by modeling practitioners could help 

identify critical gaps to be addressed by future development 

of environmental data for accurate bio-physical models. 

Such a survey should account for the availability of required 

environmental data relative to major feature categories, their 

geographic extent, and spatial grain.

Synthesizing data for ecosystem management

Measuring and mapping the spatial distribution of species 

and community types across landscapes is just the first 

step toward conserving biodiversity. Understanding these 

patterns, the underlying processes that shape them and 

potential human-induced alterations of them must be our 

ultimate goal because such understanding is essential for 

effective ecosystem management. Understanding underlying 

ecological processes requires knowledge of the ecology or 

life history of the species we are attempting to conserve. 

There have been many independent and widely scattered 

efforts to characterize species according to critical ecological 

attributes (such as reproductive guilds, physiological 

tolerances, plant life forms, and so forth) that determine their 

distribution and abundance across the landscape. 

Closing data gaps

There exists no concerted effort to generate standardized 

characterizations for a wide range of taxonomic groups, 

and important structural and functional features of the 

biota of our nation or the world. To close this gap a major 

collaborative effort is needed among state and federal 

agencies and research institutions to synthesize widelyscattered and incomplete knowledge into a more centralized, 

hierarchical schema for classifying species, one that 

accounts for their key ecological attributes. Formulating 

independent or collaborative funding and research initiatives 

to support local, regional, and national biological survey or 

environmental data development efforts would necessarily 

lead to enhancements in biodiversity modeling efforts. In 

addition, a synthesis of our existing knowledge of critical 

structural and functional attributes of our nation’s plants 

and animals would provide the key to moving from “simple” 

mapping conceptions of biodiversity to more formal 

understanding of the processes that drive the creation and 

destruction of this diversity.

The NBII should facilitate the filling of priority data gaps 

through several initiatives including: conducting surveys 

of data needs and data availability, hosting a forum for 

exchange of data and information through regional or 

national conferences or via the Web, facilitating the 

creation/enhancement of environmental and ecological 

data sets by serving as a coordinating entity and a potential 

funding source, and operating as a broker to discover other/

new funding sources and to mate them with data projects 

requiring additional resources. 

c. Advocate and fund data digitization

Digitizing non-digital data, and transforming existing 

digital data into more usable formats (such as georeferencing textual locality descriptions into spatial 

coordinate systems) still represent the most fundamental 

challenges to using many existing data resources. These 

data-availability problems exist in both the biotic and 

abiotic (environmental) data domains. The NBII should 

work actively with its partners to identify non-digitized 

data resources, to acquire needed funding, and to facilitate 

digitizing these data.

3. Improve data integration through schema element or 

ontology reuse

Improved access to biodiversity data and information 

relies upon greater standardization of data structures 

(or at least standardized external views) and formats. It 

also requires standard mechanisms to describe data and 

standard programmatic methods for accessing those data. 

The ultimate goal of this standardization is to provide more 

nearly seamless integration to users across sources. The 

NBII’s biodiversity modeling clearinghouse should adopt 

the goal of standardizing of schemas and/or ontologies, or 

more correctly, promoting ontology and schema element 

reuse, to help improve the current state of disparity (limited 

compatibility) between existing data sets. Similarly, there 

is a need to develop a multiplicity of data and model 

service interfaces, the programmatic interfaces used by 

machines to connect with data and simulation models, or 

other programmatic services. 

Proposed constituent actions:

a. Develop and manage a federated data schema

End members and current practice in data integration

The simplest approach to begin schema standardization is 

to build a catalog of metadata relevant to the available data 

sources (such as the Biological Extension to the FDGC 

Metadata Standard, and the NBII Web Resources Catalog), 

without committing effort to cross-walk specific data 

elements. As stated above, this approach is necessary for 

Findings
12

Biodiversity Modeling Workshop: Results and Recommendations 

documenting the data universe, but it does not address the 

labor-intensive step of combining data resources on a caseby-case basis. 

At the opposite extreme, the alternative is to develop a 

single, all-encompassing standard data schema, to which 

virtually all existing data sources can (and must) be 

mapped. This approach has had some success when applied 

within a specific subset of data sources where control and/

or willing compliance is high. For example, the museum 

collection community is collaborating to develop networks 

of distributed data resources that are based on a federation 

schema. A federation schema can be thought of as a set of 

generalized, but still well defined, data elements common 

to a broad range of otherwise different data resources. By 

having data providers map (conform) their heterogeneous 

resources to a federation schema, a large amount of work is 

distributed across a broad population of data managers. 

Systems such as the Species Analyst <http://

speciesanalyst.net/index.html> use federation schemas 

to integrate conceptually overlapping portions of data 

resources. The Darwin Core <http://tsadev.speciesanalys

t.net/DarwinCore/darwin_core.asp>, which underlies the 

Species Analyst (and now Distributed Generic Information 

Retrieval [DiGIR], see below), is a simple schema. It 

applies to a comparatively large number of records 

because it is relatively abstract, but it is insufficient to 

represent (and transport) data concepts that apply to more 

specialized data sets. However, when considering the much 

broader domain of biodiversity and environmental data 

resources of potential interest to the NBII and the modeling 

communities it can serve, history and experience suggest 

strongly that the development of such a monolithic data 

schema to accommodate all (or almost all) data sources 

unlikely to be successful, at least within the foreseeable 

future.

The alternative – an adaptive, practical approach to 

data integration

A better solution lies between the extremes of building 

either a simple catalog or one all-encompassing standard 

data schema. Rather than devising a single monolithic 

schema into which all data resources must be mapped 

(conformed), several (perhaps fewer than one or two 

dozen) “standard” schemas could be developed that 

would collectively accommodate the majority of wellknown kinds of data and data uses (such as models). As 

described above, the Darwin Core is one such schema that 

already exists. Although intended primarily for Natural 

History Collections’ data resources, this schema can 

accommodate a wide variety of data types, wherein each 

record represents an occurrence of a particular taxon at a 

particular place and time. Thus, whereas Natural History 

Collections’ data will map “robustly” (many matching 

elements) to the Darwin Core, other data types might 

map somewhat less robustly, but still map well enough to 

provide meaningful data on taxon distribution over time. 

Still other types, such as those containing exclusively 

abiotic geospatial, environmental data, might not map 

to the Darwin Core at all. Note that a similar approach, 

ontology languages, under development as part of the 

Semantic Web, are capable of expressing a greater range 

of relationships among data elements. It is likely that 

as federated XML schema approaches migrate to being 

expressed in next generation Semantic Web forms, some 

of the difficult problems of “mapping” between differing 

vocabularies and concepts will become more amenable to 

automated cross-walking and semantic processing. 

A federated solution - multiple standard data schemas 

(MSDS)

There would likely be substantial overlap of specific data 

elements among different schemas. For example, fields 

associated with taxonomic identification, and/or fields 

associated with spatial coordinates, will likely appear 

in many if not most of the different schemas. Other data 

elements specific to a certain class of data sources (such 

as natural history collection data, population monitoring 

data, environmental parameter data) would likely appear in 

only one or several of the standard schemas. As indicated 

in Figure 1. each data source could potentially conform 

(map), to a greater or lesser degree, to more than one data 

schema. (Note that bold lines in the diagram represent the 

most robust mappings; dashed lines represent the least 

robust mappings.) With the right set of standard data 

schemas, each of which is intended to address a particular 

class of data resource, at least one, if not multiple standard 

schemas, could accommodate most data.

Implementing MSDS

The rationale for establishing these multiple standard 

schemas is to reduce the plethora of protocols among 

existing data sources and their respective schemas into 

a more manageable number of alternate schemas. As 

opportunity and need allows, individual data sources 

should be manually mapped to one or more of the standard 

data schemas. Once a data source is mapped (conformed) 

to a standard schema, it will subsequently become available 

via that standard schema for all future users. When a 

user needs access to data, instead of simply identifying 

potentially hundreds or thousands of disparate data sources 

Findings
Biodiversity Modeling Workshop: Results and Recommendations

13

and manually conforming each one to a single schema, 

the user need only identify which one, or which of several, 

of the standard data schemas contains elements useful 

for the intended purpose. Thereby the user gains direct 

access to all relevant data sets without the need for manual 

transformation on a source-by-source basis. In this way, 

schema standardization offers an intermediate solution 

that, while requiring some human mediation (in the form 

of mapping existing data sets to one or several standard 

schemas), also enhances automation of data access (once 

mapped to a standard schema).

Identifying and developing automated users and 

support tools

The NBII should review the extant metadata catalog to 

determine how to cluster data resources into optimal 

data type, in order to clarify, specify, and prioritize 

schema development. This should be done in concert 

with a similar analysis and classification of the inputs 

required by modeling tools, so that classes of “automated 

users” or data consumers are identified and used as 

guidance. (For examples see “Lifemapper” system, <http:

//www.lifemapper.org/>), based on the Darwin Core 

federation schema and the GARP modeling tool for 

predicting species’ distributions.) The NBII should further 

facilitate the development of those various standardized 

schema, as particular needs or classes of data resources 

are identified through workshops and other forums 

for discussion among interested and knowledgeable 

parties. The NBII can help the process by supporting the 

development of software tools (“Wizards”) to assist data 

holders in the process of mapping (conforming) their 

data source schemas to one or more of the standardized 

schemas. Similar “Wizards” would assist researchers in 

designing schemas for new data sets in such a way that 

they maximally conform to one or more of the existing 

standard schemas and/or identify needs for new schemas 

or extensions of existing standard schemas. Finally, the 

NBII should maintain a network for communication with 

data holders about modifications to existing standard 

data schemas and the introduction of new standard data 

schemas.

b. Registry support and data services

Beyond passive discovery and secondary 

characterization of data and services

A registry system allows an active means to identify the 

existence and location of both data resources and data 

services. Active registration of data sets alleviates the 

need for passive discovery and secondary characterization 

(metadata generation) of information resources. Also, 

registration of data services (standardized programmatic 

interfaces to data resources) allows the reuse of general 

data services that may have broad application for many 

data resources and data access needs. In either case, 

the NBII should adopt a centralized registry based on 

a common, well-known standard, such as Universal 

Description, Discovery, and Integration (UDDI), to remove 

potentially significant development activity required to 

build a custom registry.

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Data Federation
14

Biodiversity Modeling Workshop: Results and Recommendations 

Registration process

Registration of data sets should be a straightforward 

process that simply involves selecting a schema and access 

Applications Program Interface (API) from the registry. To 

support this there should be some sort of validation process 

that is invoked during the registration process to ensure 

that: 1) data are accessible; 2) data conform to the selected 

schema(s); and 3) data values are in the expected range 

(where appropriate). The NBII should explore options for 

data resource registration, and seek to either support and 

coordinate with existing registry efforts or to develop new 

registry efforts where none exist.

Registration authority and mediation

The NBII’s proposed registry of data services should 

support and document the location of services, description 

of services, and responsible parties for both services 

and service descriptions. It is critical that duplication of 

service descriptions does not occur (such as two service 

descriptions that describe the same service type). Hence, 

some authority should be designated to ensure that this 

does not occur. Registration of new service types in the 

registry should not be a trivial process and should require 

human mediation, rather than being an automatic or 

autonomous process. 

4. Promote data quality, data exchange, and peer review

Generation of data schemas for existing data sets 

contributed to the NBII’s biodiversity modeling 

clearinghouse should be made as simple as possible and 

should provide significant incentives for participation by 

data producers. Examples of incentives might include 

data cleaning or visualization services. Comparison with 

existing data sets may be useful in some cases and software 

tools need to be developed to assist this process. These 

tools are more likely to be adopted if they are designed for 

cross-platform use, and maintained and improved through 

an open source process. Tools to assist with the creation 

of new data sets should be provided to help reduce schema 

diversity or better, simply promote reuse of existing 

schema components where possible. Again, cross platform 

and open source tool development will help encourage 

broad adoption.

A peer review process should be implemented by the 

NBII for some types of data sets, such as “standard” 

environmental layers and critical distribution. A large part 

of data quality evaluation tasks should be achieved with the 

kind of automated tools described above. Peer review will 

help ensure data sets and their metadata are meaningful, 

complete, and accurate. Peer review of data sets should 

not be allowed to become an impediment or precondition 

limiting accessibility of data sets. Instead, data sets should 

simply be tagged to indicate their peer review status and 

then offered freely.

5. Provide education on biodiversity information 

management and modeling

Educational efforts toward implementation of models 

and modeling should relate to the two general NBII 

objectives of advancing research appropriate for resources 

management and providing the results of this research in a 

format useful for resources managers. Educating end users 

about the importance of models, how they are developed, 

their uses and limitations, and their interpretation should 

be an inherent part of the design of any NBII modeling 

services interface.

Proposed constituent actions:

a. Use metadata to educate model users

Providing incentives and enforcing requirements for 

model developers to follow appropriate metadata and 

documentation standards (specifying the major approaches 

and assumptions in the models) would not only benefit 

end users, but would also encourage modelers to consider 

appropriate ways to explain models to novice or new users. 

This scheme therefore provides benefit to both researchers 

and end users by providing model metadata listing key 

assumptions and approaches pertaining to each model 

(including model outputs). The NBII can assist managers 

in assessing the relative utility of a given model for their 

particular application. Doing so would also be prerequisite 

to full implementation of a decision support tree that assists 

users to decide which of many modeling approaches and 

models might be most appropriate to address a specific 

question/problem (see “Establish decision support system 

for selecting and using biodiversity models” above). 

b. Develop multi-scale consciousness and ecological 

perspective

[Reviewer’s comment: One challenge for incorporating 

modeling facilitation services within the NBII arises due to 

the hierarchy of management.] Managers using models at 

broad spatial extent may have a quite different perspective 

about use of models and data than managers who deal with 

a particular, more spatially-limited location. Local resource 

managers may not understand why information generated 

from modeling at broad spatio-temporal extent may be 

necessary to address what appears to them to be a strictly 

local problem. [Reviewer’s comment: This suggests the 

need for different means to deliver education to different 

Findings
Biodiversity Modeling Workshop: Results and Recommendations

15

potential users, although having a national perspective 

represented even at regional gatherings may be helpful 

so that regional meetings don’t become focused solely on 

local ecological processes or management issues.]

c. Educate collaborators about biological and 

ecological informatics

Delivering education associated with the modeling 

components of the NBII can precede in a number of ways. 

One may be to collaborate, for instance, with the USGS 

Research Units or Cooperative Ecosystem Study Units 

at universities and state agencies to develop biological 

and ecological informatics courses or programs, or 

to integrate coursework from existing programs at a 

university into other programs. Existing programs suitable 

for this delivery might include those teaching Conservation 

Biology, Ecology, Biology, Fisheries and Wildlife, and 

Natural Resources Management, or existing extension 

programs adapted for state and local managers. This might 

also include the development of short courses for state 

agencies that would be run out of, and in collaboration 

with, the Co-op Units, but involve NBII staff or 

collaborators.

d. Train and expose users to biological and 

ecological informatics

Alternatively, the NBII might consider helping to establish 

training workshops/short courses on biological and 

ecological informatics at key centers where current 

research efforts are focused, including universities with 

expansive research groups in the area of biodiversity 

modeling, or at nodes with this expertise available inhouse. “Road shows” by key model developers who have 

excellent “success stories” about how modeling associated 

with NBII data management efforts has aided managers 

would be useful. These could be held at state agencies 

and regional gatherings associated with large national 

societies (such as land-use planners), or co-hosted with 

organizations such as the Institute for Ecosystems Studies 

<http://www.ecostudies.org> at the Cary Arboretum in 

Milford, NY, or held in association with the Council of 

State Governments, the American Association for the 

Advancement of Science (AAAS), or the Organization of 

Fish and Wildlife Information Managers (OFWIM).

e. Develop a teaching text

The NBII should work to help overcome an important 

constraint on educational objectives: the lack of good texts 

on bioinformatics that clearly relate modeling and data, 

particularly in a spatio-temporal context. One remedy 

would be to encourage authoritative people to compose 

such texts. Another would be to organize a conference, 

perhaps associated with such organizations as the AAAS 

or the Ecological Society of America (ESA), the presenters 

at which would write chapters to be included in a text 

designed to advance the field. Again, success stories would 

be helpful to ensure that the text would meet the broad 

needs of educating resource professionals about the range 

of information management and modeling issues. As 

another potential means to create a biodiversity modeling 

text, hold a strategy session at the annual NBII meeting 

on educating those external to the NBII about modeling 

capabilities facilitated by the NBII. 

f. Found and sponsor an electronic journal

The NBII should consider early the influential option of 

helping to establish an electronic journal (associated with 

the NBII) that covers issues of information management, 

biodiversity modeling, and information transfer. Such 

an e-journal could provide an incentive for individual 

researchers to participate in an NBII-based collaboratory. 

If editors of this journal required peer review, it would 

strengthen the quality assurance in models contributed to 

the NBII, and provide an additional incentive to researchers 

who participate. These inducements are valuable because 

modelers often observe that there are few natural outlets 

for publishing detailed models. This approach could also 

build a valuable “research utilization bridge” by helping 

to overcome both the reluctance of researchers to ensure 

that their results become formally integrated into (applied) 

natural resource management, and the reluctance of 

managers to keep up-to-date with current science.

 

6. Provide a service outlet to develop models and maps 

operating through distributed biodiversity modeling 

systems

In early stages of its action plan (following), a primary 

concern of the NBII should be to promote and deploy 

current-practice, interoperable, modeling schemes.

These schemes will support the development of systems 

that entail the automated acquisition of distributed data 

and execution of models on multiple remote computing 

systems. This might build upon the infrastructure already 

provided by the NBII nodes for geographic information 

systems, adding other functionalities. 

Proposed constituent actions:

a. Provide scaled, remote computation service

The NBII should provide a range of computational 

capabilities at a level of service that is appropriate, 

supportable, and utilized. Recommended service actions, 

Findings
16

Biodiversity Modeling Workshop: Results and Recommendations 

in order of increasing levels of effort, are:

• Create a File Transfer Protocol (FTP) site for 

download (distribution) of modeling software.

• Demonstrate implementation of algorithms.

• Implement a test bracket that allows users to check 

the validity of their (own) implementation of a 

modeling algorithm based on test data.

• Allow users to run their own data on NBII–hosted 

algorithms via single user interface.

• Provide a standard middle-ware Web service 

that allows users to build their own interfaces to the 

computational service.

• Provide a distributed computational service

such as Lifemapper, or other grid-based system, for 

computational-intensive algorithms.

b. Provide mapping and modeling services via the Web

The NBII should provide a service for end users to develop 

ecological niche models and distribution maps. This would 

entail a substantial expansion of the current role of the 

NBII as a server of data. It would require customization 

of data and models to suit the needs of users, working at 

a range of scales from local (such as conservation of rare 

species) to global/international (such as invasive species 

threats).

To scale such a commitment, a prioritized list of candidate 

tasks should be developed for modeling activities to 

be supported and related useful algorithms. Possible 

candidates, in no implied order, might include:

• Ecological niche modeling including logistic

regression, General Algebraic Modeling Systems 

(GAMs), GARP, decision trees, neural nets, 

BIOCLIM, and perhaps co-kriging or other 

explicitly spatial statistical services.

• Population Viability Analysis.

• Reserve optimization algorithms – incorporating 

such principles as complimentarity.

• Spatial data service – distributing custom sets of 

georeferenced spatial data suitable for biodiversity 

modeling.

c. Build user interfaces using interoperable middleware

The NBII should design clearinghouse interfaces that 

ensure adoption of modeling capabilities by target user 

groups. This will require focusing on an interoperable 

middleware layer upon which a range of interfaces could 

be built. The Open GIS Consortium Web Mapping Service 

(OGC: WMS) is an example of this approach in GIS, but 

could also be addressed in other complex algorithms such 

as Physiognomic modeling, Population Viability Analysis, 

and so on. Target audiences and their needs might include:

• Simple stateless interfaces that produce a single 

image or result,

• Detailed interfaces that expose all parameters, and

• Application-specific interfaces such as for invasive 

species or species conservation.

The development of such interoperable systems will entail 

a number of important considerations and will require 

additional study. Several notable initial considerations are:

• Interoperable data schema or ontologies,

• Development of Web services,

• Standardization of application program interfaces 

(APIs), and 

• Web service registration systems such as the UDDI.

d. Implement distributed biodiversity modeling systems

The NBII could adopt a very central role in influencing 

the form and evolution of an integrated computing 

infrastructure to support biodiversity modeling. The 

NBII has ready access to modeling user groups who 

would benefit from greater access to advanced computing 

services, and in return, could review and test the 

developing components. The NBII also has previous 

experience in standards-based data development, 

together with significant expertise in facilitating Internetbased collaborative work that could be used to develop 

a community dedicated to implementing distributed 

biodiversity modeling systems.

To accomplish this, the NBII needs to improve its ability 

to leverage funds in the information sciences though 

participation in such programs as the National Science 

Foundation’s (NSF’s) Information Technology Research 

(ITR). This might be achieved though outreach to the 

scientific community by hosting workshops, creating an 

electronic journal, and proactive involvement in existing, 

funded computational technology projects and ITRs, such 

as SEEK <http://seek.ecoinformatics.org/>, SPIRE 

< http://spire.umbc.edu>, and others.

Findings
Biodiversity Modeling Workshop: Results and Recommendations

17

III. Implementation

A. Adopt terms of engagement and proposed NBII 

Action Plan - Status and expectations

Workshop participants recognized that successful 

implementation of recommendations for “A. Modeling 

the distribution of species” and “B. Data and information 

supporting biodiversity modeling” would require 

meeting key preconditions with regard to institutional 

and management attitudes, commitment, and readiness. 

Most important in this regard is one underlying criterion 

for success: that action on the recommendations in this 

report be approached as not usual or routine, and therefore, 

incapable of providing lasting benefit unless particular, 

deliberate, and prior adaptations are made. 

Recommendations:

7. Adopt a leadership role in improving biodiversity 

modeling 

It is proposed here that the NBII adopt a leadership role to 

support and improve biodiversity modeling. This role of 

facilitating biodiversity modeling through collaborative 

development and application is a new role not previously 

filled by the NBII or any other agency or authority. Thus 

it is one that will require effective interaction between 

the NBII and a host of other individuals and institutions 

operating in one or more teams designated according to the 

tasks outlined in the following action plan. Anticipating 

this new set of operational requirements and planning to 

properly accommodate them should be accomplished by a 

structured, proactive, and flexible response by the NBII. 

8. Proactively and systematically engage the 

biodiversity community

Successful support of biodiversity modeling enterprises 

by the NBII will require careful assessment of modeling 

requirements established by relevant communities of 

modelers and user, and by participating communities of 

scientists and decision-makers. These requirements will 

need to be periodically reassessed relative to the strategic 

and institutional opportunities and constraints of the NBII 

and its partner agencies. The optimum match of modeling 

requirements to institutional opportunities/constraints will 

be most successfully translated to effective action if two 

conditions are met: making an explicit and overt effort 

to determine how the NBII may establish good practices 

that are specifically suited to the support of modeling 

enterprises, and recognizing that such activities are new 

ones that may not be fully congruent with, or optimized to, 

existing institutional practices and procedures at the NBII. 

9. Adapt NBII institutional interfaces to ensure 

successful leadership

Following are specific “criteria for success” recommended 

as necessary preliminary steps toward successful NBII 

implementation.

a. Designate leaders, managers, and ombudsmen

Advocates should be named who will be imbued with 

both the responsibility and the authority to ensure that all 

reasonable efforts are made for successful implementation 

of the recommendations and related actions described here.

b. Make NBII’s contact persons apparent and accessible 

Knowledgeable persons should be known, or be made 

easily identifiable, both inside and outside of the NBII and 

its partner organizations. All practical effort should be 

made to avoid repeated rediscovery of persons appropriate 

to handle inquiries and problems.

c. Define responsibilities and chain of authority 

Routing of requests and inquiries, as well as pathways 

toward solutions and decisions, should be predictable and 

widely known as standard operating procedures within 

the NBII and its partner organizations. Awareness about, 

and revision of, these procedures should be continual and 

routine.

d. Orient staff and management with ongoing internal 

education efforts

A high level of understanding about enterprises 

implemented to support biodiversity modeling should 

be achieved and maintained, recognizing that such 

commitments will be new and constantly evolving. 

e. Establish, monitor, and implement strategic and 

tactical plans

Planning should be proactive, persistent, and adaptive. 

Business as usual will not provide adequate management 

support.

10. Begin three-year start-up phase with proposed 

 schedule of effort

Workshop participants concluded that the 

recommendations in this report would be more valuable, 

and perhaps easier to implement, if some sense of priority 

and relative effort were attached. Participants did not 

conclude that ranking recommendations by order of 

importance would be feasible or desirable at this early 

stage. Priorities are expressed in terms of the year in which 

each recommendation should be started and its period of 

duration. No calendar dates are suggested, since progress 

Implementation
18

Biodiversity Modeling Workshop: Results and Recommendations 

will need to be measured from an as-yet unknown inception 

date to be determined by the NBII. Instead, start dates are 

shown by”project year.” Similarly, levels of effort that might 

be expected to be invested to achieve satisfying results are 

shown as day, month, or year units of effort by individuals, 

teams, or groups. In some cases it is implied that authority 

be invested in one person responsible to supervise and 

accomplish a designated task (recommendation). It is 

assumed that this schedule of effort will be revised through 

time to reconcile resources and constraints. However, 

it is strongly suggested that revision be accomplished 

comprehensively and systematically, with consultation 

by a core group of workshop participants, rather than 

incrementally and autonomously by institutional managers. 

There is danger of defeat by attrition if any change does not 

anticipate the scope and consequences of all changes.

Proposed schedule of effort:

Revise/refine biodiversity modeling problem statement

• Year 1: 1 day team effort

Generate goals and objectives of the NBII 

pertaining to modeling 

• Year 1: 1-3 months team effort. Review report 

from modeling workshop and formulate a strategy 

that outlines subsequent goals and objectives.

Define universe of biodiversity models and modeling 

activity

• Year 1: 3-6 months. To be done in conjunction with 

identifying goals and objectives.

Assess information needs and data gaps pertaining to 

biodiversity modeling

• Year 1: 3-12 months, individual or team effort. 

Conduct surveys of data availability and data needs. 

• Year 1 and/or 2: team effort. Provide forum for 

exchange of data and information at regional or 

national conferences or via the Web.

• Year 2-3: team effort. Facilitate creation of 

geospatial and ecological data sets by serving as a 

coordinating entity and a potential funding source. 

Note: The above roles could be distributed among the 

regional NBII nodes to ensure a more thorough and focused 

means of addressing these difficult, yet important, issues.

Build hierarchical classifications of ecological 

(functional) attributes of species.

• Year 1: Ongoing, team effort. To be accomplished 

via workshops, literature review, and written 

documents presented to key scientists for review.

Identify and catalog data universe

• Year 1: 1 coordinator, 2-3 collaborators, 12 

months minimum. Review the status of clearinghouse repositories, identify gaps in coverage, and 

develop alternative approaches to filling those gaps 

(such as automated resource description).

Standardize interfaces to data and models

• Year 2: 1 coordinator, 2-3 collaborators, 12 months 

minimum. Undertake data resource classification 

and analysis (first draft for review).

• Year 2: 1 coordinator (different skills than above), 

2-3 collaborators, 12 months minimum. Undertake a

similar analysis and classification of the inputs

required by modeling tools, so that classes of

“automated users” or data consumers are identified 

and used to create focus (see “Lifemapper” 

system, based on “Darwin Core” federation schema 

and the GARP modeling tool for predicting species’ 

distributions).

Establish multiple standard data schemas and 

ontologies

• Year 1: Ongoing, 1 coordinator, plus cooperators 

focusing on the various schemas. Facilitate the

development of the various standardized schemas, 

as particular needs or classes of data resources are 

identified, through workshops and other forums for 

discussion among interested and knowledgeable 

parties.

• Year 1: Ongoing, same team as above. Facilitate the

process of mapping individual data resources to

standardized schemas, through node partnership 

participation, on-site visits and consultations, and 

grants of hardware where needed.

• Year 2/3: 6-12 months, same team as above. 

Develop tools (“Wizards”) to assist data holders in 

the process of mapping their data source schemas to

one or more of the standardized schemas, and 

evaluate the suitability of generalizing them using

ontology languages. Also develop tools (“Wizards”)

to assist researchers in designing the schemas or 

ontologies for new data sets in such a way that they 

maximally conform to one or more of the

existing standard schemas and/or identify needs 

for new schemas or extensions of existing standard 

schemas.

• Year 1: Ongoing. Maintain a network for

communication with data holders about 

modifications to existing standard data schemas and 

the introduction of new standard data schemas.

Implementation
Biodiversity Modeling Workshop: Results and Recommendations

19

Establish standard APIs

• Year 1: Ongoing, 3-9 months, 1 person. Review 

standards for data access and interoperability.

Establish Registry support for standardization

• Year 1: Ongoing, 6-9 months, 1 person with outside 

cooperative assistance. Adopt a centralized registry 

based on a common, well-known standard such

as UDDI to remove potentially significant 

development activity required to build a custom 

registry.

• Year 1: Ongoing, team. Develop a review team 

with a repository manager having significant 

expertise in programming and data management/

storage.

Document data quality, provide peer review

• Year 1: Ongoing, 6-12 months, 1 coordinator.

Develop data quality documentation guidelines.

• Year 1: Ongoing, 1 coordinator. Develop an expert 

review panel and evaluate guidelines.

Provide education on information management and 

modeling

• Year 1, 12 months, 1-2 people. Collaborate with

the USGS Cooperative Research Units or 

Cooperative Ecosystem Study Units at universities 

and state agencies to develop informatics courses 

or programs, or integrate coursework from existing 

informatics programs at a university. 

• Year 1, 12 months, 1-2 people. Develop short 

courses for state agencies that would be run out of,

and in collaboration with, the Coop Units, but 

involve NBII staff or collaborators.

• Year 1, 12 months, 1-2 people. Establish training 

workshops/short courses on informatics at key 

centers. 

• Year 1, 12 months, 1-2 people. Mount road shows 

of key model developers who have excellent 

“success stories.”

• Year 1, 12 months, 1-2 people. Facilitate 

development of texts on ecoinformatics. 

• Year 1, 12 months, 1-2 people. Host NBII meeting 

focused on educating those external to the NBII on 

the modeling capabilities associated with the NBII. 

• (Effort and duration TBD). Establish an electronic 

journal associated with the NBII that covers issues 

of information management, modeling, and 

information transfer. 

Provide a service outlet to develop models and maps 

operating through distributed biodiversity modeling 

systems

• Year 1, within 12 months, team. Assess options for 

computational capabilities and determine which of 

the below options may work for the NBII:

o FTP site for download of software

o Demonstration implementation of algorithms

o Implementation of a test bracket that allows

 users to check the validity of their 

 implementation of modeling algorithm on test 

 data

o Users allowed to run own data on NBII nodes 

 via single user interface

o Provision of a standard middle-ware Web 

 service that allows users to build their own 

 interfaces to the computational service

o Provision of a distributed computational service 

 such as Lifemapper or other grid-based system 

 for computational intensive algorithms.

• Year 2: team, with cooperators. Implement one or

more of the above options for facilitating data

access and modeling (Production system level 

service).

• Year 1, within 12 months, team. Assess and

prioritize list of candidates for activities to be 

supported and potential algorithms.

• Year 1, 1-6 months, team. Assess target audiences 

and needs.

• Year 2: Ongoing, technical team. Design 

appropriate interfaces for each target user group. 

• Year 2: Ongoing, technical team. Develop strategy 

to ensure the interoperability of systems including: 

standardization of data schema, development of

Web services, standardization of application 

program interfaces, and Web service registration 

systems such as the UDDI.

• Year 1: Ongoing, technical team. Ensure the

infrastructure initially developed by the NBII is 

adopted by NBII partners. 

• Year 1: Ongoing, administrative team. Enable 

greater involvement with information technology 

research. This might be achieved though outreach 

to the scientific community, through hosting of

workshops, creation of an electronic journal,

proactive involvement in existing funded 

computational technology projects and ITRs such 

as SEEK and others.

Implement a clearinghouse for existing models

• Year 1: 3-12 months, administrative and technical 

Implementation
20

Biodiversity Modeling Workshop: Results and Recommendations 

team. Develop strategic plan outlining logistics for 

initial and full implementation.

• Year 2: Ongoing, administration and technical 

team. Implement clearinghouse.

Establish a decision support system for selecting and using 

biodiversity models

• Year 1, 1-3 months, 1 person, followed by peer 

review. Develop specifications for decision support.

• Year 1, 6-12 months, tech team. Implement decision 

support system.

Systematically assess the current state of biodiversity 

modeling thought and practice, inclusive of data and 

information requirements

• Year 1, 3-6 months, 1 coordinator with team. Assess 

current state of modeling activities.

• Year 1: Ongoing, 1 coordinator and technical team. 

Facilitate movement toward mechanistic modeling 

activities.

Track reuse of biodiversity models

• Year 2: Ongoing, 1 person. Implement procedures 

for tracking, version stamping and updating models.

• Year 2: Ongoing, 1 person. Track requests for 

models.

B. Concluding Remarks 

This workshop venue gave attendees the freedom to 

explore the potential for a well developed modelingsupport enterprise within the NBII. Attendees agreed 

that this is needed and would be welcome by the 

community. However, the group was not constrained 

in any way by the realities of building such a system 

and hence laid out a very broad and grand vision which 

if fully implemented would be a valuable asset to the 

community. In addition, many steps were identified 

that would launch the NBII on its journey to meet the 

vision. Thus in the early stages of this program the 

NBII is encouraged to address two key goals identified 

from the workshop. First, further engage the modeling 

community, that is bring the results of the workshop 

to other venues and engage a far broader audience in 

the dialog. This will help to attain buy-in and also to 

better frame the issues. Second, follow-on workshops 

should be used to identify discrete and doable projects 

that can be undertaken by the NBII to accomplish 

this broader vision in time. This approach will help 

create technologies and information infrastructure 

needed to address the NBII’s larger goals, in a stepwise organized fashion that can evolve to address 

community needs. 

Implementation
Biodiversity Modeling Workshop: Results and Recommendations

21

IV. Appendices

A. Acronyms used

API Applications Program Interface

AAAS American Association for the Advancement of Science 

BDEI Biodiversity and Ecosystem Informatics (federal inter-agency initiative)

BIOCLIM (name of a model)

CBD Convention on Biological Diversity (UN-sponsored international convention)

CBI Center for Biological Informatics (of the NBII)

DGVM Dynamic General Vegetation Model

DiGIR Distributed Generic Information Retrieval (protocol)

EML Ecological Metadata Language

ESA Ecological Society of America

FGDC Federal Geographic Data Committee

FTP File Transfer Protocol

GAMs General Algebraic Modeling System

GAP Gap Analysis Program (program of the U.S. Geological Survey)

GARP Genetic Algorithm for Rule Production 

GBIF Global Biodiversity Information Facility 

GIS Geographic Information System (family of spatial analysis capabilities)

GISP Global Invasive Species Program (UNEP Program)

GISIN Global Invasive Species Information Network

IABIN Inter-Americas Biodiversity Information Network (Summit of the Americas)

ITR Information Technology Research (National Science Foundation program)

IUCN World Conservation Union (formerly International Union for the Conservation of Nature)

LTER Long Term Ecological Research (National Science Foundation program)

MSDS Multiple standard data schemas (mnemonic device)

NAC-BDEI North American Consortium for Biodiversity and Ecosystem Informatics

NBII National Biological Information Infrastructure (federal inter-agency framework)

NISC National Invasive Species Council (federal inter-agency initiative)

NRC National Research Council (federal agency)

NSF National Science Foundation

ODE Ordinary Differential Equations

OGC: WMS The Open GIS Consortium Web Mapping Service

OFWIM Organization of Fish and Wildlife Information Managers

OWL Web Ontology Language

PBIN Pacific Basin Information Node (of the NBII)

PCAST Presidents Committee of Advisors on Science and Technology

PFT Plant Functional Type

RDF Resource Description Framework (protocol)

SEEK Science Environment for Ecological Knowledge (protocol)

SPIRE Semantic Prototypes in Research Ecoinformatics (protocol)

UDDI Universal Description, Discovery and Integration (protocol)

USGS United States Geological Survey

WDC World Data Center (for Biodiversity and Terrestrial Ecosystems)

XML Extensible Markup Language (W3C protocol)

Appendices
22

Biodiversity Modeling Workshop: Results and Recommendations 

B. Workshop participants

1. Contributors in attendance

Blum Stan sblum@CalAcademy.Org 415-750-7145 CAL Acad Sci

Crosier Catherine crosier@nrel.colostate.edu 970-491-5630 USGS

Davies Rhyn rdavies@usgs.gov 808-984-3718 USGS/PBIN

Dorfman Dan dorfman@hawaii.edu 808-956-6616 Hawaii Natural Heritage Program

Frame Mike mike_frame@usgs.gov 703-648-4164 USGS-CBI

Fornwall Mark mark_fornwall@usgs.gov 808-875-2435 USGS/PBIN

Gross Louis gross@tiem.utk.edu 865-974-4295 University of Tennessee

Helweg Dave david_helweg@usgs.gov 808-956-9588 USGS-BRD-PIERC

Mosesso John john_mosesso@usgs.gov 703-648-4079 NBII

Mulligan Mike mike_mulligan 303-202-4242 USGS-CBI

Neilson Ron neilson@fsl.orst.edu 541-750-7303 USDA Forest Service

Parks Bradley bparks@colorado.edu 303-497-6330 UCB-CIRES/NOAA-NGDC

Pyle Richard deepreef@bishopmuseum.org 808-848-4115 Bishop Museum

Sowa Scott scott_sowa@usgs.gov 573-441-2791 MoRAP, University of Missouri

Stockwell David davids@sdsc.edu 858-822-0942 Univ. of California, San Diego

Sugimura Tak tak@mhpcc.edu 808-879-5077 MHPCC

Turner Robb rsturner@utk.edu 865-974-4585 SAIN, ORNL

Vieglais Dave vieglais@ku.edu 785-864-7792 University of Kansas

White Denis white.denis@epa.gov 541-754-4476 US EPA

Willet Karen kbwillett@ucdavis.edu 530-752-5678 UC Davis / CAIN

2. Contributors not in attendance

Kineman John John.j.kineman@noaa.gov 303-497-6900 NOAA-NGDC

Quinn James jfquinn@ucdavis.edu 530-752-8027 Univ. of California, Davis

C. Workshop prospectus

Biodiversity Modeling Workshop, Maui, April 2003

Goal

Convene a working meeting of researchers and practitioners concerned with predicting the occurrences of ecologically 

important biological elements in order to assess related opportunities and constraints and to shape a research agenda, 

establish pilot applications, and develop a vision for the role of the NBII in such future activity. 

Purpose

The purpose of this workshop is to provide a forum and a venue for scientists to discuss current biodiversity 

modeling challenges and potential role of the NBII to science support, data and information resources, computational 

infrastructure, and to enhance organizational capabilities. This first year workshop will provide valuable input to 

the NBII and will test whether such a workshop should become a regular activity of the NBII (see following detailed 

statement of workshop problem and focus).

Background-NBII 

• The NBII is the nation’s information infrastructure for biodiversity information.

• One key goal of the NBII is to promote data use and development of applications.

• The time is right for the NBII to begin bridging key data sets and their applications. 

Appendices
Biodiversity Modeling Workshop: Results and Recommendations

23

Motivating elements for an NBII biodiversity modeling initiative

• The NBII should establish a venue for dialog with the biodiversity modeling community and a way for enlisting 

that community in its development (particularly models and related data tools).

• As the NBII continues to develop, it will be important for the NBII to demonstrate its contribution to modeling 

(the theme of this workshop) and to decision support. 

• There is a convergence of several agency interests to bring a group together to discuss modeling, supercomputing, 

and the NBII.

Objectives

• A strategy for integrating needs of the biodiversity modeling community into the NBII (includes program 

direction, community data needs, and dialog on integration of models into the NBII architecture),

• Identification of supportive projects that should be undertaken within the NBII or in partnership with NSF, NASA, 

or other interested partners, and

• Recommendations on the future of biodiversity modeling and NBII potential contributions to modeling initiatives, 

based upon the discussions of the meeting. 

Outcomes needed

• A model-based biodiversity research framework for the NBII,

• One-to-several pilot projects under this framework, and

• A strategic and short-term focus for the NBII. 

D. Workshop program

Sunday – July 27, 2003

8:00 - 9:00 pm Evening social hour (travel schedules permitting)

Goal – Introductions / acquaintance

Day 1, Monday – July 28

6:30 – 7:00 am Conveners meeting (hosts only, as needed)

7:00 – 8:00 am Breakfast, group (please join us)

8:00 – 8:30 am Preview

(15’) Welcome & charge (Mark Fornwall, CBI)

(25’) Self-introductions / profiles (participants)

(10’) Purpose & outcomes needed (Brad Parks, UC-Boulder)

(10’) Comments and revisions (participants)(limit relative to next item below)

9:00 – 11:30 am Reconnaissance: “map” the BD modeling universe and identify domain(s) of interest

(Identify and structure points of discussion to follow, first pass)

10:00 – 10:15 am Break

11:30 am – 1:00 pm Lunch

1:00 – 3:00 pm Informal presentations of modeling/mapping applications by participants (TBD)

3:00 – 3:15 pm Break

3:15 – 5:30 pm Informal presentations of modeling/mapping applications by participants (TBD)

5:30 pm Adjourn for dinner

6:00 pm Dinner, group (location TBD)

7:00 – 7:30 pm Conveners meeting (hosts only)

Day 2, Tuesday – July 29

6:30 – 7:00 am Conveners meeting (hosts only, as needed)

7:00 – 8:00 am Breakfast, group (please join us)

8:00 – 8:15 am Preview & recap progress

Appendices
24

Biodiversity Modeling Workshop: Results and Recommendations

Day 2, Tuesday – July 29 (continued)

8:15 – 10:00 am Discussion: state of theory and practice supporting or limiting biodiversity modeling 

10:00 – 10:15 am Break

10:15 – 11:30 am Reconsider: “map” of BD modeling universe and domain(s) of interest

(Identify and structure points of discussion to follow, second pass)

11:30 am – 1:00 pm Lunch

1:00 – 3:00 pm Discussion: evaluate and rank alternate modeling approaches 

3:00 – 3:15 pm Break

3:15 – 4:00 pm Discussion: CBI/NBII modeling mission/vision opportunities and constraints

4:00 – 5:00 pm Discussion: other institutional modeling missions/visions, linkages 

5:00 – 5:30 pm Review progress / results

5:30 pm Adjourn for dinner

6:00 pm Dinner, group (location TBD)

7:00 – 7:30 pm Conveners meeting (hosts only)

7:30 – 8:30 pm Special interest meetings (as needed)

Day 3, Wednesday – July 30

6:30 – 7:00 am Conveners meeting (hosts only, as needed)

7:00 – 8:00 am Breakfast, group (please join us)

8:00 – 8:15 am Preview & recap progress

8:15 – 10:00 am Discussion: matching opportunities to constraints for CBI/NBII modeling support

10:00 – 10:15 am Break

10:15 – 11:30 am Cont. Discussion: matching opportunities to constraints for CBI/NBII modeling support

11:30 am – 1:00 pm Lunch

1:00 – 3:00 pm Recommendations: action plan for CBI/NBII biodiversity modeling support

3:00 – 3:15 pm Break

3:15 – 5:00 pm Recommendations: action plan for CBI/NBII biodiversity modeling support

5:00 – 5:30 pm Assign completion tasks, follow-up actions

5:30 pm Adjourn for dinner / departures

6:30 pm Dinner, group (travel schedules permitting, location TBD)

7:00 – 7:30 pm Conveners meeting (hosts only, travel schedules permitting)

Day 4, Thursday – July 31

7:00 – 8:00 am Breakfast, working (conveners/hosts only)

8:00 – 9:00 am Review key results, consolidate notes, reallocate remaining time

9:00 – 10:00 am Fit presentation framework to results (Outline, schematics, etc.)

10:00 – 12:00 noon Map results into presentation framework

12:00 – 1:00 pm Lunch, working (conveners/hosts only)

1:00 – 2:00 pm Map results into presentation framework (cont.)

2:00 – 3:00 pm Assign completion tasks, follow-up actions

3:00 pm Adjourn

Appendices