pith. sign in

arxiv: 2511.15721 · v2 · submitted 2025-11-13 · 🧬 q-bio.PE

8 quick tips for data-model integration in ecology

Laurinne J Balstad , Joe Brennan , Marissa L. Baskett , Mattea K. Berglund , Mei Z. Blundell , Jessica A. Bolin , Amy A. Briggs , Mary C. Fisher
show 7 more authors
Christopher M. Heggerud Madeline Jarvis-Cross Lauren Mossman Andrea N. Odell Jennifer Paige Sophia Pelletier Mikaela M. Provost
This is my paper

Pith reviewed 2026-05-17 21:36 UTC · model grok-4.3

classification 🧬 q-bio.PE
keywords data-model integrationtheoretical ecologyecological modelingmultiple data sourcesuncertaintymodel iterationbest practicestransparent communication
0
0 comments X

The pith

Theoretical ecologists can strengthen models by iterating with data, drawing on multiple sources, and reporting uncertainty transparently.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper supplies eight practical tips to guide theoretical ecologists who want to bring empirical data into their models at different stages. These tips cluster around three main ideas: cycling between data and model adjustments, pulling information from several data sets at once, and treating uncertainty as a central part of the work. The authors argue that every modeling decision must be explained clearly so that readers can judge how the choices shape the results and their meaning. Greater volumes of ecological data now make such guidance timely for anyone building or refining theoretical models.

Core claim

The authors present eight tips organized under the themes of iteration in the data-model process, leveraging multiple data sources, and understanding uncertainty, with the overarching requirement that all modeling choices be communicated in a transparent, justifiable, and defensible way so that others can properly interpret the model and its implications.

What carries the argument

The eight tips for data-model integration, grouped into three themes and centered on transparent communication of choices.

Load-bearing premise

The eight tips, drawn from the authors' shared experience as early-career theoretical ecologists, will prove broadly useful and effective across many different ecological systems even without formal tests of their effect on model results.

What would settle it

A controlled comparison in which one group of modelers follows the eight tips and another does not, then measuring differences in model predictive accuracy, fit to independent data, or clarity of reported limitations.

read the original abstract

Theoretical ecologists have long leveraged empirical data in various forms to advance ecology. Recently increased volumes and access to ecological data present an expanding set of opportunities for theoreticians to inform model development, framing, and interpretation. Whereas statisticians have collective guidance on best practices for data use, theoreticians might lack formal education on how to integrate diverse types of data into a single ecological model. As a group of predominantly early-career theoretical ecologists, we have developed guiding principles and practical tips to support theoretical ecologists in synthesizing multiple types of data at different phases of the modeling process. Our rules fall into three overarching themes: iteration in the data-model integration process, leveraging multiple sources of data), and understanding uncertainty. Across these rules, we emphasize that the data-model integration requires transparent, justifiable, and defensible communication of modeling choices to support readers in appropriately contextualizing the model and its implications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript presents eight practical tips for data-model integration in ecology, developed by a group of predominantly early-career theoretical ecologists. The tips are organized under three themes—iteration in the data-model integration process, leveraging multiple sources of data, and understanding uncertainty—with repeated emphasis on transparent, justifiable, and defensible communication of modeling choices to aid reader contextualization.

Significance. If adopted, the guidance could help theoreticians better incorporate diverse empirical data into model development, framing, and interpretation amid growing data availability. The advisory synthesis from collective experience addresses a noted gap in formal training and promotes practices that support more robust and interpretable ecological models.

minor comments (2)
  1. [Abstract] Abstract: the phrasing 'leveraging multiple sources of data), and understanding uncertainty' contains an extraneous closing parenthesis that should be corrected.
  2. The tips would be strengthened by including one brief, concrete ecological example (with citation) for at least a subset of the eight rules to demonstrate application.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and constructive review, which recognizes the value of our practical guidance for theoretical ecologists. We appreciate the recommendation for minor revision and will incorporate any specific suggestions for clarity or additional examples in the revised manuscript.

Circularity Check

0 steps flagged

No significant circularity in advisory guidelines

full rationale

The paper presents eight practical tips for data-model integration synthesized from collective experience of early-career theoretical ecologists, organized under themes of iteration, multiple data sources, and uncertainty, with emphasis on transparent communication of choices. It contains no equations, derivations, predictions, or first-principles claims that could reduce to fitted inputs or self-citations. The work is explicitly advisory guidance rather than a validated methodology or mathematical result, with no load-bearing self-referential steps or uniqueness theorems invoked from prior author work. This makes the content self-contained as practical advice without any circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a best-practices guide rather than a formal derivation or empirical study, so the central claim rests on no free parameters, mathematical axioms, or newly invented entities.

pith-pipeline@v0.9.0 · 5517 in / 1036 out tokens · 47073 ms · 2026-05-17T21:36:26.616488+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages

  1. [1]

    Data! Data! Data! I can't make bricks without clay

    Department of Mathematics, University of Manitoba, Winnipeg Canada R3T 2N2; 7. Department of Mathematics, University of California, Davis USA 95616 8. California Department of Fish and Wildlife, West Sacramento USA 95605 “Data! Data! Data! I can't make bricks without clay.” – Arthur Conan Doyle, The Adventure of the Copper Beeches Introduction Theoretical...

    work page 1974

  2. [2]

    Statistical Rules of Thumb

    Belle G van. Statistical Rules of Thumb. 2nd ed. John Wiley & Sons; 2011. 286 p

    work page 2011

  3. [3]

    Ten iterative steps in development and evaluation of environmental models

    Jakeman AJ, Letcher RA, Norton JP. Ten iterative steps in development and evaluation of environmental models. Environ Model Softw. 2006;21(5):602–14

    work page 2006

  4. [4]

    Data feminism

    D’Ignazio C, Klein LF. Data feminism. MIT Press; 2023. 328 p

    work page 2023

  5. [5]

    Contrasting patterns of demography and population viability among Gopher Tortoise populations in Alabama

    Folt B, Goessling JM, Tucker A, Guyer C, Hermann S, Shelton ‐ Nix E, et al. Contrasting patterns of demography and population viability among Gopher Tortoise populations in Alabama. J Wildl Manag. 2021;85(4):617–30

    work page 2021

  6. [6]

    Metrics matter: The effect of parasite richness, intensity and prevalence on the evolution of host migration

    Shaw AK, Sherman J, Barker FK, Zuk M. Metrics matter: The effect of parasite richness, intensity and prevalence on the evolution of host migration. Proc R Soc B Biol Sci. 2018;285(1891):20182147

    work page 2018

  7. [7]

    Modeling food dependent symbiosis in Exaiptasia pallida

    Kaare-Rasmussen JO, Moeller HV, Pfab F. Modeling food dependent symbiosis in Exaiptasia pallida. Ecol Model. 2023;481:110325

    work page 2023

  8. [8]

    Coexistence of bacteria with a competition-colonization tradeoff on a dynamic coral host

    Gibbs TL, Dahlin KJM, Brennan J, Silveira CB, McManus LC. Coexistence of bacteria with a competition-colonization tradeoff on a dynamic coral host. bioRxiv [Preprint]. 2024 bioRxiv [posted 2024 Sept 16]. Available from: https://www.biorxiv.org/content/10.1101/2024.09.15.612558v1 doi: 10.1101/2024.09.15.612558

    work page doi:10.1101/2024.09.15.612558v1 2024

  9. [9]

    Quantifying the relative importance of variation in predation and the environment for species coexistence

    Shoemaker LG, Barner AK, Bittleston LS, Teufel AI. Quantifying the relative importance of variation in predation and the environment for species coexistence. Snyder R, editor. Ecol Lett. 2020;23(6):939–50

    work page 2020

  10. [10]

    Multiple resiliency metrics reveal complementary drivers of ecosystem persistence: An application to kelp forest systems

    Arroyo ‐ Esquivel J, Adams R, Gravem S, Whippo R, Randell Z, Hodin J, et al. Multiple resiliency metrics reveal complementary drivers of ecosystem persistence: An application to kelp forest systems. Ecology. 2024;105(12)

    work page 2024

  11. [11]

    Anthropogenic climate change will likely outpace coral range expansion

    Vogt-Vincent NS, Pringle JM, Cornwall CE, McManus LC. Anthropogenic climate change will likely outpace coral range expansion. Sci Adv. 2025;11(23):eadr2545

    work page 2025

  12. [12]

    From individual Fuzzy Cognitive Maps to Agent Based Models: Modeling multi-factorial and multi-stakeholder decision-making for water scarcity

    Mehryar S, Sliuzas R, Schwarz N, Sharifi A, Van Maarseveen M. From individual Fuzzy Cognitive Maps to Agent Based Models: Modeling multi-factorial and multi-stakeholder decision-making for water scarcity. J Environ Manage. 2019;250:109482

    work page 2019

  13. [13]

    Population viability analysis for two species of imperiled freshwater turtles

    Gregory KM, Darst C, Lantz SM, Powelson K, Ashton D, Fisher R, et al. Population viability analysis for two species of imperiled freshwater turtles. Chelonian Conserv Biol. 2024;23(1)

    work page 2024

  14. [14]

    Optimal salmon lice treatment threshold and tragedy of the commons in salmon farm networks

    Kragesteen TJ, Simonsen K, Visser AW, Andersen KH. Optimal salmon lice treatment threshold and tragedy of the commons in salmon farm networks. Aquaculture. 2019;512:734329

    work page 2019

  15. [15]

    Global warming and flowering times in Thoreau’s Concord: A community perspective

    Miller-Rushing AJ, Primack RB. Global warming and flowering times in Thoreau’s Concord: A community perspective. Ecology. 2008;89(2):332–41

    work page 2008

  16. [16]

    Unpredictable Evolution in a 30-Year Study of Darwin’s Finches

    Grant PR, Grant BR. Unpredictable Evolution in a 30-Year Study of Darwin’s Finches. Science. 2002;296(5568):707–11

    work page 2002

  17. [17]

    Introgressive hybridization as a mechanism for species rescue

    Baskett ML, Gomulkiewicz R. Introgressive hybridization as a mechanism for species rescue. Theor Ecol. 2011;4(2):223–39

    work page 2011

  18. [18]

    Predation drives complex eco-evolutionary dynamics in sexually selected traits

    Lerch BA, Servedio MR. Predation drives complex eco-evolutionary dynamics in sexually selected traits. PLOS Biol. 2023;21(4):e3002059

    work page 2023

  19. [19]

    Collecting and analyzing qualitative data for system dynamics: methods and models

    Luna ‐ Reyes LF, Andersen DL. Collecting and analyzing qualitative data for system dynamics: methods and models. Syst Dyn Rev. 2003;19(4):271–96

    work page 2003

  20. [20]

    Knowledge coevolution: generating new understanding through bridging and strengthening distinct knowledge systems and empowering local knowledge holders

    Chapman JM, Schott S. Knowledge coevolution: generating new understanding through bridging and strengthening distinct knowledge systems and empowering local knowledge holders. Sustain Sci. 2020;15(3):931–43

    work page 2020

  21. [21]

    Two ‐ Eyed Seeing

    Reid AJ, Eckert LE, Lane J, Young N, Hinch SG, Darimont CT, et al. “Two ‐ Eyed Seeing”: An Indigenous framework to transform fisheries research and management. Fish Fish. 2021;22(2):243–61

    work page 2021

  22. [22]

    A heuristic model of socially learned migration behaviour exhibits distinctive spatial and reproductive dynamics

    MacCall AD, Francis TB, Punt AE, Siple MC, Armitage DR, Cleary JS, et al. A heuristic model of socially learned migration behaviour exhibits distinctive spatial and reproductive dynamics. ICES J Mar Sci. 2019;76(2):598–608

    work page 2019

  23. [23]

    Five Questions to understand epistemology and its influence on integrative marine research

    Moon K, Cvitanovic C, Blackman DA, Scales IR, Browne NK. Five Questions to understand epistemology and its influence on integrative marine research. Front Mar Sci. 2021;8

    work page 2021

  24. [24]

    Two-Eyed Seeing and other lessons learned within a co-learning journey of bringing together indigenous and mainstream knowledges and ways of knowing

    Bartlett C, Marshall M, Marshall A. Two-Eyed Seeing and other lessons learned within a co-learning journey of bringing together indigenous and mainstream knowledges and ways of knowing. J Environ Stud Sci. 2012;2(4):331–40

    work page 2012

  25. [25]

    Making room and moving over: Knowledge co-production, Indigenous knowledge sovereignty and the politics of global environmental change decision-making

    Latulippe N, Klenk N. Making room and moving over: Knowledge co-production, Indigenous knowledge sovereignty and the politics of global environmental change decision-making. Curr Opin Environ Sustain. 2020;42:7–14

    work page 2020

  26. [26]

    integration

    Nadasdy P. The Politics of TEK: Power and the “integration” of knowledge. Arctic Anthropology 1999; 36(1-2):1-18

    work page 1999

  27. [27]

    Cultural Competence in Interdisciplinary Collaborations: A Method for Respecting Diversity in Research Partnerships

    Reich SM, Reich JA. Cultural Competence in Interdisciplinary Collaborations: A Method for Respecting Diversity in Research Partnerships. Am J Community Psychol. 2006;38(1–2):1–7

    work page 2006

  28. [28]

    Unbecoming Claims: Pedagogies of Refusal in Qualitative Research

    Tuck E, Yang KW. Unbecoming Claims: Pedagogies of Refusal in Qualitative Research. Qual Inq. 2014;20(6):811–8

    work page 2014

  29. [29]

    On the role of traditional ecological knowledge as a collaborative concept: a philosophical study

    Whyte KP. On the role of traditional ecological knowledge as a collaborative concept: a philosophical study. Ecol Process. 2013;2(1):7

    work page 2013

  30. [30]

    A framework for co-production of knowledge in the context of Arctic research

    Yua E, Raymond-Yakoubian J, Daniel RA, Behe C. A framework for co-production of knowledge in the context of Arctic research. Ecol Soc. 2022;27(1):art34

    work page 2022

  31. [31]

    Knowledge co-production: A pathway to effective fisheries management, conservation, and governance

    Cooke SJ, Nguyen VM, Chapman JM, Reid AJ, Landsman SJ, Young N, et al. Knowledge co-production: A pathway to effective fisheries management, conservation, and governance. Fisheries. 2021;46(2):89–97

    work page 2021

  32. [32]

    Contributions of Indigenous Knowledge to ecological and evolutionary understanding

    Jessen TD, Ban NC, Claxton NX, Darimont CT. Contributions of Indigenous Knowledge to ecological and evolutionary understanding. Front Ecol Environ. 2022;20(2):93–101

    work page 2022

  33. [33]

    The strategy of model building in population biology

    Levins R. The strategy of model building in population biology. Am Sci. 1966;54(4):421–31

    work page 1966

  34. [34]

    At what spatial scales are alternative stable states relevant in highly interconnected ecosystems? Ecology

    Karatayev VA, Baskett ML. At what spatial scales are alternative stable states relevant in highly interconnected ecosystems? Ecology. 2020;101(2):e02930

    work page 2020

  35. [35]

    Six personas to adopt when framing theoretical research questions in biology

    Shaw AK, Bisesi AT, Wojan C, Kim D, Torstenson M, Naven Narayanan, et al. Six personas to adopt when framing theoretical research questions in biology. Proc R Soc B Biol Sci. 2024;291(2031):20240803

    work page 2024

  36. [36]

    Environmental variability promotes coexistence in lottery competitive systems

    Chesson PL, Warner RR. Environmental variability promotes coexistence in lottery competitive systems. Am Nat. 1981;117(6):923–43

    work page 1981

  37. [37]

    In defense of Type I functional responses: The frequency and population-dynamic effects of feeding on multiple prey at a time

    Novak M, Coblentz KE, DeLong JP. In defense of Type I functional responses: The frequency and population-dynamic effects of feeding on multiple prey at a time. Am Nat. 2025;206(4)

    work page 2025

  38. [38]

    Host plant limitation of butterflies in highly fragmented landscapes

    Crone EE, Schultz CB. Host plant limitation of butterflies in highly fragmented landscapes. Theor Ecol. 2022;15(3):165–75

    work page 2022

  39. [39]

    Beyond authorship: attribution, contribution, collaboration, and credit

    Brand A, Allen L, Altman M, Hlava M, Scott J. Beyond authorship: attribution, contribution, collaboration, and credit. Learn Publ. 2015;28(2):151–5

    work page 2015

  40. [40]

    Public Participation in Scientific Research: a Framework for Deliberate Design

    Shirk JL, Ballard HL, Wilderman CC, Phillips T, Wiggins A, Jordan R, et al. Public Participation in Scientific Research: a Framework for Deliberate Design. Ecol Soc. 2012;17(2):art29

    work page 2012

  41. [41]

    Negotiating the ethical-political dimensions of research methods: a key competency in mixed methods, inter- and transdisciplinary, and co-production research

    West S, Schill C. Negotiating the ethical-political dimensions of research methods: a key competency in mixed methods, inter- and transdisciplinary, and co-production research. Humanit Soc Sci Commun. 2022;9(1):294

    work page 2022

  42. [42]

    A taxonomy and treatment of uncertainty for ecology and conservation biology

    Regan HM, Colyvan M, Burgman MA. A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl. 2002;12(2):618–28

    work page 2002

  43. [43]

    The role of sensitivity analysis in ecological modelling

    Cariboni J, Gatelli D, Liska R, Saltelli A. The role of sensitivity analysis in ecological modelling. Ecol Model. 2007;203(1–2):167–82

    work page 2007

  44. [44]

    Handbook of uncertainty quantification

    Ghanem R, Higdon D, Owhadi H, editors. Handbook of uncertainty quantification. Cham: Springer International Publishing; 2017. 616p

    work page 2017

  45. [45]

    Sensitivity analysis of environmental models: A systematic review with practical workflow

    Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, et al. Sensitivity analysis of environmental models: A systematic review with practical workflow. Environ Model Softw. 2016;79:214–32

    work page 2016

  46. [46]

    Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices

    Saltelli A, Aleksankina K, Becker W, Fennell P, Ferretti F, Holst N, et al. Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environ Model Softw. 2019;114:29–39

    work page 2019

  47. [47]

    Testing ecological models: The meaning of validation

    Rykiel EJ. Testing ecological models: The meaning of validation. Ecol Model. 1996;90(3):229–44

    work page 1996

  48. [48]

    Sensitivity analysis in practice: A guide to assessing scientific models

    Saltelli A, Tarantola S, Campolongo F, Ratto M. Sensitivity analysis in practice: A guide to assessing scientific models. John Wiley & Sons; 2004. 234 p

    work page 2004

  49. [49]

    How to avoid a perfunctory sensitivity analysis

    Saltelli A, Annoni P. How to avoid a perfunctory sensitivity analysis. Environ Model Softw. 2010;25(12):1508–17

    work page 2010

  50. [50]

    Introduction to sensitivity analysis

    Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, et al. Introduction to sensitivity analysis. John Wiley & Sons, Ltd; 2007. 292 p

    work page 2007

  51. [51]

    Robust misinterpretation of confidence intervals

    Hoekstra R, Morey RD, Rouder JN, Wagenmakers EJ. Robust misinterpretation of confidence intervals. Psychon Bull Rev. 2014;21(5):1157–64

    work page 2014

  52. [52]

    The (Mis)understanding of scientific uncertainty? How experts view policy-makers, the media and publics

    Landström C, Hauxwell-Baldwin R, Lorenzoni I, Rogers-Hayden T. The (Mis)understanding of scientific uncertainty? How experts view policy-makers, the media and publics. Sci Cult. 2015;24(3):276–98

    work page 2015

  53. [53]

    Ecologists should not use statistical significance tests to interpret simulation model results

    White JW, Rassweiler A, Samhouri JF, Stier AC, White C. Ecologists should not use statistical significance tests to interpret simulation model results. Oikos. 2014;123(4):385–8

    work page 2014

  54. [54]

    Communicating scientific uncertainty

    Fischhoff B, Davis AL. Communicating scientific uncertainty. Proc Natl Acad Sci. 2014 Sept 16;111(suppl. 4):13664–71

    work page 2014

  55. [55]

    Epistemology of computational biology: Mathematical models and experimental predictions as the basis of their validity

    Dougherty ER, Braga-Neto U. Epistemology of computational biology: Mathematical models and experimental predictions as the basis of their validity. J Biol Syst. 2006;14(01):65–90

    work page 2006

  56. [56]

    Co-producing knowledge with Indigenous Peoples: challenges and solutions

    Grimm J, Jarvis-Cross M, Bailey M, Ban NC, Bartlett M, Cadman R, et al. Co-producing knowledge with Indigenous Peoples: challenges and solutions. Trends Ecol Evol. 2025; Forthcoming

    work page 2025

  57. [57]

    Sacred ecology

    Berkes F. Sacred ecology. 4th ed. New York: Routledge; 2017. 394 p

    work page 2017

  58. [58]

    Coming full circle: Indigenous knowledge, environment, and our future

    McGregor D. Coming full circle: Indigenous knowledge, environment, and our future. Am Indian Q. 2004;28(3):385–410

    work page 2004

  59. [59]

    Guidance for Federal Departments and Agencies on Indigenous Knowledge

    Prabhakar A, Mallory B. Guidance for Federal Departments and Agencies on Indigenous Knowledge. 2022

    work page 2022

  60. [60]

    Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services

    IPBES. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Brondizio ES, editor. IPBES secretariat, Bonn, Germany; 2019. 56p

    work page 2019

  61. [61]

    System sensitivity and uncertainty analysis

    Loucks DP, van Beek E. System sensitivity and uncertainty analysis. In: Loucks DP, van Beek E, editors. Water Resource Systems Planning and Management: An Introduction to Methods, Models, and Applications. Cham: Springer International Publishing; 2017. p. 331–74

    work page 2017

  62. [62]

    Calibration, sensitivity and uncertainty analysis of ecological models--a review

    Malchow AK, Hartig F. Calibration, sensitivity and uncertainty analysis of ecological models--a review. Authorea [Preprint], 2025. Authorea [posted 2024 Nov 6]. Available from: https://www.authorea.com/doi/full/10.22541/au.173090741.12160653 doi: 10.22541/au.173090741.12160653/v1

    work page doi:10.22541/au.173090741.12160653 2025

  63. [63]

    Deciphering culprits for cyanobacterial blooms and lake vulnerability in north-temperate lakes

    Serpico J, Zambrano-Luna BA, Milne R, Heggerud CM, Hastings A, Wang H. Deciphering culprits for cyanobacterial blooms and lake vulnerability in north-temperate lakes. arXiv [Preprint]. 2025. arXiv [posted 2024 Oct 24, revised 2025 Jun 9]. Available from: https://arxiv.org/abs/2410.20757 doi: 10.48550/arXiv.2410.20757

    work page doi:10.48550/arxiv.2410.20757 2025

  64. [64]

    Symbiont diversity may help coral reefs survive moderate climate change

    Baskett ML, Gaines SD, Nisbet RM. Symbiont diversity may help coral reefs survive moderate climate change. Ecol Appl. 2009;19(1):3–17

    work page 2009