Burns, Jean H.

Che-Castaldo, Judy

Childs, Dylan Z.

Ezard, Thomas H.G.

Hernandez-Yanez, Haydee

Hodgson, David J.

Jones, Owen R.

Jongejans, Eelke

Kendall, Bruce E.

Knight, Tiffany

Merow, Cory

Ramula, Satu

Salguero-Gómez, Roberto

Stott, Iain

Vindenes, Yngvild

Yokomizo, Hiroyuki

Jones, Owen R

Kendall, Bruce E

Burns, Jean H

Childs, Dylan Z

Ezard, Thomas HG

Hodgson, David J

eScholarship - University of California

Comments to “Persistent problems in the construction of matrix population models”

University of Lincoln Institutional Repository

Southampton (e-Prints Soton)

Jongejans, E.

NARCIS 

Che-Castaldo, J.

Jones, O.R.

Kendall, B.E.

Burns, J.H.

Childs, D.Z.

Ezard, T.H.G.

Hernandez-Yanez, H.

Hodgson, D.J.

Knight, T.

Merow, C.

Ramula, S.

Stott, I.

Vindenes, Y.

Yokomizo, H.

Salguero-Gómez, R.

White Rose Research Online

This is a repository copy of Comments to “Persistent problems in the construction of matrix population models”.White Rose Research Online URL for this paper:https://eprints.whiterose.ac.uk/186299/Version: Accepted VersionArticle:Che-Castaldo, J., Jones, O.R., Kendall, B.E. et al. (13 more authors) (2020) Comments to “Persistent problems in the construction of matrix population models”. Ecological Modelling, 416. 108913. ISSN 0304-3800 https://doi.org/10.1016/j.ecolmodel.2019.108913Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can’t change the article in any way or use it commercially. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Title: Comments to “Persistent problems in the construction of matrix population models”   Authors: Judy Che-Castaldo* (1), Owen R. Jones (2), Bruce E. Kendall (3), Jean H. Burns (4), Dylan Z. Childs (5), Thomas H.G. Ezard (6), Haydee Hernandez-Yanez (1), David J. Hodgson (7), Eelke Jongejans (8), Tiffany Knight (9), Cory Merow (10), Satu Ramula (11), Iain Stott (12), Yngvild Vindenes (13), Hiroyuki Yokomizo (14), & Roberto Salguero-Gómez (15)  (1) Alexander Center for Applied Population Biology, Conservation & Science Department, Lincoln Park Zoo, Chicago, IL 60614-4712 USA (2) Interdisciplinary Center on Population Dynamics & Department of Biology, Campusvej 55, University of Southern Denmark, 5230 Odense M, Denmark (3) Bren School of Environmental Science & Management, University of California Santa Barbara, Santa Barbara, CA 93106-5131 USA (4) Department of Biology, Case Western Reserve University, Cleveland, OH  44106-7080 USA (5) Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK (6) Ocean & Earth Science, National Oceanography Centre Southampton, University of Southampton Waterfront Campus, Southampton SO14 3ZH, UK (7) Centre for Ecology & Conservation, College of Life and Environmental Sciences, University of Exeter, Cornwall, UK (8) Animal Ecology and Physiology Department, Radboud University, Nijmegen, The Netherlands (9) Institute of Biology, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany; Department of Community Ecology, Helmholtz Centre for Environmental Research-UFZ, Theodor-Lieser-Straße 4, 06120 Halle (Saale), Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany (10) Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA (11) Department of Biology, University of Turku, FI-20014, Turku, Finland (12) School of Life Sciences, University of Lincoln, Brayford Pool, LN6 7TS, Lincoln UK (13) Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway (14) Center for Health and Environmental Risk Research, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan (15) Department of Zoology, 11a Mansfield Rd, University of Oxford, OX1 3SZ, Oxford, UK  *Corresponding author: jchecastaldo@lpzoo.org; 301-351-8290    *MPMs = matrix population models The use of matrix population models (MPMs*) to summarize and analyze the empirically 1 observed demography of both plant and animal populations has exhibited rapid growth in recent 2 decades (Salguero-Gómez et al. 2015, Salguero-Gómez et al. 2016). Comparative analyses 3 using independent collections of MPMs have generated valuable ecological insights (e.g., 4 Franco & Silvertown 1990, Franco & Silvertown 2004, Ramula et al. 2008, Buckley et al. 2010, 5 Burns et al. 2010, Salguero-Gómez & Casper 2010, Stott et al. 2010, Crone et al. 2011, Zeigler 6 et al. 2013), leading to the recognition that a comprehensive MPM dataset would be a valuable 7 resource for the ecological research community. We therefore launched the COMPADRE Plant 8 Matrix Database (Salguero-Gómez et al. 2015) and the COMADRE Animal Matrix Database 9 (Salguero-Gómez et al. 2016; http://compadre-db.com/), which comprise 1204 plant and animal 10 species and 10949 MPMs as of October 2019.  11  12 Our initial goal with COMPADRE and COMADRE was to present MPMs as intended by the 13 authors of the original publications, relying on the authors and the peer review process to 14 ensure that the published population projection matrix correctly described the intended life 15 history. Thus, our error-checking procedure to date has focused for the most part on eliminating 16 typographical errors introduced during digitization, rather than errors of MPM construction made 17 by the original authors. However, Kendall et al. (2019) have shown that a substantial fraction of 18 animal MPMs in peer-reviewed papers contain errors in construction, such that model 19 components are missing and/or the model does not accurately represent the described life 20 history of the species. Plant MPMs are also prone to errors in construction, including dormant 21 life stages (most commonly a seed bank) that are missing (Doak et al. 2002, Nguyen et al. 22 2019) or incorrectly incorporated, leading to a one-year delay in the life cycle (Caswell 2001, p. 23 60). Although some construction errors may not dramatically affect estimates of demographic 24 parameters in some cases, they can be crucial in others. Estimates of generation time and 25 population growth rate, in particular, can be sensitive to missing components or life stages 26 *MPMs = matrix population models (Kendall et al. 2019, Nguyen et al. 2019). Construction errors may also lead to directional 27 biases, such as underestimating the elasticities of population growth rate to survival when 28 fertility coefficients do not include survival (Kendall et al. 2019), and these biases may affect 29 results in comparative analyses. Moving forward, there is a clear need for better training 30 materials to help ecologists and conservation scientists (many with little experience in 31 mathematical modeling) construct MPMs from their data. But how can we ensure that previously 32 published MPMs, including those already compiled in COMPADRE and COMADRE and those 33 remaining to be digitized, correctly represent the intended life histories, and thus support 34 meaningful synthetic analyses? 35  36 We believe that an important principle in big data analyses can be summed up as “Just because 37 you can, does not mean you should”. That is, although large volumes of data and tools that 38 enable their fast analysis are available, they should not be used without careful consideration 39 and evaluation. There are many possible sources of error whenever large datasets are 40 compiled, and we have previously discussed some of these (including errors in matrix 41 construction by authors) as a cautionary note to data users (Salguero-Gómez et al. 2016). We 42 also maintain an open, frequently updated digitization protocol, and an extensive user guide 43 (both available on our GitHub repository, https://github.com/jonesor/compadreDB). These open, 44 transparent policies are essential for users to fully understand the data and choose the subsets 45 of MPMs that are most appropriate for addressing their specific research questions.  46  47 In our collective cases, comparative analyses of demographic data from COMPADRE and 48 COMADRE (e.g., Vindenes and Engen 2017, Beckman et al. 2018, Che-Castaldo et al. 2018, 49 Davidson et al. 2019, Healy et al. 2019, Jones et al. 2019, Nguyen et al. 2019, Roper et al. 50 2019, Pistón et al. 2019) have each resulted in a separate sample size because the research 51 questions required a different set of selection criteria. For instance, for some questions related 52 *MPMs = matrix population models to estimating various moments of longevity, the subcomponents of fertility are not necessary (if 53 one assumes no trade-offs between reproduction and survival), and so MPMs that do not 54 include those (indicated by the metadata field “MatrixFec”) can be used. Similarly, to calculate 55 the population growth rate from an MPM, the subcomponents of survival, sexual reproduction 56 and clonality do not need to be separated (indicated by the metadata field “MatrixSplit”), but to 57 estimate life history traits and vital rate sensitivities/elasticities, it is fundamental that they are 58 separated. Thus, the set of selection criteria used for a specific study must be carefully 59 designed before the analysis begins. Moreover, the availability of digitized data should not be a 60 substitute for examining the original sources, which may provide important details of interest for 61 the user’s specific goals. For this reason (and to facilitate the citation of original sources), 62 COMPADRE and COMADRE include bibliographic information for the source studies. 63  64 Nevertheless, we agree that addressing the newly-identified MPM construction errors in 65 COMPADRE and COMADRE would be a useful service for the scientific community. In fact, in 66 recent years we have started to include information on data quality in the metadata. For 67 example, there is a field (“SurvivalIssue”) for noting when the summed survival for a given 68 age/stage exceeds 1.0, which may indicate a typographical error or a sensitivity matrix being 69 incorrectly identified as an MPM in the original source. Other issues, such as when GPS 70 coordinates are approximate rather than accurate, or environmental conditions that, while 71 naturally occurring, may not be representative of typical conditions for a given population (e.g., 72 fires, herbivory, droughts), are noted in the unstructured “Observations” field. In the coming 73 months, we will take further steps including updating the metadata to record (1) whether the 74 MPM is based on a pre-breeding or post-breeding census whenever the information is available, 75 and (2) whether the MPM in the database has been altered from the original published one and 76 why [e.g., typo in the original source; inclusion of an unnecessary seed stage where no 77 permanent seed bank exists (Caswell 2001, p. 60)], or whether the MPM was calculated from 78 *MPMs = matrix population models other published data along with reference to those data (e.g., a life table, integral projection 79 model, individual-based model). We have updated the digitization protocol to include Kendall et 80 al. (2019) as essential reading so that our team can avoid the identified common errors when 81 constructing MPMs from published life cycle data. Future releases of the database will include a 82 unique identification number for each existing and new MPM, so that users can look up a 83 specific MPM and all of its associated observations and metadata. They can also share with the 84 community any additional metadata they collect for their own analyses, which could be linked 85 back to the full dataset via the unique ID. 86  87 In the longer term, our plans include identifying MPMs that are likely to have construction issues 88 following the protocol presented in Appendix B of Kendall et al. (2019). If the publication 89 contains sufficient data, we will attempt to correct the MPMs and indicate why and how we have 90 done so. This way, users will be able to identify the modified matrices and determine whether 91 those modifications are appropriate for their use. In the last year, we have transitioned to a 92 relational database that will allow us to keep a record of changes to the MPM data, so that we 93 can record the MPMs as presented by original authors and also update them to fix known errors 94 such as those mentioned above. However, often the published paper and supplementary 95 materials do not include the necessary information (e.g., a separate life table or mean 96 development time for each life history stage) to help us contextualize the MPMs and determine 97 specific construction methods. Our digitization team invests a significant amount of time 98 contacting authors for clarification and additional information. Without the original researchers’ 99 input, there may be many MPMs that we cannot correct or even flag with potential issues. This 100 highlights the need for authors to consider providing as much detail on the life cycles, data 101 collection, and biogeographic context for their species and populations as possible to enable 102 reproducible research. 103   104 *MPMs = matrix population models Although finding errors like the ones reported by Kendall et al. (2019) is worrisome, we 105 emphasize that an evaluation of data quality issues and their extent (not to mention large-scale 106 assessments in general) would not be possible without the existence of a database for MPMs. A 107 database also allows us to implement solutions systematically across the entire set of 108 demographic studies represented in the database. As a centralized repository of MPM data, 109 COMPADRE and COMADRE can: (1) serve as a point of reference for recording whether a 110 particular publication may have miscalculated MPMs, (2) pool efforts from independent groups 111 or individuals to identify and/or fix the miscalculated MPMs, and (3) enable users to access and 112 benefit from previously corrected MPMs. The database will only improve as we continue to 113 aggregate data, assess data quality, and make corrections. Moreover, the mission of the 114 COMPADRE and COMADRE team goes beyond releasing large volumes of data, and includes 115 teaching workshops around the globe about MPMs and engaging the demographic community. 116 We can therefore use our existing platform, including educational materials on our GitHub 117 repository (see link above) and our regular user engagement workshops, to help raise 118 awareness and promote more careful construction of MPMs.  119   120 We encourage and welcome the community to help validate the MPM data in COMPADRE and 121 COMADRE, as the databases are currently supported by limited funding and volunteered 122 researcher time. Conducting a comprehensive check in the databases for construction errors 123 will require substantial effort, and we are only beginning to understand the extent of these 124 issues. We are developing website tools (such as the new function for reporting data errors and 125 corrections) to gather input and facilitate collaborative data validation. Participation from users 126 and original authors will be vital in this effort to help us deliver reliable, open-access MPM data 127 for demographic researchers and the broader scientific community. 128  129  130 *MPMs = matrix population models Literature Cited 131 Buckley, Y. M. et al. 2010. Causes and consequences of variation in plant population growth 132 rate: a synthesis of matrix population models in a phylogenetic context. Ecology Letters 13, 133 1182–1197. 134  135 Burns, J. H. et al. 2010. Empirical tests of life-history evolution theory using phylogenetic 136 analysis of plant demography. Journal of Ecology 98, 334–344. 137  138 Beckman N, Bullock J, Salguero-Gómez R. 2018. High dispersal ability is related to fast life 139 history strategies. Journal of Ecology 4, 1349–1362. 140  141 Caswell, H. 2001. Matrix Population Models: Construction, Analysis, and Interpretation, 2nd 142 edn. Sinauer Associates, Inc, Sunderland, MA, USA 143  144 Che-Castaldo, J., Che-Castaldo, C., Neel, M. C. 2018. Predictability of demographic rates 145 based on phylogeny and biological similarity. Conservation Biology 32, 1290–1300. 146  147 Crone, E. E. et al. 2011. How do plant ecologists use matrix population models? Ecology Letters 148 14, 1–8. 149  150 Davison R., Stadman M., Jongejans E. 2019. Stochastic effects contribute to population fitness 151 differences. Ecological Modelling 408:108760. 152  153 Doak, D.F., Thomson, D., Jules, E.S. 2002. Population viability analysis for plants: 154 Understanding the demographic consequences of seed banks for population health. 155 in: Beissinger S.R., McCullough D.R. (eds.) Population viability analysis. The University 156 *MPMs = matrix population models of Chicago Press, Chicago, Illinois, USA. 157  158 Franco, M. & Silvertown, J. 1990. Plant demography: What do we know? Evolutionary Trends in 159 Plants 4, 74–76. 160  161 Franco, M. & Silvertown, J. 2004. A comparative demography of plants based upon elasticities 162 of vital rates. Ecology 85, 531–538. 163  164 Healy K., Ezard T., Jones O., Salguero-Gómez R., Buckley Y. 2019. Animal life history is 165 shaped by the pace of life and the distribution of age-specific mortality and reproduction. Nature 166 Ecology & Evolution 3, 1217–1224. 167  168 Jones O.R., Ezard T.H.G., Dooley C., Healy K., Hodgson D.J., Mueller M., Townley S., 169 Salguero-Gomez R. 2019. My family and other animals: Human demography under a 170 comparative cross-species lens. In "Human Evolutionary Demography", Burger O., Lee R., & 171 Sear R. (eds). Open Book Publishers. Cambridge. UK. 172  173 Kendall, B.E., Fujiwara, M., Diaz-Lopez, J., Schneider, S., Voigt, J., Wiesner, S. 2019. 174 Persistent problems in the construction of matrix population models. Ecological Modelling 406, 175 33–43 https://doi.org/10.1016/j.ecolmodel.2019.03.011 176  177 Nguyen V., Buckley Y.M., Salguero-Gómez R., Wardle G.M. 2019. Consequences of neglecting 178 cryptic life stages from demographic models. Ecological Modelling 408, 108723. 179  180 *MPMs = matrix population models Pistón N., de Bello F., Dias A., Götzenberger L., de Mattos E., Rosado B., Salguero-Gómez R., 181 Carmona C. 2019. Multidimensional ecological analyses demonstrate how interactions between 182 functional traits shape fitness and life history strategies. Journal of Ecology 107: 2317–2328. 183  184 Ramula, S., Knight, T. M., Burns, J. H. & Buckley, Y. M. 2008. General guidelines for invasive 185 plant management based on comparative demography of invasive and native plant populations. 186 Journal of Applied Ecology 45, 1124–1133. 187  188 Roper M., Capdevila P., Salguero-Gómez R. 2019. Senescence - still an unsolved problem of 189 biology. Biorxiv: DOI 10.1101/739730 190  191 Salguero-Gómez, R. & Casper, B. B. 2010. Keeping plant shrinkage in the demographic loop. 192 Journal of Ecology 98, 312–323. 193  194 Salguero-Gómez, R. et al. 2015. The COMPADRE Plant Matrix Database: an open online 195 repository for plant demography. Journal of Ecology 103:202–218 196  197 Salguero-Gómez, R. et al. 2016. COMADRE: a global data base of animal demography. Journal 198 of Animal Ecology 85:371–384 199  200 Stott, I., Franco, M., Carslake, D., Townley, S. Hodgson, D. 2010. Boom or bust? A comparative 201 analysis of transient population dynamics in plants. Journal of Ecology 98, 302–311. 202  203 Vindenes, Y., Engen, S. 2017. Demographic stochasticity and temporal autocorrelation in the 204 dynamics of structured populations. Oikos 126, 462–471. 205  206 *MPMs = matrix population models Zeigler, S., Che-Castaldo, J. Neel, M. 2013. Actual and potential use of population viability 207 analysis in recovery of plant species listed under the U.S. Endangered Species Act. 208 Conservation Biology 27, 1265–1278. 209  210 

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