COMADRE : a global data base of animal demography

1Understanding the drivers and consequences of variation in reproduction and survival throughout the life cycle is fundamental for population biology, evolution, ecology and allied fields (e.g. Stearns 1989; Gaillard et al. 2005; Metcalf & Pavard 2007; Salguero‐Gómez & de Kroon 2010). Although demography is essential to understand and predict population dynamics, no single open‐data repository integrates this information for animal species world‐wide. This is mainly because most biological data sources are scattered and biological data types are heterogeneous (Hoffmann et al. 2014). Moreover, demographic data pose challenges for standardization due to the different formats (e.g. life table, matrix model, individual‐level records, population sizes, etc.) and terminology (Lebreton et al. 2012). This makes it challenging to create a single demographic data repository across multiple species. However, important efforts in this direction already exist, such as the Global Population Dynamics Database (GPDD, Inchausti & Halley 2001) or the Living Planet Index (LPI, Collen et al. 2009), BIDDABA (Lebreton et al. 2012) and the Primate Life History Database (PLHD, Strier et al. 2010) containing demographic information for birds and primates, respectively. These remarkable efforts are important contributions to population biology, but are limited in either demographic detail (GPDD, LPI) or taxonomic scope (BIDDABA, PLHD, WBI).

2A mechanistic understanding of how and why populations invade, grow, decline, or go locally extinct, requires data and methods that provide insights into age‐/size‐/ontogeny‐based structure, such as Matrix Population Models (MPMs hereafter; Caswell 2001). MPMs have become a staple method describing the structured demography of animal populations. The widespread use of MPMs stems from their well‐understood mathematical foundations and tractability (Caswell 2001), coupled with the clear biological interpretations of the analytical outputs (e.g. growth rates, population structure and reproductive values; sensitivity and elasticity of demographic outputs; decomposition of treatment effects using LTRE analysis; measures of population viability and extinction risk; selection gradients in quantitative genetics and adaptive dynamics; and rates of spread of invasive species. See Caswell (2001), and Morris & Doak (2002) for detailed discussions and examples). Briefly, an MPM classifies the life cycle of a species into discrete stages and projects its population(s) through based on the probabilities of survival, transitions among stages and the contributions to sexual or clonal reproduction at each stage. The stages of the life cycle are typically chosen based on the biology of the species, and the projection interval can vary from days (e.g. Buston & García 2007) to years (e.g. Edmunds 2015), depending on the data available, species and questions.

3As is the case with plants (Salguero‐Gómez et al. 2015), a large number of MPMs have been published on species in the animal kingdom since the models were introduced in the 1940s (Bernardelli 1941; Leslie 1945) (Fig. 1). Underlining the general utility of MPMs, these models have been used to address diverse topics including conservation biology (e.g. Crouse, Crowder & Caswell 1987; Bessa‐Gomes et al. 2003; Colin & Lebreton 2005; Jenouvrier et al. 2012), evolutionary biology (e.g., Kawecki 1995; Gaillard et al. 2005; Gamelon et al. 2015), ecotoxicology (e.g., Charles et al. 2009), invasion biology (e.g., Neubert & Parker 2004) and resource management (e.g., Salomon et al. 2013). MPMs have been employed to study species as taxonomically distinct as Caenorhabditis elegans, Loxodonta africana and Homo sapiens, and in geographically diverse regions with studies in every major biome (Fig. 2a and b).

4Despite the growing availability of published MPMs and the fact that such models are inherently comparable (Caswell 2001), there have been few attempts to use MPMs in comparative analyses. Notable exceptions are the work by Sæther & Bakke (2000) or Bessa‐Gomes et al. (2003) on birds, and Heppell, Caswell & Crowder (2000) and Gamelon et al. (2014, 2014) on mammals, Vélez‐Espino, Fox & McLaughlin (2006) on bony fish, Mollet & Cailliet (2002) on cartilaginous fish, and van de Kerk et al. (2013) on order Carnivora. These works illustrate the power of comparative approaches for robust generalizations by relating demographic estimates made from MPMs to interactions with the environment that form the basis for the evolution of life histories. One reason for the lack of comparative studies has historically been the paucity of readily available demographic data, compared with genetic data (e.g. Benson et al. 2013). This changed recently, when the COMPADRE Plant Matrix Database was released (Salguero‐Gómez et al. 2015). Since its foundation in 1990, COMPADRE has prompted over 35 comparative plant demography studies ranging from senescence (Silvertown, Franco & Perez‐Ishiwara 2001; Jones et al. 2014), to short‐term population dynamics (Stott, Townley & Hodgson 2011), to the link between functional traits and demography (Adler et al. 2014; Salguero‐Gómez et al. 2015). Here, we announce the release of COMPADRE's sister data base, the COMADRE Animal Matrix Database, containing MPMs and associated metadata from the animal kingdom.

5The main objectives of the COMADRE team are (i) to find, digitize and systematically error‐check published animal MPMs and supplement them with additional information (Table 1); (ii) to offer such information on an open‐data framework; and (iii) to develop tools to facilitate comparative analyses. The data described here are available at www.comadre-db.org. In this paper, we briefly describe the COMADRE Animal Matrix Database and highlight the major differences and similarities with its sister data base, COMPADRE. In addition, we briefly report some geographic, taxonomic and modelling biases in the animal demography literature that are revealed by the compilation of MPMs in COMADRE. Finally, we share our vision for how this resource may expand and develop in the future, linking to other already existing open‐access data bases to address timely questions in animal ecology and evolution.

6The accumulated number of publications reporting MPMs for animals has increased dramatically since MPMs were introduced in the 1940s (Fig. 1). Important contributions to this history come from the introduction of new types of MPMs, and new methods for analysing them.

7Matrix population models were largely ignored for twenty years after the work of Leslie (1945) (but see for instance Thompson 1959). The rediscovery of MPMs in the 1960s can be credited to Keyfitz (1964), Lefkovitch (1965) and Rogers (1966), whose works focused on animals. Keyfitz (1964) presented MPMs as tools for projecting population growth; his book (Keyfitz 1968) influenced a generation of animal ecologists. The first presentations of MPMs had assumed that age was the only individual state (i‐state; Metz & Diekmann 1986) variable. Lefkovitch (1965), based on studies of laboratory populations of stored product insect pests, explicitly proposed stage‐classified models based on the life cycle stages of insects. Rogers (1966) introduced spatial, or multiregional, models for human populations, classifying individuals by age and spatial location, and modelling survival, fertility and migration.

8Other types of MPMs were introduced in the following years. The first seasonal, periodic MPM appeared in 1964 (Darwin & Williams 1964) in a study of seasonal harvesting as a control strategy for rabbits. The first density‐dependent models appeared in 1969: Pennycuick (1969) analysed a population of great tits (Parus major), while Rabinovich (1969) compared several density‐dependent models, including a MPM to analyse laboratory populations of a parasitoid wasp. The first stochastic model for an animal population was the analysis by Cohen, Christensen & Goodyear (1983) on recruitment fluctuations in striped bass (Morone saxatilis). Invasion models, using matrix integrodifference equations, were first applied to bird populations by Caswell, Lensink & Neubert (2003).

9Analytical methods have developed in parallel with their applications to animal populations. Some of these developments have provided new ways of constructing models (photograph‐identification methods, mark–recapture methods, vec‐permutation matrix methods). Others have provided ways to extract additional information from the resulting MPM (perturbation analyses; LTRE decompositions; stability and bifurcation for nonlinear models; Markov chain methods for the analysis of longevity, heterogeneity and individual stochasticity; reactivity and amplification analyses). Further important contributions are detailed in Fig. 1.

10The COMADRE Animal Matrix Database was founded at the Max Planck Institute for Demographic Research (MPIDR) (Appendix S1, Supporting information). The motivation for the creation of a data base containing MPMs for animals was based on substantial contributions of its sister data base, the COMPADRE Plant Matrix Database (Salguero‐Gómez et al. 2015), to plant ecology and evolution. Four years after its foundation, the COMADRE digitization team has digitized, standardized, error‐checked and supplemented information contained in over 400 species. As with the commitment for its plant sister data base, more data will be released periodically (Fig. 1) at www.comadre-db.org.

11The COMADRE portal (www.comadre-db.org) provides access to the data to important news (e.g. version releases), the user's guide and digitalization protocol, announcements on workshops and open‐access scripts for analyses. The current version of the COMADRE data is provided in a structured Rdata object format (Box 1, Fig. S1); this will migrate to an SQL infrastructure eventually. The information in COMADRE is obtained mostly (>99%) from published peer‐reviewed manuscripts obtained from searches of ISI, Scopus and Google Scholar with keywords frequently used in publications containing MPMs (e.g. ‘elasticity’, ‘sensitivity’, ‘LTRE’, ‘population growth rate’, ‘matrix population model’, ‘projection matrix’). The portal contains a list of all species included in the current release of the data, as well as those still being digitized/error‐checked. Users are encouraged to email their works containing MPMs at comadre-contact@demogr.mpg.de if not cited in the aforementioned list. The demographic information is digitized, re‐organized (see equation 2) and error‐checked (below), and then supplemented with additional sources (e.g. taxonomy, ecoregion; Table 1). The COMADRE user's guide details the organization of the data object, the meaning and range of possible values for these variables, as well as information on error‐checks and quality controls that are carried out. Additionally, Frequently Asked Questions (FAQs) can be found at the online portal (http://www.comadre-db.org/Help).

12The basic data item of COMADRE is the population projection matrix. A basic (i.e. linear and time‐invariant) MPM can be written as where n is a vector describing the abundance of a set of age/size/ontogenetic classes and A is a population projection matrix. The structure of the projection matrix A depends on the choice of life cycle stages and the projection interval.

13In COMADRE, the projection matrix is decomposed as where U is the matrix describing transitions and survival of extant individuals, and F and C are the matrices describing production of new individuals by sexual and clonal reproduction, respectively. Some studies do not measure reproduction, reporting only the transition matrix U. In these cases, this is reflected in the variable MatrixFec (see Table 1 and COMADRE User's Guide for details). The column sums of U give the survival probabilities of the stages, and thus should not exceed 1 (below).

14The simple model (eqn 1) can be extended in several ways. Seasonal MPMs divide the year into seasons and report a projection matrix B(i) for season i during the m seasons/periods in the year; the data base entries for such seasonal models report all of the seasonal matrices, and when necessary, we also calculated the consequent annual matrix for inclusion in COMADRE:

15Stochastic, density‐dependent and environment‐dependent MPMs are increasingly common in animal studies (e.g. Cushing et al. 2003; Jenouvrier et al. 2009; Hunter et al. 2010; Barraquand et al. 2014). In such cases, the MPM can be written as in equation 4, where E(t) corresponds to environmental conditions. Such a model is associated not with a single projection matrix, but with a function that returns an A matrix given a time and/or environment and/or population vector. Because such functions require a different data structure, such models are not directly included in COMADRE 1.0.0. However, in some cases, static MPMs are presented at particular values of density or environmental conditions, as specified in the variable Treatment (Table 1). These are not stochastic, density‐dependent or environment‐dependent models, although they might eventually be used to construct such models.

16Associated with each MPM is a set of descriptive information (metadata). These metadata are contained in the R object COMADRE_v.1.0.0.Rdata as a list object that contains four further subhierarchical objects: metadata, matrixClass, mat and version (Box 1; See User's guide). The metadata object (a data.frame) can be accessed in R with the command comadre$metadata, and it includes information about taxonomy, additional details of the study including its source, geolocation and some details about the specific MPMs (Variables 1 through 54 inTable 1). Information about the classes used to construct the specific MPM is contained in the matrixClass object (a list of data.frames), which can be accessed with the R command comadre$matrixClass. The MPMs are held in a list of lists that can be retrieved with the command comadre$mat. Data pertaining to particular MPMs can be obtained using R’s standard data indexing facilities; for instance, comadre metadata[n,] and comadre$matrixClass[[n]] will return the metadata and class information pertaining to the nth matrix (comadre$mat[[n]]). This is illustrated in Box 1, where we demonstrate how to obtain data for the snowshoe hare (Lepus americanus; Meslow & Keith 1968). We provide further examples on how to query COMADRE to run comparative analyses in Appendix S4.1–4.7. The last object within COMADRE is the list version, which contains summary information about the version for replicability purposes, as well as a link to the User's Agreement.

17In some cases, the original data source provided information that allowed us to split the full life cycle matrix (A) into non‐reproductive, survival‐dependent transition probability processes (U), sexual reproduction (F), and clonal reproduction, (C) as described in equation 2. Whether this was possible or not is indicated in by the variable MatrixSplit (Table 1). This set of four matrices (A,U,F and C) are stored as a list (with elements named matA, matU, matF and matC, respectively) within the elements of the comadre$matlist. Thus, the matrices can be obtained with ease: comadre mat[[n]] matA, comadre$mat[[n]]$matU, etc. Splitting the matrices in this way allows for automated calculation of demographic output of various kinds from hundreds of records in a few seconds (Appendix S4).

18The core data in both COMADRE and COMPADRE are the projection matrices that appear in MPMs. A comparison of Table 1 in this manuscript and Table 1 in the introduction to COMPADRE Plant Matrix Database (Salguero‐Gómez et al. 2015) reveals a number of similarities. Moreover, the data quality controls are the same for COMPADRE and for COMADRE (below).

19Despite the similarities, biological differences between plants and animals mean that animal data cannot be fully accommodated by the data base framework of COMPADRE. The key differences between the two data bases are as follows: (i) the variables GrowthType, DicotMonocot and AngioGymno are specific to plants and so do not exist in COMADRE; (ii) variables related to taxonomic validation and cross‐referencing (1–14 in Table 1) were crosschecked with The Catalogue of Life (CoL) (http://www.catalogueoflife.org) in COMADRE, instead of The Plant List (http://www.theplantlist.org) as done for COMPADRE; and (iii) we have added in COMADRE the variable MatrixFec, which indicates whether the reproductive component (matrices F and/or C) of the matrix model is missing or not. This variable was not present in COMPADRE 3.0.0, but it is now in 3.2.1; (iv) unlike in COMPADRE, where we reconstructed a phylogeny for plant species, a tree for most animal species in COMADRE has been recently published (Hedges et al. 2015). Furthermore, species‐level resolved trees also exist for some taxonomic groups such as mammals (Bininda‐Emonds et al. 2007), birds (Jetz et al. 2012) or reptiles (Pyron, Burbrink & Wiens 2013).

20To facilitate transparency in the data entry and error‐checking process, we provide the protocol used internally by our team in the Online Appendix (Supporting information). Most of these checks were detailed in the publication of the COMPADRE Plant Matrix Database (Salguero‐Gómez et al. 2015). Important error‐checks include making sure that equation 2 remains correct for all MPMs and that stage‐specific survival (i.e., the column sums of the matrix U) does not exceed 1. In order to eliminate potential typographical errors, a double‐blind check, whereby the same MPM is digitized by two team members, each unaware that the other is doing so, and of what the results are, is carried out for ca. 50% of the original MPM sources. Additionally, each datum digitized by the COMADRE team is reviewed and error‐checked three times by the team leaders before the data are released. We do note that typos may remain despite our efforts. Users are encouraged to contact us at comadre-contact@demogr.mpg.de to inform us of potential errors.

21Users wishing to run ‘big data analyses’ with COMADRE must keep in mind that, although hundreds of open‐data demographic records are available in COMADRE, these may not all be appropriate for a particular analyses. Data selection is perhaps the most important step in comparative research; thus, users must ask carefully which variables in Table 1 are irrelevant, relevant or missing for their research goals. Careful data selection criteria will allow for fair large analyses, the gold standard in comparative analyses. For instance, does the research require demographic data from various populations per species or is one population per species sufficient (NumberPopulations)?.Should there be a minimum threshold to the study length (StudyDuration)? We provide several examples of R code in the Online Appendix 4 to help users subset data based on various selection criteria.

22We emphasize the importance of variables SurvivalIssue and MatrixFec (Table 1) to facilitate detection of issues related to survival‐ or reproduction‐dependent life history traits, respectively. SurvivalIssue reports the maximum stage‐specific survival value in the U matrix. This is important because the stage‐specific survival of any column sums of the U matrix is constrained to be between 0 and 1, and values greater than 1 render most analyses impossible, particularly those on survival and longevity. During data entry, when probabilities exceeded the error margin for rounding error and were considerably greater than 1, contributing authors were contacted for clarification. In some cases (<13% of MPMs with this issue), these personal communications have resulted in amendments from the originally published matrices or in the re‐assignment of proportions of each matrix element in A to the submatrices U,F and C (eqn 2). MPMs where this issue is still pending (IssueSurvival >1) may contain information provided by the authors in the variable ‘Observation’ (Table 1). Currently, only 0·9% of the MPMs (15 of the 1625) in version 1.0.0 have at least one stage‐specific survival >1. MatrixFec = ‘No’ is used to indicate MPMs for which reproduction was not modelled, to distinguish those cases from instances where reproduction was measured, but no recruitment occurred in t + 1. In the former case, the MPM cannot be used to quantify metrics that depend on reproduction, such as population growth rate λ, its elasticities/sensitivities, damping ratio ρ and net reproductive rate. However, other metrics are still valid in these models (e.g. life expectancy ηe from matU; Table 1). In version 1.0.0, only 4·6% of the MPMs (75 of 1625) contain this issue in the submatrix F.

23The study designs, data sources and estimation procedures used to estimate MPMs are incredibly diverse. The research questions that can be asked and the analytical methods that exist to address them are equally diverse. Not all of the information relevant to a given analysis can be included in COMADRE. Just as with any scientific study, a user must take care that the data used are appropriate for the question and the analysis. This may require revisiting original sources (Appendix S5) to clarify aspects of the original study. A non‐exhaustive list of potential issues includes the effect of processes not included in the model (e.g. permanent emigration that may be confounded with mortality), the relative precision of estimates obtained by different methods (e.g. the difference between sessile and motile organisms) and the methods used to obtain measurements of age or stage. Reproduction in MPMs in COMADRE has not been categorized into pre‐breeding, post‐breeding or birth‐flow categories (Caswell 2001, p. 130); these classifications are not relevant to all studies, but may be of interest for some purposes. Thus, although MPMs from peer‐reviewed publications are included only after passing our error‐checks, some may still contain issues related to the original author's calculations.

24COMADRE contains an unprecedented sample of information on animal population dynamics: 1625 MPMs from 402 studies corresponding to 345 taxonomically accepted species according to the Catalogue of Life (Appendix 4.2). This represents a substantial improvement in sample size and ancillary information to date (Table 1), including important comparative works examining various aspects of life history traits and population dynamics of mammals (50 species in Heppell, Caswell & Crowder 2000; 111 in Gamelon et al. 2014), birds (49 species in Sæther & Bakke 2000), fish (88 species in Vélez‐Espino, Fox & McLaughlin 2006) and order Carnivora (285 species in van de Kerk et al. 2013). It must also be noted that the information analysed in the aforementioned studies was not made open access with the exception of van de Kerk et al. (2013), who archived it into COMADRE.

25COMADRE offers a broad geographic coverage of animal population dynamics (Fig. 2). Information in COMADRE 1.0.0 includes MPMs from all continents except Antarctica – although MPMs for Antarctic species do exist and will be released in future version of COMADRE (e.g. Emperor penguin, Jenouvrier et al. 2012; Antarctic petrel, Decamps et al. 2015). Importantly, geographic gaps do exist in our knowledge of animal demography in certain regions, including Oceania (8·11% of MPMs), and Asia (2·49%; Fig. 2b). Together, the USA (31·7%), Canada (8·7%), Australia (5·3%) and Kenya (4·8%) comprise over half the MPMs in COMADRE 1.0.0. Unfortunately, few studies report MPMs from biodiversity hotspots such as Honduras, Guatemala, the Democratic Republic of Congo, Paraguay, India and Indonesia. Furthermore, some developed countries, such as Saudi Arabia, Italy, Greece, Ireland and Brazil, are under‐represented.

26Individual and seasonal population projection matrices (Table 1 #42) together total over 50% of the matrices in COMADRE 1.0.0, representing unique combinations of studies × species × populations × treatments × periods (Fig. S2A). The remaining 772 projection matrices are element‐by‐element arithmetic means of other matrices, or constructed based on data from multiple sources (‘pooled’). Given the spatial (Fig. 3a), interannual (Fig. 3b) and intraannual replication (Fig. 3c) in many studies, the high proportion of mean and pooled matrices suggests a tendency in animal demographic studies to publish only summary MPMs. We encourage authors to publish their original MPMs in the supplementary materials for their papers. Authors can archive MPMs in COMADRE by submitting them directly at comadre-contact@demogr.mpg.de.

27Most studies in COMADRE are of natural populations in the wild (89%, Fig. S2B) and under unmanipulated conditions (i.e. no researcher‐imposed treatment) (80%; Fig. S2C). Most of the demographic studies in COMADRE are based on females only (67%; Fig. S2D); this is common practice in animal demographic studies (particularly in mammals), as quantifying reproduction and assigning paternity are challenging in males. We have noted, in the variable ‘Observations’ (Table 1 #52), when the primary sex ratio was stated by the author as differing from 1:1 (female:male). For the vast majority of matrices (95%; Fig. S2E), we have successfully split the full matrix A into its subcomponents of survival (U), sexual reproduction (F) and clonal reproduction (C) as per equation 2, and only 4·6% of the MPMs do not incorporate reproductive information (Fig. S2F).

28The data in COMADRE 1.0.0 represent a wide range of animal groups (Fig. S2G). However, even though the digitization in COMADRE has not been prioritized by taxonomic group or geographic region, some strong taxonomic biases exist. Mammals represent 44·8% of the MPMs in the current version of COMADRE, followed by birds (17·5%), bony fish (Actinopterygii 10·6%) and reptiles (6·1%). COMADRE includes few MPMs for amphibians (0·9%), despite global concerns for their conservation status (Beebee & Griffiths 2005; Wake & Vredenburg 2008) or for insects (2·7%), despite their high species richness, estimated to comprise the majority of the animal kingdom (Hedges et al. 2015). The latter is particularly surprising a important early developments of MPMs focused on insects thanks to their clearly structured population dynamics (Lefkovitch 1965; Rabinovich 1969). This may reflect the widespread use of seasonal life table methods for insects, due to their annual life cycles (e.g. Dempster 1975), prior to the introduction of periodic matrix models specifically aimed at annual organisms (Caswell 2001; Chap. 13.2). Aside from bony fish, we lack significant amounts of demographic information on marine organisms in COMADRE, including corals (5·7%), bivalves (1·3%), sponges (0·8%), sea urchins (0·1%) and cartilaginous fish (0·2%). No information in COMADRE 1.0.0 exists for the order Struthioniformes (kiwis, emu, ostriches, etc.) or infraclass Marsupialia (kangaroos, wallabies, possums, opossums, wombats, etc., with the exception of the koala Phascolarctos cinereus; Baxter et al. 2006; Ng et al. 2014; Appendix S4.1).

29The replication of studies through time and over space is highly variable in COMADRE. Yet, the average duration of studies in COMADRE 1.0.0 (10·97 years ± 0·68 SE; Fig. 3b, Fig. S3) is greater than in plant MPM studies (5·60 ± 0·23 years; Salguero‐Gómez et al. 2015). Long duration is essential for many demographic studies in the animal kingdom, as some animals, such as the clam Arctica islandica, giant tortoises (Geochelone nigra, G. gigantea), some rockfish species (e.g. Sebastes aleutianus) and the bowhead whale (Balaena mysticetus), can reach over 150 years of age (de Magalhaes & Costa 2009). Notable demographic studies using MPMs parameterized with long time series include Vipera aspis (17 years, Altwegg et al. 2005), Ursus americanus (22 years, Mitchell et al. 2009), Delphinus delphis (35 years, Mannocci et al. 2012), Recurvirostra avosetta (40 years, Hill 1988), Elatobium abietinum (41 years, Estay et al. 2012), Marmota flaviventris (44 years, Ozgul et al. 2009), Haliaeetus albicilla (62 years, Krüger, Grünkorn & Struwe‐Juhl 2010), Diomedea exulans (51 years, Barbraud et al. 2013) and Aythya affinis (72 years, Koons et al. 2006).

30In contrast to the duration, the average number of populations considered in each study is relatively low, averaging 2·52 ± 0·42 (SE). The low spatial replication currently limits much‐needed understanding of the geographic variability of demographic rates within species. It is perhaps not surprising that the animal studies with highest spatial replication in COMADRE 1.0.0 focus on humans, with the foundational archive of human MPMs compiled by Keyfitz & Flieger (1968), which covers populations from 157 countries. We note that analyses of spatial and other kinds of variability in animal population studies are becoming more sophisticated due to the use of model selection methods to explicitly include environmental variables (e.g. Thomson, Cooch & Conroy 2009), and the concept of ‘spatial replication’ used in plant studies may acquire a different meaning to that used in most animal studies.

31I'm not interested in your data; I'm interested in merging your data with other data. Your data will never be as exciting as what I can merge it withTim Berners‐Lee

32I'm not interested in your data; I'm interested in merging your data with other data. Your data will never be as exciting as what I can merge it with

33The potential of the COMADRE Animal Matrix Database does not reside exclusively in its hundreds of MPMs, or on the frequent future updates with new species and studies, but also in the many outputs that can be derived from them, and the possibility to place them in a broader spatial, ecological and evolutionary context using other open‐data sources. Users of COMADRE can find R scripts to manipulate and interact with MPMs and derive basic demographic outputs (Appendix S4 and updated scripts in our GitHub repository). Users are encouraged to explore these and other open‐source libraries (Stubben & Milligan 2007; Stott, Hodgson & Townley 2012; Metcalf et al. 2013), and to apply the rich repertoire of analytical methods for MPMs (Caswell 2001; Morris & Doak 2002).

34The schedules of growth, survival and reproduction and the associated population performance metrics available through COMADRE will enable further comparative analyses of life history variation and population performance relative to the environment. For example, information in COMADRE could be integrated with existing repositories for other data such as genetic sequences (GenBank; Benson et al. 2013), distribution and occurrences (GBIF; Flemons et al. 2007), and conservation status and threats (BirdLife, http://www.birdlife.org/datazone; IUCN Red List, http://www.iucnredlist.org). Data on species‐level life history traits are also available for specific taxonomic groups including vertebrates (AnAge; de Magalhaes & Costa 2009), mammals (Ernest 2003; PanTHERIA, Jones et al. 2009), amphibians (Trochet et al. 2014), fish (FishBase) and reptiles (SCALETOOL, www.scale-project.net). In addition to the rapidly growing body of data, a diverse set of tools are emerging that will facilitate these large‐scale comparative analyses including the R packages taxize (Chamberlain & Szöcs 2013), letsR (Vilela & Villalobos 2015) which facilitate taxonomic matching and macroecological analyses, respectively.

35We hope that the compilation of demographic data in COMADRE will also enable ecologists to identify the gaps in our knowledge in animal population dynamics and will catalyse new studies at broad spatial scales. The open‐data nature of both COMPADRE and COMADRE will facilitate further comparative demographic analyses across plant and animal kingdoms (see Jones et al. 2014) enabling tests of life history and population dynamics theory across a wide range of species with contrasting life histories, mobility/dispersal and architecture. We suggest that researchers revisit the canonical tenets of animal life history to confront established theories with data compilations that are vastly richer than was available 30 years ago. We have already identified over 900 additional animal species with MPMs (Fig. 1), and our ongoing efforts will release them as they become fully digitized, error‐checked and supplemented in the coming years. Finally, researchers using the data archived in COMADRE are strongly encouraged to cite also the original sources (Appendix S5).

36The data associated with this manuscript can be accessed at www.comadre-db.org.