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Synthesising trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Despite the well-recognised importance of traits for addressing ecological and evolutionary questions, trait-based approaches still struggle with several basic data requirements to deliver openly accessible, reproducible, and transparent science. Here, we introduce the Open Traits Network (OTN) – a decentralised alliance of international researchers and institutions focused on collaborative integration and standardisation of the exponentially increasing availability of trait data across all organisms. The OTN embraces the use of Open Science principles in trait research, particularly open data, open source, and open methodology protocols and workflows, to accelerate the synthesis of trait data across the Tree of Life. Increased efforts at all levels – from individual scientists, research networks, scientific societies, funding agencies, to publishers – are necessary to fully exploit the opportunities offered by Open Science in trait research. Democratising access to data, tools and resources will facilitate rapid advances in the biological sciences and our ability to address pressing environmental and societal demands.
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The Open Traits Network: Using Open Science principles to accelerate trait-based
science across the Tree of Life
Gallagher, R. V.1, Falster, D. S.2, Maitner, B. S.3, Salguero-Gómez , R.4,5,6, Vandvik,
V.7,8, Pearse, W. D.9, Schneider, F. D.10, Kattge, J.11,12, Alroy, J.1, Ankenbrand, M. J.13,14,
Andrew, S. C.15, Balk, M.16, Bland, L. M.17, Boyle, B. L.3, Bravo-Avila, C. H.18,19,
Brennan, I.20, Carthey, A. J. R.21, Catullo, R.20, Cavazos, B. R.22, Chown, S.23, Fadrique,
B.24, Feng, X.3, Gibb, H.25, Halbritter, A. H.7,8, Hammock, J.26, Hogan, J. A.27, Holewa,
H.15, Hope, M.15, Iversen, C. M.28, Jochum, M.29,30,31, Kearney, M.32, Keller, A.13, Mabee,
P.33, Madin, J. S.34, Manning, P.35, McCormack, L.36, Michaletz, S. T.37, Park, D. S.38,
Penone, C.29, Perez, T. M.24,39, Pineda-Munoz, S.40, Poelen, J.41, Ray, C. A.42, Rossetto,
M.43, Sauquet, H.43,44, Sparrow, B.45, Spasojevic, M. J.46, Telford, R. J.7,8, Tobias, J. A.47,
Violle, C.48, Walls, R.49, Weiss, K. C. B.42, Westoby, M.1, Wright, I. J.1, Enquist, B. J.3,50
1Department of Biological Sciences, Macquarie University, Sydney, NSW, Australia
, 2Evolution and Ecology Research Centre, School of Biological, Earth and
Environmental Sciences, University of New South Wales, Sydney,
Australia, 3Department of Ecology and Evolutionary Biology, University of Arizona,
Tucson, Arizona, USA, 4Department of Zoology, Oxford University, Oxford,
UK, 5Centre for Biodiversity and Conservation Science, University of Queensland,
Queensland, Australia, 6Evolutionary Demography Laboratory, Max Plank Institute for
Demographic Research, Rostock, Germany, 7Department of Biological Sciences,
University of Bergen, Bergen, Norway, 8Bjerknes Centre for Climate Research,
University of Bergen, N-5020 Bergen, Norway, 9Ecology Center and Department of
Biology, Utah State University, Utah, USA, 10Niederstr. 23 64285 Darmstadt,
Germany, 11Max Planck Institute for Biogeochemistry, Jena, Germany,12German Centre
for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig,
Germany,13Department of Bioinformatics, Biocenter, University of Wuerzburg,
Wuerzburg, Germany, 14Center for Computational and Theoretical Biology, Biocenter,
University of Wuerzburg, Wuerzburg, Germany,15Commonwealth Scientific and
Industrial Research Organisation (CSIRO), Canberra, Australia, 16Bio5 Institute,
University of Arizona, Tucson, Arizona, USA, 17School of Life and Environmental
Sciences, Centre for Integrative Ecology, Deakin University, Victoria,
Australia, 18Department of Biology, University of Miami, Coral Gables, Florida,
USA, 19Fairchild Tropical Botanic Garden, Coral Gables, Florida, USA, 20Research
School of Biology, Australian National University, Canberra, Australia, 21Department of
Biological Sciences, Macquarie University, North Ryde, NSW, Australia , 22Department
of BioSciences, Program in Ecology and Evolutionary Biology, Rice University,
Houston, Texas, USA, 23School of Biological Sciences, Monash University, Victoria,
Australia, 24Department of Biology, University of Miami, Florida, USA, 25Department of
Ecology, Environment and Evolution and Centre for Future Landscapes, La Trobe
University, Victoria,26National Museum of Natural History, Smithsonian Institution,
Washington DC, USA, 27International Center for Tropical Botany, Department of
Biological Sciences, Florida International University, Florida, USA,28Climate Change
Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory,
Tennessee, USA, 29Institute of Plant Sciences, University of Bern, Bern,
Switzerland, 30German Centre for Integrative Biodiversity Research (iDiv)
Halle?Jena?Leipzig, Leipzig, Germany, 31Leipzig University, Institute of Biology,
Leipzig, Germany, 32School of BioSciences, The University of Melbourne, Victoria,
Australia,33Department of Biology, University of South Dakota, South Dakota,
USA, 34Hawai‘i Institute of Marine Biology, University of Hawai‘i at Manoa, Hawai‘i,
USA, 35Senckenberg Biodiversity and Climate Research Centre (SBiK-F),
Frankfurt,, 36Center for Tree Science, The Morton Arboretum, Illinois,
USA, 37Department of Botany and Biodiversity Research Centre, University of British
Columbia, British Columbia, Canada,38Department of Organismic and Evolutionary
Biology and Harvard University Herbaria, Harvard University, Cambridge,
Massachusetts, USA, 39Fairchild Tropical Botanic Garden, Florida, USA, 40School of
Biological Sciences, School of Earth & Atmospheric Sciences, Georgia Institute of
Technology, Georgia, USA, 41400 Perkins Street Apt 104, Oakland, California, United
States, 42School of Life Sciences, Arizona State University, Tempe, Arizona,
USA, 43National Herbarium of New South Wales, Royal Botanic Gardens and Domain
Trust, Sydney, NSW, Australia, 44Ecologie Systématique Evolution, Univ. Paris-Sud,
CNRS, AgroParisTech, Universite? Paris-Saclay, Orsay, France, 45TERN / School of
Biological Sciences, Faculty of Science, The University of Adelaide, Adelaide,
Australia, 46Department of Evolution, Ecology, and Organismal Biology, University of
California Riverside, California, USA, 47Department of Life Sciences, Imperial College
London, Silwood Park, UK, 48CEFE, CNRS, Univ Montpellier, Université Paul Valéry
Montpellier 3, EPHE, IRD, Montpellier, France, 49CyVerse, University of Arizona,
Tucson, AZ, USA, 50Santa Fe Institute, Santa Fe, New Mexico, USA
Synthesising trait observations and knowledge across the Tree of Life remains a grand
challenge for biodiversity science. Despite the well-recognised importance of traits for
addressing ecological and evolutionary questions, trait-based approaches still struggle
with several basic data requirements to deliver openly accessible, reproducible, and
transparent science. Here, we introduce the Open Traits Network (OTN) a
decentralised alliance of international researchers and institutions focused on
collaborative integration and standardisation of the exponentially increasing availability
of trait data across all organisms. The OTN embraces the use of Open Science principles
in trait research, particularly open data, open source, and open methodology protocols
and workflows, to accelerate the synthesis of trait data across the Tree of Life. Increased
efforts at all levels from individual scientists, research networks, scientific societies,
funding agencies, to publishers are necessary to fully exploit the opportunities offered
by Open Science in trait research. Democratising access to data, tools and resources will
facilitate rapid advances in the biological sciences and our ability to address pressing
environmental and societal demands.
Traits, broadly speaking, are attributes or characteristics of organisms. Traits related to
functionsuch as leaf size, body mass, or growth formare often used to understand
how organisms interact with the environment and with other species via key vital rates
such as survival, development, and reproduction1-6. Trait-based approaches have long
been used in systematics and macroevolution to delineate taxa and reconstruct ancestral
morphology and function7-9 and to link candidate genes to phentoypes10-12. The broad
appeal of the trait concept is its ability to facilitate quantitative comparisons of biological
form and function and to mechanistically link organismal responses to abiotic and biotic
factors using measurements that are, in principle, relatively easy to capture across large
numbers of individuals. For example, appropriately chosen and defined traits can help
identify different lineages that share similar life-history strategies for a given
environmental regime13,14. Thus, documenting and understanding the diversity and
composition of traits in ecosystems directly contributes to our understanding of
organismal and ecosystem processes, functionality, productivity, and resilience in the
face of environmental change15-20.
Functional traits are important indicators for the socio-economic value of
ecosystems and their services. They are increasingly used to model food and energy
security, and to inform conservation decision-making21-25. Traits are also key to
improving modelling of earth systems and their responses to climate change by linking
energy and resource fluxes between organisms and the environment26,27. In short, trait
data can help bridge disciplines within biology, and link biology to the physical sciences
and human systems.
In recent decades there has been an acceleration in the collection, compilation,
and availability of trait data for a variety of organisms. Substantial trait databases now
exist for plants28-31, reptiles32,33, invertebrates31,34-37, fish38,39, corals40, birds31,41,42,
amphibians43, mammals31,42,44,45, and fungi31 (see also and parallel efforts are no doubt underway
for other taxa. Though considerable effort has been made to quantify traits for some key
groups (e.g., Fig. 1), substantial work remains. In order to advance knowledge by
developing and testing theory in biodiversity science much greater effort is needed to
combine and integrate data46. There are two central questions that together define the
scope and nature of the challenge to trait science. First, how can we most effectively
advance the synthesis of trait data within and across disciplines to address questions of
global significance (Box 1)? Second, how can we best deliver accurate and
understandable biodiversity knowledge to non-academic audiences who should have
equitable access to quality data on the traits of Earths’ species?
Figure 1. Mammal, bird, and plant phylogenies coloured according to the number of
traits for which we have data for each species and lineage. Trait data were downloaded
from 33,42,47, the number of traits present across these datasets for each species counted,
and then mapped onto single phylogenies from the posteriors of 45,48, and a random
subset of plant species within a single phylogeny from49. Terminal branches (representing
species) and ancestral lineages (using ancestral state reconstruction50) were then coloured
according to the number of (reconstructed) traits. Note that this is an exploratory
analysis, conducted purely to show the variation across taxonomic groups in the amount
of available trait data.
Using Open Science principles to accelerate trait-based science
This is an exciting time to advocate for greater coordination and transparency in trait-
based research. Indeed, across the sciences, Open Science principles are rapidly emerging
and being adopted. Open Science principles (Fig. 2) outline a movement towards making
all aspects of the scientific process transparent and accessible to a wide audience51,52.
Figure 2. The six core principles of Open Science which guide the Open Traits Network.
In this context, knowledge is considered open if anyone can freely access, use, modify,
and share it subject, at most, to measures that preserve provenance and openness
( Several pronouncements about Open Science principles
have already been made, including the Berlin (
Declaration), Bouchout ( and
Denton Declarations ( on open access to
science data. Other initiatives champion some open practices such as the Bari Manifesto
on interoperability53 and the FORCE 11 network, which developed the 'Joint
Declaration of Data Citation Principles’
( and ‘FAIR’ principles
( The FAIR principles address
several of the major challenges facing trait-based research, namely making data
Findable, Accessible, Interoperable, and Reusable.
While the adoption of Open Science Principles has the potential to rapidly
advance global trait synthesis by democratising access to data, tools, and resources,
numerous hurdles remain. Trait-based science continues to struggle with realising these
principles because of several issues including: 1) a lack of readily available, machine-
accessible primary data released under a clear license arrangement; 2) the need for
standardised protocols, handbooks or metadata formats for data collection,
documentation and management, but see54-56; and 3) the complexity of integrating
existing legacy data from disparate sources (e.g., specimens, published literature, citizen
science initiatives57,58, large scale digitisation efforts (e.g., Biodiversity Heritage Library)
which will have systematic differences in the error rates, validation, context,
reproducibility, and objectivity relative to field-collected trait observations.
For many researchers and institutions, Open Science principles are increasingly
becoming part of their everyday approach to trait research. Connecting those wishing to
transition to an Open Science model whether individuals, research groups, or
institutions to those already actively adopting these principles will facilitate transfer of
skills and knowledge. These connections could be made via model examples, standards,
and networks. Trait science has clear potential to rapidly increase its taxonomic,
phylogenetic, and spatial scope, by stronger advocacy of Open Science and greater
connection between researchers, institutions, publishers, and funding bodies.
Introducing the Open Traits Network
The Open Traits Network (OTN) is a decentralised network accessible to any
international researcher, institution, or research infrastructure provider focused on
collaboration and standardization in the collection of trait data based on Open Science
principles (Fig. 2). The primary goal of the OTN is to increase cross-community
exchange of ideas, tools, resources, and data.
Network diagrams can represent the way researchers interact when building
interlinked trait sources and databases; they show how individual nodes (researchers,
groups, institutions) are connected (Fig. 3).
Figure 3. Architectures of three alternative networks in which research groups (nodes) interact in
collecting and organising trait data. Black nodes are individuals, groups or institutions pursuing trait
projects. Light green nodes are those synthesising data and protocols, where node size is proportional to
available resources. Dark green nodes are synthesising nodes, which benefit from the flow of standardised
trait data and knowledge. (A) Groups are disconnected and decentralised, risking duplication of effort
(often the status quo); (B) Groups are inflexibly linked to a centralised repository, potentially limiting
innovation; (C) The Open Traits Network, which is represented by orange connecting lines. Nodes are
linked together within biological domains (e.g., plants, corals) and/or disciplines (e.g., systematics,
palaeobiology), allowing for more efficient and specialised decisions about trait collection. However, data
synthesis across domains or disciplines is then facilitated by joining nodes based on common workflows
and data sharing protocols which adhere to the guiding principles of the Open Traits Network. Central
synthesising nodes may be, for example, the Smithsonian Encyclopedia of Life
In many instances, groups building smaller-scale databases do so in isolation,
using their own tools and workflows tailored to their research question (Fig. 3A). These
decentralised researchers are often best placed to determine which traits are most useful
in their study system and how they should be compiled. However, there is little formal
support or interaction across this style of network, so researchers often collect redundant
data, use different data structures, and may develop similar tools for data collection,
cleaning, and integration in isolation, which can lead to duplication of effort. There are
many small, isolated, and heterogeneous data sources of this sort, increasing the
disconnect between pools of trait data59.
For some organisms, centralised hubs already exist which aggregate and
standardise trait data from across disparate sources (e.g.,29,40,60-63) (Fig. 3B). These trait
repository hubs have increasingly become the main access point for trait data, but they
remain mostly isolated from each other, limiting the sharing of expertise. Other large-
scale informatic challenges have also followed the centralised and connected network
model (e.g., the Global Biodiversity Information Facility;
GenBank and mandate strict data exchange
protocols to facilitate synthesis. Standardized, controlled vocabularies such as Darwin
Core and Humboldt Core have been essential to the explosive growth of biodiversity
data because they facilitate free exchange of information using common data
formats46,58,64. Further, ontologies necessary to represent traits have been developed (e.g.,
Uberonthe multispecies anatomy ontology for animals65, TOP the Thesaurus of
Plant characteristics66) that enable the integration with genetic and environmental data
through corresponding ontologies (e.g., Gene Ontology67; Environmental Ontology68).
Although the centralised and connected model (Fig. 3B) has clear advantages, it can lack
the flexibility to dynamically connect trait data where ontologies and exchange formats
do not exist. The result is that established trait networks will remain isolated and
The OTN (represented by the orange connections in Fig. 3C) maintains the key
advantages of a decentralised network (e.g., taxon/discipline specific decision-making)
while enhancing the level of connectivity among groups, allowing for easier aggregation
and sharing of expertise, tools, and data. These network characteristics may also buffer
against node loss (e.g., due to lack of funding). Decentralised and connected networks are
also known to be characterised by socially-mediated improvements in learning69. The
OTN is, in part, about capitalising on existing robust connections within disciplines to
more efficiently disseminate granular knowledge about traits.
Principles of the Open Traits Network
The OTN can strengthen and bridge between current initiatives in trait science and
encourage efficient new enterprises by serving as a platform for sharing principles,
methods, tools, examples, and approaches to support the wider traits community in
developing its scientific practice. The OTN is based on core principles of Open Science
(Fig. 2), namely:
1. Openly sharing data, methods, protocols, code, and workflows;
2. Appropriate citation of original data collectors, providing scholarly credit;
3. Provision of appropriate metadata together with trait observations;
4. Collection of trait data following reproducible, standardised methods and
protocols (when available) or commitment to their development;
5. Providing training resources in trait collection and database construction using
Open Science principles.
Below, we highlight key activities for the OTN designed to empower researchers to
gather and make better use of trait data.
Key activities for the Open Traits Network
Activity 1: Maintaining a global registry of trait-based initiatives
The OTN maintains a global registry of trait-based initiatives (https://protect-
11; Table 1)
to help (i) connect the research community, (ii) identify data and knowledge gaps, (iii)
prioritise trait collection, and (iv) allow researchers to avoid inadvertent duplication of
efforts when collating trait data. The heterogeneous ways in which trait data have been
collected to date have resulted in a patchy and unrepresentative trait landscape across
trait types, taxa, regions, and times of the year70. These gaps impede synthetic analyses
across taxa, space, and ontogeny.
The OTN Registry contains information on existing datasets so that gaps can be
easily identified, and ultimately filled, through collective effort. Core information for the
registry includes trait name, geographic extent, taxonomic coverage, and temporal
period (Table 1), and existing knowledge from Ecological Metadata Language
language.html) and Darwin Core is adopted. Critically, the OTN Registry provides the
opportunity for contributors to identify where code to process and manipulate their raw
data is located (see Activity 2 below). The OTN Registry will also link to the ontology
resource OBO Foundry ( Thus, the OTN registry maps to
several Open Science principles (Fig. 2; e.g., Open Source, Open Data, Open Access)
and is designed, from the ground up, to support resolving the issue of data integration.
Activity 2: Sharing reproducible workflows and tools for aggregating trait data
The OTN leverages collaborative software development via platforms like GitHub
( to create a toolbox of modular open source software for access
and harmonisation and re-use of trait data, with seamless piping of data from one tool to
the next. OTN contributors have already developed several open source tools. For
instance, the traitdataform package assists R users to format their data and harmonise
units (; the code for the Coral Traits
database40 ( could be easily modified to guide the
creation of databases on other organisms; and the FENNEC project provides a tool for
accessing and viewing community trait data as a self-hosted website service71
( The OTN acts as a connector for interactions
between developers and the broader community seeking to synthesise trait data,
facilitating the training of scientists in all aspects of reproducible data management.
Activity 3: Advocating for a free flow of data and appropriate recognition of efforts
A goal of the OTN is to improve how researchers receive credit, via citations, for the
effort they have made to collect or synthesise primary data on species traits. Without
effective reward or motivation for collecting new trait observations or liberating legacy
data (e.g., observations from field guides, specimens, publications without data
supplements) a broad trait synthesis across the Tree of Life will remain unattainable.
Currently, however, motivation for collecting and sharing new primary data is not
The OTN can strengthen the attribution of credit to data providers via two paths.
Firstly, by encouraging citation back to primary source via CC-BY licensing. There is an
important distinction between sharing data within a network and making data publicly
available under an open license. Clear license arrangements increase visibility and
promote fair attribution/citation (e.g., using creative commons licenses such as CC-BY
or CC0). CC-BY requires attribution (i.e., citation) to the original creator, whereas CC0
doesn’t legally require users of the data to cite the source, but it does not affect the ethical
norms for attribution in scientific and research communities
( However,
identifying where credit for prior work should be directed for legacy data is complicated,
particularly where data involve a chain of expertise (e.g., when trait data are extracted
from taxonomic treatments which involve specimen collectors, digitisers, taxonomists,
and curators).
Secondly, the OTN Trait Registry (Activity 1) can be used to identify high-value
data gaps, helping would-be collectors of primary data on traits to have studies pre-
registered via the Centre for Open Science ( Already as of
March 2019, 168 journals are willing to give in-principle acceptance before field or
experimental work is conducted but following pre-review of the study design.
Approximately ten of these participating journals regularly feature papers on trait-based
science, including BMC Ecology and Ecology and Evolution.
Activity 4: Advocating a common metadata standard across nodes
Given the highly contextual nature of trait data, metadata are as important as the
measurements themselves. The OTN provides a platform for the development of
metadata standards, controlled vocabularies, and a suite of trait ontologies which can be
recorded in the OTN Registry (Activity 1). Several initiatives have developed metadata
standards (e.g., Darwin Core64; Humboldt Core58; Ecological Metadata Language72).
However, these metadata standards are yet to be commonly applied in trait-based data
publications and syntheses. Using referencing terms from anatomy or phenotype
ontologies (e.g., the Plant Ontology73; the Vertebrate Trait Ontology74) relates traits
semantically to publicly-defined terms and allows data thus annotated to be processed
computationally75-77. Over time, the further development and implementation of
metadata standards in the OTN will help to avoid downstream issues in data re-use and
Table 1. Structure of the OTN global registry of trait-based initiatives 1
Field name
A unique identifier for each dataset
Location of the dataset or database on the
internet, e.g. the database website or the address
of the dataset on a file hosting service
Corresponding author or maintainer for the
dataset or database (name)
Characters separated by vertical bar space
| (if multiple names)
Corresponding author or maintainer for the
dataset or database (email address)
Characters by vertical bar space | (if
multiple names)
A legal document giving official permission to do
something with the resource
(; see
List of traits. Names should correspond to
existing controlled vocabulary (when applicable)
Characters separated by vertical bar space
| (if multiple traits)
A list (concatenated and separated) of geographic
names less specific than the information captured
in the locality term. (from:
Characters separated by vertical bar space
| (if multiple traits)
The geographic latitude (in decimal degrees, using
the spatial reference system given in
geodeticDatum) of the geographic centre of a
Location. Positive values are north of the
Equator, negative values are south of it. Legal
Decimal degrees
values lie between -90 and 90, inclusive.
The geographic longitude (in decimal degrees,
using the spatial reference system given in
geodeticDatum) of the geographic center of a
Location. Positive values are east of the
Greenwich Meridian, negative values are west of
it. Legal values lie between -180 and 180,
Decimal degrees
A group of organisms (sensu
considered by taxonomists to form a
homogeneous unit. (see
Characters separated by vertical bar space
| (if multiple groups)
The date-time or interval during which an Event
occurred. For occurrences, this is the date-time
when the event was recorded. Not suitable for a
time in a geological context. (from
Use a date that conforms to ISO
8601:2004E (see
Immutable document object identifier of the
dataset or database; or actual citation (if
collection of multiple datasets, DOIs or citations
must appear in metadata)
Description of the dataset or any other useful
List of all taxa with some trait information
Characters separated by vertical bar space
| (if multiple taxa)
Any other useful information for improved
searching (e.g., life stage, body part, inferred traits
Automated data consolidation (e.g., Darwin Core
or Ecological Trait-Data Standard)
Links to scripts for data standardization
Character (using a standard that can be
Activity 5: Facilitating consistent approaches to measuring traits within major groups 4
The OTN will advocate for the development of protocols and handbooks for major 6
clades that standardise approaches to capture traits. Protocols are necessary because 7
downstream activities such as developing metadata standards (Activity 4) will be 8
impossible to create if trait measurement protocols do not exist. Some trait-research 9
communities have already adopted standardised lists of terms66 and trait data collection 10
protocols (e.g., plants28,56,78-80, invertebrates37,55,81,82, mammals44, aquatic life38,40 11 New protocols and handbooks may not 12
emerge rapidly and should have the flexibility to be open to innovation through a 13
commitment to creating versions and updates as techniques evolve (e.g., from83 to56, or as 14
in 15
Standardising approaches to trait measurement a priori across communities of 16
researchers will reduce ambiguity when aggregating data and improve the quality of 17
resulting datasets. Also, integrating trait standardization and databasing in to taxonomic 18
workflows represents both a considerable challenge and opportunity8, but also holds the 19
promise of bridging a long disconnect between structural and functional traits. The 20
presence of a range of biodiversity collections personnel in the OTN, and an open 21
invitation for more to join, has the potential to catalyse the adoption of trait-based 22
thinking into taxonomic practices. 23
Concluding remarks 25
The creation of an open, trait-based view of global diversity is now possible given the 26
myriad tools and approaches to data mobilisation and aggregation, harmonisation, and 27
processing. These advances in technology are also accompanied by numerous emerging 28
opportunities to work with institutions seeking to deliver biodiversity information to the 29
public, and with citizen scientists84 keen to gather trait data through platforms like (e.g., 30
Zooniverse; iNaturalist 31
The OTN aims at supporting a reciprocal exchange of expertise and outputs between 32
researchers, institutions, and citizen scientists based on Open Science principles to 33
accelerate a cross-taxa, worldwide, trait-based data resource to examine, understand, and 34
predict nature’s responses. 35
Ideas presented stem from initial discussions at three separate international meetings 38
the Australian National Climate Change Adaptation Research Facility Roundtable on 39
Species Traits, the iDigBio ALA Traits workshop, and a preliminary Open Traits 40
workshop held at the Ecological Society of America. RVG is supported by an Australian 41
Research Council DECRA Felllowship (DE170100208). DSF is supported by an 42
Australian Research Council Future Felllowship (FT160100113). WDP is supported by 43
NSF ABI-1759965, NSF EF-1802605, and USDA Forest Service agreement 18-CS-44
11046000-041. CMI was supported by the Biological and Environmental Research 45
program in the United States Department of Energy’s Office of Science. MJ was 46
supported by the German Research Foundation within the framework of the Jena 47
Experiment (FOR 1451) and by the Swiss National Science Foundation. AK received 48
financial support for MJA by the German Research Foundation (DFG KE1743/7-1). 49
STM is supported by SERDP project RC18-1346. CP is supported by the DFG Priority 50
Program 1374. 51
There are no original data associated with this manuscript. 54
Box 1: Potential research programs that could be carried out with comprehensive
trait data across the Tree of Life
A multi-kingdom analysis of adult size exploring mechanistic constraints and
ecological correlates.
Whole-ecosystem or multi-trophic analyses of common traits which influence
function (e.g., relating traits to ecosystem processes, such as how differences in
traits and migration patterns in birds can influence soil nutrient cycling).
Prediction of community assembly processes across time and space (e.g., from
deep-time via the fossil record to present day human-dominated systems).
Reserve selection optimised for protecting function (e.g., reserve design in both
marine and terrestrial realms based functional attributes of the broad community
of organisms).
Developing better Earth System models, biophysical niche models, and process-
based mortality models through the integration of trait data.
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4 Adler, P. B. et al. Functional traits explain variation in plant life history strategies. 71
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... Functional ecology requires standardized and reproducible classification schemes to characterize species' niches [76][77][78]. Rather than relying on expert opinion for the assignment of trophic groups, which often results in variable assignments, we demonstrate that the categorization of discrete trophic guilds and pairwise trophic interactions can be achieved with a quantitative, reproducible framework grounded in empirical data across biogeographic regions. We employed network analysis to partition 535 tropical coral reef fish species into 8 trophic guilds based on a synthesis of globally distributed, community-wide fish dietary analyses, and then we applied a Bayesian phylogenetic model that predicts trophic guilds based on phylogeny and body size, attaining a 5% misclassification error. ...
... Trophic guilds are among the most commonly applied trait to assess ecosystem functioning because they directly relate to energy and nutrient fluxes across trophic levels. Thus, our standardized framework represents a major step forward for coral reef functional ecology, while heeding the call for openly accessible, reproducible trait databases [31, 78,101]. As trait-based ecology continues to be used to examine disturbances and implement management strategies, our cohesive and accessible framework can provide key insights into the trajectory of coral reef communities. ...
... Further, our results can serve as the foundation for an online platform that permits researchers to collate, update, and utilize trait-based data on coral reef fishes. Similar to current initiatives across the entire tree of life [78], the creation of an online, user-maintained dietary database will facilitate collaboration and traceability in trait-based reef fish research. One challenge will lie in merging visual fish gut content analysis databases with molecular data, such as gut content DNA metabarcoding (e.g., [83]), and biochemical data, such as stable isotope analysis (e.g., [102]), and short-chain fatty acid profiles (e.g., [103]), which indicate nutritional assimilation rather than the simple ingestion of prey items [81]. ...
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Understanding species’ roles in food webs requires an accurate assessment of their trophic niche. However, it is challenging to delineate potential trophic interactions across an ecosystem, and a paucity of empirical information often leads to inconsistent definitions of trophic guilds based on expert opinion, especially when applied to hyperdiverse ecosystems. Using coral reef fishes as a model group, we show that experts disagree on the assignment of broad trophic guilds for more than 20% of species, which hampers comparability across studies. Here, we propose a quantitative, unbiased, and reproducible approach to define trophic guilds and apply recent advances in machine learning to predict probabilities of pairwise trophic interactions with high accuracy. We synthesize data from community-wide gut content analyses of tropical coral reef fishes worldwide, resulting in diet information from 13,961 individuals belonging to 615 reef fish. We then use network analysis to identify 8 trophic guilds and Bayesian phylogenetic modeling to show that trophic guilds can be predicted based on phylogeny and maximum body size. Finally, we use machine learning to test whether pairwise trophic interactions can be predicted with accuracy. Our models achieved a misclassification error of less than 5%, indicating that our approach results in a quantitative and reproducible trophic categorization scheme, as well as high-resolution probabilities of trophic interactions. By applying our framework to the most diverse vertebrate consumer group, we show that it can be applied to other organismal groups to advance reproducibility in trait-based ecology. Our work thus provides a viable approach to account for the complexity of predator–prey interactions in highly diverse ecosystems.
... These data are needed to monitor long-term processes, including those related to climate change and non-linear effects of human impacts (see e.g. frameworks proposed by Rund et al., 2019 andGallagher et al., 2019 for initiatives applicable to sandy beach ecology). ...
... Finally, we 3) identify the phylogenetic clades and geographical regions for which we have different amounts of trait and genetic data, allowing us to contrast the clades and parts of the world for which we have relatively poor and broad coverage. While plant scientists may all agree that there are gaps in our knowledge of plants (Meyer et al. 2016, Gallagher et al. 2019, Saatkamp et al. 2019, they are unlikely to know where the most gaping holes occur (as found in systematic surveys of botanical trait knowledge; FitzJohn et al. 2014). By highlighting the clades and regions in most urgent need of attention, we hope to turn what appears an insurmountable task into a manageable checklist of gaps to be filled. ...
The era of big biodiversity data has led to rapid, exciting advances in the theoretical and applied biological, ecological and conservation sciences. While large genetic, geographic and trait databases are available, these are neither complete nor random samples of the globe. Gaps and biases in these databases reduce our inferential and predictive power, and this incompleteness is even more worrisome because we are ignorant of both its kind and magnitude. We performed a comprehensive examination of the taxonomic and spatial sampling in the most complete current databases for plant genes, locations, and functional traits. To do this, we downloaded data from The Plant List (taxonomy), the Global Biodiversity Information Facility (locations), TRY (traits) and GenBank (genes). Only 17.7% of the world's described and accepted land plant species feature in all three databases, meaning that more than 82% of known plant biodiversity lacks representation in at least one database. Species coverage is highest for location data and lowest for genetic data. Bryophytes and orchids stand out taxonomically and the equatorial region stands out spatially as poorly represented in all databases. We have highlighted a number of clades and regions about which we know little functionally, spatially and genetically, on which we should set research targets. The scientific community should recognize and reward the significant value, both for biodiversity science and conservation, of filling in these gaps in our knowledge of the plant tree of life. This article is protected by copyright. All rights reserved.
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Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET,, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.
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Aquatic ecologists face challenges in identifying the general rules of the functioning of ecosystems. A common framework, including freshwater, marine, benthic, and pelagic ecologists, is needed to bridge communication gaps and foster knowledge sharing. This framework should transcend local specificities and taxonomy in order to provide a common ground and shareable tools to address common scientific challenges. Here, we advocate the use of functional trait-based approaches (FTBAs) for aquatic ecologists and propose concrete paths to go forward. Firstly, we propose to unify existing definitions in FTBAs to adopt a common language. Secondly, we list the numerous databases referencing functional traits for aquatic organisms. Thirdly, we present a synthesis on traditional as well as recent promising methods for the study of aquatic functional traits, including imaging and genomics. Finally, we conclude with a highlight on scientific challenges and promising venues for which FTBAs should foster opportunities for future research. By offering practical tools, our framework provides a clear path forward to the adoption of trait-based approaches in aquatic ecology. © 2020 The Authors. Limnology and Oceanography published by Wiley Periodicals LLC. on behalf of Association for the Sciences of Limnology and Oceanography.
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Interspecific competition, a dominant process structuring ecological communities, acts on species' phenotypic differences. Species with similar traits should compete intensely (trait-similarity), while those with traits that confer competitive ability should outcompete others (trait-hierarchy). Either or both of these mechanisms may drive competitive exclusion within a community, but their relative importance and interacting effects are rarely studied. We show empirically that spatial associations (pairwise co-occurrences) between an invasive ant Solenopsis invicta and 28 other ant species across a relatively homogenous landscape are explained largely by an interaction of trait-similarity and trait-hierarchy in one morphological trait. We find that increasing trait-hierarchy leads to more negative associations; however these effects are counteracted when species are sufficiently dissimilar (by 37-95%) in their trait ranges. We also show that a model of species co-occurrences integrating trait-similarity and trait-hierarchy consolidates predictions of different theoretical assembly rules. This highlights the explanatory potential of the trait-based co-occurrence approach.
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1.Trait‐based approaches are widespread throughout ecological research as they offer great potential to achieve a general understanding of a wide range of ecological and evolutionary mechanisms. Accordingly, a wealth of trait data is available for many organism groups, but this data is underexploited due to a lack of standardisation and heterogeneity in data formats and definitions. 2.We review current initiatives and structures developed for standardising trait data and discuss the importance of standardisation for trait data hosted in distributed open‐access repositories. 3.In order to facilitate the standardisation and harmonisation of distributed trait datasets by data providers and data users, we propose a standardised vocabulary that can be used for storing and sharing ecological trait data. We discuss potential incentives and challenges for the wide adoption of such a standard by data providers. 4.The use of a standard vocabulary allows for trait datasets from heterogeneous sources to be aggregated more easily into compilations and facilitates the creation of interfaces between software tools for trait‐data handling and analysis. By aiding decentralised trait‐data standardisation, our vocabulary may ease data integration and use of trait data for a broader ecological research community and enable global syntheses across a wide range of taxa and ecosystems. This article is protected by copyright. All rights reserved.
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Essential Biodiversity Variables (EBV) are fundamental variables that can be used for assessing biodiversity change over time, for determining adherence to biodiversity policy, for monitoring progress towards sustainable development goals, and for tracking biodiversity responses to disturbances and management interventions. Data from observations or models that provide measured or estimated EBV values, which we refer to as EBV data products, can help to capture the above processes and trends and can serve as a coherent framework for documenting trends in biodiversity. Using primary biodiversity records and other raw data as sources to produce EBV data products depends on cooperation and interoperability among multiple stakeholders, including those collecting and mobilising data for EBVs and those producing, publishing and preserving EBV data products. Here, we encapsulate ten principles for the current best practice in EBV-focused biodiversity informatics as ‘The Bari Manifesto’, serving as implementation guidelines for data and research infrastructure providers to support the emerging EBV operational framework based on trans-national and cross-infrastructure scientific workflows. The principles provide guidance on how to contribute towards the production of EBV data products that are globally oriented, while remaining appropriate to the producer's own mission, vision and goals. These ten principles cover: data management planning; data structure; metadata; services; data quality; workflows; provenance; ontologies/vocabularies; data preservation; and accessibility. For each principle, desired outcomes and goals have been formulated. Some specific actions related to fulfilling the Bari Manifesto principles are highlighted in the context of each of four groups of organizations contributing to enabling data interoperability - data standards bodies, research data infrastructures, the pertinent research communities, and funders. The Bari Manifesto provides a roadmap enabling support for routine generation of EBV data products, and increases the likelihood of success for a global EBV framework.
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The Gene Ontology resource (GO; provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the 'GO ribbon' widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.
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1.Functional traits are widely recognised as a useful framework for testing mechanisms underlying species community assemblage patterns and ecosystem processes. Functional trait studies in the plant and animal literature has burgeoned in the past 20 years, highlighting a need for standardised ways to measure ecologically meaningful traits across taxa and ecosystems. However, standardised measurements of functional traits are lacking for many organisms and ecosystems, including fungi. 2.Basidiomycete wood fungi occur in all forest ecosystems worldwide, where they are decomposers and also provide food or habitat for other species, or act as tree pathogens. 3.Despite their major role in the functioning of forest ecosystems, the understanding and application of functional traits in studies of communities of wood fungi lags behind other disciplines. As the research field of fungal functional ecology is growing, there is a need for standardised ways to measure fungal traits within and across taxa and spatial scales. 4.This handbook reviews pre‐existing fungal trait measurements, proposes new core fungal traits, discusses trait ecology in fungi, and highlights areas for future work on basidiomycete wood fungi. 5.We propose standard and potential future methodologies for collecting traits to be used across studies, ensuring replicability and fostering between‐study comparison. Combining concepts from fungal ecology and functional trait ecology, methodologies covered here can be related to fungal performance within a community and environmental setting. 6.This manuscript is titled ‘a start with’ as we only cover a subset of the fungal community here, with the aim of encouraging and facilitating the writing of handbooks for other members of the macrofungal community, e.g. mycorrhizal fungi. This article is protected by copyright. All rights reserved.
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Motivation: The Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade‐offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained: The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain: Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub‐Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain: All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement: Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format: csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database ( to be included in the next version release.
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Essential Biodiversity Variables (EBVs) allow observation and reporting of global biodiversity change, but a detailed framework for the empirical derivation of specific EBVs has yet to be developed. Here, we re-examine and refine the previous candidate set of species traits EBVs and show how traits related to phenology, morphology, reproduction, physiology and movement can contribute to EBV operationalization. The selected EBVs express intra-specific trait variation and allow monitoring of how organisms respond to global change. We evaluate the societal relevance of species traits EBVs for policy targets and demonstrate how open, interoperable and machine-readable trait data enable the building of EBV data products. We outline collection methods, meta(data) standardization, reproducible workflows, semantic tools and licence requirements for producing species traits EBVs. An operationalization is critical for assessing progress towards biodiversity conservation and sustainable development goals and has wide implications for data-intensive science in ecology, biogeography, conservation and Earth observation.
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Data needed for macroecological analyses are difficult to compile and often hidden away in supplementary material under non-standardized formats. Phylogenies, range data, and trait data often use conflicting taxonomies and require ad hoc decisions to synonymize species or fill in large amounts of missing data. Furthermore, most available data sets ignore the large impact that humans have had on species ranges and diversity. Ignoring these impacts can lead to drastic differences in diversity patterns and estimates of the strength of biological rules. To help overcome these issues, we assembled PHYLACINE, The Phylogenetic Atlas of Mammal Macroecology. This taxonomically integrated platform contains phylogenies, range maps, trait data, and threat status for all 5,831 known mammal species that lived since the last interglacial (~130,000 years ago until present). PHYLACINE is ready to use directly, as all taxonomy and metadata are consistent across the different types of data, and files are provided in easy-to-use formats. The atlas includes both maps of current species ranges and present natural ranges, which represent estimates of where species would live without anthropogenic pressures. Trait data include body mass and coarse measures of life habit and diet. Data gaps have been minimized through extensive literature searches and clearly labelled imputation of missing values. The PHYLACINE database will be archived here as well as hosted online so that users may easily contribute updates and corrections to continually improve the data. This database will be useful to any researcher who wishes to investigate large-scale ecological patterns. Previous versions of the database have already provided valuable information and have, for instance, shown that megafauna extinctions caused substantial changes in vegetation structure and nutrient transfer patterns across the globe.
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1.Species composition assessment of ecological communities and networks is an important aspect of biodiversity research. Yet often ecological traits of organisms in a community are more informative than scientific names only. Furthermore, other properties like threat status, invasiveness, or human usage are relevant for many studies, but cannot be evaluated from taxonomy alone. Despite public databases collecting such information, it is still a tedious manual task to enrich community analyses with such, especially for large‐scaled data. 2.Thus we aimed to develop a public and free tool that eases bulk trait mapping of community data in a web browser, implemented with current standard web and database technologies. 3.Here we present the Fennec, a workbench that eases the process of mapping publicly available trait data to the user's communities in an auto mated process. Usage is either by a local self‐hosted or a public instance ( covering exemplary traits. Alongside the software we also provide usage and hosting documentation as well as online tutorials. 4.The Fennec aims to motivate public trait data submission and its reuse in meta‐analyses. Further, it is an open‐source development project with the code freely available to use and open for community contributions ( This article is protected by copyright. All rights reserved.
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1. Trait-based approaches are widespread throughout ecological research, offering great potential for trait data to deliver general and mechanistic conclusions. Accordingly, a wealth of trait data is available for many organism groups, but, due to a lack of standardisation, these data come in heterogeneous formats. 2. We review current initiatives and infrastructures for standardising trait data and discuss the importance of standardisation for trait data hosted in distributed open-access repositories. 3. In order to facilitate the standardisation and harmonisation of distributed trait datasets, we propose a general and simple vocabulary as well as a simple data structure for storing and sharing ecological trait data. 4. Additionally, we provide an R-package that enables the transformation of any tabular dataset into the proposed format. This also allows trait datasets from heterogeneous sources to be harmonised and merged, thus facilitating data compilation for any particular research focus. 5. With these decentralised tools for trait-data harmonisation, we intend to facilitate the exchange and analysis of trait data within ecological research and enable global syntheses of traits across a wide range of taxa and ecosystems.
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We developed new methods for parameter estimation-in-context and, with the help of 125 authors, built the AmP (Add-my-Pet) database of Dynamic Energy Budget (DEB) models, parameters and referenced underlying data for animals, where each species constitutes one database entry. The combination of DEB parameters covers all aspects of energetics throughout the full organism’s life cycle, from the start of embryo development to death by aging. The species-specific parameter values capture biodiversity and can now, for the first time, be compared between animals species. An important insight brought by the AmP project is the classification of animal energetics according to a family of related DEB models that is structured on the basis of the mode of metabolic acceleration, which links up with the development of larval stages. We discuss the evolution of metabolism in this context, among animals in general, and ray-finned fish, mollusks and crustaceans in particular. New DEBtool code for estimating DEB parameters from data has been written. AmPtool code for analyzing patterns in parameter values has also been created. A new web-interface supports multiple ways to visualize data, parameters, and implied properties from the entire collection as well as on an entry by entry basis. The DEB models proved to fit data well, the median relative error is only 0.07, for the 1035 animal species at 2018/03/12, including some extinct ones, from all large phyla and all chordate orders, spanning a range of body masses of 16 orders of magnitude. This study is a first step to include evolutionary aspects into parameter estimation, allowing to infer properties of species for which very little is known.
Ecology has joined a world of big data. Two complementary frameworks define big data: data that exceed the analytical capacities of individuals or disciplines or the “Four Vs” axes of volume, variety, veracity, and velocity. Variety predominates in ecoinformatics and limits the scalability of ecological science. Volume varies widely. Ecological velocity is low but growing as data throughput and societal needs increase. Ecological big-data systems include in situ and remote sensors, community data resources, biodiversity databases, citizen science, and permanent stations. Technological solutions include the development of open code- and data-sharing platforms, flexible statistical models that can handle heterogeneous data and sources of uncertainty, and cloud-computing delivery of high-velocity computing to large-volume analytics. Cultural solutions include training targeted to early and current scientific workforce and strengthening collaborations among ecologists and data scientists. The broader goal is to maximize the power, scalability, and timeliness of ecological insights and forecasting. © The Author(s) 2018. Published by Oxford University Press on behalf of the American Institute of Biological Sciences. All rightsreserved.