Realising the promise of large data and complex models

Abstract

In an era of rapid change, ecologists are increasingly asked to provide answers to big, urgent questions of global concern (Solé & Levin, 2022; Sutherland et al., 2013; Yates et al., 2018). Concurrently, technological advances allow ecological data to be collected at increasingly high resolutions (e.g. temporal and/or spatial scales), leading to both new types of data and larger datasets becoming available (Farley et al., 2018). These data provide the opportunity to investigate new, and even previously unanswerable, questions, including those concerning animal movements (Nathan et al., 2022) and those addressing conservation and sustainability issues (Runting et al., 2022). Increasingly, realistic models need to be developed and fitted to these data (Fer et al., 2018), pushing the boundaries of the type and intricacy of questions that can be explored (Niu et al., 2020). However, big data and big models can lead to big troubles across multiple aspects, from storing and processing the data to fitting of complex models to data and interpreting the output. Close collaborations between ecologists, statisticians, mathematical modellers, computer scientists and other disciplines offer exciting ways forward to solve these problems, leading to mutually beneficial advancements. For example, computer scientists can aid in the efficient storage and extraction of data, and the development of new algorithms; statisticians can help and guide ecologists in the analysis of data, fitting complex models to the data via efficient computational algorithms and propagating or quantifying uncertainties throughout the process; mathematicians can ensure models are constructed in the most suitable fashion for the specific questions asked and demonstrate suitable properties (such as realistic territorial ranges or population predictions); and ecologists can guide mathematical scientists on the biological characteristics of the systems studied and ecological interpretation of the corresponding results, thus informing future models and influencing policy decisions. The need to answer important ecological questions is unprecedented, with declines in biodiversity and ecosystem services which will impact our ability to meet Sustainable Development Goals (Reyers & Selig, 2020), and it is through interdisciplinary collaborations that the biggest steps forward can be made. Data analysis challenges arise across the full data analytic pipeline, including processing and visualising the data, developing ecologically relevant and interpretable models to fit to the data, adapting the associated algorithms to fit models to data efficiently and obtaining meaningful interpretations of the output. In practice, there are often many trade-offs between these different aspects due to the challenges that arise during the data analysis pipeline. For example, within the initial processing of the data, decisions may need to be made regarding cleaning the data (e.g. to remove recorded data errors) or the summarised form of the processed data to report (e.g. the temporal and/or spatial scale). This itself can be challenging and there will often be uncertainty within the process, leading to potential new errors being introduced. The decisions made will typically impact the model fitted to these data. For example, for motion-sensor camera trap data, there may be a trade-off between the level of initial data processing (i.e. the level of advanced tools used for uniquely identifying individuals via, e.g. machine learning techniques) and associated models that may be fitted to incorporate the amount of uncertainty in the preprocessed data (e.g. from assuming no error in the matches; to incorporating matching uncertainty; to allowing for both marked and unmarked individuals). Alternatively, complex models often require computationally intensive algorithms for them to be fitted to the data, which may not scale as datasets increase in size. This may lead to the consideration of a simpler model that can be more easily fitted, thus reducing the level of fine-detail that may be extracted from the data; or adaptations to the model-fitting process such as using some form of approximate model-fitting approach that aims to be robust to the approximations used, but potentially could lead to biased parameter estimates. This Special Feature provides a combination of review papers and scientific articles that address one or more of the challenges of modern day analyses of large and/or complex ecological data. Echoing the challenges facing the discipline, we present these in the natural statistical cycle, starting with the challenges of new types of data, to the limitations of statistical models and associated algorithms (and computer packages) used to fit the models to the data to the interpretation and presentation of the corresponding model outputs. We consider each of the themes identified in turn relating to (i) data; (ii) statistical models and model-fitting; and (iii) visualisation and interpretation. However, we also emphasise that these are very closely interlinked and although we have used these coarse ‘pigeonholes’, there are many overlapping aspects and challenges. Ecology, like environmental sciences and other branches of biology, has entered into an era of big data, with enormous possibilities for a better understanding of environmental state (Runting et al., 2022). Data can be ‘big’ due to different characteristics. The ‘Four Vs Framework’ (see discussion in Farley et al. (2018) and references therein) discuss four distinct aspects: (1) volume: quantity of data (2) velocity: time-varying data; (3) variety: multiple data types with complex relationships; and (4) veracity: trustworthiness of the data. These different aspects often do not occur in isolation, leading to multiple intricate data challenges when analysing ecological data. We highlight just some of the problems and approaches to address specific associated ‘V’ challenges that the authors of the papers within this Special Feature have encountered and discussed. Biologging sensor technologies have been at the forefront of creating large volumes of available data, frequently at a range of different scales. Thus, the analysis of biologging data is often pioneering within ecology in relation to big data, with the potential to rapidly transform our understanding of the ecology, particularly in their application to animal movements (Nathan et al., 2022; Williams et al., 2020). A key limitation of most current systems, however, is the trade-off between collecting ultra-fine sub-second scale movement and behaviour data over shorter periods of time vs. more coarse but longer-term movement and space use data. Wild et al. (2023) take advantage of rapid developments in the field of the Internet of Things (i.e. methods for attaching electronic sensor devices, connected to a network, to everyday objects) to overcome key limitations in current biologging data networking systems and present new Wi-Fi solutions, combined with smart embedded software, for big biologging data. The authors are able to demonstrate orders of magnitude of improvement in data retrieval efficiency, which is the biggest limitation of animal biologging systems. In particular, Wild et al. (2023) discuss in detail challenges and solutions concerning software architecture, on-board processing of biologging sensor data, difficulties of time synchronisation and the data transmission concept and the pros and cons of different Wi-Fi infrastructures. Advances in technology has also led to (perhaps less foreseen) forms of data gathering mechanisms gaining momentum, and associated build-up of large quantities of data, with the rise of citizen (or community) science initiatives. The resulting data from such initiatives are typically very varied in nature, often involving multiple data collection protocols with more limited/reduced structure than compared to traditional survey methods, including data arising from opportunistic events. While analysing citizen science data from designed surveys requires carefully developed methods, difficulties increase markedly with data from semi-structured projects, for example without fixed data collection protocols or data collected by observers of any degree of observer knowledge. This leads to new challenges across the whole spectrum of the 4 ‘V's. While these challenges have some commonality in terms of similar issues to address and overcome, due to the large expanse of types of data collection techniques, the specific challenges and associated data analytic approaches will vary. Johnston et al. (2023) summarise four overarching categories of challenges: (i) observer behaviour, including, for example spatial bias, observer or reporting differences, and false-positive errors; (ii) data structures, relating to both measures of detectability and procedures for validation; (iii) statistical models, including not only the opportunities provided by data integration and multispecies models but also sources of bias and computational limitations; and (iv) communication, motivated by the application of citizen science within biodiversity monitoring. The veracity of data within biodiversity also arises in less obvious ways, outside the sphere of data collection protocols ‘in the field’, which are most commonly considered as the reason for querying the trustworthiness of the data. In particular, there is a wealth of information contained with many ecological and biodiversity databases. However, to combine this information, data must typically be uniquely associated with specific species and taxa. This in itself raises methodological challenges, due to, for example dynamic species names, the discovery of new species, changing biological attributes, etc. As a result, homonyms, synonyms and errors may accumulate while for many taxa a general consensus on an accepted name and taxonomic and phylogenetic relationships may not have been reached so that taxonomy itself may resemble a confusingly intricate tangled bank. To address such issues, Grenié et al. (2023) provide an extensive review of the tools, databases and best practices for harmonising taxon names in biodiversity studies. In particular, they categorise the ‘wild world’ of existing publicly available taxonomic databases and resources, along the axes of taxonomic breadth and spatial scope, and discuss the associated strengths and caveats of each database. In addition, on the practical computation side, they review the existing computational tools provided in different R packages for taxonomic harmonisation, and, perhaps rather fittingly, provide a ‘taxonomy’ of the R packages, classifying them according to their associated functions. A vast array of different statistical models have been developed and fitted to ecological data in the last decade or so (Guisan et al., 2017; Hooten et al., 2017; Kery & Royle, 2016; MacKenzie et al., 2018; McCrea & Morgan, 2015; Royle et al., 2014; Schaub & Kéry, 2021), often with limited critical review of the characteristics and associated disadvantages and challenges of each. The advancement in models and associated model-fitting tools reflect the changing quantity of the data (as highlighted above), quality of the data (e.g. increased spatial/temporal resolution), emerging forms of data from new technologies (e.g. earth observation and/or drone data, eDNA) and advanced computational techniques (and associated computational power). Thus, summary overviews of these emerging and advancing areas are important and timely for ecologists and statisticians to be able to understand what can, and often importantly, what cannot (or should not), be done and also provide tools for fitting such models to different data. These models encompass all areas of ecology from population and community ecology to landscape and ecosystem ecology. Interrogation of the associated modelling ideas motivates further advances in addressing the challenges and model development to account for additional data complexities or efficient model-fitting tools, for example. We briefly summarise here some of the types of models and associated challenges that arise across a range of different types of models, and data, within this Special Feature. Developing or adapting general statistical models that can be applied to different forms of data can be very scientifically efficient. Such approaches also often permit the use of readily available software packages, for example NIMBLE (de Valpine et al., 2017), R-INLA Lindgren and Rue (2015) and inlabru (Bachl et al., 2019) as well as specific application focused packages, such as MARK/RMARK (for capture–recapture models; Laake, 2013); momentuHMM (for hidden Markov models [HMMs] applied to movement data; McClintock & Michelot, 2018) and Distance (for distance sampling; Thomas et al., 2010). Areas which have accessible software are witnessing substantial statistical development, enhanced by the flexibility of the computational tools provided. For example, R-INLA and inlabru have been used by both Laxton et al. (2023) and Torney et al. (2023), while Newman et al. (2023) discusses the relative merits of available software tools for fitting models. However, et al. (2023) take one further from the of readily accessible computer packages, that model fitting is not the rather that the models being used by ecologists need to be considered as models, which can be used and easily datasets or statistical of the provides a by which these important challenges can be the between such general statistical models and specific ecological models can be as can be the data into the general models that have been applied within ecological models are the closely and models these types of models are used in ecological in the of data et al., McClintock et al., of these models within the ecological is that they both the distinct ecological and/or This often the model the consideration of the A between these models to the are to be (for or although we that this is not ecological areas these models have been but are from limited to, et al., population et al., animal movement et al., 2017; et al., et al., and surveys 2014; McCrea & Morgan, et al. (2023) and Newman et al. (2023) provide a methodological (and review of and In particular, et al. (2023) highlight the potential difficulties that may be encountered when for different systems, including issues which arise when model are not and the challenges of and fitting a suitable model in an when the hidden process in of these general statistical models that can be applied to a of different forms of ecological data and associated discussion of issues to be of are a very for particularly when the that may The rapid of the application of has also been by associated efficient model-fitting due to the structure of the model et al., The practical issues of fitting general and assuming a ecological process, is highlighted and by Newman et al. they discuss and a of model-fitting techniques, on the of the In particular, they model-fitting algorithms that can more complex modelling such as and/or Such models are less within the ecological most due to the associated model-fitting challenges, such adaptations of have potential for the modelling of ecological data. The important of what software can be used to fit such complex models is also highlighted in the The challenges of fitting models to data can concern both the associated algorithms (as for and the increase in computational particularly as the of the model increasingly large such as those collected in or biologging (see Wild et al., many methods and cannot be is a to and suitable to computational and adapting traditional (and developing methods to big data. and the associated that most including for example, computational et al., and as in et al. as well as model that the within the data, are The challenges that arise regarding due to large (and datasets are also an opportunity for the development and use of machine learning However, algorithms may not be or may be as by et al. (2023), so additional developments may be for ecological For example, it will be important to incorporate ecological within the data are and trade-offs in complex models to increase on the ecological For example, Laxton et al. (2023) use the very species models to highlight the of model on ecological The authors the of a marked process which the of key population dynamic to ecological to landscape structure and the range of movements of the and highlight the of an understanding of the and of each model to ensure and ecological Alternatively, Torney et al. (2023) in relation to the of movement behaviour, including complex mechanisms animal into the statistical models can increase model and they demonstrate that the between model and model is the of robust procedures for models. is to fit a wealth of complex models to but is the between fitting a model for and the is for an understanding of the by the In many could a model be more Such questions are in many including ecology models to be developed to the data ecological these will be a of more complex models to meaningful and interpretable ecological In there is a trade-off between the of the model being fitted and the associated intricacy of the information that can be extracted suitable and available statistical learning (or machine techniques are rapidly in their and within ecology & 2022; & with such techniques often but at the of ecologically interpretable is becoming important to interpretable and meaningful from models fitted to data, combined with within and the scientific for example, with of ecology in which model leads to further challenges is that of such models have been used to a between a species and the that it in to an understanding of or to the of environmental However, there has been for these models to a species in and to between species et al., et al., and/or the mechanisms et al., in to of multispecies models. However, in the of the the associated of the model can more To address this et al. (2023) use a approach to community structure within modelling approaches to the practical of multispecies models. the of to it is to of not only which models but also this is the within more complex dynamic Laxton et al. (2023) that any increased in the model to be in ecological This in turn the different mechanisms or of each of the model can be identified leading to increased interpretable ecological As models and data more complex and high obtaining meaningful and of the data and/or model for also more methods, such as and may lead more and/or intricate ecological being or even lead to in their presentation et al., 2014; & This is particularly challenging in more complex structures, such as or For example, visualisation should allow to an understanding of the structure of and provide into the detail of the however, current approaches to the structure and cannot provide the To address some of these challenges, and (2023) methods for visualising increasingly complex (and other by and Alternatively, et al. (2023) have developed the which to efficiently and landscape and more issues arise when to that multiple distinct that the (e.g. when analysing rather than this Thomas et al. (2023) a on consensus analysis to be able to summarise and in they also provide an R to permit and impact within the large scientific The opportunities for gaining an understanding of ecological systems from the range of different forms of available data (and new emerging are However, to on these addressing the associated challenges and and a approach the whole data analytic is We discuss a of important aspects that will to advancing ecological and address important issues we that this is from an in the ecological approach has the potential for within the The of for example ecologists, data collection statisticians advanced modelling techniques to the available data and survey and computer scientists in machine learning and provides the opportunity for the of new and exciting approaches to address challenging ecological Close with mathematical ecologists a better realistic of models to ecological important is the with ecologists at the model to that the are is important to ensure that data analytic methods are being developed to the most of the and of ecological data being efficiently collected at scale and quantity et al., However, the advancement of data collection technology at a rapid and the associated data analytic tools are developed at a is no in developing analytic tools for data that do not and/or cannot be an interdisciplinary will help identifying data collection tools and methods not used in ecology. has been a natural development datasets within a model in et al., both multiple data types of a species et al., and data from multiple species & This that one of the biggest challenges facing statistical ecologists is to the types of data being combined in an analysis are they have quality and will this the model For example, will datasets with large data, for example from the help to the bias in the or the in the et al., This to the to provide In particular, we this to the that being able to fit complex statistical models to data through advances in associated not that the models are (or for the data. is a need to consider the of fit a model to a and it is and/or the ecological of and available data. in should model or methods Such approaches are to have an important in the future of methods in the ecological & particularly when is a However, such methods should not be applied to with is important that there is a methodological their The of such models is more challenging due to the of the algorithms and of ecological or for example. and uncertainty in the and best practices of these approaches particularly in relation to and is a need to increase into machine learning and approaches so that their can be for ecology and For example, understanding from carefully fitted and machine learning methods could be more often also used to guide the development of new This is an increasingly of statistical The type of software ranges from general statistical packages to which ecological models and data analyses can be (such as inlabru et al., or NIMBLE Valpine et al., 2017), to packages for very specific problems et al., However, the of computer packages (and in different such as R or or leads to additional challenges of identifying the most relevant and/or efficient for the at regarding the and disadvantages of different approaches is a particularly often as there may be many different data and decisions in The of for addressing and the challenges of citizen science data are highlighted in Johnston et al. In particular, the authors on the of new statistical methods the limited of This requires also in software development and and also that a of data analysis may the by the of data collection through citizen science and help the most of these data, which has to be one of the most issues facing statistical ecologists at this current The papers in this Special Feature only the of the challenges present with large data and complex models, and some approaches for with different issues and our ecological These areas of will to provide a and of challenges for ecological but the challenges, interdisciplinary data analytic and interpretable will ensure the by this community will full leading to in our ecological and be a for policy This from and at the of by the for Ecology, and the and Special in in is by The review for this is available at

Topics & Concepts