Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology

Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology Janine B. Illian 0 1 David F. R. P. Burslem 0 1 0 School of Biological Sciences, University of Aberdeen , Aberdeen , UK 1 School of Mathematics and Statistics, University of Saint Andrews , St Andrews , UK The last few decades have seen an increasing interest and strong development in spatial point process methodology, and associated software that facilitates model fitting has become available. A lot of this progress has made these approaches more accessible to users, through freely available software. However, in the ecological user community the methodology has only been slowly picked up despite its obvious relevance to the field. This paper reflects on this development, highlighting mutual benefits of interdisciplinary dialogue for both statistics and ecology. We detail the contribution point process methodology has made to research on biodiversity theory as a result of this dialogue and reflect on reasons for the slow take-up of the methodology. This primarily concerns the current lack of consideration of the usability of the approaches, which we discuss in detail, presenting current discussions as well as indicating future directions. 1 Introduction 1.1 Point process models in the modern world In the past, complex statistical methods were not directly accessible to the applied user. Data analyses that required statistical approaches beyond those covered in introductory statistics textbooks would be done using software tailor-made for the specific application by an expert. Nowadays, freely available, sophisticated software packages such as R are in common use, and at the same time—in these days of Big Data—increasing amounts of data are collected (Tippmann 2015; Decan et al. 2015) and new journals have emerged to communicate advances in statistical methods to non-statistical audiences. As a result, users have both a stronger need for data analysis and an increasing awareness of the existence of the advanced methodology since it is now less “hidden” from them in inaccessible statistical journals. Hence, in ecology, in particular, it is now relatively common for non-specialists to use advanced statistical methodology and associated software without direct support from a statistician (Zuur et al. 2010). Some might argue that this is a retrograde step and to avoid misuse, complex statistical methods should only be used by experts. However, this argument has been largely pre-empted by the high demand for new statistical methods and their widespread uptake among user communities—as a result researchers outside statistics are already using complex methods, and higher education institutions are expanding their provision of statistical training for undergraduate and postgraduate biologists. Rather than worrying about the misuse of methods, we argue that statisticians should consider the usability of methodology an important part of method development. The issue is not misuse of methodology or the ignorance of the user community—we cannot possibly expect them to become experts as well, considering how long it has taken each one of us to get where we are. The issue is rather that methods have often been developed from the point of view of a statistician, and little thought has gone into their relevance and the practicality of applying them. We outline in this paper how we believe that it is possible to improve the “usability” of methodology, i.e. its suitability for use by non-experts, by considering the relevance and practicability of statistical methodology through an ongoing interdisciplinary dialogue. Communicating practical advice in an accessible way is an important component of statistical method development. More specifically, while it is now relatively common to publish an R-library associated with a specific statistical development, we still need to appreciate that making statistical methods accessible to the research community involves not only the development of methodology and providing the software but also considering the practicalities of using it and communicating these details in a style that is accessible to intended users. Making model fitting feasible by developing computationally efficient approaches to reduce running times is clearly a step in the right direction (Pélissier and Goreaud 2015) . However, enhancing the practicality of modelling requires not only improvements to model fitting. The practicality of the entire statistical analysis from model construction via prior choice to interpretation is equally relevant for users with real data sets and real scientific questions. Similarly, the increasing complexity of the methodology not only makes model fitting more complex, but also implies that all steps in a statistical analysis become more complex. An effective impact on the practicalities of statistical analysis hence requires an understanding of what th (...truncated)