We congratulate the authors for their comprehensive review of the statistical summary characteristics for marked spatial point processes. These characteristics are valuable for understanding spatial dependencies between points, marks, and points and marks. It is essential that summary characteristics are developed and made available for more general marks, such as object-valued marks, and for non-Euclidean spaces, such as linear networks.To add to the already impressive list of summary characteristics, we would like to mention the work Rajala and Illian ( 2012), where they studied indices for patterns with a high component count (e.g. rainforest data), including the mingling function mentioned here. There are also some third-order point process characteristics available for multitype point processes (Ayala and Simó 2020; Comas et al. 2010). Moreover, the authors call for characteristics to inspect local behaviour of marks. We note that the mark sum measures, as considered in Illian et al. (2008) and Myllymäki (2009), may be viewed as simple local characterisations of the potentially spatially varying mark distribution.Marked summary characteristics play a pivotal role in the preliminary data analysis of marked point patterns. To interpret the information contained in a chosen characteristic, it is necessary to compare the empirical characteristic to its counterpart under a specific hypothesis, such as random labelling or random superposition as considered by the authors. In addition to the analysis performed by the authors, formal statistical tests, e.g. the traditional deviation tests (Diggle 2013;Myllymäki et al. 2015) and the global envelope tests, which aid in interpreting the test results through a graphical interpretation (Myllymäki et al. 2017;Myllymäki and Mrkvička 2023), can be performed. Here, when testing hypotheses with complex statistical characteristics, it is worth bearing in mind that even the very simplest This is a commentary to the article