Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)—mental models that graphically represent causal relationships between a system’s factors—are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure—finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect—possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored

How theory can help to understand the potential impact of food environment policies on socioeconomic inequalities in diet: an application of Bourdieu's capital theory and the scarcity theory.

Government policies that promote healthy food environments are considered promising to reduce socioeconomic inequalities in diet. Empirical evidence of effects on these inequalities, however, is relatively scarce and, with a few exceptions, tends to be inconclusive. We use two contemporary theories that help to understand socioeconomic inequalities in health and health-related behaviours (Bourdieu&#8217;s capital theory and Mullainathan and Shafir&#8217;s scarcity theory) to reason how policies influencing food environments may differentially impact lower and higher socioeconomic groups. In essence, these theories enable us to understand how specific elements of broader daily living conditions (e.g. social practices that lead to habitus formation, material conditions that shape experiences of scarcity) may lead to a greater benefit of certain food environment policies for the healthfulness of diets of lower or higher socioeconomic groups. We conclude that the application of theories on the mechanisms underlying socioeconomic inequalities in health can help to guide future empirical studies in testing theory-based hypotheses on differential effects of policies, and thereby enhance the development of effective policies tackling socioeconomic inequalities in dietary&nbsp;intakes.</p

Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data

In our paper "Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data" (Global Ecology and Biogeography) we use GPS tracking data from 1,498 from 49 different species to evaluate the expert-based habitat suitability data from the International Union for Conservation of Nature (IUCN). Therefore, we used the GPS tracking data to estimate two measures of habitat suitability for each individual animal and habitat type: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN’s classification into suitable, marginal and unsuitable habitat types. Our results showed that IUCN habitat suitability data were in accordance with the GPS data (>95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a >50% probability of agreement based on proportional habitat use and selection ratios, respectively. These findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, our study shows that GPS tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data. In this dataset we provide the measures of habitat suitability for each individual and each habitat type, calculated using different methods. In addition, we provide data on the body mass and IUCN Red List category of the species, as well as whether the species can be considered a habitat specialist or habitat generalist

Data of "Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data"

In our paper "Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data" (Global Ecology and Biogeography) we use GPS tracking data from 1,498 from 49 different species to evaluate the expert-based habitat suitability data from the International Union for Conservation of Nature (IUCN). Therefore, we used the GPS tracking data to estimate two measures of habitat suitability for each individual animal and habitat type: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN’s classification into suitable, marginal and unsuitable habitat types. Our results showed that IUCN habitat suitability data were in accordance with the GPS data (>95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a >50% probability of agreement based on proportional habitat use and selection ratios, respectively. These findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, our study shows that GPS tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data. In this dataset we provide the measures of habitat suitability for each individual and each habitat type, calculated using different methods. In addition, we provide data on the body mass and IUCN Red List category of the species, as well as whether the species can be considered a habitat specialist or habitat generalist