Global relationships between language range area, language range extent, language richness, and latitude.

<p>(A) Changes in median language-range area (determined with mid-point method with 2 degree bands) at different latitudes (Correlation coefficient (using absolute latitude and Spearman's rank correlation): 0.63) (B) Changes in median range extent (determined with mid-point method and 2-degree lat bands) of language at different latitudes (Correlation coefficient (using absolute latitude and Spearman's rank correlation): 0.69) (C) Latitudinal gradient in language richness (number of languages per 2-degree latitude band) (D) Median language-range area versus log-language richness.</p

Spearman rank correlations between language-range sizes and latitude and language richness.

<p>All correlations in boldface type significant at P<.01; otherwise significant at P<.05; ns, nonsignificant.</p><p>Spearman rank correlations between language-range sizes and latitude and language richness.</p

Language-range area distribution.

<p>(A) Untransformed range areas (note: y-axis is log(frequency)). (B) Log-10 transformed range areas. (C) Deviation from normal following log-10 transformation.</p

Distribution of the world's languages.

<p>Each point represents the center of the range of one language.</p

A New Approach to Identifying the Drivers of Regulation Compliance Using Multivariate Behavioural Models

<div><p>Non-compliance with fishing regulations can undermine management effectiveness. Previous bivariate approaches were unable to untangle the complex mix of factors that may influence fishers’ compliance decisions, including enforcement, moral norms, perceived legitimacy of regulations and the behaviour of others. We compared seven multivariate behavioural models of fisher compliance decisions using structural equation modeling. An online survey of over 300 recreational fishers tested the ability of each model to best predict their compliance with two fishing regulations (daily and size limits). The best fitting model for both regulations was composed solely of psycho-social factors, with social norms having the greatest influence on fishers’ compliance behaviour. Fishers’ attitude also directly affected compliance with size limit, but to a lesser extent. On the basis of these findings, we suggest behavioural interventions to target social norms instead of increasing enforcement for the focal regulations in the recreational blue cod fishery in the Marlborough Sounds, New Zealand. These interventions could include articles in local newspapers and fishing magazines highlighting the extent of regulation compliance as well as using respected local fishers to emphasize the benefits of compliance through public meetings or letters to the editor. Our methodological approach can be broadly applied by natural resource managers as an effective tool to identify drivers of compliance that can then guide the design of interventions to decrease illegal resource use.</p></div

Model fit indices for models tested to explain fisher compliance with regulations.

<p>Model fit indices for models tested to explain fisher compliance with regulations.</p

Location of the Marlborough Sounds, New Zealand [29].

<p>The boundaries of the Marlborough Sound’s recreational blue cod fishery are indicated by shading and the study sites are also shown. New Zealand map sourced from LINZ (Crown Copyright Reserved) and Marlborough Sounds map modified from the Ministry of Primary Industries [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163868#pone.0163868.ref030" target="_blank">30</a>].</p

Overview of seven behavioural models of fisher compliance decisions.

<p>Overview of seven behavioural models of fisher compliance decisions.</p

Model of pro-environmental behaviour from Bamberg and Möser [21].

<p>Model of pro-environmental behaviour from Bamberg and Möser [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163868#pone.0163868.ref021" target="_blank">21</a>].</p

Graphical output of the selected best-fitting model for fishers’ compliance with the size limit.

<p>Numerical values on arrows are standardized regression coefficients (<i>β</i>) and values in the top right of ovals representing the constructs are coefficients of determination (R²).</p