Is what you see what you get? Visual vs. measured assessments of vegetation condition

Same Viewpoint Different Perspectives—A Comparison of Expert Ratings with a TLS Derived Forest Stand Structural Complexity Index

Forests are one of the most important terrestrial ecosystems for the protection of biodiversity, but at the same time they are under heavy production pressures. In many cases, management optimized for timber production leads to a simplification of forest structures, which is associated with species loss. In recent decades, the concept of retention forestry has been implemented in many parts of the world to mitigate this loss, by increasing structure in managed stands. Although this concept is widely adapted, our understanding what forest structure is and how to reliably measure and quantify it is still lacking. Thus, more insights into the assessment of biodiversity-relevant structures are needed, when aiming to implement retention practices in forest management to reach ambitious conservation goals. In this study we compare expert ratings on forest structural richness with a modern light detection and ranging (LiDAR) -based index, based on 52 research sites, where terrestrial laser scanning (TLS) data and 360° photos have been taken. Using an online survey (n = 444) with interactive 360° panoramic image viewers, we sought to investigate expert opinions on forest structure and learn to what degree measures of structure from terrestrial laser scans mirror experts’ estimates. We found that the experts’ ratings have large standard deviance and therefore little agreement. Nevertheless, when averaging the large number of participants, they distinguish stands according to their structural richness significantly. The stand structural complexity index (SSCI) was computed for each site from the LiDAR scan data, and this was shown to reflect some of the variation of expert ratings (p = 0.02). Together with covariates describing participants’ personal background, image properties and terrain variables, we reached a conditional R2 of 0.44 using a linear mixed effect model. The education of the participants had no influence on their ratings, but practical experience showed a clear effect. Because the SSCI and expert opinion align to a significant degree, we conclude that the SSCI is a valuable tool to support forest managers in the selection of retention patches.

Measuring the accuracy of management effectiveness evaluations of protected areas

Evaluating the effectiveness of protected area management to help improve management outcomes is becoming an increasingly common practice. The evaluation tools developed and implemented in over 100 countries generally rely on the expert judgements of protected area managers. Despite the growing collection and use of management effectiveness evaluation data, there have been no previous attempts to measure the accuracy of these data. We measured the accuracy of managers' judgements about the conditions in their reserves by collecting independent field data. We also assessed how accurately the evaluation tool reflected managers' views by conducting semi-structured interviews with 23 protected area managers from New South Wales, Australia. We found that managers made highly accurate judgements of the extent of a common weed species, Rubus fruticosus (blackberry), but often misinterpreted the scope, scale and timeframe of the evaluation. These framing effects can lead to error being introduced into the evaluation dataset, affecting the precision of evaluations such that they cannot be reliably compared among reserves. We suggest that the wording of evaluation questions needs to be explicit about the assessment frame to minimize the influence of framing effects on management effectiveness evaluations.