Heidi van Deventer

Knowledge co-production and boundary work to promote implementation of conservation plans

Knowledge co-production and boundary work offer planners a new frame for critically designing a social process that fosters collaborative implementation of resulting plans. Knowledge co-production involves stakeholders from diverse knowledge systems working iteratively toward common vision and action. Boundary work is a means of creating permeable knowledge boundaries that satisfy the needs of multiple social groups while guarding the functional integrity of contributing knowledge systems. Resulting products are boundary objects of mutual interest that maintain coherence across all knowledge boundaries. We examined how knowledge co-production and boundary work can bridge the gap between planning and implementation and promote cross-sectoral cooperation. We applied these concepts to well-established stages in regional conservation planning within a national scale conservation planning project aimed at identifying areas for conserving rivers and wetlands of South Africa and developing an institutional environment for promoting their conservation. Knowledge co-production occurred iteratively over 4 years in interactive stake-holder workshops that included co-development of national freshwater conservation goals and spatial data on freshwater biodiversity and local conservation feasibility; translation of goals into quantitative inputs that were used in Marxan to select draft priority conservation areas; review of draft priority areas; and packaging of resulting map products into an atlas and implementation manual to promote application of the priority area maps in 37 different decision-making contexts. Knowledge co-production stimulated dialogue and negotiation and built capacity for multi-scale implementation beyond the project. The resulting maps and information integrated diverse knowledge types of over 450 stakeholders and represented >1000 years of collective experience. The maps provided a consistent national source of information on priority conservation areas for rivers and wetlands and have been applied in 25 of the 37 use contexts since their launch just over 3 years ago. When framed as a knowledge co-production process supported by boundary work, regional conservation plans can be developed into valuable boundary objects that offer a tangible tool for multi-agency cooperation around conservation. Our work provides practical guidance for promoting uptake of conservation science and contributes to an evidence base on how conservation efforts can be improved.

Reducing Leaf-Level Hyperspectral Data to 22 Components of Biochemical and Biophysical Bands Optimizes Tree Species Discrimination

The high dimensionality of hyperspectral data constitutes a challenge for species classification. This study assessed 1) whether tree species classification can be optimized with the selection of bands which relate to known plant properties and 2) whether a partial least square (PLS) transformation improve species classification above principal component analysis (PCA). Leaf spectra between 400 and 2500 nm were measured for six evergreen tree species in the spring of 2011, in the KwaZulu-Natal Province of South Africa. Twenty-two bands which relate to pigment, foliage biomass, nutrients, and leaf structural components were selected from the hyperspectral data set. The 2100 bands of 1 nm were resampled to 421 bands at 5 nm spectral resolution, ensuring the number of variables are less than the number of samples. The random forest (RF) classification algorithm was used to assess the accuracy for both PCA and PLS transformations on the 421 and 22 bands. The accuracy of individual species classes was calculated as the average of ten iterations, for each data reduction option. The three 22-band models resulted in comparable accuracies to the 421-band classifications (OA of 84 ± 4.9% for untransformed, 78 ± 5% for PCA, and 84 ± 4% for PLS) and no significant differences between the 421 and 22-band models (p > 0.4). The optimized PLS model (22 bands, 8 components) showed a 6% (p <; 0.01) increase in accuracy above the optimized PCA model (22 bands, 3 components). Reducing hyperspectral data to bands which relate to plant properties, and the use of PLS for data transformation, optimizes species classification.