The MacKinnon Lists Technique: An efficient new method for rapidly assessing biodiversity and species abundance ranks in the marine environment

lines. In this study, the effort needed to analyse video footage to calculate relative abundance and species richness was similar for both methods (one-person hour per 60 min video). When measuring species richness and relative abundance, both methods require little technology and are comparable in terms of time required for analysis. Therefore, both methods are likely to be feasible options in environments where survey costs, staff time, availability of technology and training is limited. In a snorkelling and diving context, the MLT may allow for a faster and more standardized sampling approach, without the challenge of considering time restrictions, swimming speed or transect length, therefore making it a much simpler approach that is easier to implement in a standardised manner. In a real-world context, areas of conservation importance often lack expertise and equip- ment to fully assess fish community composition. MLT has been shown to generate consistent relative abundance estimates across a range of personnel experience [34,37]. We suggest that using MLT in the marine environment could allow personnel with a lack experience or scien- tific support to focus on being able to confidently identify species of key conservation impor- tance in the field, rather than on the more complex methodological requirements of other techniques. This should then enable such observers to help assess the spatial and temporal Table 6. Comparison of ability of MaxN and MTL methods to detect significant effects on community composition with lower sampling effort. PERMANOVA

results of square root transformed relative abundance data generated by MaxN and MLT. Significant values are highlighted in bold. The full experimental design was reduced to five videos for all habitats. By reducing the sample size of the fished sites at both depths to five, maintaining ROA samples at five, following by reducing fished and ROA video deployments to three and ultimately two. P(MC) denotes Monte Carlo permutations. Significant values are highlighted in bold. Video/ habitat MaxN MLT 5 Source df MS Ps-F P(MC) Source df MS Ps-F P(MC) Status 1 5923.7 3.8 0.014 St 1 7841.9 2.3 0.010 Depth 1 7087.4 4.1 0.010 De 1 8569.1 3.5 0.001 Site(Status) 6 1537.6 0.8 0.711 Si(St) 5 2972.8 0.9 0.613 StatusxDepth 1 3503.2 2.0 0.096 StxDe 1 4324.5 1.9 0.028 DepthxSite(Status) 5 1738.1 0.9 0.593 DexSi(St) 5 2101.8 0.7 0.966 Residuals 4 1902.7 Vi(Si(St)xDe) 7 3338.0 1.2 0.089 Total 18 Res 61 2710.4 Total 81 3 Source df MS Ps-F P(MC) Source df MS Ps-F P(MC) Status 1 2932.3 2.5 0.086 St 1 4864.6 2.8 0.005 Depth 1 5683.7 3.0 0.072 De 1 10760.0 8.2 0.001 Site(Status) 3 1134.0 0.5 0.842 Si(St) 2 1480.2 0.4 0.991 StatusxDepth 1 4273.6 2.2 0.121 StxDe 1 6526.5 5.9 0.001 DepthxSite(Status) 3 1858.9 0.9 0.613 DexSi(St) 2 836.0 0.2 0.999 Residuals 2 2185.7 Vi(Si(St)xDe) 4 3808.9 1.4 0.049 Total 11 Res 41 2751.6 Total 52 2 Source df MS Ps-F P(MC) Source df MS Ps-F P(MC) Status 1 2976.4 2.7 0.202 St 1 3214.4 2.0 0.119

Depth 1 5159.4 2.5 0.233 De 1 6017.1 3.6 0.017 Site(Status) 1 1023.0 0.5 0.697 Si(St) 1 1713.1 0.4 0.923 StatusxDepth 1 2349.7 1.4 0.413 StxDe 1 3365.5 2.5 0.053 DepthxSite(Status) 1 1693.2 0.8 0.538 DexSi(St) 1 1400.3 0.4 0.961 Residuals 2 2185.7 Vi(Si(St)xDe) 2 4375.9 1.5 0.065 Total 7 Res 26 2833.6 Total 33 https://doi.org/10.1371/journal.pone.0231820.t006 PLOS ONE A new method for monitoring marine biodiversity PLOS ONE | https://doi.org/10.1371/journal.pone.0231820 April 22, 2020 11 / 15