Accounting for habitat structural complexity improves the assessment of performance in no-take marine reserves

Abstract

Seascape variability may confound assessments on the effectiveness of no-take marine reserves (NTMRs) in conserving biodiversity. In most cases baseline data are lacking, resulting in evaluations of NTMR effectiveness being Control Impact (CI) assessments. Even with independent replicate areas among management zones, this approach can make it difficult to detect zone effects if seascape attributes, such as habitat structural complexity varies among experimental areas. To determine the importance of structural complexity in evaluations of NTMR effectiveness we performed assessments on the abundance of a targeted fish, yellowtail kingfish (Seriola lalandi), in the Lord Howe Island Marine Park (LHIMP). We compared assessments which did and did not account for structural complexity, quantified using high resolution multibeam bathymetry. Despite almost 3 times more S. lalandi in NTMRs, the traditional CI assessment explained only 3% of the variation in the abundance of S. lalandi and revealed no clear effect of protection. Incorporating structural complexity into the assessment increased the deviance explained to 65% and uncovered an important interaction between zone and structural complexity. Greater abundances of S. lalandi were detected in NTMRs compared to fished zones but only on highly complex reefs. By accounting for structural complexity, we demonstrate that the precision and accuracy of NTMR assessments can be improved, leading to a better understanding of ecological change in response to this conservation strategy. Consequently, where marine park zones vary greatly in structural complexity, we strongly advocate for quantifying and accounting for such variability in assessments of NTMR performance. HIGHLIGHTS 22 No-take marine reserve assessments were compared with and without habitat structure 23  Abundances of yellowtail kingfish were greater in NTMRs but only on complex reef 24  Including habitat structure increased deviance explained from 3% to 65% 25  This reduced the standard error of mean abundances in NTMRs by 40% 26  Assessment precision & accuracy improved, providing robust outcomes for 27 management 28 29 ABSTRACT 30Seascape variability may confound assessments on the effectiveness of no-take marine 31 reserves (NTMRs) in conserving biodiversity. In most cases baseline data are lacking, resulting in 32 evaluations of NTMR effectiveness being Control Impact (CI) assessments. Even with independent 33 replicate areas among management zones, this approach can make it difficult to detect zone effects if 34 seascape attributes, such as habitat structural complexity varies among experimental areas. To 35 determine the importance of structural complexity in evaluations of NTMR effectiveness we 36 performed assessments on the abundance of a targeted fish, yellowtail kingfish (Seriola lalandi), in 37 the Lord Howe Island Marine Park (LHIMP). We compared assessments which did and did not 38 account for structural complexity, quantified using high resolution multibeam bathymetry. Despite 39 almost 3 time...

Seascape topography slows predicted range shifts in fish under climate change

“…For example, rates of redistribution in the poleward edge of core environmental habitat for species with the strongest associations with topographic habitat (bonito and kingfish) were reduced by 60.4 and 58.3 km per decade, respectively, while a reduction of 7.1 km per decade was identified for dolphinfish, which had a weak association with seascape topography. These findings are supported by the positive effect of topographic features on the relative abundance of bonito and kingfish found by Hobday and Campbell (2009) and Rees et al (2018), and suggest that pelagic fishes that do not exhibit these habitat preferences may be undergoing more rapid range shifts than fishes that actively associate with topographic features. Thus, the effect of topographic habitat on pelagic fish distributions may be an important driver of variation in rates of climatedriven range shifts, which may manifest in punctuated rather than gradual shifts in species distributions depending on the presence or absence of topographic features throughout the seascape.…”

A review of seascape complexity indices and their performance in coral and rocky reefs

“…These mostly include Rugosity , VerRelief, depth heterogeneity indices , FractalDim , different substrate hole properties (amount, size, density etc.) and visual estimation (Almany, 2004; Ferreira et al., 2001; Nash et al., 2013; Rees et al., 2018; Walker et al., 2009; Wilson et al., 2007). We found that more than half of the studies reviewed incorporated only one index to quantify complexity, and that the use of several indices remains relatively low (Figure 3).…”

Taking a deeper look: Quantifying the differences in fish assemblages between shallow and mesophotic temperate rocky reefs

“…The swath acoustic data were beneficial in selecting reefs to sample, but they can also be used to derive metrics that can possibly predict the spatial distribution of species richness, species of interest and all fishery target species pooled together. The derivation of habitat metrics from swath acoustic data can provide various levels of explanatory or predictive ability relating to fish assemblage composition and distribution [ 27 , 31 , 34 , 78 , 79 ]. In this study, rugosity, slope or relief were selected in the ‘best’ models explaining the variability in many aspects of the fish assemblages that were sampled.…”

“…Temperate mesophotic reefs have important biodiversity, social and economic values [ 16 , 25 ], so understanding the characteristics of their associated fish assemblages is fundamental to effectively managing them. Habitat type (coral, sponge, bare) and complexity (relief, rugosity, curvature) are known to be important in structuring fish assemblages [ 26 – 31 ]. Habitat complexity is considered as the variance in surface structure of the reef and can be defined in terms of relief, slope, rugosity, surface area, and other factors [ 28 , 32 ].…”