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Hammill, E., & Clements, C. F. (2020 Hammill, E. , & Clements, C. F. (2020). Imperfect detection alters the outcome of management strategies for protected areas. Ecology Letters, 23(4), 682-691. https://doi.org/10.1111/ele.13475 Peer reviewed version Link to published version (if available): 10.1111/ele.13475 Link to publication record in Explore Bristol Research PDF-document This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Wiley at https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13475. Please refer to any applicable terms of use of the publisher. University of Bristol - Explore Bristol Research General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/ 1 Imperfect detection alters the outcome of management strategies for protected areas 2 Edd Hammill1 and Christopher F. Clements2 3 1Department of Watershed Sciences and the Ecology Center, Utah State University, 5210 Old 4 Main Hill, Logan, UT, USA 5 2School of Biological Sciences, University of Bristol, Bristol, BS8 1TQ, UK 6 Statement of Authorship. The experiment was conceived by EH following multiple 7 conversations with CFC. EH conducted the experiment and ran the analyses relating to species 8 richness, probability of predators, and number of extinctions. CFC designed and conducted all 9 analyses relating to sampling protocols. EH wrote the first draft of the MS, both authors 10 contributed substantially to editing the MS. 11 Running title: Patch configuration versus sampling regime 12 Correspondence to Edd Hammill - [email protected], 435 265 5946, Department of 13 Watershed Sciences, 5210 Old Main Hill, Logan, UT, USA, 84322 14 Abstract - 149 15 Main body text – 4988 16 Tables - 0 17 Figures – 5 18 References – 64 19 Supplementary Materials - Appendix S1 20 Keywords: SLOSS, FLOMS, protected areas, predators, experiments, protected area design 21 22 Statement on data accessibility 23 Data have been submitted to the Dryad Data repository, DOI 24 https://doi.org/10.5061/dryad.stqjq2c0d 25 26 27 Abstract 28 Designing protected areas configurations to maximize biodiversity is a critical conservation goal. 29 The configuration of protected areas can significantly impact the richness and identity of the 30 species found there; one large patch supports larger populations but can facilitate competitive 31 exclusion. Conversely, many small habitats spreads risk but may exclude predators that typically 32 require large home ranges. Identifying how best to design protected areas is further complicated 33 by monitoring programs failing to detect species. Here we test the consequences of different 34 protected area configurations using multi-trophic level experimental microcosms. We 35 demonstrate that for a given total size, many small patches generate higher species richness, are 36 more likely to contain predators, and have fewer extinctions compared to single large patches. 37 However, the relationship between the size and number of patches and species richness was 38 greatly affected by insufficient monitoring, and could lead to incorrect conservation decisions, 39 especially for higher trophic levels. 40 41 Introduction 42 The designation of protected areas remains at the heart of conservation efforts (Watson et al. 43 2016). When designing protected areas, managers are tasked with deciding whether greater 44 outcomes may be achieved by investing in a small number of large, or a large number of small 45 protected areas (Diamond 1975). A small number of large protected areas may allow species to 46 exist at higher population abundances, thereby reducing extinction risk (Mccarthy et al. 2011). 47 This issue of home range may be especially true of apex predators, which may require larger 48 home ranges than species in lower trophic levels (Mcnab 1963; Fauvelle et al. 2017). However, 49 having a small number of protected areas may increase stochasticity, demographic and 50 otherwise, potentially decreasing the number of species protected through extinctions (Whittaker 51 & Fernández-Palacios 2007). In addition, a small number of large protected areas may increase 52 the chance that the whole network becomes dominated by a few highly competitive species 53 (Schippers et al. 2015). Given the potentially negative outcomes for a small number of large 54 protected areas, managers may instead opt for a large number of smaller protected areas (Higgs 55 & Usher 1980; Simberloff & Abele 1982; Wintle et al. 2019). However, a large number of 56 smaller locations may be unable to support species with large resource requirements, and runs 57 contrary to principles from species-area relationships and the theory of island biogeography 58 (Diamond 1975; Tjørve 2010). The question of “single/few large” or “several/many small” (The 59 SLOSS or FLOMS debate) has received a substantial amount of attention in the decades since its 60 inception, but whether greater biodiversity outcomes are achieved through a small number of 61 large protected areas, or a large number of small protected areas remains unresolved. It therefore 62 becomes critical to test the consequences of different protected area configurations using a 63 replicated, manipulable system that contains a suite of species embedded in a complex, 64 multitrophic foodweb. 65 Having sufficient information relating to the distribution of species is critical when 66 making any decisions about the importance of different habitat patches (Maxwell et al. 2015). 67 Insufficient sampling may fail to detect species when they are present, leading to biased 68 estimates of site occupancy (Mackenzie et al. 2003; Loehle & Weatherford 2017). In addition, 69 monitoring efforts should be designed so as to achieve sufficient temporal and spatial coverage 70 to understand the state of the system (Legg & Nagy 2006; Rhodes & Jonzén 2011). For example, 71 while targeted monitoring may provide information as to whether or not a particular species of 72 interest is present, it may fail to detect species that exist in non-targeted locations, leading to an 73 underestimate of total biodiversity (Nichols & Williams 2006; Nielsen et al. 2009). Given the 74 difficulties associated with monitoring wild systems, understanding whether better conservation 75 outcomes are achieved through the designation of few large or several small protected areas 76 becomes difficult. It therefore becomes necessary to test how allocating protected areas using 77 imperfect information affects conservation outcomes compared to those generated using perfect 78 information. 79 Laboratory-based systems provide an ideal arena to test questions relating to the 80 SLOSS/FLOMS debate, and the issue of monitoring, as populations of multiple interacting 81 species can be assembled and closely monitored. One system used to test broad ecological 82 concepts is that based on freshwater ciliate protozoa due to their small size, ease of manipulation, 83 and high replicability (Drake & Kramer 2012; Altermatt et al. 2015). In addition, the global 84 biomass of protists has been estimated to be double that of the animal kingdom (Bar-On et al. 85 2018), highlighting their ecological relevance. The short generation times of the species used 86 means that multi-generational data can be rapidly collected, making the system ideally suited for 87 studies on population biology (Lawler & Morin 1993; Hammill et al. 2015), community ecology 88 (Clements et al. 2013a; Carrara et al. 2015), metacommunity dynamics (Holyoak & Lawler 89 2005; Resetarits et al. 2018), biodiversity-ecosystem function experiments (DeLong & Gibert 90 2019), and conservation issues (Fryxell et al. 2006; Benton et al. 2007). The protist system can 91 also contain complex food webs, consisting of up to four trophic levels that incorporate 92 omnivory and specialization (Forbes and Hammill 2013, Fig 1). Protist microcosms represent an 93 excellent system to address the SLOSS/FLOMS question as customized microcosms of a range 94 of different sizes can be easily and accurately produced. The individual microcosms can then be 95 combined to produce portfolios of microcosms that are representative of suites of protected areas 96 that have no connectivity, a situation present in ~50% of global protected areas (Santini et al. 97 2016; Saura et al. 2018). 98 We performed an experiment to test how the configuration of simulated protected areas 99 performs in terms of the amount of species represented, and how different monitoring protocols 100 affect our ability to detect what species were present. The experiment utilized custom designed 101 protist microcosms of varying volumes that were inoculated with nine species. Each microcosm 102 (or patch) was disconnected from each other, and is intended to represent a single, isolated 103 protected area. Individual patches were periodically sampled to assess the abundances of the 104 species present. These patch-level data were then used to construct protected area portfolios 105 (analogous to "experimental landscapes" used in Pasari et al. 2013; Hammill et al. 2018) that 106 allowed us to investigate whether for a given total area, better biodiversity outcomes were 107 achieved through few large or many small patches. Protected area portfolios were produced by 108 combining (without replication) the abundance data for between 1 and 8 of the patches, and the 109 number
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