Some optimal designs for sampling in two dimensions

Bellhouse, David R.

doi:10.1093/biomet/64.3.605

Cited by 70 publications

(26 citation statements)

References 4 publications

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“…Because spatial sampling theory suggests that hyperdispersed sampling design (e.g., a square lattice of sample locations) best captures spatial variability in a landscape (Quenouille 1949, Das 1950, Bellhouse 1977, Olea 1984, we constructed protected area systems in one of two ways: selecting planning units at random to protect, and maximizing hyperdispersion by placing protected areas with equal spacing between them. We used r ¼ 0.2 and c ¼ 0.1 throughout all of the simulations.…”

Section: Square Species Distributionsmentioning

confidence: 99%

The role of scale in designing protected area systems to conserve poorly known species

2015

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Possingham. 2015. The role of scale in designing protected area systems to conserve poorly known species. Ecosphere 6(11):237. http://dx.doi.org/10.1890/ES15-00346.1Abstract. Systematic conservation planning has a substantial theoretical underpinning that allows optimization of tradeoffs between biodiversity conservation and other socioeconomic goals. However, this theory assumes perfect spatial information about the locations of biodiversity features (e.g., species distributions). In practice, planners represent well-known taxa and other biodiversity ''surrogates'' in protected area systems, hoping that unmapped species will also be conserved. However, empirical research finds that surrogates predict species presence imperfectly, and sometimes rather poorly, at scales relevant to planning, and existing theory provides no further guidance. We developed new theory, explicitly incorporating aspects of spatial scale, for the representation problem when the locations of species distributions are unknown. Using probability theory and simulated and real species distributions, we found that the probability of adequately representing an unmapped species in a protected area system will be low unless the total fraction of the region being protected is larger than the species representation target. Furthermore, successful conservation depended critically on the relative sizes of the species distribution and of the individual protected areas; fewer, larger protected areas allowed the entire species distribution to fall into an unprotected gap. This scale-dependence varied with the configuration of the protected area system, with the conservation objective most likely to be attained if the individual protected areas were hyperdispersed (evenly spaced across the planning region). Using these results, we developed three design principles for representing unmapped species in protected areas: (1) The fraction of the region placed in protected areas should be substantially larger than the species-level representation target; (2) Individual protected areas must be at least one to two orders of magnitude smaller than the unmapped species' distribution; and (3) Protected areas should be evenly dispersed over geographic space. We also performed preliminary investigations of the effects of surrogates and socio-economic cost data on the probability of adequately representing unmapped species, finding that the primary effect of surrogates may simply be to promote hyperdispersion of protected areas across the planning region, and that seeking to minimize opportunity costs gives poorer conservation results than random protected area placement.

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Section: Square Species Distributionsmentioning

confidence: 99%

The role of scale in designing protected area systems to conserve poorly known species

2015

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“…Statistical studies (Madow and Madow 1944, Cochran 1946, Yates 1948, Matérn 1960, Bellhouse 1977 concurred that systematic sampling schemes are more precise in autocorrelated populations. Some of this work (Madow andMadow 1944 , Cochran 1946 ) was done assuming linear populations were to be sampled, such as an alphabetic list of households to be surveyed by telephone.…”

Section: Introductionmentioning

confidence: 98%

Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms

McGarvey¹,

Burch²,

Matthews³

2015

Ecological Applications

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“…See e.g. Quenouille (1949), Bellhouse (1977), Iachan (1985) and Stevens and Olsen (2004). We shall, however, not assume an ordering of rows or columns and shall not refer further to this literature.…”

Section: Introductionmentioning

confidence: 99%