A theory for ecological survey methods to map individual distributions

Economo

2020

Ecography

Self Cite

Pattern in space and time is central to ecology, and adequately designed ecological sampling is needed to resolve those patterns, pursue ecological questions and design conservation strategies. Recently, there has been an explosion of various ecological data due to the proliferation of online data‐sharing platforms, citizen science programs and new technology such as unmanned aerial vehicles (UAVs), but data reliability, consistency and the error properties of the sampling method are usually uncertain. While there are a number of standard survey protocols for different taxa, they often subjectively designed and standardization is meant to facilitate repeatability rather than produce a quantitative evaluation of the data (e.g. error properties). Here, we describe an ecological survey scheme consisting of an ‘algorithm' to be followed in the field that will result in a standard set of data as well as the error properties of the data. While many such sampling schemes could be developed that target different types of organisms, we focus on one case of a moving observer attempting to detect a species in the field (e.g. a birder, UAV, etc.) with the goal of producing a presence–absence map. The multiscale model developed is spatially explicit and accommodates inherent survey tradeoffs such as sampling speed, detectability and map resolution. Given a set of sampling parameters, the model provides estimates of the total sampling time and map accuracy translated into the probability of false negative. Additionally it also provides an actual and sampled occupancy–area curve across mapping resolutions that can be utilized to discuss sampling effects. While the proposed sampling framework is simple, the same general approach could be adapted for other conditions to meet the needs of a particular taxon. If a set of ‘canonical' sampling algorithms could be developed with known mathematical properties, it would enhance reliability and usage of ecological datasets.

Section: Methodsmentioning

confidence: 99%

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Developing generalized sampling schemes with known error properties: the case of a moving observer

Economo

2020

Ecography

Self Cite

“…3a). From Eqs (19,20) and with the aid of Fig. 3b, we calculate the number of species N s found within a sampling unit with area πr 2 s , as…”

Section: Species Area Relationshipmentioning

confidence: 99%

A geometric approach to scaling individual distributions to macroecological patterns

Kusumoto

Kubota

et al. 2018

Preprint

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Understanding macroecological patterns across scales is a central goal of ecology and a key need for conservation biology. Much research has focused on quantifying and understanding macroecological patterns such as the species-area relationship (SAR), the endemic-area relationship (EAR) and relative species abundance curve (RSA). Understanding how these aggregate patterns emerge from underlying spatial pattern at individual level, and how they relate to each other, has both basic and applied relevance. To address this challenge, we develop a novel spatially explicit geometric framework to understand multiple macroecological patterns, including the SAR, EAR, RSA, and their relationships, using theory of point processes. The geometric approach provides a theoretical framework to derive SAR, EAR, and RSA from species range distributions and the pattern of individual distribution patterns therein. From this model, various well-documented macroecological patterns are recovered, including the tri-phasic SAR on a log-log plot with its asymptotic slope, and various RSAs (e.g., Fisher s logseries and the Poisson lognormal distribution). Moreover, this approach can provide new insights such as a single equation describing the RSA at an arbitrary spatial scale, and explicit forms of the EAR with its asymptotic slope. The theory, which links spatial distributions of individuals and species with macroecological patterns, is ambiguous with regards to the mechanism(s) responsible for the statistical properties of individual distributions and species range sizes. However, our approach can be connected to mechanistic models that make such predictions about lower-level patterns and be used to scale them up to aggregate patterns, and therefore is applicable to many ecological questions. We demonstrate an application of the geometric model to scaling issue of beta diversity.

A perspective on biodiversity data and applications for spatio-temporally robust spatial planning for area-based conservation

Kusumoto

2023

Discov Sustain

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The growing availability of high-resolution biodiversity data is enhancing our ability to implement biodiversity conservation more effectively. Spatial planning has widely utilized such fine-scale biodiversity data, and proposals of finely-organized protected area networks have been increasing. However, a naive adoption of such fine-scale data for conservation may not only degrade the utility of the data, but may even risk reduction of long-term efficacy of conservation efforts. This is due to inherent tradeoffs between the efficacy of conservation actions over short-term and its persistence over long-term that is characterized by the management scale of spatial planning associated with the resolution of the data used. To demonstrate this argument, the spatiotemporal ecosystem dynamics must be described, but such discussions are limited in the literature. Here, we discuss the potential issues associated with naive uses of fine-scale biodiversity data to establish fine-tuned spatial planning. We then emphasize the importance of matching the data resolution with an appropriate scale of spatial planning that is realized by transforming the data resolution. This method is readily applicable for widely used decision-support tools for spatial planning. A simple worked example is provided to demonstrate its utility with a long-term conservation efficacy in spatial planning. Guided by the recent explosion of biological data, our discussion provides new insights into the ways to maximize the utility of these data, and further improve biodiversity conservation.