A comparative analysis of common methods to identify waterbird hotspots

Abstract

Hotspot analysis is a commonly used method in ecology and conservation to identify areas of high biodiversity or conservation concern. However, delineating and mapping hotspots is subjective and various approaches can lead to different conclusions with regard to the classification of particular areas as hotspots, complicating long‐term conservation planning. We present a comparative analysis of recent approaches for identifying waterbird hotspots, with the goal of developing insights about the appropriate use of these methods. We selected four commonly used measures to identify persistent areas of high use: kernel density estimation, Getis‐Ord Gi*, hotspot persistence and hotspots conditional on presence, which represent the range of quantitative hotspot estimation approaches used in waterbird analyses. We applied each of the methods to aerial survey waterbird count data collected in the Great Lakes from 2012–2014. For each approach, we identified areas of high use for seven species/species groups and then compared the results across all methods and to mean effort‐corrected counts. Our results indicate that formal hotspot analysis frameworks do not always lead to the same conclusions. The kernel density and Getis‐Ord Gi* methods yielded the most similar results across all species analysed and were generally correlated with mean effort‐corrected count data. We found that these two models can differ substantially from the hotspot persistence and hotspots conditional on presence estimation approaches, which were not consistently similar to one another. The hotspot persistence approach differed most significantly from the other methods but is the only method to explicitly account for temporal variation. We recommend considering the ecological question and scale of conservation or management activities prior to designing survey methodologies. Deciding the appropriate definition and scale for analysis is critical for interpretation of hotspot analysis results as is inclusion of important covariates. Combining hotspot analysis methods using an integrative approach, either within a single analysis or post hoc, could lead to greater consistency in the identification of waterbird hotspots.

Perceptions of Public Officers Towards the Effects of Climate Change on Ecosystem Services: A Case-Study From Northern Portugal

“…For each map we specifically annotated the spatial location of each ES and climate change-related drivers based on the letters and numbers assigned to each dot on the maps (see section "Participatory Approach"). We then combined each set of digitized locations and applied a Kernel Density function (based on point features) to identify the most prominent locations ("hotspots") for ES supply as well as the locations most affected by climate change-related drivers (Fagerholm and Palomo, 2017;Sussman et al, 2019). The Kernel function was applied considering a minimum mapping unit (m.m.u.)…”

Marine hotspots of activity inform protection of a threatened community of pelagic species in a large oceanic jurisdiction

“…These hotspots occurred primarily around breeding islands and submarine geographical features and covered only 25% of the EEZ, which should be considered of extraordinary value for marine conservation. Identifying hotspots is thus an effective approach to minimize the area required for protection and thus reduce the opportunity cost resulting from the protection of these areas of outstanding biodiversity value (Queiroz et al, 2016;Sussman et al, 2019;Yurkowski et al, 2019).…”

“…To identify statistically significant clusters of cells with relatively high intensity of use by the study species within the Tristan EEZ, we used an indicator of spatial association, the Getis-Ord G i * (Anselin, 1995;Getis & Ord, 1992), which is a robust approach that has recently been used to identify other marine hotspots (Kuletz et al, 2015;Queiroz et al, 2016;Sussman et al, 2019;Yurkowski et al, 2019). This statistic compares the intensity of use of a given cell with the value of each adjacent cell within a given fixed radiusthe cells in this radius are referred to as the spatial neighbourhood.…”

Evaluating the use of marine protected areas by endangered species: A habitat selection approach

“…Often, satellite tracking efforts can reveal high‐use areas for foraging or breeding which can be summarized for changes in conservation policy (Hays et al., 2019; Lea, Humphries, Brandis, Clarke, & Sims, 2016). Delineating areas of high use or high biodiversity, commonly referred to as hotspot analysis, has become a fundamental component of conservation planning due to its feasibility and cost‐effectiveness, as protecting the full range of biodiversity is never a realistic target (Marchese, 2015; Sussman et al., 2019). While typically designated on a case‐by‐case basis, hotspots are broadly defined as geographic areas with persistent high levels of either species abundance, richness or endemism (Possingham & Wilson, 2005).…”