Frequency distribution curves and the identification of hotspots: response to comments

Bartolino, Valerio; Maiorano, Luigi; Colloca, Francesco

doi:10.1007/s10144-011-0273-6

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Unlike traditional hot spot identification methods that require a user‐defined threshold (Bartolino et al. ,b, Cayuela et al. ), the CRFD method identifies the hot spot threshold as a function of the CRFD curve.…”

Section: Methodsmentioning

confidence: 99%

Models of spatiotemporal variation in rabbit abundance reveal management hot spots for an invasive species

Brown

Wells

Roy‐Dufresne

et al. 2020

Ecological Applications

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The European rabbit (Oryctolagus cuniculus) is a notorious economic and environmental pest species in its invasive range. To better understand the population and range dynamics of this species, 41 yr of abundance data have been collected from 116 unique sites across a broad range of climatic and environmental conditions in Australia. We analyzed this time series of abundance data to determine whether interannual variation in climatic conditions can be used to map historic, contemporary, and potential future fluctuations in rabbit abundance from regional to continental scales. We constructed a hierarchical Bayesian regression model of relative abundance that corrected for observation error and seasonal biases. The corrected abundances were regressed against environmental and disease variables in order to project high spatiotemporal resolution, continent‐wide rabbit abundances. We show that rabbit abundance in Australia is highly variable in space and time, being driven primarily by internnual variation in temperature and precipitation in concert with the prevalence of a non‐pathogenic virus. Moreover, we show that internnual variation in local spatial abundances can be mapped effectively at a continental scale using highly resolved spatiotemporal predictors, allowing “hot spots” of persistently high rabbit abundance to be identified. Importantly, cross‐validated model performance was fair to excellent within and across distinct climate zones. Long‐term monitoring data for invasive species can be used to map fine‐scale spatiotemporal fluctuations in abundance patterns when accurately accounting for inherent sampling biases. Our analysis provides ecologists and pest managers with a clearer understanding of the determinants of rabbit abundance in Australia, offering an important new approach for predicting spatial abundance patterns of invasive species at the near‐term temporal scales that are directly relevant to resource management.

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Section: Methodsmentioning

confidence: 99%

Models of spatiotemporal variation in rabbit abundance reveal management hot spots for an invasive species

Brown

Wells

Roy‐Dufresne

et al. 2020

Ecological Applications

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“…Bartolino, Maiorano, and Colloca () proposed cumulative relative frequency distributions (CRFDs) as a simple way of delineating spatial hotspots in a continuous parameter of interest (e.g., density, diversity). The method is objective and preferable to the more traditional Getis–Ord Gi* or Moran's I , which rely heavily on a priori user‐selected settings (Bartolino, Maiorano, & Colloca, ). We obtained CRFD curves by plotting the relative values of fish abundance indices in each bioregion against their own frequency distribution.…”

Section: Methodsmentioning

confidence: 99%

“…The method is objective and preferable to the more traditional Getis-Ord Gi* or Moran's I, which rely heavily on a priori user-selected settings (Bartolino, Maiorano, & Colloca, 2011a). We obtained CRFD curves by plotting the relative values of fish abundance indices in each bioregion against their own frequency distribution.…”

Section: Hotspot Detectionmentioning

confidence: 99%

Continental‐scale hotspots of pelagic fish abundance inferred from commercial catch records

Bouchet

Meeuwig

Huang

et al. 2017

Global Ecol Biogeogr

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Aim Protected areas have become pivotal to the modern conservation planning toolbox, but a limited understanding of marine macroecology is hampering their efficient design and implementation in pelagic environments. We explored the respective contributions of environmental factors and human impacts in capturing the distribution of an assemblage of commercially valuable, large‐bodied, open‐water predators (tunas, marlins and mackerels). Location Western Australia. Time period 1997–2006. Major taxa studied Pelagic fishes. Methods We compiled 10 years of commercial fishing records from the Sea Around Us Project and derived relative abundance indices from standardized catch rates while accounting for confounding effects of effort, year and gear type. We used these indices to map pelagic hotspots over a 0.5°‐resolution grid and built random forests to estimate the importance of 33 geophysical, oceanographic and anthropogenic predictors in explaining their locations. We additionally examined the spatial congruence between these hotspots and an extensive network of marine reserves and determined whether patterns of co‐occurrence deviated from random expectations using null model simulations. Results First, we identified several pelagic hotspots off the coast of Western Australia. Second, geomorphometrics explained up to 50% of the variance in relative abundance of pelagic fishes, and submarine canyon presence ranked as the most influential variable in the North bioregion. Seafloor complexity, geodiversity, salinity, temperature variability, primary production, ocean energy, current regimes and human impacts were also identified as important predictors. Third, spatial overlap between hotspots and marine reserves was limited, with most high‐abundance areas primarily found in zones where anthropogenic activities are subject to few regulations. Main conclusions This study reveals geomorphometrics as valuable indicators of the distribution of mobile fish species and highlights the relevance of harnessing static topography as a key element in any blueprint for ocean zoning and spatial management.

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“…We designated hotspots following a spatial frequency distribution method developed by Bartolino et al (2010) [ 31 ], in R Version 3.0.1. This method has an advantage over traditional spatial hotspot delineation techniques in that a hotspot threshold is defined from the geometric properties of a cumulative relative frequency distribution (CRFD) curve rather than a more subjective user-defined threshold [ 31 – 34 ]. For each year (2003–2010), we approximated a CRFD curve by plotting the relative value of the biodiversity measure (species richness or Shannon diversity, H′) against the frequency distribution for that value ( S2 File ) [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Evaluating Temporal Consistency in Marine Biodiversity Hotspots

et al. 2015

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With the ongoing crisis of biodiversity loss and limited resources for conservation, the concept of biodiversity hotspots has been useful in determining conservation priority areas. However, there has been limited research into how temporal variability in biodiversity may influence conservation area prioritization. To address this information gap, we present an approach to evaluate the temporal consistency of biodiversity hotspots in large marine ecosystems. Using a large scale, public monitoring dataset collected over an eight year period off the US Pacific Coast, we developed a methodological approach for avoiding biases associated with hotspot delineation. We aggregated benthic fish species data from research trawls and calculated mean hotspot thresholds for fish species richness and Shannon’s diversity indices over the eight year dataset. We used a spatial frequency distribution method to assign hotspot designations to the grid cells annually. We found no areas containing consistently high biodiversity through the entire study period based on the mean thresholds, and no grid cell was designated as a hotspot for greater than 50% of the time-series. To test if our approach was sensitive to sampling effort and the geographic extent of the survey, we followed a similar routine for the northern region of the survey area. Our finding of low consistency in benthic fish biodiversity hotspots over time was upheld, regardless of biodiversity metric used, whether thresholds were calculated per year or across all years, or the spatial extent for which we calculated thresholds and identified hotspots. Our results suggest that static measures of benthic fish biodiversity off the US West Coast are insufficient for identification of hotspots and that long-term data are required to appropriately identify patterns of high temporal variability in biodiversity for these highly mobile taxa. Given that ecological communities are responding to a changing climate and other environmental perturbations, our work highlights the need for scientists and conservation managers to consider both spatial and temporal dynamics when designating biodiversity hotspots.

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Frequency distribution curves and the identification of hotspots: response to comments

Cited by 5 publications

References 5 publications

Models of spatiotemporal variation in rabbit abundance reveal management hot spots for an invasive species

Models of spatiotemporal variation in rabbit abundance reveal management hot spots for an invasive species

Continental‐scale hotspots of pelagic fish abundance inferred from commercial catch records

Evaluating Temporal Consistency in Marine Biodiversity Hotspots

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