Abstract:Very high-resolution satellite (VHR) imagery is a promising tool for estimating the abundance of wildlife populations, especially in remote regions where traditional surveys are limited by logistical challenges. Emperor penguins Aptenodytes forsteri were the first species to have a circumpolar population estimate derived via VHR imagery. Here we address an untested assumption from Fretwell et al. (2012) that a single image of an emperor penguin colony is a reasonable representation of the colony for the year t… Show more
“…We also note that our estimate includes only the proportion of the population present in late spring (and is therefore a conservative index of adult emperor penguins), which includes an unknown quantity of failed-breeders, pre-breeders and non-breeders. Figure 1Index of global abundance of emperor penguins (number of adult birds in attendance at colonies in the springtime [19]) per year calculated by combining area of ‘penguin pixels’ from VHR imagery within a Bayesian modelling framework over the study period (2009–2018). The blue ribbon represents the 95% equal-tailed CI for the annual index, while the central line represents the median of the posterior distribution.…”
Section: Resultsmentioning
confidence: 99%
“… Index of global abundance of emperor penguins (number of adult birds in attendance at colonies in the springtime [ 19 ]) per year calculated by combining area of ‘penguin pixels’ from VHR imagery within a Bayesian modelling framework over the study period (2009–2018). The blue ribbon represents the 95% equal-tailed CI for the annual index, while the central line represents the median of the posterior distribution.…”
Section: Resultsmentioning
confidence: 99%
“…increasing the spatial resolution of the multi-spectral image by merging it with its higher-resolution, panchromatic pair [ 33 ]) and projected to Antarctic Polar Stereographic (EPSG code 3031). We then followed semi-automated methods already established [ 12 , 13 , 19 ]: briefly, we loaded VHR imagery into ArcGIS 10.8 (Esri), identified the location of emperor penguins on the image and then clipped images to the extent of the colony. We conducted a supervised classification by manually training the program with shapefile points representing pixels of guano, snow and penguin.…”
Section: Methodsmentioning
confidence: 99%
“…for Antarctic krill, Euphausia superba; Antarctic toothfish, Dissostichus mawsoni) [18]. We analysed 460 VHR images [12][13][14][15][16][19][20][21], aerial surveys across several colonies [22][23][24], ground surveys [25] and remote camera data [26]. Remote sensing analysis involved a semi-automated, supervised classification of VHR images combined with aerial and ground validation to convert the area covered by 'penguin pixels' into indices of population size (number of adult birds present in colonies during aerial surveys) [12,19].…”
Section: Introductionmentioning
confidence: 99%
“…We analysed 460 VHR images [12][13][14][15][16][19][20][21], aerial surveys across several colonies [22][23][24], ground surveys [25] and remote camera data [26]. Remote sensing analysis involved a semi-automated, supervised classification of VHR images combined with aerial and ground validation to convert the area covered by 'penguin pixels' into indices of population size (number of adult birds present in colonies during aerial surveys) [12,19]. We integrated available survey data in a Bayesian state-space population model, allowing us to estimate annual population indices and trends from 50 colony locations (that were discovered prior to 2015) during the period 2009-2018.…”
Like many polar animals, emperor penguin populations are challenging to monitor because of the species' life history and remoteness. Consequently, it has been difficult to establish its global status, a subject important to resolve as polar environments change. To advance our understanding of emperor penguins, we combined remote sensing, validation surveys and using Bayesian modelling, we estimated a comprehensive population trajectory over a recent 10-year period, encompassing the entirety of the species’ range. Reported as indices of abundance, our study indicates with 81% probability that there were fewer adult emperor penguins in 2018 than in 2009, with a posterior median decrease of 9.6% (95% credible interval (CI) −26.4% to +9.4%). The global population trend was −1.3% per year over this period (95% CI = −3.3% to +1.0%) and declines probably occurred in four of eight fast ice regions, irrespective of habitat conditions. Thus far, explanations have yet to be identified regarding trends, especially as we observed an apparent population uptick toward the end of time series. Our work potentially establishes a framework for monitoring other Antarctic coastal species detectable by satellite, while promoting a need for research to better understand factors driving biotic changes in the Southern Ocean ecosystem.
“…We also note that our estimate includes only the proportion of the population present in late spring (and is therefore a conservative index of adult emperor penguins), which includes an unknown quantity of failed-breeders, pre-breeders and non-breeders. Figure 1Index of global abundance of emperor penguins (number of adult birds in attendance at colonies in the springtime [19]) per year calculated by combining area of ‘penguin pixels’ from VHR imagery within a Bayesian modelling framework over the study period (2009–2018). The blue ribbon represents the 95% equal-tailed CI for the annual index, while the central line represents the median of the posterior distribution.…”
Section: Resultsmentioning
confidence: 99%
“… Index of global abundance of emperor penguins (number of adult birds in attendance at colonies in the springtime [ 19 ]) per year calculated by combining area of ‘penguin pixels’ from VHR imagery within a Bayesian modelling framework over the study period (2009–2018). The blue ribbon represents the 95% equal-tailed CI for the annual index, while the central line represents the median of the posterior distribution.…”
Section: Resultsmentioning
confidence: 99%
“…increasing the spatial resolution of the multi-spectral image by merging it with its higher-resolution, panchromatic pair [ 33 ]) and projected to Antarctic Polar Stereographic (EPSG code 3031). We then followed semi-automated methods already established [ 12 , 13 , 19 ]: briefly, we loaded VHR imagery into ArcGIS 10.8 (Esri), identified the location of emperor penguins on the image and then clipped images to the extent of the colony. We conducted a supervised classification by manually training the program with shapefile points representing pixels of guano, snow and penguin.…”
Section: Methodsmentioning
confidence: 99%
“…for Antarctic krill, Euphausia superba; Antarctic toothfish, Dissostichus mawsoni) [18]. We analysed 460 VHR images [12][13][14][15][16][19][20][21], aerial surveys across several colonies [22][23][24], ground surveys [25] and remote camera data [26]. Remote sensing analysis involved a semi-automated, supervised classification of VHR images combined with aerial and ground validation to convert the area covered by 'penguin pixels' into indices of population size (number of adult birds present in colonies during aerial surveys) [12,19].…”
Section: Introductionmentioning
confidence: 99%
“…We analysed 460 VHR images [12][13][14][15][16][19][20][21], aerial surveys across several colonies [22][23][24], ground surveys [25] and remote camera data [26]. Remote sensing analysis involved a semi-automated, supervised classification of VHR images combined with aerial and ground validation to convert the area covered by 'penguin pixels' into indices of population size (number of adult birds present in colonies during aerial surveys) [12,19]. We integrated available survey data in a Bayesian state-space population model, allowing us to estimate annual population indices and trends from 50 colony locations (that were discovered prior to 2015) during the period 2009-2018.…”
Like many polar animals, emperor penguin populations are challenging to monitor because of the species' life history and remoteness. Consequently, it has been difficult to establish its global status, a subject important to resolve as polar environments change. To advance our understanding of emperor penguins, we combined remote sensing, validation surveys and using Bayesian modelling, we estimated a comprehensive population trajectory over a recent 10-year period, encompassing the entirety of the species’ range. Reported as indices of abundance, our study indicates with 81% probability that there were fewer adult emperor penguins in 2018 than in 2009, with a posterior median decrease of 9.6% (95% credible interval (CI) −26.4% to +9.4%). The global population trend was −1.3% per year over this period (95% CI = −3.3% to +1.0%) and declines probably occurred in four of eight fast ice regions, irrespective of habitat conditions. Thus far, explanations have yet to be identified regarding trends, especially as we observed an apparent population uptick toward the end of time series. Our work potentially establishes a framework for monitoring other Antarctic coastal species detectable by satellite, while promoting a need for research to better understand factors driving biotic changes in the Southern Ocean ecosystem.
Emperor penguins (Aptenodytes forsteri) are under increasing environmental pressure. Monitoring colony size and population trends of this Antarctic seabird relies primarily on satellite imagery recorded near the end of the breeding season, when light conditions levels are sufficient to capture images, but colony occupancy is highly variable. To correct population estimates for this variability, we develop a phenological model that can predict the number of breeding pairs and fledging chicks, as well as key phenological events such as arrival, hatching and foraging times, from as few as six data points from a single season. The ability to extrapolate occupancy from sparse data makes the model particularly useful for monitoring remotely sensed animal colonies where ground-based population estimates are rare or unavailable.
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