Monitoring Variation in Small Coastal Dolphin Populations: An Example from Darwin, Northern Territory, Australia

Brooks, Lyndon O; Palmer, Carol; Griffiths, Anthony D.; Pollock, Kenneth H.

doi:10.3389/fmars.2017.00094

Cited by 13 publications

(14 citation statements)

References 52 publications

(77 reference statements)

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“…Confidently assessing population trends of wild or elusive animals remains very challenging (Nussear & Tracy, 2007;Wanyama et al, 2010;Brooks et al, 2017). This study confirmed that the mountain gorilla population of the Virunga Massif continued growing, reaching a minimum of 604 gorillas, and more likely reaching an estimated 639-669 total individuals as of June 2016.…”

Section: Discussionsupporting

confidence: 67%

Estimating abundance and growth rates in a wild mountain gorilla population

Granjon

Robbins

Arinaitwe

et al. 2020

Animal Conservation

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Monitoring population size and growth over time is vital for the conservation of endangered species. Mountain gorillas Gorilla beringei beringei remain in two small populations that span the borders of the Democratic Republic of Congo, Rwanda and Uganda. Each population contains two subpopulations that receive differing levels of protection: the monitored groups are visited daily by park staff and researchers and can be counted by sight, whereas the number and growth rate of unmonitored gorillas must be estimated indirectly. Here, we re-analyze published data from a survey in 2010 combined with new results from a survey conducted during two sampling occasions in 2015 and 2016 to estimate mountain gorilla abundance and growth in the Virunga Massif between 2010 and 2016. Using genetic analysis of non-invasively collected samples and two capture-markrecapture estimates, we estimated that the 186 detected genotypes represented 221 (95% credible interval: 204-243) to 251 (205-340) unmonitored gorillas in 2016. Together with the 418 monitored gorillas, the overall population of the Virunga Massif thus reached 639 (622-661) to 669 (623-758) individuals. We estimated the growth of the entire Virunga Massif population at about 3% per year, but determined that the growth of the monitored gorillas (4.4%) mainly drove that increase. In contrast, the trend of the unmonitored subpopulation could not be determined with confidence because both models provided 95% CI that encompassed zero: 0.5% per year (À0.7% to +1.7%) and 1.1% (À2.7% to +4.4%). While the overall growth rate represents a rare success story for primate conservation, our results highlight the need for greater protection of unmonitored gorillas.

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Section: Discussionsupporting

confidence: 67%

Estimating abundance and growth rates in a wild mountain gorilla population

Granjon

Robbins

Arinaitwe

et al. 2020

Animal Conservation

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“…This issue is exacerbated in tropical environments, where reports of inconspicuous or uncommon taxa are typically sparse and of lesser quality due to cryptic (e.g., boat-shy) animal behavior, uneven sampling effort, as well as the technical difficulties associated with surveying remote and vast seascapes (Braulik et al, 2018). Species that naturally occur in low numbers also make it difficult to assess population trends over time (Brooks et al, 2017). Snubfins are a compelling example of this, as few studies of the animals' abundance or geographic range have been undertaken since the species was formally described in 2005, except at a few key sites (Parra et al, 2006a,b;Cagnazzi et al, 2013b;Palmer et al, 2014a;Brown et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, the EOO/AOO are suitable for assessing conservation status even when there is limited information on local threatening processes and the distribution of a taxon is only known from opportunistic observations, unlike other IUCN metrics (Dauby et al, 2017). In particular, criterion B tends to be more suitable than abundance metrics (i.e., IUCN criterion A) for coastal dolphin populations whose sizes are challenging to estimate but predicted to be declining (Brooks et al, 2017), and/or where such estimates are not available over most of the species' range (Parra and Cagnazzi, 2016); both of which apply directly to snubfins. Nonetheless, crowdsourcing disparate datasets brings forth a number of challenges related to variability in both data quality (e.g., taxonomic misidentifications, measurement/recording errors, differences in field protocols) and quantity (e.g., spatio-temporal bias in sampling effort, different data storing/sharing/access policies) (Akçakaya et al, 2000;Guttmacher, 2016;Rueda-Cediel et al, 2018;Fletcher et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Regional Assessment of the Conservation Status of Snubfin Dolphins (Orcaella heinsohni) in the Kimberley Region, Western Australia

Bouchet

Thiele

Marley³

et al. 2021

Front. Mar. Sci.

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Implementing conservation measures for data-limited species is a fundamental challenge for wildlife managers and policy-makers, and proves difficult for cryptic marine animals occurring in naturally low numbers across remote seascapes. There is currently scant information on the abundance and habitat preferences of Australian snubfin dolphins (Orcaella heinsohni) throughout much of their geographical range, and especially within the Kimberley region of northern Western Australia. Such knowledge gaps curtail rigorous threat assessments on both local and regional scales. To address this and assist future conservation listings, we built the first comprehensive catalog of snubfin dolphin sightings for the Kimberley. We used these data to estimate the species’ extent of occurrence (EOO) and area of occupancy (AOO) along the region’s 7,000 km coastline, following a simple Bootstrap bivariate kernel approach to combine datasets of varying quality and quantify uncertainty. Our catalog consists of 1,597 visual detections of snubfin dolphins made over a period of 17 years (2004–2020) and collated from multiple sources, including online biodiversity repositories, peer-reviewed scientific articles, citizen science programs, as well as dedicated marine wildlife surveys with local Indigenous communities and Ranger groups. Snubfin dolphins were consistently encountered in shallow waters (<21 m depth) close to (<15 km) freshwater inputs, with high detection rates in known hotspots (e.g., Roebuck Bay, Cygnet Bay) as well as in coastal habitats suspected to be suitable (e.g., Prince Regent River and surrounds, King Sound, Doubtful Bay, Napier Broome Bay and the upper Cambridge Gulf). Bootstrap estimates of EOO and AOO were 38,300 (95% CI: 25,451–42,437) km2 and 700 (656–736) km2 respectively, suggesting that snubfin dolphins in the Kimberley are likely Vulnerable under IUCN criteria B2 at a regional scale, in keeping with their global classification. Our study offers insights into the distribution of a vulnerable coastal cetacean species and demonstrates the value of integrating multiple data sources for informing conservation assessments in the face of uncertainty.

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“…The Australian humpback dolphin (hereafter "humpback dolphin") is endemic to shallow (typically < 30 m) coastal waters of tropical northern Australia and southern Papua New Guinea 30 . Studies in selected areas throughout the Australian range of humpback dolphins indicate that populations are small [typically 50 to 150 individuals, sometimes fewer [31][32][33][34][35][36] with limited gene flow 32,37 , and relatively small home ranges (< 300 km 2 ; 32,38 )]. The IUCN Red List of Threatened Species recently listed the Australian humpback dolphin as 'Vulnerable' due to the species' small population sizes and cumulative exposure to human activities 39 .…”

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confidence: 99%