The endangered North Atlantic right whale (NARW) faces threats from lethal and sublethal stressors. Prey limitation has been linked to reduced growth and fecundity, while anthropogenic noise has been associated with acoustic behavior changes and increased physiological stress. We combined disparate data sets collected in Cape Cod Bay, Massachusetts, USA, across multiple years, quantifying links between prey, calling behavior, noise, and foraging behavior to answer 3 questions: (1) What are the characteristics of noise and prey density in the patches where NARW forage? Are prey density, call rate, and noise related to (2) the probability of being in a foraging state and (3) the size of the foraging group? We found links between predicted prey density and 2 measures of NARW foraging: probability of being in a foraging state and foraging group size. There were also significant non-linear relationships between lagged calling behavior and these same measures. For group size, an interaction term between calling behavior and prey density suggested a complex interrelationship between these variables. When more ambient noise was measured near a patch in the 4 h prior to a sighting of foraging whales, whales foraged in lower-quality zooplankton patches. Elevated noise ahead of aggregated sightings was also associated with a smaller group size. This is consistent with the hypothesis that increased ambient noise disturbs foraging and/or reduces communication space, limiting the ability of NARWs to fully exploit their environment. Our findings suggest pathways for future experiments to explicitly test the mechanisms underpinning foraging processes in endangered NARW.

Deep-sea mining activities in the Clarion-Clipperton Zone (CCZ), Pacific Ocean, are expected to generate continuous noise pollution across multiple depth zones, with potential impacts on biodiversity. Impact studies have primarily focused on sediment plumes and habitat destruction, leaving consequences of industrial-scale deep-sea mining noise largely unexplored. Many marine taxa, including invertebrates, fish, and mammals, rely on sound for communication, navigation, and predator avoidance. Our systematic literature review highlights that noise sensitivity is widespread across taxa, yet only 35 % of taxonomic classes known in the CCZ have been studied for noise impacts. Soniferous fish, which rely on acoustic communication, are particularly vulnerable to noise. Chronic exposure to mining noise may have cascading ecological consequences, disrupting key behaviors and physiological processes in an environment characterized by low anthropogenic stressors. By identifying knowledge gaps and quantifying taxa likely to be vulnerable to industrial noise, this review provides the foundation to guide evidence-based management required to safeguard deep-sea ecosystems. Given the uncertainty surrounding deep-sea biotic responses to prolonged noise exposure, we emphasize the urgent need for a transparent transfer of knowledge on noise characteristics of deep-sea mining. These data are essential for assessing risks from mining noise to species, communities, and ecosystem functions and services.

Petrochronological investigations of granulite-facies metapelitic rocks from the eastern contact of the Closepet granite in the Central Dharwar Craton (CDC), southern India, provide new pressure-temperature-age (P-T-t) constraints on two seemingly discrete Archean metamorphic events during the Neoarchean and late Paleoarchean eras. Phase equilibrium modelling and conventional thermobarometry coupled with in situ monazite and garnet geochronology constrain Neoarchean (ultrahigh-temperature; UHT) metamorphic peak conditions to similar to 930 degrees C and similar to 6.7 kbar at c. 2.63 Ga, then cooling and limited decompression to similar to 5.7 kbar at <= 810 degrees C. Monazite inclusions in garnet least affected by Neoarchean recrystallisation have distinct positive Eu anomalies and yield ages of c. 3.2 Ga, whereas garnet cores interpreted to have grown at the same time have slightly younger apparent ages of c. 3.1 Ga. We interpret this age mismatch to be the result of extensive resorption of garnet during Neoarchean UHT metamorphism, which led to widespread modification of the initial Lu-Hf systematics in garnet to produce younger apparent ages. The effect of retention and inward intracrystalline diffusion of Lu on the isotopic composition of garnet is most pronounced close to the resorbed grain margins and decreases towards the core, as reflected by progressively younger apparent single-spot garnet dates from core to rim. Despite extensive overprinting of the sample at c. 2.63 Ga, the trace element composition of Paleoarchean monazite indicates growth in equilibrium with garnet but in the absence of feldspar, which is predicted to occur over a broad stability range at P >= 8 kbar and T <= 700 degrees C. Such P-T conditions are uncommon in the metamorphic rock record prior to the Neoarchean, but are typical of Barrovian-type metamorphism, which is considered to be an expression of accretionary-to-collisional orogenesis. Rocks of similar age and metamorphic grade have been reported from the core of the Western Dharwar Craton (WDC) and may reflect regional subduction at the margins of the CDC and WDC since the late Paleoarchean. Neoarchean UHT metamorphism in the Dharwar Craton is coeval with a cluster of other UHT occurrences at 2.7-2.6 Ga, indicating the existence of globally elevated thermal gradients at that time.

North Atlantic right whales are an endangered species. Their entire population is estimated to be approximately 372 individuals, and they are subject to major anthropogenic threats. They feed on zooplankton species whose distribution shifts in a dynamic and warming oceanic environment. Because right whales in turn follow their shifting food resource, it is necessary to jointly study the distribution of whales and their prey. The innovative joint species distribution modelling (JSDM) contribution here is different from anything in the large JDSM literature, reflecting the processes and data we have to work with. Specifically, our JSDM supplies a geostatistical model for the expected amount of zooplankton collected at a site. We require a point pattern model for the intensity of right whale abundance. The two process models are linked through a latent conditional-marginal specification. Furthermore, each species has two data sources informing its respective distribution, necessitating a novel data fusion approach. The result is a complex multi-level model. Through simulation, we demonstrate that our joint specification effectively identifies model unknowns and improves the estimation of species distributions compared to modelling them separately. We then apply our model to real data from Cape Cod Bay, Massachusetts, USA.

Sound is assumed to be the primary modality of communication among marine mammal species. Analyzing acoustic recordings helps to understand the function of the acoustic signals as well as the possible impact of anthropogenic noise on acoustic behavior. Motivated by a dataset from a network of hydrophones in Cape Cod Bay, Massachusetts, utilizing automatically detected calls in recordings, we study the communication process of the endangered North Atlantic right whale. For right whales an "up-call" is known as a contact call, and ensuing counter-calling between individuals is presumed to facilitate group cohesion. We present novel spatiotemporal excitement modeling consisting of a background process and a counter-call process. The background process intensity incorporates the influences of diel patterns and ambient noise on occurrence. The counter-call intensity captures potential excitement, that calling elicits calling behavior. Call incidence is found to be clustered in space and time; a call seems to excite more calls nearer to it in time and space. We find evidence that whales make more calls during twilight hours, respond to other whales nearby, and are likely to remain quiet in the presence of increased ambient noise.

Marine mammals are increasingly vulnerable to human disturbance and climate change. Their diving behavior leads to limited visual access during data collection, making studying the abundance and distribution of marine mammals challenging. In theory, using data from more than one observation modality should lead to better informed predictions of abundance and distribution. With focus on North Atlantic right whales, we consider the fusion of two data sources to inform about their abundance and distribution. The first source is aerial distance sampling which provides the spatial locations of whales detected in the region. The second source is passive acoustic monitoring (PAM), returning calls received at hydrophones placed on the ocean floor. Due to limited time on the surface and detection limitations arising from sampling effort, aerial distance sampling only provides a partial realization of locations. With PAM, we never observe numbers or locations of individuals. To address these challenges, we develop a novel thinned point pattern data fusion. Our approach leads to improved inference regarding abundance and distribution of North Atlantic right whales throughout Cape Cod Bay, Massachusetts in the US. We demonstrate performance gains of our approach compared to that from a single source through both simulation and real data.

The International Whaling Commission (IWC) carried out blue whale research within its annual Southern Ocean Whale and Ecosystem Research (SOWER) cruises between 1996 and 2010. Over 700 sonobuoys were deployed to record blue whale vocalisations during 11 Antarctic and three low‐latitude blue whale cruises off Australia, Madagascar and Chile. The recorded acoustic files from Antarctic deployments were collated and reviewed to develop a database of digital acoustic files and the associated deployment station metadata of 7,486 acoustic files from 484 stations. Acoustic files were analysed using the automated detection template and visual verification method. We found a significant difference between the total number of acoustic recording hours (2,481) reported in the associated cruise reports and the currently available number of acoustic recording hours (1,541). Antarctic blue whale vocalisations (9,315 D‐calls and 24,902 Z‐calls) were detected on 4,183 out of the total 7,486 acoustic files. December had the lowest call rates; January and February yielded high call rates. While most sonobuoys (63%) were deployed between 1800hrs and 0600hrs, the majority of calls (62%) were detected during observation periods between 0600hrs and 1800hrs. The difference between the available and reported data is a significant concern. Reconciliation of these and any future IWC acoustic data is strongly recommended.

Detrital zircon grains provide a useful tool to determine the maximum age of sedimentary strata and can elucidate tectonic setting and basin evolution. However, high-grade metamorphism can overprint the mineral chemistry of the detrital cargo and reset both primary age and compositional information. Incomplete resetting of the U-Pb isotopic system, fluid alteration, and complex internal structures due to granulite-facies metamorphism makes dating and interpretation of the obtained zircon ages challenging. Here, we use trace element chemistry, U-Pb geochronology (LA-ICP-MS and SHRIMP), textural observations, and inclusion characterization from detrital zircon crystals in the Snowys Dam Formation (Albany-Fraser Orogen; AFO) to deconvolve its strongly overprinted sedimentary history, and to evaluate the likely geodynamic setting for basin genesis. Detrital zircons that survived metamorphic overprinting show that the main detrital load was sourced from Paleoproterozoic lithologies from neighbouring regions, with rare Archean Yilgarn crystals. The youngest detrital grains correlate with the age of plagiogranites in the Madura Province to the east of the AFO and imply a maximum depositional age of c. 1390 Ma, indicating that the Fraser Zone temporally correlates to an oceanic arc and thus best fits a back-arc basin setting. High-grade metamorphism during Stage I of the orogeny (c. 1330-1260 Ma) promoted recrystallization of pre-existing detrital zircons together with neoblastic growth of metamorphic zircon rims. At c. 1245 Ma, during early Stage II of the AFO, anatexis of the metasedimentary rocks produced rare neocrystallized magmatic zircon. Late-stage fluids are indicated by elevated LREE for these grains, which also contain a hydrous inclusion assemblage. High volume sampling techniques such as LA-ICP-MS often drilled through multiple age domains that can hinder geological interpretation. Thus, a lower volume sampling technique (e.g., SHRIMP) and verification with mineral chemistry is highly instructive in accurately resolving primary age groups in high-grade terrains.

Analytical methods, images, data tables, and supporting plots.

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Animal songs can change within and between populations as the result of different evolutionary processes. When these processes include cultural transmission, the social learning of information or behaviours from conspecifics, songs can undergo rapid evolutions because cultural novelties can emerge more frequently than genetic mutations. Understanding these song variations over large temporal and spatial scales can provide insights into the patterns, drivers and limits of song evolution that can ultimately inform on the species' capacity to adapt to rapidly changing acoustic environments. Here, we analysed changes in fin whale (Balaenoptera physalus) songs recorded over two decades across the central and eastern North Atlantic Ocean. We document a rapid replacement of song INIs (inter-note intervals) over just four singing seasons, that co-occurred with hybrid songs (with both INIs), and a clear geographic gradient in the occurrence of different song INIs during the transition period. We also found gradual changes in INIs and note frequencies over more than a decade with fin whales adopting song changes. These results provide evidence of vocal learning in fin whales and reveal patterns of song evolution that raise questions on the limits of song variation in this species. Editor's evaluation This study is a valuable contribution to our understanding of vocal variation in acoustic displays of male baleen whales, part of a developing story about cultural change in songs in species other than the relatively well studied humpback whales. The authors present solid evidence of changes at various timescales in 20-Hz song note intervals and call center frequency over decadal time scales and large spatial scales. https://doi.org/10.7554/eLife.83750.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Animal songs, often used as acoustic sexual displays, can change within and between populations through different evolutionary processes. These processes can be selective (i.e. sexual, cultural, or natural selection), favouring song changes that confer advantages to singers, or non-selective (i.e. cultural or genetic drift), causing random changes in songs. Both selective and non-selective processes may result in rapid and gradual population-wide shifts in the structure, complexity, frequency, and temporal properties of songs (Garland et al., 2011; Otter et al., 2020; Whiten, 2019; Williams et al., 2013), although song evolution can also be constrained by the species' genetic variation and mechanical design (Podos et al., 2004). The best-known examples of song evolution are found among songbirds, which field studies started decades ago and have led to extensive literature on the topic. Songs from many songbirds are culturally transmitted through vocal learning, wherein animals learn to sing by hearing and imitating conspecifics (Williams, 2021). Vocal learning and specific patterns of dispersal are largely responsible for the geographic variation found in songs of many bird species (Podos and Warren, 2007). The formation of local dialects is, in part, a consequence of certain mechanisms of song learning (i.e., copying 'errors') that generate vocal novelties (Podos and Warren, 2007). Learned songs may also undergo rapid evolutions within populations, basically because cultural novelties can emerge more frequently than genetic mutations (Wilkins et al., 2013). One example of rapid song evolution is found in the white-throated sparrow (Zonotrichia albicollis), in which a new doublet ending song spread across the North America continent in less than 20 years, completely replacing the established triplet-ending song. This fast spread is believed to have occurred because birds singing the old and new songs overwintered in the same grounds and learned from each other. Although it remains unclear why the new song overturned the old song, one possible explanation for this rapid song transition is that certain innovations are adopted non-randomly by all males to maintain female interest (Otter et al., 2020). Birdsong properties can also show a gradual directional evolution in response to specific evolutionary process. A clear example are birds from urban areas, which song elements increase in frequency (Hz) in response to noisy environmental conditions (Slabbekoorn, 2013). Directional song evolution can also cause directional song changes (e.g. faster trill rates, broader frequency bands, and lower frequency trills) driven by sexual selection operating through male-male interactions, mate choice by females or both (de Kort et al., 2009; Illes et al., 2006; Williams et al., 2013). Yet, all these song variations are constrained by the singers' morphological (e.g. beak shape or body size) and neurological limitations that sometimes can hinder the animals' adaptation to rapid human induced changes in the environment (e.g. urban noise; Luther and Derryberry, 2012; Podos et al., 2004). A parallelism to birdsong evolution can be found in the marine realm. Songs from humpback whales (Megaptera novaeangliae) differ across ocean regions (Winn et al., 1981), evolve gradually over time (Payne et al., 1983; Payne and Payne, 1985) and can go through revolutionary changes (Noad et al., 2000). During song revolutions, a population song type is rapidly replaced by a novel song type introduced from a neighbouring population (Garland et al., 2011; Noad et al., 2000). Most authors agree that these spatial and temporal patterns in humpback whale song changes can only be explained by vocal learning (Garland et al., 2011; Janik and Knörnschild, 2021; Noad et al., 2000; Tyack, 2008). Yet, the learning capacity of novel songs in humpback whales may be limited because song complexity always decreases in each revolutionary event (Allen et al., 2018). Compared to humpback whales and most songbirds, fin whales (Balaenoptera physalus) produce simpler songs consisting of a stereotyped repetition of a few low-frequency note types. These songs are also believed to act as mating displays (Thompson et al., 1992; Watkins et al., 1987), because they are produced by males (Croll et al., 2002) and intensify during the breeding season (Lockyer, 1984; Širović et al., 2013; Thompson et al., 1992; Watkins et al., 1987). In this species, the song inter-note interval (INI) is the most distinctive parameter between regions (Castellote et al., 2012; Delarue et al., 2009; Hatch and Clark, 2004; Širović et al., 2017; Watkins et al., 1987) and has been used to differentiate stocks and populations (Castellote et al., 2012; Delarue et al., 2009; Morano et al., 2012; Širović et al., 2017; Wood and Širović, 2022). Previous studies showed that fin whale song INIs differed between western, central, and eastern North Atlantic areas, as well as between these and the Mediterranean Sea (Castellote et al., 2012; Delarue et al., 2009; Hatch and Clark, 2004; Morano et al., 2012). These results partially agree with genetic data that indicate significant levels of heterogeneity in the mitochondrial DNA between the Mediterranean Sea, the eastern (Spain), and the western (Gulf of Maine and Gulf of St Lawrence) North Atlantic; however, samples from West Greenland and Iceland could not be assigned to either of the two North Atlantic areas, suggesting a mixture of subpopulations in these feeding grounds (Bérubé et al., 1998). Another large-scale study combining fin whale genetic and song data from the Northeast Pacific, North Atlantic, and Mediterranean Sea showed that acoustic differentiation among fin whales were not always reflected in estimates of genetic divergence (Hatch and Clark, 2004). These authors concluded that differences in songs may reflect differences in fin whale movements and/or social and vocal behaviours that occur at shorter timescales than genome evolution. In fact, fin whale song INIs can change abruptly from one year to the next in the same region (Delarue et al., 2009; Hatch and Clark, 2004; Helble et al., 2020; Morano et al., 2012; Širović et al., 2017) and have been progressively changing over time in different ocean regions (Best et al., 2022; Helble et al., 2020; Leroy et al., 2018a; Weirathmueller et al., 2017). Also, the center frequencie of two fin whale song components, the 20 Hz note and the higher frequency (~130 Hz) upsweep (hereafter HF note; Hatch and Clark, 2004), have been decreasing gradually over the last decade in different ocean basins (Leroy et al., 2018a; Weirathmueller et al., 2017; Wood and Širović, 2022). Currently, we do not understand the mechanisms and drivers of fin whale song variations nor how these variations may be affected by the species' physiological and morphological constraints of vocal performance. Broad-scale studies matching the known scales of fin whale natural history and ecology can shed light into the species' population structure and demography, even before genetic differentiation is evident, and elucidate patterns, drivers and limits of song evolution that can ultimately inform on the species' capacity to adapt to human-induced changes in their acoustic habitats (e.g. anthropogenic noise and climate change). Our study attempts to address these issues by investigating changes over two decades of three fin whale song parameters (INIs and peak frequencies of the 20 Hz and HF note types) in a wide area of the North Atlantic Ocean. Our work provides evidence of social learning in fin whale songs and shows: (i) a rapid evolution in song INIs across a vast area of the central North Atlantic in just four singing seasons, with the existence of hybrid songs (including both INIs) and a clear geographic gradient of song INIs during the transition period; (ii) a gradual evolution of song parameters showing an increase in INIs and a decrease in frequencies of the 20 Hz and HF notes over more than a decade in the central and eastern North Atlantic; and (iii) the adoption of both rapid and gradual song changes by fin whales from a wide region. We conclude by discussing song changes under the scope of cultural transmission, song function and the limits of song variation. Results The processing across all acoustic data resulted in 379 songs, from which 39680 INIs and its corresponding note frequencies were measured, and 143 songs, from which 9185 HF note peak frequencies were measured (Supplementary file 1a; 'Materials and methods'). The greatest numbers of INIs came from the SE and Azores locations in the Oceanic Northeast Atlantic (ONA) region, with ~32% and~1% respectively. Contributions from the remaining locations of the ONA region ranged from 3% to 7%, while contributions from locations outside the ONA region ranged from 0.6% to 7%. Among the locations in which the measurement of the HF note was possible, the Azores and SE Greenland locations contributed the most (~46% and~22% respectively; Table 1). Table 1 Asking author for title. For each location within each region this table shows: sampled period, duty cycle, sampling rate (Samp. rate), total number of recording hours (Rec. hours), number of measured inter-note intervals (INIs) (Num. INIs), contribution to total number of INIs measured (Contr. INIs), number of measured high frequency (HF) note peak frequencies (Num. HF note) and percent contribution to total number of measured HF note peak frequencies (Contr. HF note). RegionLocationSampled periodDuty cycle(%)Samp. rate (Hz)Rec.hoursNum.INIsContr. INIs(%)Num.HF noteContr. HF note (%)SE GreenlandSE Greenland01/10/2007 - 14/03/2008Cont.2000439228417.1204022.2SE IcelandSE Iceland04/01/2007 - 31/03/2007Cont.400020882910.71691.9Celtic SeaNorth Porcupine01/10/2015 - 03/11/2016102000520.82860.72152.3South Porcupine01/10/2015 - 03/11/2016102000520.86741.76276.9ONANE01/10/2002 - 31/03/2003Cont.250434426506.7NANANW01/10/2002 - 31/03/2003Cont.250434427547.1NANACE01/10/2002 - 31/03/2003Cont.250434428177.1NANACW01/10/2002 - 31/03/2003Cont.250434429307.3NANAAzores01 –31/01/2006; 01-31/01/2007;01-31/01/2008Cont.200022325731.4NANA01/10/2008 - 06/03/2011102000650.47491.8133514.515/10/2011 - 06/03/20124320001497.610172.56777.301/10/2012 - 18/10/2012292000122.4120.02162.323/02/2017 - 31/03/2020252000213623826203622.1Total6638.4473311.9426446.4SE08/02/1999 - 31/03/2005Cont.110317041299632.7NANASW31/12/2002 - 31/03/2003Cont.110218415173.8NANASW PortugalSW Portugal001/12/2007 - 29/02/2008Cont.10021848182.1NANA01/10/2015 - 31/03/2016202000878.411953.1129414Canary IslandsCanary Islands01/11/2014 - 29/02/2015Cont.100216019915.1NANABarents SeaSvalbard02/10/2014 - 31/01/2016Cont.50028822600.61651.8Vesterålen01/01/2018 - 28/02/2018Cont.40014169272.34114.5Total74944.4396801009185100 Transition in song INIs in the Offshore Northeast Atlantic region Results showed a rapid shift in song INIs in the SE location from the ONA region (Figure 1A), previously noted by Nieukirk et al., 2011, where songs with 19 s INIs were completely replaced by songs with 12 s INIs in just four singing seasons (Figure 1B and C). In 1999 and 2000, the 19s-INI song was the only song present in this location. By 2004, the 19s-INI song had disappeared from this location (Figure 1B) and was not detected in any of the sampled regions from 2006 to 2020, except from a single song in 2008 Figure 3A. During the transition period, 12s- and 19s INI-songs co-existed and there was a notable percentage of songs containing both INIs, which we refer as 'hybrid songs'. Hybrid songs showed two INIs in variable ratios and no apparent pattern, either mixed within the same song sequences (i.e. series of consecutive 20 Hz notes separated by periods of silence; Watkins et al., 1987) or found separated in different sequences from the same song. The singing season with the most hybrid songs was 2002/2003, when there were ~30% of hybrids. Figure 1 Download asset Open asset Transition in song INIs in the Offshore Northeast Atlantic region. (A) Map showing the SE location (red circle) of the Oceanic Northeast Atlantic region. (B) Percentage of songs with each inter-note interval (INI) type (19 s, hybrid (hyb), 12 s) in this location during the song INI shift in 1999 – 2005. (C) INIs from 1999 to 2005 for this same location. Points represent mean values per song and error bars are standard deviations. Six locations of the ONA region, with simultaneous data from the 2002/2003 singing season, were used to analyse the spatial pattern in song INIs. In this period, the prevalence of songs with each INI type showed a clear spatial gradient across the entire ONA region. The 19s-INI song largely dominated in the SW ONA, with only 9% of hybrid songs, and no detection of 12s-INI songs. The proportion of 19s-INI songs decreased progressively to the east, reaching 0–4% in the easternmost locations (CE and NE), where the 12s-INI songs were prominent (90 and 83%). Hybrid songs were more abundant (17–23%) at central ONA (NW, CW, and SE) than in easternmost locations (NE and CE; 10–13%; Figure 2). Figure 2 Download asset Open asset Map showing the percentages of fin whale songs with each inter-note interval (INI) type for six locations within the Oceanic Northeast Atlantic region during the 2002/2003 singing season. Gradual changes in song INIs and notes frequencies After the song transition, we found a gradual change in three fin whale song parameters over more than a decade, with most regions fitting the trend. The only exception was the Barents Sea where INIs differed from the rest of the sampled area showing a bimodal pattern. From 2006 to 2021, INIs increased at 0.21 s/yr (Adj. R-sq.=0.4; p<0.001) (Figure 3A and Figure 3—figure supplement 1). Peak frequencies of the 20 Hz note decreased at a rate of –0.06 Hz/yr (Adj. R-sq.=0.1 from 2009 to 2020; p<0.001) (Figure 3B and Figure 3—figure supplement 1) while peak frequencies of the HF note decreased at a rate of –0.35 Hz/yr (Adj. R-sq.=0.8; p<0.001) from 2007 to 2020, with all regions fitting the trend including the Barents Sea region (Figure 3C and Figure 3—figure supplement 1). Figure 3 with 2 supplements see all Download asset Open asset Gradual changes in song INIs and notes frequencies. (A) Inter-note intervals (INIs) from 2006 to 2020 for all regions sampled. INIs increased at a mean rate of 0.21 s/yr. (B) Peak frequencies of the 20 Hz note for SW Portugal (2015/2016) and Azores locations (Oceanic Northeast Atlantic region) sampled with Ecologic Acoustic Recorders (Lammers et al., 2008) (2008–2020); these changed at a mean rate of –0.06 Hz/yr. (C) Peak frequencies of the High Frequency (HF) note for all regions sampled; these changed at a mean rate of –0.36 Hz/yr. Points represent average values per song, error bars are standard deviations and black lines represent the fitted linear regression model with confidence intervals in shadowed grey. Differences in fin whale song parameters between regions When comparing data from different regions (SE Iceland, SE Greenland, ONA, SW Portugal, Canary Islands, Barents and Celtic Sea) with simultaneous recordings (i.e. in the same singing season) results showed unimodal overlapping distributions in INIs and HF note peak frequencies (Figure 4). The only exception was the Barents Sea region, where INIs differed from the Canary Islands in 2014/2015 (Barents Sea:~9 s and ~14 s; Canary Islands:~15 s), from SW Portugal and the Celtic Sea in 2015/2016 (Barents Sea:~10 s and ~15 s; SW Portugal and Celtic Sea:~15 s) and from the ONA region in 2017/2018 (Barents Sea:~10 s and 16 s; ONA:~16 s; Figure 4A). Figure 4 Download asset Open asset Histograms of (A) inter-note intervals (INIs) and (B) higher frequency (HF) note peak frequencies by singing season (Oct-Mar) from regions with concurrent data. Discussion The rapid and gradual changing patterns of different fin whale song parameters reported here for a wide area of the central and eastern North Atlantic provides evidence of vocal learning in this species. Decoupled variations in song INIs and frequency (i.e. INIs changed abruptly but frequencies did not) reveal the complex interplay between different selective pressures and shed some light on the potential limits of song variation. The rapid replacement of fin whales' song INIs (from 19s to 12s) described here for the ONA region cannot be explained by environmental causation. The shift in INIs found in the ONA region seemed to occur simultaneously at northern feeding grounds, in the so-called Northeast North Atlantic (NENA) region (Hatch and Clark, 2004). This variation in INI patterns during the same singing season between neighbouring locations within ONA, together with an identical shift in INIs documented for the same period in the environmentally distant NENA region (Hatch and Clark, 2004), strongly suggest that the transition in INIs was not a response to local acoustic environments. Fin whale song INIs are regionally distinct (Castellote et al., 2012; Constaratas et al., 2021; Delarue et al., 2009; Hatch and Clark, 2004; Morano et al., 2012; Pereira et al., 2020; Širović et al., 2017; Víkingsson and Gunnlaugsson, 2006) and the shift in INIs found in our study could have been caused by a population replacement. However, if this was the case, we would not find hybrid songs containing both INIs during the transition period, as the new song pattern would simply substitute the former, as documented for fin whale songs off Southern California (Širović et al., 2017). Multiyear and seasonal alternation of different fin whale song INIs, with presence of hybrid songs, have also been reported in two regions of the Northwest Atlantic. In both cases, authors suggest INI shifts occurred within the same population (Delarue et al., 2009; Morano et al., 2012). Thus, we suggest that the rapid turnover of fin whale song INIs along a spatial gradient in the ONA region, with males adopting the new song INI, and the existence of hybrid songs, is the result of cultural transmission, the social learning of information or behaviours from conspecifics (Rendell and Whitehead, 2001). Our study also shows that fin whale song INIs from the distant Barents Sea region differ from the rest of the sampled area (central and eastern North Atlantic). Geographic differentiation in song INIs (Hatch and Clark, 2004) with fin whales within a certain area conforming the same INI has been widely documented (Castellote et al., 2012; Delarue et al., 2009; Hatch and Clark, 2004; Širović et al., 2017; Wood and Širović, 2022). Bird songs also vary geographically, and this variation can be largely attributed to their ability of learning to vocalize through imitation (Kroodsma, 2004; Podos and Warren, 2007). When songbirds learn their songs from models (i.e. conspecifics) inhabiting the same geographic area where they set their breeding territories, local similarities in song structure can arise (i.e. dialects). This learning can occur after dispersal with birds learning or retaining the dialects sang in the breeding grounds where they set (Nelson et al., 2001). Thus, in most species in which vocal learning occurs, the distribution of learned songs may reflect the social interactions among birds, not the genetic structure of the populations (Kroodsma, 2004). A decoupling between patterns of cultural (songs) and genetic variation has also been reported for fin whales (Hatch and Clark, 2004), further suggesting that song INIs may be socially learned in this species. Learning of novel rhythms (i.e. INIs; Vernes et al., 2021) can also be found in sperm whales (Physeter macrocephalus), which can match their clicks to the rhythm of a ship echosounder (Backus and Schevill, 1996), and use codas (i.e. rhythmic patterns of clicks) that are unique to each vocal clan and are socially learned (Rendell and Whitehead, 2003). Fin whales may also be able to learn songs from other populations that not only differ in their INIs but also in their note composition (Helble et al., 2020). After the song transition from 1999 to 2005, we found a gradual increase in song INIs along with a decrease in peak frequencies of the 20 Hz and HF notes. These findings are in line with the gradual trends of decreasing frequencies (Best et al., 2022; Leroy et al., 2018b; Weirathmueller et al., 2017) and increasing INIs (Best et al., 2022; Morano et al., 2012; Širović et al., 2017; Weirathmueller et al., 2017) described for fin whale songs in other ocean basins and in the Mediterranean Sea. Contrarily to the rapid changes in INIs, a global-scale process of cultural transmission cannot explain these directional changes. First, changes in INIs and frequencies occur at different rates in different oceans and there is no convergence in song acoustic characteristics across populations (Leroy et al., 2018a; Širović et al., 2017; Weirathmueller et al., 2017). Second, a similar pattern of decreasing frequencies and increasing INIs has been described for blue whale (B. musculus) songs (Jolliffe et al., 2019; Malige et al., 2020; McDonald et al., 2009), and decreasing frequencies have been reported for bowhead whales (Balaena mysticetus) calls (Thode et al., 2017). Such gradual song changes in multiple species and different ocean basins suggest an adaptation to a common selective pressure, which does not mean that within-region conformity in song characteristics does not result from cultural transmission. Mathematical modelling of the linear decrease in blue whale song frequencies suggest a simultaneous effect from two selection processes: conformity and sexual selection (Malige et al., 2022). Conformity would occur because individuals would be more likely to share variants of a cultural trait with nearby individuals than with more distant ones. This could be caused either by a conformist bias, which occurs when individuals select common variants from those available more often than would be expected by chance, or by more simple processes, such as only learning from nearby individuals (Morgan and Laland, 2012). Sexual selection would drive males to sing lower frequency songs than other whales, presumably because females prefer bigger males that are able to sing lower pitch songs (Malige et al., 2022). Increased blue whale body size in a post-whaling recovery scenario has also been proposed as a potential explanation for this species' song changes; yet blue whale body size distributions should have returned to near pre-whaling values by now and song frequencies continue to decrease (McDonald et al., 2009). Also, it is very unlikely that changes in whale body size evolved in such a straight line at this timescale (Malige et al., 2022). Fin whale songs may evolve in a similar way as blue whale songs do, but so far, none of the proposed hypotheses can convincingly explain the slow frequency song changes in these species (McDonald et al., 2009; Thode et al., 2017). Large-scale and long-term datasets would help understanding if fin whale song INIs and frequencies are constantly evolving or started changing recently in response to a new driver. The rapid and gradual evolution of fin whale song parameters found in this, and other studies (Hatch and Clark, 2004; Širović et al., 2017; Weirathmueller et al., 2017), resemble the patterns of song evolution of some bird species and humpback whales. Evidence from songbirds suggest that these different trajectories in song evolution (rapid versus gradual) occur within certain boundaries because learned songs are subject to a combination of strong stabilizing selection and underlying genetic variation that prevent incremental change for long periods of time (McEntee et al., 2021). In humpback whales, song complexity increases as songs evolve gradually over time, but decreases when revolutions occur (i.e. periods of rapid song changes), suggesting that learning capacities in this species are limited (Allen et al., 2018). After the rapid shift in fin whale song INIs, from 19s to 12s, a gradual reset towards the 19s-INIs seems to be occurring in all sampled areas, except from the Barents Sea. In the northwest Atlantic Ocean, rapid shifts in fin whale song INIs occurred between 15 s and 9 s (Delarue et al., 2009; Morano et al., 2012). Perhaps, like in birdsongs and humpback whales, changes in fin whale song INIs are also limited by learning constrains and genetic predispositions. Our results show that fin whale song INIs from the Barents Sea region differ from the rest of the sampled area. Yet, satellite tracking data from 2015 to 2019 showed that some fin whales summering in Svalbard (Barents Sea) migrate to the SW Portugal region in fall and winter (Lydersen et al., 2020), so some degree of mixing between males from these two acoustic populations occur. Also, a recent study from Svalbard revealed that fin whale song INIs differed between singing seasons, which suggests that either fin whales from that area switch between INIs or different populations use the area sequentially (Papale et al., 2023). Investigating the changing patterns of fin whale song INIs in these two regions (Barents seas and SW Portugal) may shed some light on the learning mechanisms of song INIs and the limitations of its variability. Compared to INIs, fin whale song frequencies of the 20 Hz and HF notes do not vary abruptly but only gradually. Fundamental frequencies of this species' songs seem constrained by the optimisation of long-range communication in pelagic environments (Clark and Garland, 2022; Payne and Webb, 1971). This song frequency limitation may be an adaptation first, to a dispersed and open water distribution of this species during the breeding season (Edwards et al., 2015; Nieukirk et al., 2004) and second, to match a particular frequency band with low levels of noise in deep waters (i.e. a quiet window in frequency) (Clark and Garland, 2022; Curtis et al., 1999). Comparatively, humpback and right whales (Eubalaena spp.) aggregate in coastal breeding grounds (Clapham, 2018; Kenney, 2009) and use higher frequency songs and calls that transmit better in shallow environments (quiet window: 100–400 Hz) (Clark and Ellison, 2004) and do not need to reach distant conspecifics (Clark, 1982; Clark and Garland, 2022). Therefore, the acoustic environment during the mating season and the species' breeding behaviour could constrain variation in song frequencies to keep them within the quiet window. In addition, the animals' physiology can constrain song frequency variation. In birds, the ability to produce low-frequency songs is linked to body size (Ryan and Brenowitz, 1985). If fin whale song frequencies continue to decrease, it can potentially reach the physiological limits of sound production. These limits in song variation can compromise song function and ultimately male fitness when the acoustic habitat in which these songs evolved is changing too rapidly to adapt. For example, the vocal adaptation ability of birds in urban environments (e.g. increasing song frequencies) affect the detection by receivers. If birds are not able to avoid the masking of their songs by noise, this may difficult the establishment and defence of a territory that can ultimately affect their fitness (Habib et al., 2007; Luther and Derryberry, 2012). Similarly, the constraints in fin whale song frequency may limit adaptation to an increasingly noisy environment. Shipping noise, the major source of ocean noise, overlaps in frequency with fin whale songs and can cause a reduction of the communication space (CS) in this species (Clark et al., 2009; Erbe et al., 2019). Models estimate a reduction of CS by vessel n

The rate of cooling of metamorphic rocks provides a first-order constraint on the tectonic processes controlling heat flow and exhumation. For example, for small crustal terranes that were subducted to ultrahigh pressure conditions during the early stages of collisional orogenesis, exhumation is generally fast with rates similar to plate velocities, such that cooling is also rapid. Similarly, rates of cooling are commonly fast (generally ~20–30°C/Myr) during exhumation of metamorphic core complexes or due to transpression. By contrast, cooling in some granulite terranes can be slow and close-to-isobaric, leading to time-integrated cooling rates of <5°C/Myr. The implication of such slow rates of cooling is that these granulite terranes were close to isostatic equilibrium as a result of sustained high mantle heat flow that limited exhumation by erosion. However, constraining initial cooling rates in granulite terranes can be difficult, particularly where the rocks reached ultrahigh temperatures (>900 °C) that exceed the closure temperature of many geochronometers. In order to overcome this difficulty, we combine U–Pb zircon geochronology with Ti-in-zircon thermometry to investigate the thermal history of metapelitic rocks from the Eastern Ghats Province of eastern India. For the combined dataset of metamorphic zircon from the samples, concordant dates decrease continuously within 2σ uncertainty from around 950 Ma to 800 Ma, consistent with c. 150 Ma of zircon crystallization. Ti-in-zircon temperatures for each dated spot during this period decrease with age, corresponding to linear cooling rates ranging from 0.26 to 0.90°C/Myr. We propose that retention of heat producing elements in the lower crust of the Eastern Ghats Province and a low net erosion rate were responsible for c. 150 Myr of ultra-slow cooling. The location of the Eastern Ghats Province on the margin of the supercontinent Rodinia may have been a contributing factor enabling the region to remain relatively undisturbed until it was exhumed during the formation of Gondwana.

Previous articleNext article No AccessMiscellaneousThe Sounds of Life: How Digital Technology Is Bringing Us Closer to the Worlds of Animals and Plants. By Karen Bakker. Princeton (New Jersey): Princeton University Press. $33.00. ix + 354 p.; index. ISBN: 9780691206288 (hc); 9780691240985 (eb). 2022.Christopher W. ClarkChristopher W. ClarkCornell Lab of Ornithology and Neurobiology & Behavior, Cornell University, Ithaca, New York Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by The Quarterly Review of Biology Volume 98, Number 3September 2023 Published in association with Stony Brook University Article DOIhttps://doi.org/10.1086/726507 Views: 2Total views on this site For permission to reuse, please contact [email protected].PDF download Crossref reports no articles citing this article.

Presented here is a broadly applicable, transparent, repeatable analytical framework for assessing relative risk of anthropogenic disturbances on marine vertebrates, with the emphasis on the sound generating aspects of the activity. The objectives are to provide managers and action-proponents tools with which to objectively evaluate drivers of potential biological risk, to identify data gaps that limit assessment, and to identify actionable measures to reduce risk. Current regulatory assessments of how human activities (particularly those that produce sound) influence the likelihood of marine mammal behavioral responses and potential injury, rely principally on generalized characterizations of exposure and effect using simple, threshold-based criteria. While this is relatively straightforward in regulatory applications, this approach fails to adequately address realistic site and seasonal scenarios, other potential stressors, and scalable outcome probabilities. The risk assessment presented here is primarily based on a common and broad understanding of the spatial-temporal-spectral intersections of animals and anthropogenic activities, and specific examples of its application to hypothetical offshore wind farms are given. The resulting species- and activity-specific framework parses risk into two discrete factors: a population’s innate ‘vulnerability’ (potential degree of susceptibility to disturbance) and an ‘exposure index’ (magnitude-duration severity resulting from exposure to an activity). The classic intersection of these factors and their multi-dimensional components provides a relativistic risk assessment process for realistic evaluation of specified activity contexts, sites, and schedules, convolved with species-specific seasonal presence, behavioral-ecological context, and natural history. This process is inherently scalable, allowing a relativistic means of assessing potential disturbance scenarios, tunable to animal distribution, region, context, and degrees of spatial-temporal-spectral resolution.

The Mount Woods Domain, in the northeastern Gawler Craton, occupies a tectonically important location in Proterozoic Australia, yet there is very little published U-Pb geochronology data from this region to underpin tectonic models. New LA-ICP-MS U-Pb monazite and detrital zircon geochronology reveal Archean to Paleoproterozoic basement in the central Mount Woods Domain, comprising metasedimentary rocks and garnet-bearing granite with protolith ages of c. 2550-2400 Ma and metasedimentary rocks deposited after c. 1855 Ma. The southern Mount Woods Domain contains younger metasedimentary sequences deposited after 1750 Ma. Metamorphic monazite and zircon geochronology combined with phase equilibria modelling show the rocks of the central Mount Woods Domain were metamorphosed to granulite facies between 1700 and 1670 Ma, reaching pressure and temperature conditions of 4.8-5.3 kbar and 800-840 degrees C. Monazite geochronology from samples located along major shear zones and in the westernmost Mount Woods Domain record amphibolite facies metamorphism and reworking at 1570-1550 Ma, with a further phase of shear zone activity along the northern margin of the Mount Woods Domain at c. 1480 Ma. Laser ablation inductively coupled plasma triple quadrupole mass spectrometry (LA-ICP-QQQ-MS) Rb-Sr biotite ages from across the Mount Woods Domain range between 1480 and 1390 Ma. The protracted geological history in the Mount Woods Domain from c. 2500-1400 Ma provides a piercing point linking different regions of Proterozoic Australia and western Laurentia during the tenure of the Nuna supercontinent.

&lt;p&gt;In situ analysis of the Rb&amp;#8211;Sr isotopic composition of biotite via triple quadropole LA&amp;#8211;ICPMS is an increasingly popular method for constraining the time through the Sr closure temperature in rocks. Although interpreting the radiogenic product can be complicated by various factors that can affect diffusion of Rb and Sr, the role of the different minerals that may be in contact with biotite in regard to local diffusion gradients is poorly understood. In this study, we show the importance of analysing Rb&amp;#8211;Sr isotopic data in the context of detailed petrographic observations, which reveals that the ratios obtained are affected by various diffusion pathways between like material and minerals that preferentially incorporate Sr. The studied samples are metapelites from the Fraser Zone (Western Australia) that have peak metamorphic conditions of about 850 &amp;#176;C and 9 kbar and a history of cryptic biotite Ar-Ar ages of ~1205 Ma, which on face value could imply exhumation rates that are some of the fastest recorded in Earth&amp;#8217;s history. However, new biotite data from in-situ Rb-Sr analysis highlights differences in Sr retentivity. While calculated isochrons may at first yield large errors, sorting based on the location of the grains in terms of surrounding minerals yields a possible solution for varying Sr values skewing the ages in the sample. This results in an average age of 1205 Ma for biotite and sillimanite surrounded grains and 1107 Ma, from biotite and sillimanite surrounded grains and quartz and K-feldspar surrounded grains. This shows that the diffusive properties of Sr between biotite and the surrounding minerals creating variable re-equilibration between the different domains surrounding biotite. The complexities of Sr diffusion within between the various phases are still unknown, but the apparent effect between the surrounding material on the biotite and the measured initial Sr values does play a key factor in the final calculated ages and the interpretations they represent.&lt;/p&gt;

Ultrahigh-temperature (UHT; T max ≥ 900 °C) metamorphism requires unusually high heat in continental crust at depths of 15–55 km, but how such extreme thermal conditions are achieved is enigmatic. In this Review, we investigate UHT metamorphism, based on advances in metamorphic petrology and numerical modelling, to identify the tectonic settings where UHT metamorphism occurs. UHT rocks are spatially related to convergent plate margins and spatially correlate with the assembly of supercontinents, such as the formation of Rodinia (1,350–850 Ma). Commonly, UHT occurrences are linked to arc–backarc systems, thinned lithosphere or orogenic plateaus. Elevated mantle heat in younger arc and backarc systems is related to slab rollback, whereas thinned lithosphere in ancient orogens is related to lithospheric peeling or shallow slab breakoff. By contrast, UHT metamorphism in orogenic plateaus is a result of radiogenic heating during thickening, sometimes with elevated mantle heat during orogenic collapse. Geophysical mapping of Moho temperature and depth beneath present-day orogens reveals the locations where UHT metamorphism is occurring, such as in the Tibetan Plateau and the North American Cordillera. Future research should include improved geodynamic modelling of UHT metamorphism and the respective tectonic settings to establish quantitative correlations between a viable heat source and the spatial extent of UHT metamorphism.

The detection range of calling animals is commonly described by the passive sonar equations. However, the sonar equations do not account for interactions between source and ambient sound level, i.e., the Lombard effect. This behavior has the potential to introduce non-linearities into the sonar equations and result in incorrectly predicted detection ranges. Here, we investigate the relationship between ambient sound and effective detection ranges for North Atlantic right whales (Eubalaena glacialis) in Cape Cod Bay, MA, USA using a sparse array of acoustic recorders. Generalized estimating equations were used to model the probability that a call was detected as a function of distance between the calling animal and the sensor and the ambient sound level. The model suggests a non-linear relationship between ambient sound levels and the probability of detecting a call. Comparing the non-linear model to the linearized version of the same model resulted in 12 to 25% increases in the effective detection range. We also found evidence of the Lombard effect suggesting that it is the most plausible cause for the non-linearity in the relationship. Finally, we suggest a simple modification to the sonar equation for estimating detection probability for single sensor monitoring applications.

There has been enormous growth in technical mechanisms for collecting, analyzing, and visualizing baleen whale acoustic behaviors. Organizing and synthesizing the import of these behaviors remain a challenge, as is the placement of such efforts within the broader framework of adaptationAdaptation, selective advantageSelective advantage, and behavioral and evolutionary ecologyEvolutionary ecology. Synthesis based on bioacoustic behaviorBioacoustic behavior includes consideration of low-frequency, physical acoustic propagationAcoustic propagation in the marine environmentPhysical environment and the resultant potential beneficial opportunities for baleen whales to communicateCommunicate, forageForage, navigateNavigate, orient, and maintain social organization. Observations of baleen whale bioacoustic behaviorsBioacoustic behavior range from singing as a male reproductive advertisement display occurring over periods of many months within a potentially enormous communicationCommunication space to non-singing events associated with short duration social contexts involving both sexes and multiple age groups. Such observations are helpful as a starting framework but should be recognized as simplifications given the high levels of behavioral variabilityBehavioral variability and complexity inherent in these long-lived, large-brained species. A variety of observations, referred to as discrepant eventsDiscrepant event, suggest that present understandings of baleen whale behavioral ecologyBehavioral ecology are insufficient to explain the spatial and temporal scales over which baleen whales engage in bioacoustic behaviorsBioacoustic behavior. Such behaviors and behavioral variabilityBehavioral variability at ocean basin scales have promoted the concepts of acoustic environmentAcoustic environment and acoustic habitatAcoustic habitat, and motivated concerns over the biological influences of anthropogenic sounds on species-specific habitats, behaviors, and survival.