The research, published in Frontiers in Mammal Science, was carried out by Julia Joukhadar, Anton Baotic and Angela S. Stoeger of the Acoustics Research Institute at the Austrian Academy of Sciences and the University of Vienna. Their team recorded a semi-free-ranging herd of seven African savanna elephants (Loxodonta africana) at the Adventures with Elephants conservation and research unit in Bela Bela, South Africa, between 4 and 28 November 2018. Across 18 daytime recording sessions, handlers divided the herd into two subgroups positioned about 200 metres apart with no line of sight between them, a standardized separation designed to elicit frequent contact rumbles while minimizing visual cues. Synchronized microphones and video cameras captured every rumble, yielding 459 annotated calls from three adult females, two adult males and two calves.

The central question was deceptively simple: does the same set of elephant rumbles look like coordinated communication no matter how you measure it? To find out, the researchers applied three previously published analytical frameworks to the identical dataset. The first, a quartile-based approach, sorts calls into overlapping, adjacent, intermittent or single categories using statistical boundaries derived from the latencies between vocalizations. The second, bout criterion interval analysis, uses log-survivorship curves to find the natural breakpoint separating calls clustered within a bout from those separated by longer silences. The third, antiphonal probability analysis, adapted from a 1977 study of white-throated sparrows, asks whether an individual produces more calls after another’s vocalization than would be expected by chance alone.

The results were strikingly divergent. Under the quartile-based method, nearly half of all rumbles — 49 percent — were classified as overlapping, meaning they began before the previous call had ended. Adjacent calls, the category most likely to represent potential antiphonal responses, were the rarest at just 14 percent. The temporal boundaries themselves shifted compared with earlier work: the upper limit for intermittent calls came out at 71 seconds, well below the 120 seconds reported in a foundational 2003 study. Because the boundaries are computed from the data’s own quartiles, a dataset rich in overlapping calls compresses the thresholds, so the same framework can carve the same behaviour into different categories depending on how aroused or urgent the animals happen to be.

The antiphonal probability analysis told a different story again. Using a 25-second response window, the team tested whether each elephant answered others more often than chance would predict. Only two of the seven individuals — the adult male Chova and the adult female Messina — produced significantly more responses than expected. The remaining five, including Shan, the herd’s most prolific caller with 115 rumbles, showed no statistically reliable evidence of turn-taking. The authors caution that this framework is inherently conservative: it assumes that answerable time windows do not overlap, which inflates the number of responses expected by chance during periods of intense calling and biases the analysis against detecting genuine antiphony. Calls from unidentified individuals, which made up 19 percent of the dataset, further complicated individual-level conclusions.

The bout criterion interval analysis produced yet another picture. A Kolmogorov–Smirnov test confirmed that the intervals between calls deviated significantly from the random, exponential pattern expected if calling were uncoordinated. A segmented regression on the log-survivorship curve identified a breakpoint at roughly 124 seconds — dramatically longer than the 1.5-second threshold reported for wild elephant family groups departing a water hole in a 2012 study. By this measure, nearly 90 percent of all recorded rumbles fell within extended group-level calling bouts, suggesting that elephant vocal activity in this context is dominated by sustained chorusing rather than neat, alternating exchanges.

Notably, the 124-second bout criterion and the 71-second quartile boundary for intermittent calls, though derived by entirely different methods, point to a similar transition zone between clustered and isolated vocal activity. The authors are careful to note that this agreement does not constitute independent validation, but it hints that some underlying temporal structure may be real even as the labels attached to it vary. Call duration, meanwhile, told its own story: rumbles ranged from just over one second to 12.44 seconds, but duration differed little across temporal categories. Instead, mixed-effects models revealed that individual identity — not timing — was the strongest driver of how long a rumble lasted, with individual identity explaining considerably more variance than recording session.

The broader implication is unsettling for the field. Terms such as chorusing, turn-taking, duetting, bouts and antiphonal calling are often used interchangeably in elephant research and across taxa, even though they capture partially overlapping aspects of vocal coordination. The 30-second response window introduced in 2003 was adopted by later studies without further testing, and the new results show that such fixed thresholds may not generalize across datasets. Because only a handful of studies have examined elephant vocal timing at all, early methodological choices risk having a disproportionate influence on how temporal coordination is defined and detected — potentially shaping the entire field’s understanding of coordinated vocal behaviour in one of Earth’s most socially complex species.

The authors argue that the way forward lies not in crowning a single universal method, but in combining complementary frameworks. Mixture models, successfully used to estimate bout criteria in humpback whale social calls, could test whether inter-call intervals form distinct temporal populations. Permutation procedures could assess whether observed response timing differs from chance expectations, while mixed-effects and hierarchical models could account for individual identity, dyadic relationships, recording session and context simultaneously. Social bonds matter here: previous work has shown that chimpanzees preferentially join the choruses of closely bonded males, and that bonobo turn-taking tracks relationship strength, so dyadic analyses may reveal patterns that group-level statistics obscure.

For now, the elephants of Bela Bela continue to rumble across their 300-hectare range, indifferent to the statistical debates their calls provoke. But the study’s message resonates far beyond one herd: when scientists ask whether animals converse, the answer may depend as much on the mathematics of the question as on the biology of the animals. In a field where a single threshold, borrowed and never revalidated, can determine whether a rumble counts as a reply or mere noise, methodological humility may be the most important instrument in the bioacoustician’s kit.

Subject of Research: Temporal coordination and antiphonal calling in African savanna elephant vocalizations

Article Title: Methodological frameworks influence the classification and interpretation of temporal coordination in African savanna elephant vocalizations

Article References: Joukhadar, J., Baotic, A., & Stoeger, A. S. (2026). Methodological frameworks influence the classification and interpretation of temporal coordination in African savanna elephant vocalizations. Frontiers in Mammal Science, 5, Article 1893031. https://doi.org/10.3389/fmamm.2026.1893031

Image Credits: AI Generated

DOI: 10.3389/fmamm.2026.1893031

Keywords: African savanna elephant, acoustic communication, antiphonal calling, vocal timing, bioacoustics, temporal coordination, turn-taking, vocal bouts, rumbles, methodology, animal behavior, Loxodonta africana

The research team, led by scientists affiliated with Hawassa University and Addis Ababa University in Ethiopia, compiled more than 6,800 occurrence records for Loxodonta africana from field surveys in Ethiopian national parks, published literature, and the Global Biodiversity Information Facility. After removing duplicate and spatially redundant records, 4,298 verified presence points remained, spanning eight East African countries: Eritrea, Ethiopia, South Sudan, Somalia, Kenya, Uganda, Tanzania, and Rwanda. The authors restricted the dataset to records documented since the 1990s to avoid contaminating the models with historical distributions that no longer reflect where elephants actually live.

To translate those sightings into a predictive map, the researchers built an ensemble species distribution model that combined seven algorithms: three regression-based methods, namely generalized linear models, generalized additive models, and multivariate adaptive regression splines, and four machine-learning approaches, including boosted regression trees, maximum entropy, random forests, and support vector machines. Each algorithm was trained on 70 percent of the occurrence data and validated on the remaining 30 percent using a ten-fold subsampling scheme, with 10,000 background points generated across the study area to represent pseudo-absences. The final ensemble prediction was produced by averaging the individual model outputs, weighting each by its true skill statistic, a standard technique for reducing the uncertainty inherent in any single algorithm.

The environmental backbone of the models came from twenty predictor variables: nineteen bioclimatic layers drawn from the WorldClim 2.1 database at roughly one-kilometer resolution, plus a human footprint index that quantifies cumulative pressure from roads, settlements, agriculture, and infrastructure. Because many climate variables are strongly correlated with one another, the team screened them for multicollinearity, retaining only predictors with pairwise correlations below 0.7 and variance inflation factors under 5. Ten variables survived the filtering. Future projections relied on the HadGEM3-GC global circulation model from the Coupled Model Intercomparison Project Phase 6, run under two shared socioeconomic pathways: SSP2-4.5, an intermediate emissions scenario, and SSP5-8.5, a very high emissions pathway in which atmospheric carbon dioxide roughly triples by 2100.

The models performed exceptionally well. The average area under the receiver operating characteristic curve reached 0.94, with random forests and support vector machines delivering the strongest individual results, and the machine-learning algorithms significantly outperformed the regression-based approaches. Sensitivity and specificity values of 0.91 and 0.86 respectively indicated that the models were both reliable at identifying where elephants can live and where they cannot. Importantly, the predicted current suitable habitat of approximately 887,000 square kilometers under the weighted-mean threshold closely matched the species’ extant range of about 889,000 square kilometers delineated by the IUCN, lending the projections considerable credibility.

Which environmental factors mattered most? Not the human footprint, surprisingly. At the broad regional scale of the analysis, the dominant drivers of elephant habitat suitability were climatic: precipitation of the warmest quarter, isothermality, the mean temperature of the driest quarter, and precipitation of the driest month. These variables govern water availability, forage productivity, and thermal stress tolerance, all of which directly shape elephant distribution, migration corridors, and survival. The authors note that while climate determines the overall pattern of suitable habitat across East Africa, human pressures such as agricultural expansion, roads, and poaching remain critical local threats that fragment landscapes and squeeze the corridors elephants need to move between resource patches.

The future projections are where the study turns alarming. Under the intermediate SSP2-4.5 scenario, mean suitable habitat is projected to decline by 51.9 percent by 2050 and 52.6 percent by 2070 compared with the current average of roughly 821,000 square kilometers. Under the worst-case combinations, losses reach as high as 71.3 percent by 2050 and 73 percent by 2070, depending on the threshold applied. New habitat gains are minimal, amounting to only about 4 to 5 percent of the current range, meaning the species faces a net contraction of nearly half its climatic niche. The Horn of Africa fares worst of all: suitable areas in South Sudan and Somalia are projected to become unsuitable entirely, and the already fragmented habitats of Eritrea and eastern Ethiopia shrink to isolated patches.

Perhaps the most sobering finding concerns protected areas. East Africa’s protected area network covers about 573,000 square kilometers, yet only 17.2 to 17.9 percent of the currently suitable elephant habitat falls inside it. More than 82 percent of predicted suitable habitat lies outside formal protection, exposed to agricultural conversion, charcoal production, and infrastructure development. The concept of Anthropocene refugia, areas that remain climatically suitable and protected over time, offers a framework for identifying the landscapes most likely to sustain elephants through the coming decades. The study found that only about 21 percent of the remaining stable habitat projected for 2050 and 2070 sits within protected areas, and these refugia themselves decline under the harsher emissions scenario, shrinking by more than 21,000 square kilometers between the moderate and severe 2050 projections.

The authors caution that legal designation alone does not guarantee viable habitat. Substantial portions of the existing protected area network are already climatically unsuitable for elephants, and many reserves face severe encroachment and anthropogenic pressure. Elephants surviving in marginal or degraded habitats may function as refugee species, confined to forests and fragments to avoid people rather than occupying the landscapes their climatic tolerances would predict. This behavioral compression can mask the true extent of range loss and complicates conservation planning, particularly in the Horn of Africa where continuous population monitoring is limited and corridor establishment remains insufficient.

The study’s conclusions point toward an urgent agenda: reassess and realign protected area boundaries so that future climatic refugia are actually captured within conservation networks, establish transboundary corridors to maintain connectivity across national borders, strengthen community engagement and stewardship, and protect current habitats even where models forecast future unsuitability, especially for the vulnerable populations of Eritrea, Somalia, and South Sudan. Neither the suitable habitats inside protected areas nor those outside them, the authors conclude, can by themselves guarantee the long-term survival of the species. With Africa’s savanna elephants already down 30 percent over recent decades according to continent-wide surveys, and roughly 415,000 individuals of both African elephant species remaining, the window for proactive, climate-informed conservation in East Africa is narrowing rapidly. Mapping where elephants can persist, and securing those places before the climate closes them off, may be the difference between a managed decline and a functional extinction across much of the species’ eastern range.

Subject of Research: Climate-driven habitat suitability and Anthropocene refugia for the African savanna elephant in East Africa

Article Title: Persistence Vulnerability of the African Savanna Elephant Loxodonta africana to Anthropocene Threats in East Africa

Article References: Ahmed, A. S., Melese, D., Aligaz, M. A., Atickm, A., & Kufa, C. A. (2026). Persistence Vulnerability of the African Savanna Elephant Loxodonta africana to Anthropocene Threats in East Africa. Ecology and Evolution, 16(9), Article e74389. https://doi.org/10.1002/ece3.74389

Image Credits: AI Generated

DOI: 10.1002/ece3.74389

Keywords: African savanna elephant, Loxodonta africana, species distribution modeling, climate change, Anthropocene refugia, East Africa, protected areas, habitat loss, ensemble models, human-elephant conflict, conservation planning, Horn of Africa