Paleoenvironmental inferences on the Late Miocene hominoid-bearing site of Can Llobateres (NE Iberian Peninsula): An ecometric approach based on functional dental traits.

Hispanopithecus laietanus from the Late Miocene (9.8 Ma) of Can Llobateres 1 (CLL1; Vallès-Penedès Basin, NE Iberian Peninsula) represents one of the latest occurrences of fossil apes in Western mainland Europe, where they are last recorded at ∼9.5 Ma. The paleoenvironment of CLL1 is thus relevant for understanding the extinction of European hominoids. To refine paleoenvironmental inferences for CLL1, we apply ecometric models based on functional crown type (FCT) variables-a scoring scheme devised to capture macroscopic functional traits of occlusal shape and wear surfaces of herbivorous large mammal molars. Paleotemperature and paleoprecipitation estimates for CLL1 are provided based on published regional regression models linking average FCT of large herbivorous mammal communities to climatic conditions. A mapping to Whittaker's present-day biome classification is also attempted based on these estimates, as well as a case-based reasoning via canonical variate analysis of FCT variables from five relevant biomes. Estimates of mean annual temperature (25 °C) and mean annual precipitation (881 mm) classify CLL1 as a tropical seasonal forest/savanna, only in partial agreement with the canonical variate analysis results, which classify CLL1 as a tropical rainforest with a higher probability. The former biome agrees better with previous inferences derived from fossil plants and mammals, as well as preliminary isotopic data. The misclassification of CLL1 as a tropical forest is attributed to the mixture of forest-adapted taxa with others adapted to more open environments, given that faunal and plant composition indicates the presence of a dense wetland/riparian forest with more open woodlands nearby. The tested FCT ecometric approaches do not provide unambiguous biome classification for CLL1. Nevertheless, our results are consistent with those from other approaches, thus suggesting that FCT variables are potentially useful to investigate paleoenvironmental changes through time and space-including those that led to the extinction of European Miocene apes.

Homo heterogenus: Variability in early Pleistocene Homo environments.

To understand the ecological dominance of Homo sapiens, we need to investigate the origins of the plasticity that has enabled our colonization of the planet. We can approach this by exploring the variability of habitats to which different hominin populations have adapted over time. In this article, we draw upon and synthesize the current research on habitats of genus Homo during the early Pleistocene. We examined 121 published environmental reconstructions from 74 early Pleistocene sites or site phases to assess the balance of arguments in the research community. We found that, while grasslands and savannahs were prominent features of Homo habitats in the early Pleistocene, current research does not place early Pleistocene Homo, in any single environmental type, but in a wide variety of environments, ranging from open grasslands to forests. Our analysis also suggests that the first known dispersal of Homo out of Africa was accompanied by niche expansion.

Reconciling taxon senescence with the Red Queen's hypothesis.

In the fossil record, taxa exhibit a regular pattern of waxing and waning of occupancy, range or diversity between their origin and extinction. This pattern appears to contradict the law of constant extinction, which states that the probability of extinction in a given taxon is independent of that taxon's age. It is nevertheless well established for species, genera and higher taxa of terrestrial mammals, marine invertebrates, marine microorganisms, and recent Hawaiian clades of animals and plants. Here we show that the apparent contradiction between a stochastically constant extinction rate and the seemingly deterministic waxing and waning pattern of taxa disappears when we consider their peak of expansion rather than their final extinction. To a first approximation, we find that biotic drivers of evolution pertain mainly to the peak of taxon expansion, whereas abiotic drivers mainly apply to taxon extinction. The Red Queen's hypothesis, which emphasizes biotic interactions, was originally proposed as an explanation of the law of constant extinction. Much effort has since been devoted to determining how this hypothesis, emphasizing competition for resources, relates to the effects of environmental change. One proposed resolution is that biotic and abiotic processes operate at different scales. By focusing attention on taxon expansion rather than survival, we resolve an apparent contradiction between the seemingly deterministic waxing and waning patterns over time and the randomness of extinction that the Red Queen's hypothesis implies.

Do species factories exist? Detecting exceptional patterns of evolution in the mammalian fossil record.

A species factory refers to the source that gives rise to an exceptionally large number of species. However, what is it exactly: a place, a time or a combination of places, times and environmental conditions, remains unclear. Here we search for species factories computationally, for which we develop statistical approaches to detect origination, extinction and sorting hotspots in space and time in the fossil record. Using data on European Late Cenozoic mammals, we analyse where, how and how often species factories occur, and how they potentially relate to the dynamics of environmental conditions. We find that in the Early Miocene origination hotspots tend to be located in areas with relatively low estimated net primary productivity. Our pilot study shows that species first occurring in origination hotspots tend to have a longer average longevity and a larger geographical range than other species, thus emphasizing the evolutionary importance of the species factories.

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes.

The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs.

Herbivore teeth predict climatic limits in Kenyan ecosystems.

A major focus in evolutionary biology is to understand how the evolution of organisms relates to changes in their physical environment. In the terrestrial realm, the interrelationships among climate, vegetation, and herbivores lie at the heart of this question. Here we introduce and test a scoring scheme for functional traits present on the worn surfaces of large mammalian herbivore teeth to capture their relationship to environmental conditions. We modeled local precipitation, temperature, primary productivity, and vegetation index as functions of dental traits of large mammal species in 13 national parks in Kenya over the past 60 y. We found that these dental traits can accurately estimate local climate and environment, even at small spatial scales within areas of relatively uniform climate (within two ecoregions), and that they predict limiting conditions better than average conditions. These findings demonstrate that the evolution of key functional properties of organisms may be more reflective of demands during recurring adverse episodes than under average conditions or during isolated severe events.

Eco-ISEA3H, a machine learning ready spatial database for ecometric and species distribution modeling.

We present the Eco-ISEA3H database, a compilation of global spatial data characterizing climate, geology, land cover, physical and human geography, and the geographic ranges of nearly 900 large mammalian species. The data are tailored for machine learning (ML)-based ecological modeling, and are intended primarily for continental- to global-scale ecometric and species distribution modeling. Such models are trained on present-day data and applied to the geologic past, or to future scenarios of climatic and environmental change. Model training requires integrated global datasets, describing species' occurrence and environment via consistent observational units. The Eco-ISEA3H database incorporates data from 17 sources, and includes 3,033 variables. The database is built on the Icosahedral Snyder Equal Area (ISEA) aperture 3 hexagonal (3H) discrete global grid system (DGGS), which partitions the Earth's surface into equal-area hexagonal cells. Source data were incorporated at six nested ISEA3H resolutions, using scripts developed and made available here. We demonstrate the utility of the database in a case study analyzing the bioclimatic envelopes of ten large, widely distributed mammalian species.

The emergence of modern zoogeographic regions in Asia examined through climate-dental trait association patterns.

The complex and contrasted distribution of terrestrial biota in Asia has been linked to active tectonics and dramatic climatic changes during the Neogene. However, the timings of the emergence of these distributional patterns and the underlying climatic and tectonic mechanisms remain disputed. Here, we apply a computational data analysis technique, called redescription mining, to track these spatiotemporal phenomena by studying the associations between the prevailing herbivore dental traits of mammalian communities and climatic conditions during the Neogene. Our results indicate that the modern latitudinal zoogeographic division emerged after the Middle Miocene climatic transition, and that the modern monsoonal zoogeographic pattern emerged during the late Late Miocene. Furthermore, the presence of a montane forest biodiversity hotspot in the Hengduan Mountains alongside Alpine fauna on the Tibetan Plateau suggests that the modern distribution patterns may have already existed since the Pliocene.

What are patterns of rise and decline?

The notions of change, such as birth, death, growth, evolution and longevity, extend across reality, including biological, cultural and societal phenomena. Patterns of change describe how success and composition of every entity, from species to societies, vary across time. Languages develop into new languages, music and fashion continuously evolve, economies rise and decline, ecological and societal crises come and go. A common way to perceive and analyse change processes is through patterns of rise and decline, the ubiquitous, often distinctively unimodal trajectories describing life histories of various entities. These patterns come in different shapes and are measured according to varying definitions. Depending on how they are measured, patterns of rise and decline can reveal, emphasize, mask or obscure important dynamics in natural and cultural phenomena. Importantly, the variations of how dynamics are measured can be vast, making it impossible to directly compare patterns of rise and decline across fields of science. Standardized analysis of these patterns has the potential to uncover important but overlooked commonalities across natural phenomena and potentially help us catch the onset of dramatic shifts in entities' state, from catastrophic crashes in success to gradual emergence of new entities. We provide a framework for standardized recognizing, characterizing and comparing patterns of change by combining understanding of dynamics across fields of science. Our toolkit aims at enhancing understanding of the most general tendencies of change, through two complementary perspectives: dynamics of emergence and dynamics of success. We gather comparable cases and data from different research fields and summarize open research questions that can help us understand the universal principles, perception-biases and field-specific tendencies in patterns of rise and decline of entities in nature.

The case of the grass-eating suids in the Plio-Pleistocene Turkana Basin: 3D dental topography in relation to diet in extant and fossil pigs.

Two separate subfamilies of Plio-Pleistocene African pigs (suids) consecutively evolved hypsodont and horizodont molars with flat occlusal surfaces, commonly interpreted as an adaptive trait to a grazing diet, similar to that of the present warthogs (Phacochoerus spp.). To investigate this in detail, we studied the 3D-dental topography of fossil specimens from the Turkana Basin, using geographic information systems-based methods. To establish baselines for interpretation of the Turkana Basin suids, topography of third molars of extant suids with known diets were analyzed: grazing warthog (Phacochoerus africanus), herbivorous mixed-feeder forest hog (Hylochoerus meinertzhageni), omnivorous generalist wild boar (Sus scrofa), omnivorous fruit and tuber eater bush pig (Potamochoerus spp.), and omnivorous fruit eater babirusa (Babyrousa spp.) In addition, we analyzed supposedly browsing Miocene suids, Listriodon spp. The same topographic measures were applied to Plio-Pleistocene specimens from the Turkana Basin, Kenya: Notochoerus euilus, Notochoerus scotti, Kolpochoerus heseloni, and Metridiochoerus andrewsi. With some differences between techniques, 3D-dental topography analysis of extant suid molars mostly predicts the dietary differences between the species correctly. The grazing P. africanus differs from both the omnivorous suids and the herbivorous mixed-feeder H. meinertzhageni in all except one metrics. The omnivorous mostly tropical suids, Potamochoerus and Babyrousa, primarily differ from the generalist, S. scrofa, in the orientation patch count analysis, showing higher occlusal complexity in the latter. Although, there might be significant gaps between the morphological changes and the ecological changes, we conclude that based on comparison of dental topography with the present-day suids, N. scotti and M. andrewsi were most likely highly specialized grazers, while N. euilus and K. heseloni retained more of their ancestral, omnivorous heritage, but consumed grasses more than the extant omnivorous suids. RESEARCH HIGHLIGHTS: Dental topography can predict different diets in present-day wild pigs. The Plio-Pleistocene pigs in the Turkana Basin had dental topography mostly similar to extant grazing warthog, although some species also had resemblances to omnivorous forest pigs.

Dental functional morphology predicts the scaling of chewing rate in mammals.

How food intake and mastication scale to satisfy the metabolic needs of mammals has been the subject of considerable scientific debate. Existing theory suggests that the negative allometric scaling of metabolic rate with body mass is compensated by a matching allometric scaling of the chewing rate. Why empirical studies have found that the scaling coefficients of the chewing rate seem to be systematically smaller than expected from theory remains unknown. Here we explain this imparity by decoupling the functional surface area of teeth from overall surface area. The functional surface area is relatively reduced in forms emphasizing linear edges (e.g., lophodont) compared with forms lacking linear structures (e.g., bunodont). In forms with reduced relative functional surface, the deficit in food processed per chew appears to be compensated for by increased chewing rate, such that the metabolic requirements are met. This compensation accounts for the apparent difference between theoretically predicted and observed scaling of chewing rates. We suggest that this reflects adaptive functional evolution to plant foods with different fracture properties and extend the theory to incorporate differences in functional morphology.

Author Correction: The rise and fall of the Old World savannah fauna and the origins of the African savannah biome.

In the version of this Article originally published, each of the five panels in Fig. 5 incorrectly contained a black diagonal line across the plot. This has now been corrected.

The rise and fall of the Old World savannah fauna and the origins of the African savannah biome.

Despite much interest in the ecology and origins of the extensive grassland ecosystems of the modern world, the biogeographic relationships of savannah palaeobiomes of Africa, India and mainland Eurasia have remained unclear. Here we assemble the most recent data from the Neogene mammal fossil record in order to map the biogeographic development of Old World mammalian faunas in relation to palaeoenvironmental conditions. Using genus-level faunal similarity and mean ordinated hypsodonty in combination with palaeoclimate modelling, we show that savannah faunas developed as a spatially and temporally connected entity that we term the Old World savannah palaeobiome. The Old World savannah palaeobiome flourished under the influence of middle and late Miocene global cooling and aridification, which resulted in the spread of open habitats across vast continental areas. This extensive biome fragmented into Eurasian and African branches due to increased aridification in North Africa and Arabia during the late Miocene. Its Eurasian branches had mostly disappeared by the end of the Miocene, but the African branch survived and eventually contributed to the development of Plio-Pleistocene African savannah faunas, including their early hominins. The modern African savannah fauna is thus a continuation of the extensive Old World savannah palaeobiome.

The phylogenetic signal in tooth wear: What does it mean?

A new study by Fraser et al (2018) urges the use of phylogenetic comparative methods, whenever possible, in analyses of mammalian tooth wear. We are concerned about this for two reasons. First, this recommendation may mislead the research community into thinking that phylogenetic signal is an artifact of some sort rather than a fundamental outcome of the evolutionary process. Secondly, this recommendation may set a precedent for editors and reviewers to enforce phylogenetic adjustment where it may unnecessarily weaken or even directionally alter the results, shifting the emphasis of analysis from common patterns manifested by large clades to rare cases.

An ecometric analysis of the fossil mammal record of the Turkana Basin.

Although ecometric methods have been used to analyse fossil mammal faunas and environments of Eurasia and North America, such methods have not yet been applied to the rich fossil mammal record of eastern Africa. Here we report results from analysis of a combined dataset spanning east and west Turkana from Kenya between 7 and 1 million years ago (Ma). We provide temporally and spatially resolved estimates of temperature and precipitation and discuss their relationship to patterns of faunal change, and propose a new hypothesis to explain the lack of a temperature trend. We suggest that the regionally arid Turkana Basin may between 4 and 2 Ma have acted as a 'species factory', generating ecological adaptations in advance of the global trend. We show a persistent difference between the eastern and western sides of the Turkana Basin and suggest that the wetlands of the shallow eastern side could have provided additional humidity to the terrestrial ecosystems. Pending further research, a transient episode of faunal change centred at the time of the KBS Member (1.87-1.53 Ma), may be equally plausibly attributed to climate change or to a top-down ecological cascade initiated by the entry of technologically sophisticated humans.This article is part of the themed issue 'Major transitions in human evolution'.

Active learning with drifting streaming data.

In learning to classify streaming data, obtaining true labels may require major effort and may incur excessive cost. Active learning focuses on carefully selecting as few labeled instances as possible for learning an accurate predictive model. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and models need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which are typically concentrated around the decision boundary. Changes occurring further from the boundary may be missed, and models may fail to adapt. This paper presents a theoretically supported framework for active learning from drifting data streams and develops three active learning strategies for streaming data that explicitly handle concept drift. They are based on uncertainty, dynamic allocation of labeling efforts over time, and randomization of the search space. We empirically demonstrate that these strategies react well to changes that can occur anywhere in the instance space and unexpectedly.

Dealing with concept drifts in process mining.

Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For the process management, it is crucial to discover and understand such concept drifts in processes. This paper presents a generic framework and specific techniques to detect when a process changes and to localize the parts of the process that have changed. Different features are proposed to characterize relationships among activities. These features are used to discover differences between successive populations. The approach has been implemented as a plug-in of the ProM process mining framework and has been evaluated using both simulated event data exhibiting controlled concept drifts and real-life event data from a Dutch municipality.