Ecology

Data and softwareThis analysis used two main data sources: (1) annual (through 2020) summaries of landings by species and by region provided by the Atlantic Coastal Cooperative Statistics Program (ACCSP), and (2) vessel-tracking data provided by Global Fishing Watch. The ACCSP is a cooperative state-federal program of U.S. states and the District of Columbia; it was established in 1995 to be the principal source of fisheries-dependent information on the Atlantic Coast of the United States. For the ACCSP data, I obtained annual landings by species for the North Atlantic region, Mid Atlantic region, and South Atlantic region (excluding landings from the Gulf of Mexico). The weekly cumulative landings data was obtained from the NOAA Fisheries Greater Atlantic Quota Monitoring website. Global Fishing Watch is an organization that provides access to information on commercial fishing activities, in particular information on the identity and location of fishing vessels34. Many large vessels use a system known as the Automatic Identification System (AIS) to avoid collisions at sea, broadcast their location to port authorities and other vessels, and to view other vessels in their vicinity. Vessels fitted with AIS transceivers can be observed by AIS base stations and by satellites fitted with AIS receivers. The US Coast Guard requires all vessels larger than 65 feet to have an AIS receiver onboard. Global Fishing Watch obtains AIS data for fishing vessels and enables users with Internet access to monitor fishing activity globally, and to view individual vessel tracks. They also partner with academic researchers to provide more fine-scale data.To obtain the vessel-tracking data for the relevant fisheries, I reviewed NOAA databases of squid and mackerel permits (2019 version; vessels with squid permits are automatically issued a Butterfish permit), and the Atlantic tuna permits (2020 version) and matched each permitted vessel to its unique Maritime Mobile Service Identity (MMSI) number, which is associated with Global Fishing Watch tracking information. I was able to identify 84% (187/224) of squid/butterfish permitted vessels (I focused on the SMB1A (Tier 1) permit category associated with the vast majority ( approx. 99%) of squid catch35), 100% of Tier 1 and Tier 2 mackerel-permitted vessels (56/56 vessels), and 74% of active tuna longline vessels (100/135 vessels). “Active” is defined as having reported successfully setting pelagic longline gear at least once between 2006 and 201236. This translates to a total of 17.55 million observations on fishing vessel locations for all three fisheries. I drop any observations that are missing either a latitude or longitude entry. For the squid and mackerel fisheries, I drop any observations with unusual longitudes ((ge 0^{circ }) and ( More

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This study describes the virological and pathological findings of Common buzzards infected with the 2020–2021 HPAI H5N8 virus. These analyses showed that the main lesions were HPAI virus-associated inflammation and necrosis in multiple tissues including brain and heart, confirming HPAI as cause of death or severe disease.The Common buzzard presents with several characteristic traits that make it a valuable bioindicator of HPAIV presence in wildlife. It is a medium-sized raptor, present almost throughout Europe. In the Netherlands, its population has been stable since 1970 with an estimated maximum winter population of 30,000–50,000 individuals16. The Common buzzard is mainly a resident bird, which generally inhabits woodlands but is adaptable to wetlands16,17. Its feeding behavior as an opportunistic predator and scavenger has the potential to expose it to HPAIV-infected prey. Given these predisposing biological traits, it is not unexpected that Common buzzards accounted for the highest number of HPAI virus detections in raptors during the 2020–2021 epizootic.Previous studies showed that HPAI viruses in raptors are highly neurotropic and cause severe neurological disease8,10,15,18,19. This study also supports those findings, as the most consistent lesion in Common buzzards was viral encephalitis, with confirmed presence of viral antigen in affected neurons. In addition to the nervous system, all the tissues tested of the Common buzzards were positive for virus based on RT-PCR and showed infection-related, histological lesions, indicating that HPAI H5N8 virus infection in the Common buzzard causes systemic disease.This study showed that HPAI H5N8 virus is also highly cardiotropic, as the myocardium of the Common buzzards contained the highest amount of virus based on RT-PCR (Table 1), and virus-associated, severe histological lesions in 63% (7/11) birds. In addition, 54% (6/11) of the Common buzzards showed virus-associated lesions in the liver and spleen.The Common buzzard is considered to be infected via the oral route by ingesting HPAIV-infected preys. Transmission of HPAIV from ingesting infected chicken meat has been experimentally confirmed in raptors20. Interestingly, the proventriculus of two birds in our study showed necrotic lesions with viral antigen. This finding further supports the oral route of infection, although we cannot exclude the possibility that the proventriculus was infected via the hematogenous route. It also provides new records of HPAIV enterotropism in wild birds. The adaptation to the intestinal tract is a mechanism recently reported for HPAI H5N8 virus, that may allow a more efficient fecal–oral transmission in wild birds5.Real time PCR (RT-PCR) is the preferred test for HPAI virus detection for active and passive bird surveillance9. In this study, cloacal and pharyngeal swabs had comparable RNA-levels, and both were adequate for the detection of the virus. The tissue analysis by RT-PCR showed that heart, brain, and air sac had highest viral RNA concentrations compared to other organs. Although not confirmed by a quantitative real time PCR, the results obtained by RT-PCR are well supported by histopathology and immunohistochemistry. Our advice for diagnostic pathologists is to collect at least a miniset of samples including brain, heart, liver and spleen, as these tissues are relatively easily sampled and were positive by both RT-PCR and for virus-antigen-associated lesions. For virus diagnosis of Common buzzards found dead (but without the interest or possibility to perform pathological examination), it is enough to collect pharyngeal and cloacal swabs, because they were positive by RT-PCR with Ct values that were comparable to those in most tissues (with exception of heart, that had higher Ct values).We did not detect antibodies against avian influenza virus NP in the sera of the Common buzzards in this study. Most of the birds (8/11) were juveniles in their first year of life, and likely they did not have protective antibodies from previous infections, as this was the first time in their lives that they experienced a HPAI epizootic. The absence of antibodies indicates also that the Common buzzards died acutely soon after infection, similarly to experimentally infected raptors that did not seroconvert before early death19. All the birds in our study were females. Females are larger than males (adult female weigh about 15% more than adult males), thus it is possible that female raptors are easier to find during surveillance or that there are sex-associated differences in feeding patterns.This study showed that HPAIV infection in Common buzzards produced severe systemic disease, and subsequent acute death based on the stage of the pathological changes and absence of serum antibodies. Cloacal and pharyngeal swabs were comparable in detecting the infection. Many organs contained viral RNA; with heart, brain and air sac containing the highest amount of viral RNA. The proventriculus of two birds showed virus-associated lesions, implying a possible adaptation of the virus to the gastro-intestinal tract. More

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Aarhus University, SORALO and KWT digitized bomas, fences and agriculture in a systematic manner using available satellite imagery (see methods). All digitization was re-checked by supervisors, to ensure that no data had been missed, and was adjusted following quality control where and when required. All data were then manually checked by conservation practitioners knowledgeable of the study area. Both the spatial resolution and temporal sampling of the data may present limitations to its accuracy and usage.Spatial resolutionFor both the KWT and SORALO datasets collected using Google Earth, we used the latest Google Earth imagery. Additionally, for KWT’s dataset, we also used the latest Bing maps imagery. However, the spatial resolution of this Google Earth and Bing maps data varies. Resolution can be as high as ~0.5 m, while a few remaining areas still rely on Landsat Imagery with a resolution of 30 m. However, the quality of the Google Earth and Bing maps imagery was generally high enough across the study area to accurately delineate bomas, fencelines and agricultural land. Figures 3 and 4 provide examples of areas that would be digitized, with the boundaries of the boma and fence lines clearly visible.The fencing data collected by Aarhus University used Landsat Imagery at 30 m resolution and smaller fences may be missing from the dataset as they are harder to distinguish. This is also true for wire fence (the predominant type of fencing around the Maasai Mara; Fig. 3C). Vegetation differences used to identify these fence lines may take some time to develop. Therefore, there may be an underestimate of the fences mapped, especially in those regions with high usage of wire fences.It must be noted that images from Google Earth have an overall positional root mean squared error of 39.7 m, which may impact the interpretation of this dataset23. We believe that these errors are acceptable for our first attempt at collecting landscape-scale data, and will be refined over time with improved imagery and ground-truthing. Landsat data has a root mean squared error usually below the size of a pixel, with 90% of pixels having less than 12 m deviation (1 https://www.usgs.gov/media/videos/landsat-collections-rmse).Temporal variationThe most likely discrepancies in data quality will arise from temporal variation in fencing placement, boma usage and placement, and agricultural change. Google Earth data were used for SORALO, using data available up to February 2020. Google Earth and Bing maps data were used for KWT, with data up to 2017. The weighted mean imagery date for SORALO (weighted by the area covered) was the 9th of September 2016 and ranged from 15th of December 2000 to 12th of February 2020 (Fig. 5). Where possible we have added a date-time stamp to the boma, agriculture and fencing dataset to best match the date the satellite imagery was acquired, or when it was collected on the ground. However, KWT and some SORALO data lack date attribute, the latter because no date stamp was found in Google Earth, and the former because no date was recorded for any data. The Aarhus University fencing data are from a Landsat Image from January 2016, and the MEP data are from on-the-ground collection. Our database is built so that as new or updated data become available, from both new satellite imagery and ground-based identification, the data layer can be adjusted (see below).Livestock enclosure validationWe used data on the location of SORALO livestock enclosures from the Magadi region24 (collected using handheld GPS devices), to estimate the accuracy of our data collection. The SORALO ground-truthed database contains 668 bomas, which have been occupied at least once during 2014–2017. In the same area, our boma points database contains 573 bomas (85%) of which 41.2% (n = 275) are within 100 m of ground-truthed points and 87.7% (n = 586) are within 500 m of the ground-truthed points. These ground-truthed points may have inaccuracies from their data collection. Also, many livestock enclosures distant from ground-truthed points are newer than the ground-truthing dataset.Agricultural land validationWe compared our agricultural data layer to a commonly used global open source data layer, the 2015 GFSAD30AFCE 30-m for Africa: Cropland Extent Product (www.croplands.org). Our layer agreed with the Cropland Extent Product across 856 km2 of cropland. However, our layer demarcated 455 km2 (34.4% of the total extent) more agricultural land than was found in the 30 m Cropland Extent Product, because many small areas of subsistence farming had not been detected by this global layer. Additionally, the Cropland Extent Product contained 468 km2 (35.3% of the Cropland Extent Product) of agricultural extent not captured in our layer. Much of this was on the periphery of large continuous agricultural areas and appears inaccurately mapped by the global product.Continual validation and improvement of databaseOngoing ground-truthing exercises by the Mara Elephant Project and other partners will improve the quality of the database over time, particularly the datasets on wire fencing in the Mara region. To do so the TerraChart app combined with a QuickCapture app (to validate fence lines and boma locations using aerial reconnaissance) are integrated into the ArcGIS online framework, and following validation both manually and using automated Python script, can be used to update the features collection database.Additionally, any data currently held in the private domain can be easily integrated into this database, and made available to the public domain with approval. Linking these features using a parent ID allows for not only the addition of new features, but improved spatial accuracy of old features, and temporal changes to features to be captured.This database will be continually improved over time. For example, current efforts from conservation partners in the region have resulted in large scale acquisition of high resolution, up-to-date, satellite imagery which will be further used to refine this database. More

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Experimental animalsGymnocypris przewalskii used in this study were obtained from the Rescue Center for Naked Carp of Lake Qinghai in Xining. Only healthy fish without visible body damage were used. Wet mass and body length were recorded before the fish were sampled. All fish were collected under permits issued by local and national authorities, and experimental procedures were in accordance with national animal care regulations. Experimental waters were prepared daily, and water qualities were measured before each experiment. The water temperature and salinity were measured using an YSI6600 multiprobe sensor (YSI Incorporated, Ohio, USA), and the carbonate alkalinity was determined by titration39. All fish (average body weight: 33.21 ± 2.74 g; average body length:14.81 ± 0.35 cm) were held in an indoor RAS system at a density of approximately 6.5 kg m−3. The holding and experimental water were filtered tap water (Canature/AC/KDF150-1–300) (salinity 0.16, pH 7.56, carbonate alkalinity 2.7 mmol L−1, temperature 17.1 ± 0.61 °C). Fish were fed daily with commercial feed. Fish husbandry and experimental procedures were approved by the Second Scientific Research Ethics Committee of East China Sea Fisheries Research Institute.Experimental designFish were placed on a 14:10-h light:dark (5:00–19:00 with light intensity of 600–1000 lx; 19:00–5:00 with light intensity of 0 lx) photoperiod aquaculture system. To examine the effect of rhythm on osmoregulation and acid–base balance, this study measured four endpoints: drinking rate, self-feeding intake, mRNA expression and the single cell expression level of osmoregulation and acid–base regulation relevant proteins. Fish held in filtered tap water were transferred directly to saline-alkaline lake water with salinities of 15 (L15, salinity 14.83, pH 8.65, carbonate alkalinity 30.54 mmol L−1) and 17 (L17, salinity 16.80, pH 9.02, carbonate alkalinity 34.61 mmol L−1), which was prepared by adding the same ratio of NaCl, MgCl2.6H2O, Na2SO4, CaCl2, KCl, NaHCO3 and Na2CO3 as in Qinghai Lake. The experimental period was 4–5 days.Drinking rate during high saline-alkaline transferFish were placed on a 14:10-h light:dark (5:00–19:00 with light intensity of 600–1000 lx;19:00–5:00 with light intensity of 0 lx) photoperiod aquaculture system. In this experiment, feeding was stopped 48 h prior to the experiments. Fresh water fish were transferred directly to L15 and L17 PEG-4000 free water for up to 4 days. For drinking rate analysis, new tanks were prepared which contained 50L saline-alkaline lake water with salinity 15 or 17 and with PEG-4000 (final concentration was 1.00 g L−1) during the day (10:00–16:00) and night (4:00–22:00) on the fourth day respectively. Nine fish per treatment were individually transferred from PEG-4000 free water to each tank which contained 1.00 g L−1 PEG-4000 at 10:00 or 4:00. Water samples were collected at 15 min after the fish were transferred to each treatment group for the determination of PEG-4000 concentration. The fish were terminally anesthetized with MS-222 (0.40 g L−1) after 6 h. The intestines were then quickly dissected out from nine individual fish per treatment group and the intestinal fluid were collected and stored at 4 °C. All fish were weighed before sampling.Self-feeding intake during high saline-alkaline transferFish were placed on a 14:10-h light:dark (5:00–19:00 with light intensity of 600–1000 lx;19:00–5:00 with light intensity of 0 lx) photoperiod aquaculture system. In this experiment, fish were kept in freshwater (FW) or acclimated to L15 for more than 15 days before the experiment started. Six RAS glass tanks (95 cm × 60 cm × 60 cm), which belong to two circulatory systems (3 tanks for FW and 3 for L15), were used for self-feeding experiment. Each tank had 15 individuals.Before the experiment, fish were trained by a custom-made self-feeding system (Fig. S2). Trained fish triggered the self-feeder when they want to feed. In the self-feeding system, the photoelectric sensor converts the change of optical signal into the change of electrical signal, and the feeder release feed by recognizing level fluctuation.During the formal experiment, we collected feed data at 5:00 and 19:00, which were the time points of the light and dark transition. Feed intakes of naked carp were calculated by weighing the feed quantities at two time points. The experiment lasted 5 days.mRNA expression of osmoregulation and acid–base regulation relevant proteins during high saline-alkaline transferFish were placed on a 14:10-h light:dark (5:00–19:00 with light intensity of 600–1000 lx;19:00–5:00 with light intensity of 0 lx) photoperiod aquaculture system. In this experiment, feeding was stopped 48 h prior to the experiments. Fresh water fish were transferred directly to L17 for up to 4 days. There were 24 fish per tank in triplicate. At the fourth day, six fish per tank were individually removed and terminally anesthetized with MS-222 (0.40 g L−1) at 4:00, 10:00, 16:00 and 22:00, respectively. The mid-intestine was quickly dissected out from six individual fish at each time point. Mid-intestine tissues for mRNA expression analyses were immediately snap-frozen in liquid N2, and stored at − 80 °C until analysis.Single cell positive rate of osmoregulation and acid–base regulation relevant proteinsFish were placed on a 14:10-h light:dark (5:00–19:00 with light intensity of 600–1000 lx;19:00–5:00 with light intensity of 0 lx) photoperiod aquaculture system. In this experiment, feeding was stopped 48 h prior to the experiments. To analyze the single cell positive rate of acid–base relevant proteins, a separate experiment was conducted. Fresh water fish were transferred directly to L17 for up to 4 days. There were 3 tanks (6 fish per tank) in this experimental group. At the fourth day, three fish per tank were individually removed and terminally anesthetized with MS-222 (0.40 g L−1) at 16:00 and 22:00, respectively. The mid-intestine was quickly dissected out from nine individual fish and immediately prepared for single-cell suspensions.Analytical techniquesDrinking rate analysisThe measurement of drinking rate was performed according to the study of Buxton et al.40. After weighing the collected intestinal fluid, it was centrifuged at 13,000g for 1 min, and 50 μL of the supernatant was taken, added dropwise to 350 μL of 72% pre-cooled (4 °C) acetone, and vortexed to mix. Samples were then centrifuged at 2000g for 10 min at 4 °C, the supernatant filtered with 0.45 μm filter paper, followed by addition of 100 μL of filtrate to 175 μL 25 mg L−1 gum arabic and vortexed to mix. Finally, 200 μL of TCA-CaCl2 (trichloroacetic acid-calcium chloride, 30% and 5% by mass) was added to the mixture and the reaction allowed to proceed at room temperature for 20 min. An Epoch microplate (Bio Tek) spectrophotometry unit was used to measure the absorbance at 650 nm. The remaining solution was weighed again after drying at 60 °C for 48 h, and the volume of intestinal fluid was determined (quantity of collected intestinal fluid-mass after drying). The same method as above was used to process the standard solution. Solute concentrations for standard curve were prepared as 0.00 g L−1, 0.10 g L−1, 0.20 g L−1, 0.40 g L−1, 0.60 g/L−1, 0.80 g L–1, 1 g L−1, and 2 g L−1 PEG-4000. The PEG-4000 concentration of intestinal fluid was calculated based on the standard curve. Drinking rate (μLg-1h-1) = 1000 × (CI × VI)/(CW × W × t), where CI is the concentration of PEG-4000 in the intestinal fluid (gL-1), VI is the volume of intestinal fluid (mL), CW is the concentration of PEG-4000 in experimental water (gL-1),W is the body weight of the fish (g), t is the duration of the experiment (h).Molecular biologyThe known sequences of the NKA-α gene of naked carp were compared with the corresponding genes of other species in GenBank, and highly conserved regions were selected for primer design (Table 1). The reference gene EF1α was used according to Yao et al.3. Previously published primers were used for SLC26A6 and SLC4A4 genes6. After extracting total RNA with Trizol (Invitrogen), the integrity of RNA was detected by 1% agarose gel electrophoresis, and the concentration and purity of total RNA were determined by a Bio Tek Epoch microplate spectrophotometer. The Rever Tra Ace-α (TOYOBO) kit was used to reverse transcribe mRNA to cDNA. Fluorescence quantitative PCR analysis was performed using a QuantStudio™ Real-Time PCR (Thermo life) with the SYBR Premix Ex TaqIII (TaKaRa) kit: total reaction volume of 10 μL, including 5 μL SYBR Premix Ex Taq, 2 μL upstream primers, 2 μL downstream Primers, and 1 μL cDNA template. The amplification procedure was as follows: 95 °C 30 s, 1 cycle; 95 °C 5 s, 60 °C 20 s, 40 cycles. Three replicates were included for each sample, with EF1α as the internal reference gene. The relative expression of each gene was calculated using the 2−ΔΔCt method41. Melting curve analysis was performed following each reaction to confirm that there was only a single product and no primer-dimer artifacts. In addition, representative samples were electrophoresed to verify that only a single product was present. Negative control reactions were performed for representative samples using RNA that had not been reverse transcribed to control for the possible presence of genomic DNA contamination. No-template control reactions were also performed to verify the absence of contaminating DNA or primer-dimer amplification in the reactions.Table 1 Nucleotide sequences of the primers used for amplification.Full size tableSingle cell staining analysisThe naked carp mid-intestine was isolated and transferred to HBSS on ice. The mid-intestine was washed by HBSS (Corning, 21-022-CV) and transferred to pre-warmed digestion medium containing 0.2 mg·mL−1 Collagenase I (Gibco, 17100-017), 0.06 mg mL−1 Collagenase II (Gibco, 17101-015) and 0.2 mg mL−1 Collagenase IV (Gibco, 17104-019), which was shaken vigorously for 30 s and further incubated at 37 °C for about 30 min in incubator with gentle shaking every 5 min to release cells. Cells were then collected by centrifuging at 300 × g for 5 min, and resuspended in D-PBS (BBI, E607009-0500). Then taken an appropriate amount of single cell suspension and dropped it on poly-L-lysine-coated slides where the experimental area was drawn with a hydrophobic marker to allow the single cells to settle freely. When the cell sedimentation density was moderate, aspirated the excess cell suspension, slides were fixed with 4% paraformaldehyde fix solution (BBI, E672002-0500) for 10 min, and blocked with 3% BSA (Sigma, B2064) for 1 h, three washes in D-PBS. Subsequently, slides were incubated in NKA-α or SLC26A6 (antibody dilution ratio was 1:100) for overnight at 4 °C. The NKA-α antibody was a commercial polyclonal rabbit Na+/K+-ATPase α antibody (Santa Cruz Biotechnology, sc-28800). The SLC26A6 antibody was a commercial polyclonal rabbit SLC26A6 antibody (Abcam, ab-172684). After the incubation, three washes in D-PBS. The secondary antibodies consisted of Alexa flour 568 goat anti-rabbit IgG (Thermo Fisher Scientific, A11036) (antibody dilution ratio was 1:400). Slides were incubated in room temperature for 1 h, followed by three washes in D-PBS. Finally, incubate with Hochest for 30 min. Cells were then photographed with a fluorescence microscope. For every fish, positive protein expression was counted using at least three pictures. Image J was used to analyze the fluorescence intensity and record the positivity rate.Statistical analysisThe data was expressed as mean ± standard error (SE). Two-way ANOVA and One-way ANOVA with LSD multiple comparison were employed to compare drinking rate, food intake and relative gene expression among different treatments and time courses respectively. Differences in single cell positive rate between 16:00 and 22:00 in L17 were evaluated by chi-square test. Assumptions for all parametric models (normality and equal residuals) were assessed via diagnostic plots. Means were considered significantly different when P  More

Phylogenetic relations and systematics of AllactaginaeIntergeneric relationsOur data produced a robust phylogeny for Allactaginae above species level and thereby firmly proved that Allactaga s.l. (as recognised by Holden and Musser17) is paraphyletic to both Pygeretmus and Allactodipus. Both of the latter taxa are morphologically distinct from Allactaga by a number of unique apomorphies: a unique molar pattern and glans penis morphology in Allactodipus as well as high-crowned terraced molars, reduction of the premolar, and particular glans penis morphology in Pygeretmus. At the same time, the morphology of all other five-toed jerboas is relatively monotonous with variation only in terms of body size, relative molar crown height, size of auditory bullae, m1 morphotype frequency, and the rate of M3 reduction1,45. Such level of differences never allowed recognition of more than one genus.Thus, allactagines represent a case when descendant lineages with derived morphology are nested within a group with overall conserved morphology. This can be compared to paraphyly of white-toothed shrews Crocidura relative to Diplomesodon46, rorquals (Balaenoptera) relative to humpback whales (Megaptera)47, or tits (Parus s.l.) relative to morphologically aberrant ground tit (Pseudopodoces humilis)48. In such cases, the taxonomy should be changed in accordance with the monophyly principle, which is achieved by combining genera (as done in whales) or splitting the genus in question into new taxa (as done in tits). Unfortunately, any decision in this context is arbitrary as it is based on subjective weighting of morphological differences. For Allactaginae, the splitting approach was implemented18, which resulted in the elevation of Scarturus and Orientallactaga to the generic rank2, despite the fact that a synapomorphy-based morphological diagnosis of Scarturus can hardly be formulated.As an alternative to the morphology-based approach, temporal banding—a method which uses node age as a measure of rank49—was suggested as a standardised method for taxonomic ranking. In the present study, the age of divergence of major Allactaginae lineages was dated to the Pliocene. However, in other groups of Myodonta, Pliocene divergences were found both among genera (as in voles50 or hamsters51) and among congeneric species (as in Sicista52). Thus, the ambiguity remains unresolved; we see no better option than to retain the generic classification established by Michaux & Shenbrot2 (Table S10). However, it should be noted that the inferred age of divergence between S. tetradactylus + S. hotsoni and the VECE clades (3.9–4.1 Mya) is comparable or even larger than the divergence time of Allactodipus from Allactaga. If the temporal criterion (sensu Avise, Johns49) is accepted, one should consider elevating the VECE clade at least to subgeneric rank, with Scarturus proper including only two species. The diagnosis of the new taxon should be polythetic (medium to small jerboas with five-toes, bullae not enlarged, glans penis with longitudinal fold, molar low-to medium crowned, M3 not reduced). Although the name Paralactaga is traditionally used as a subgeneric for the S. euphraticus group and therefore may have been applied to the whole VECE clade, we believe that this is incorrect. The type species of Paralactaga—P. anderssoni Young, 1927—was described from the Late Miocene of China, which is inconsistent with the estimated time of origin of the VECE clade. Apparently all similarities between S. euphraticus group and Paralactaga proper are because of plesiomorphy. Therefore, we suggest that Paralactaga should be attributed to fossil taxa only.Species groups within ScarturusIn the present study, we analysed in detail the phylogenetic reconstructions and divergence times estimations for the species and species groups of the genus Scarturus. Our study is the first to examine the phylogenetic position of the enigmatic taxon described from Afghanistan and which is currently termed Scarturus williamsi caprimulga. The mitochondrial data provided clear evidence that this taxon is not closely related to any member of the S. euphraticus species group including S. williamsi. Instead, it belongs to a separate divergent lineage of Scarturus, which should be considered a separate species, Scarturus caprimulga. It also includes the jerboa from Kopet Dag provisionally classified by Hamidi et al25 as Paralactaga cf. williamsi. The mitochondrial difference between specimens from Afghanistan and those from Kopet Dag suggested a potential subspecies rank of the latter form, which is provisionally referred to as S. aff. caprimulga. More research on the distribution and genetic structure of this species is needed for further clarification. Our study has added more representative genetic data on the poorly known S. vinogradovi and confirmed it as a separate divergent branch within Scarturus s.l. and likely a distant sister group of S. caprimulga.Previous phylogenetic reconstructions of the S. euphraticus species group based on mtDNA data recovered a divergent branch within S. euphraticus53, which was subsequently classified as S. aulacotis2. With further addition of comprehensive nuclear data, the full species rank of this taxon is now completely supported. The relationships among the three species in the S. euphraticus group correspond to a hard trichotomy dated to the late Early Pleistocene.Nuclear data strongly support deep structuring within the S. elater species group, as previously demonstrated using mtDNA19,22,54, and confirmed the species status of S. indicus and S. heptneri. The divergence between S. elater and S. indicus estimated based on the nuclear loci was dated to approximately 1.5 Mya, which was slightly older than the 1.26 Mya inferred from mtDNA by Bannikova et al.22. Both S. indicus and S. elater included allopatric lineages that have separated 600–800 kya (i.e. dzungariae and strandi within elater, and aralychensis within indicus). Their formal taxonomic rank appears controversial: the level of divergence apparently conforms to species rank, whereas genetic data indicates potential gene flow between them. Thus, the mtDNA haplotypes of Scarturus specimens from the Zaisan depression (S. e. zaisanicus) form a subclade within S. elater s.str., whereas nuclear data suggest that S. e. zaisanicus is relatively close to S. e. dzungariae. This pattern suggests that the Zaisan population, while being a derivative of the Dzungar form, experienced mtDNA capture as a result of a past hybridisation event with S. elater. Gene flow between S. strandi and S. elater proper was indicated by the occurrence of elater mtDNA haplotypes in certain populations of strandi from north-western Kyzylkum22. All these taxa require additional research to produce a more accurate evaluation of gene flow intensity. Nevertheless, we suggest that dzungariae, strandi, and aralychensis should be considered semispecies or species in statu nascendi. Taxonomically, we regard them as parts of elater and indicus superspecies and refer to them as S. (elater) dzungariae, S. (elater) strandi, and S. (indicus) aralychensis, respectively.Phylogenetic relations within OrientallactagaWithin Orientallactaga, O. bullata and O. balikunica were supported as sister taxa based on nuclear data, which is consistent with their common morphology (enlarged bullae). However, mtDNA suggested that O. bullata is a sister taxon to O. sibirica, and the reason for this discrepancy is unclear, with ancient mtDNA introgression being the most obvious explanation. The crown age of Orientallactaga was dated to the early Early Pleistocene (Gelasian). Neither O. bullata nor O. balikunica show substantial intraspecific variation.In contrast, O. sibirica consists of several genetic lineages, which partly correspond to recognised subspecies. The mtDNA data tentatively supported subdivision of O. sibirica into western and eastern groups separated by the Tianshan–Altay zoogeographic boundary. The structure of variation in the eastern portion of the range (Mongolia, China) is well-studied23; however, the genetic data on the western portion are still fragmentary. Available mtDNA data provisionally support recognition of western subspecies such as O. s. ognevi (north-eastern to central Kazakhstan), O. s. dementjevi (Issyk-Kul region), and O. s. altorum (central Tianshan). The latter two forms are distributed in high-altitude areas of Tianshan, thus indicating that, in contrast to most other jerboa species, mountain areas might serve as foci of diversification in O. sibirica.The westernmost part of the range (western Kazakhstan, Qyzylkum) was assumed to be inhabited by a single O. s. suschkini subspecies after morphological revision1. However, three divergent mtDNA lineages were recovered based on the preliminary analysis of mtDNA data retrieved from museum specimens from the area, which suggests that the diversity of western populations is likely underestimated and in need of further examination.The crown age of O. sibirica was estimated at 500–600 kya, which was substantially younger than 2.2–3.2 Mya as inferred by Cheng et al.23; this discrepancy, however, can be explained by mtDNA saturation effects and usage of inaccurate secondary calibrations in their study.Variation within Allactaga and PygeretmusConsidering the phylogenetic position of Pygeretmus, our data firmly corroborated its separate phylogenetic position and rejected any affinity with Orientallactaga bullata as reconstructed by Wu et al.55. The latter result should be attributed to identification error. In our study, all three species of Pygeretmus were analysed to confirm phylogenetic proximity of P. shitkovi and P. platyurus relative to P. pumilio. Thus, the subgeneric status of Alactagulus containing the latter species was not contradicted; however, the split age between Pygeretmus s.str. and Alactagulus is relatively young, dated as Pliocene/Pleistocene boundary, indicating that morphological and life history traits of the former (e.g. slower locomotion) have evolved rather recently.A further taxon demonstrating a complex structure is Allactaga major. Our mtDNA data indicated that A. major consisted of several genetic lineages partly corresponding to morphological subspecies (A. m. spiculum, A. m. djetysuensis). A high level of divergence was observed between specimens from the northern Caucasus and Kazakhstan. A specimen of morphologically distinct A. m. spiculum (north-eastern Kazakhstan, western Siberia) was placed as a sister species to all other A. major with a divergence level compatible with species status.Several other species included unexpected genetic lineages that were apparently divergent at subspecies level (e.g. a southern Uzbekistan lineage of A. severtzovi and an Ili lineage of P. shitkovi). However, the resolving power of the employed set of 15 nuclear genes is insufficient for clarifying relationships within species. Therefore, these cases should be studied using larger samples and further nuclear loci.Divergence time estimates within AllactaginaeOur estimated divergence times were generally more recent than those produced by most previous studies. The root node of crown Allactaginae was dated to 7.7 (5.4–9.9) Mya by Wu et al.55, 8.1 (4.2–12.7) Mya by Zhang et al.56, or 8.87 (8.3–9.85) Mya by Pisano et al.4. The results by Wu et al.55 may be affected by a node density effect as their re-analysis with reduced taxon sampling of Allactaginae and Dipodinae produced younger dating at 5.8 (3.1–8.6) Mya. The latter two studies used only one to four nuclear loci and calibrated their analysis using non-Dipodidae calibration points. In both cases, the Early Miocene age of Sicista primus was used to calibrate crown Sicista, which lacks proper justification and may result in upward bias, as argued by Rusin et al.57.The earliest Allactaginae appeared in the Early Miocene and, in the Middle Miocene, the members of the primitive genus Protalactaga Young, 1927 became a common element of the Asian fauna3. During the Late Miocene, the diversity of allactagines persisted, and new genera emerged including Paralactaga Young, 1927 which is morphologically similar to Allactaga and is often considered its subgenus3,45. However, as can be derived from our results, all but one of the Middle and Late Miocene lineages went extinct without leaving any recent descendants, and all current diversity is a product of the Pliocene–Pleistocene evolution. This diversification pattern is unlike that observed in a different jerboa subfamily, Dipodinae, which includes lineages that had diverged in the Middle and early Late Miocene (Paradipus and Dipus, respectively)4,58.As estimated here, the onset of radiation among crown Allactaginae occurred in the latest Messinian and thus was nearly coincident with the Messinian crisis. However, it remains unclear how (or whether at all) climatic perturbations at the Miocene /Pliocene boundary affected the evolution of Allactaginae. The results of the diversification analysis suggested that, throughout the Pliocene and Pleistocene, the rate and mode of speciation in five-toed jerboas remained constant, indicating high tolerance of this group towards the climatic changes of this period.The minimum age of split observed between sympatric species was approximately 1 Mya as demonstrated by heptneri versus elater s.str. (and strandi). This was the estimate for the minimum time necessary for formation of effective reproductive barriers in allactagines (post- or pre-zygotic). Other phylogenetically close sympatric species pairs were S. elater/S. indicus (1.5 My), O. bullata/O. balikunica (1.5 My), and A. major/A. severtzovi (2.0 My).Geography of speciationOf 17 analysed episodes of speciation in Pliocene–Pleistocene, the patterns of range fragmentation in 10 episodes matched well to the classical vicariance scenario and those of six episodes matched to the founder-event speciation scenario; in one episode, both scenarios were equally probable. As the location of arising isolation barriers within the ancestor range seemed incidental, only in three cases the ancestors’ range was subdivided into two parts which were more or less equal in size: first, into East and West Central Asia; second, into Turan and Iran; third, into Anatolia with trans-Caucasus and northern Zagros and Levant with northern Mesopotamia and southern Zagros. In all other cases, the ancestors’ range was subdivided into the main part and relatively small peripheral isolates. As can be expected from the modern patterns of species diversity of Allactaginae, the discovered speciation events were unequally distributed: one episode in North Africa, one in the eastern part of Central Asia, three in the Middle East, four in the Iranian highland, four in Turan, and five in Kazakhstan. In most cases, range fragmentation coincided with extreme climate conditions within the analysed time periods: warmest and wettest (decrease of the area of arid lands: nodes 2–3, 5, 10, 12, and 14–15) or coldest and driest (closing narrow mountain passages due to mountain glaciation: nodes 4, 6–9, 13, and 16–18). In one case (node 11), fragmentation of the range coincided with moderate climate conditions.Successful modelling of fragmentation of geographic ranges as a base of speciation events seemed to agree with the hypothesis of Peterson et al.15, which states that ecological niches evolve little at or around the time of speciation events, whereas niche differences accumulate later. This hypothesis was supported by Peterson’s analysis59 of data published between 1999 and 2008 which demonstrated that niche conservatism was found in more than 70% of comparisons within species and between sister species, but in less than 50% of comparisons among closely-related (but not sister) species and across monophyletic lineages of species. Moreover, analysis of habitat niche evolution of arvicoline rodents16 demonstrated that closely related species with allopatric or parapatric distribution demonstrated small niche differences, whereas they were larger in species with sympatric distribution. This is a clear indication that interspecific competition forces natural selection to increase niche differences resulting in species co-occurrence. It was demonstrated that niche divergence/conservatism can be differently expressed between different niche/resource axes60. In voles, which have a highly specialised folivorous diet, habitat segregation seems to be the only type of niche differentiation. Closely related Allactaginae species are similar in diet and typically occur in allopatric or parapatric distribution patterns1, which may indicate their niche conservatism. The only exception to a pattern where species with similar diets show widely overlapping geographic distributions are Scatrurus elater and S. heptneri (these two species are similar in both, macro- and micro-habitat niches, and it is unclear which mechanisms allow them to co-occur22). Distantly related sympatric species typically show similarities regarding macro-habitat niches but marked differences in terms of micro-habitat niches (Allactaga major and Orientallactaga sibirica; O. sibirica and O. bullata; O. sibirica and O. balikunica; Pygerethmus pumilio and P. platyurus; P. pumilio and P. shitkovi; personal observations) and diet (Allactaga and Allactodipus; Allactaga and Scarturus; Allactaga and Pygeretmus; Orientallactaga and Pygeretmus; Scarturus and Pygeretmus1,61). Thus, macro-habitat niche conservatism may be expected even in sympatric species. More