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Distribution of large carnivores in Europe 2012 - 2016: Distribution map for Golden Jackal (Canis aureus)
<p><strong>Abstract</strong></p> <p>Regular assessments of species’ status are an essential component of conservation planning and adaptive management. They allow the progress of past or ongoing conservation actions to be evaluated and can be used to redirect and prioritize future conservation actions. Most countries perform periodic assessments for their own national adaptive management procedures or national red lists. Furthermore, the countries of the European Union have to report on the status of all species listed on the directives of the Habitats Directive every 6 years as part of their obligations under Article 17. However, these national level assessments are often made using non-standardized procedures and do not always adequately reflect the biological units (i.e., the populations) which are needed for ecologically meaningful assessments.</p> <p>Since the early 2000’s the Large Carnivore Initiative for Europe (a Specialist Group of the IUCN’s Species Survival Commission) has been coordinating periodic surveys of the status of large carnivores across Europe (e.g., von Arx et al. 2004; Salvatori & Linnell 2005, Kaczensky et al. 2013). These have covered the Eurasian lynx (<em>Lynx lynx</em>), the wolf (<em>Canis lupus</em>), the brown bear (<em>Ursus arctos</em>) and the wolverine (<em>Gulo gulo</em>). The golden jackal (<em>Canis aureus</em>) has been added to the LCIE prerogatives in 2014. The species is rapidly expanding in Europe (Trouwborst <em>et al.</em> 2015; Männil & Ranc 2022), a large-scale phenomenon that resembles that of the other large carnivores. Golden jackals are thriving in human-dominated landscapes (Ćirović <em>et al.</em> 2016; Lanszki <em>et al.</em> 2018; Fenton <em>et al.</em> 2021), where they are often functioning as the top predators, despite having smaller body size that is typical for large carnivores. The expansion of the species triggers many questions among scientists, stakeholders, and policy makers (Trouwborst <em>et al.</em> 2015; Hatlauf <em>et al.</em> 2021), that are closely connected to those raised by the other large carnivores (e.g., potential conflicts with livestock or hunting). In this context, monitoring the species’ expansion, delineating populations, assessing the species' legal and protection status, and addressing the concerns raised by this rapidly expanding carnivore requires a high level of coordination among regional experts.</p> <p>These surveys involve the contributions of the best available experts and sources of information. While the underlying data quality and field methodology varies widely across Europe, these coordinated assessments do their best to integrate the diverse data in a comparable manner and make the differences transparent. They also endeavor to conduct the assessments on the most important scales. This includes the continental scale (all countries except for Russia, Belarus, Moldova and the parts of Ukraine outside the Carpathian Mountain range), the scale of the EU 28 (where the Habitats Directive operates) and of the biological populations which reflect the scale at which ecological processes occur (Linnell et al. 2008). In this way, the independent LCIE assessments provide a valuable complement to the ongoing national processes.</p> <p>Our last assessments covered the period 2006-2011 (Kaczensky et al. 2013; Chapron et al. 2014) but, at the time, did not include golden jackals. The current assessment is based on the period 2012-2016 and broadly follows the same methodology. Explicit distinctions are made between classification based on empirical data and expert opinion. The population definitions used in this report follow those proposed in (Ranc <em>et al.</em> 2018); areas whose presence category was defined by expert opinion were not assigned to a specific population, though. </p> <p> </p> <p><strong>Methods</strong></p> <p>The mapping approach follows the methods described in Chapron et al. (2014) and Kaczensky et al. (2013). It updates the published Species Online Layers (SPOIS) to the period 2012-2016.</p> <p>In short, large carnivore presence was mapped at a 10x10 km ETRS89-LAEA Europe grid scale. This grid is widely used for the Flora-Fauna-Habitat reporting by the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2</p> <p>The map encompasses the EU countries plus the non-EU Balkan states, Switzerland, Norway, and the Carpathian region of Ukraine. Presence in a grid cell was ideally mapped based on carnivore presence and frequency in a cell resulting in:</p> <p>1 = Permanent (presence confirmed in >= 3 years in the last 5 years OR in >50% of the time OR reproduction confirmed within the last 3 years)</p> <p>3 = Sporadic (highly fluctuating presence) (presence confirmed in <3 years in the last 5 years OR in <50% of the time)</p> <p>5 = Expert-based presence (high confidence) (expert-based opinion; very suitable habitat near permanent presence areas)</p> <p>6 = Expert-based presence (low confidence or unconfirmed records) (expert-based opinion; suitable habitat near presence areas or unconfirmed C3 records of jackal presence)</p> <p>7 = Expert-based absence (high confidence) (jackal presence according to coarse-resolution hunting bag data but experts think, with high confidence, the species is not present)</p> <p>8 = Expert-based absence (low confidence) (jackal presence according to coarse-resolution hunting bag data but experts think the species is not present)</p> <p>Where grid cells were assigned different values between neighboring countries; the “disputed” cells were given the “higher” presence values e.g., a cell categorized as “sporadic” by one country and “permanent” by another was categorized as “permanent”. Data-based categories (1,3) were given priority over expert-based categories (5 through 8).</p> <p>To assess the quality of carnivore signs we used the SCALP criteria developed for the standardized monitoring of Eurasian lynx (<em>Lynx lynx</em>) in the Alps (Molinari-Jobin et al. 2012):</p> <p>Category 1 (C1): “Hard facts”, verified and unchallenged large carnivore presence signs (e.g., dead animals, DNA, verified camera trap images);</p> <p>Category 2 (C2): Large carnivore presence signs controlled and confirmed by a large carnivore expert (e.g., trained member of the network), which requires documentation of large carnivore signs; and</p> <p>Category 3 (C3): Unconfirmed category 2 large carnivore presence signs and all presence signs such as sightings and calls which, if not additionally documented, cannot be verified.</p> <p>See Hatlauf and Böcker (2022) for best practices regarding golden jackal records.</p> <p> </p> <p><strong>Usage Notes</strong></p> <p>The data available consists of a shapefile at a 10 x 10 km resolution compiled for the period 2012-2016 for the Large Carnivore Initiative of Europe IUCN Specialist Group and for the IUCN Red List Assessment.</p> <p> </p> <p><strong>References</strong></p> <p>Boitani, L., F. Alvarez, O. Anders, H. Andren, E. Avanzinelli, V. Balys, J. C. Blanco, U. Breitenmoser, G. Chapron, P. Ciucci, A. Dutsov, C. Groff, D. Huber, O. Ionescu, F. Knauer, I. Kojola, J. Kubala, M. Kutal, J. Linnell, A. Majic, P. Mannil, R. Manz, F. Marucco, D. Melovski, A. Molinari, H. Norberg, S. Nowak, J. Ozolins, S. Palazon, H. Potocnik, P.-Y. Quenette, I. Reinhardt, R. Rigg, N. Selva, A. Sergiel, M. Shkvyria, J. Swenson, A. Trajce, M. Von Arx, M. Wolfl, U. Wotschikowsky and D. Zlatanova. 2015. Key actions for Large Carnivore populations in Europe. Institute of Applied Ecology (Rome, Italy). Report to DG Environment, European Commission, Bruxelles. Contract no. 07.0307/2013/654446/SER/B3</p> <p>Ćirović, D., A. Penezić and M. Krofel. 2016. Jackals as cleaners: Ecosystem services provided by a mesocarnivore in human-dominated landscapes. <em>Biological Conservation</em>, 199: 51–55.</p> <p>Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., López-Bao, J.V., Adamec, M., Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, P., Bego, F., Blanco, J.C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., Dutsov, A., Engleder, T., Fuxjäger, C., Groff, C., Holmala, K., Hoxha, B., Iliopoulos, Y., Ionescu, O., Jeremić, J., Jerina, K., Kluth, G., Knauer, F., Kojola, I., Kos, I., Krofel, M., Kubala, J., Kunovac, S., Kusak, J., Kutal, M., Liberg, O., Majić, A., Männil, P., Manz, R., Marboutin, E., Marucco, F., Melovski, D., Mersini, K., Mertzanis, Y., Mysłajek, R.W., Nowak, S., Odden, J., Ozolins, J., Palomero, G., Paunović, M., Persson, J., Potočnik, H., Quenette, P.-Y., Rauer, G., Reinhardt, I., Rigg, R., Ryser, A., Salvatori, V., Skrbinšek, T., Stojanov, A., Swenson, J.E., Szemethy, L., Trajçe, A., Tsingarska[1]Sedefcheva, E., Váňa, M., Veeroja, R., Wabakken, P., Wölfl, M., Wölfl, S., Zimmermann, F., Zlatanova, D. and Boitani, L. 2014. Recovery of large carnivores in Europe’s modern human-dominated landscapes. <em>Science</em> 346: 1517-1519.</p> <p>Fenton, S., Moorcroft, P.R., Ćirović, D., Lanszki, J., Heltai, M., Cagnacci, F., Breck, S., Bogdanović, N., Pantelić, I., Ács, K. and Ranc, N. 2021. Movement, space-use and resource preferences of European golden jackals in human-dominated landscapes: insights from a telemetry study. <em>Mammalian Biology</em>, 101: 619–630.</p> <p>Hatlauf, J. and Böcker, F. 2022. Recommendations for the documentation and assessment of golden jackal (<em>Canis aureus</em>) records in Europe. BOKU reports on wildlife research and willdife management 27. Ed: Institute of Wildlife Biology and Game Management (IWJ), University of Natural Resources and Life Sciences, Vienna. ISBN: 978-3-900932-94-7</p> <p>Hatlauf, J., Bayer, K., Trouwborst, A. and Hackländer, K. 2021. New rules or old concepts? The golden jackal (<em>Canis aureus</em>) and its legal status in Central Europe. <em>European Journal of Wildlife Research</em>, 67, 25.</p> <p>Kaczensky, P., Chapron, G., Von Arx, M., Huber, D., Andrén, H. and Linnell, J. 2013. Status, management and distribution of large carnivores - bear, lynx, wolf and wolverine - in Europe. Istituto di Ecologia Applicata, Rome, Italy.</p> <p>Lanszki, J., Schally, G., Heltai, M. and Ranc, N. 2018. Golden jackal expansion in Europe: first telemetry evidence of a natal dispersal. <em>Mammalian Biology</em>, 88: 81–84.</p> <p>Linnell, J.D.C., Salvatori, V. and Boitani, L. 2008. Guidelines for population level management plans for large carnivores in Europe. A Large Carnivore Initiative for Europe report prepared for the European Commission (contract 070501/2005/424162/MAR/B2).</p> <p>Männil, P. and Ranc, N. 2022. Golden jackal (<em>Canis aureus</em>) in Estonia: development of a thriving population in the boreal ecoregion. <em>Mammalian Research,</em> 67: 245-250.</p> <p>Molinari-Jobin, A., Kéry, M., Marboutin, E., Molinari, P., Koren, I., Fuxjäger, C., Breitenmoser-Würsten, C., Wölfl, S., Fasel, M., Kos, I., Wölfl, M. and Breitenmoser, U. 2012. Monitoring in the presence of species misidentification: the case of the Eurasian lynx in the Alps. <em>Animal Conservation </em>15: 266-273.</p> <p>Ranc, N., Krofel, M. and Cirovic, D. 2018. IUCN Red List Mapping for the regional assessment of the Golden Jackal (<em>Canis aureus</em>) in Europe. IUCN Red List Threatened Species, 13.</p> <p>Salvatori, V. and Linnell, J.D.C. 2005. Report on the conservation status and threats for wolf (Canis lupus) in Europe. Council of Europe Report T-PVS/Inf (2005) 16.</p> <p>Trouwborst, A., Krofel, M. and Linnell, J.D.C. 2015. Legal implications of range expansions in a terrestrial carnivore: the case of the golden jackal (<em>Canis aureus</em>) in Europe. <em>Biodiversity Conservation</em>, 24: 2593–2610.</p> <p>von Arx, M., Breitenmoser-Würsten, C., Zimmermann, F. and Breitenmoser, U. 2004. Status and conservation of the Eurasian lynx (<em>Lynx lynx</em>) in Europe in 2001. KORA Report 19e: 1-330.</p> <p> </p> <p><strong>Contact information</strong></p> <p>Nathan Ranc, nathan.ranc@inrae.fr</p>
Data from: Metabarcoding analysis provides insight into the link between prey and plant intake in a large alpine cat carnivore, the snow leopard
<p>Species of the family Felidae (a group represented by cats) are thought to be obligate carnivores, specialized for hunting and consuming other animals. However, the detection of plants in the feces of felids raises questions about the role of plants in their diet. This is particularly true for the snow leopard (Panthera uncia), a big cat native to central and South Asia's high mountains. Our study aimed to comprehensively identify the prey and plants consumed by snow leopards as well as six other sympatric mammals. We applied DNA metabarcoding methods on 126 fecal samples collected from the Sarychat-Ertash Nature Reserve in Kyrgyzstan. We found that among the three most common plant families in snow leopard feces, Tamaricaceae (genus Myricaraia) was consumed often by snow leopards. The genus Myricaria frequently appeared in samples lacking any animal prey DNA, indicating that snow leopards might have consumed this plant especially when their digestive tracts were empty. We also observed a significant difference in plant composition between male and female snow leopards, and potentially between sampling seasons. We provide a comprehensive overview of the prey and plants detected in the feces of snow leopards and sympatric mammals. We believe our findings will help in formulating hypotheses and guiding future research to understand the adaptive significance of plant-eating behavior in felids and animal-plant relationships in the ecosystem.</p>
Predicting potential distributions of large carnivores in Kenya: An occupancy study to guide conservation
<p><span><strong>Aim</strong>:</span><span> Species distribution maps are frequently the foundation upon which species-specific conservation strategies are developed, however, mapping species distribution is challenging, especially across large spatial extents. Our aim was to use a novel empirical approach to predict the national distribution for all six large carnivore species </span><span>found in Kenya to guide conservation and management decisions by identifying knowledge and conservation gaps.</span></p> <p><span><strong>Location</strong>:</span><span> Kenya</span></p> <p><span><strong>Methods</strong>:</span><span> Data on carnivore presence and absence were collected through questionnaires and sightings-based surveys. These data were combined and analysed using single-season false-positive occupancy models, which account for imperfect detections and false positives. </span><span>To inform conservation strategies, </span><span>we used the occupancy outputs to make predictions for unsampled areas and create occupancy-based distribution </span><span>maps, where ψ>0.50, </span><span>to</span><span> (1) quantify differences with IUCN Red List range maps, (2) quantify overlap with wildlife areas and (3) </span><span>identify areas of high carnivore richness</span><span>.</span></p> <p><span><strong>Results</strong>:</span><span> Large carnivore occupancy was associated with land conversion, habitat, and prey availability. Our results suggest that all six species are widely distributed across Kenya and reveal substantial differences in distribution maps compiled by the IUCN Red List. </span><span>More specifically, our occupancy-based distribution maps predict a </span><span>much larger distribution for African wild dog (5.09X), lion (4.77X), and leopard (1.46X), similar distribution for cheetah, and smaller distribution for spotted hyaena (0.84X) and striped hyaena (0.65X). For all large carnivores, the vast majority (~80%) of their predicted distribution falls outside wildlife areas and northern Kenya is predicted to have the highest large carnivore richness.</span></p> <p><span><strong>Main conclusions</strong>:</span> <span>Our results are encouraging as large carnivores may be widely distributed across Kenya, in some cases potentially more so than previously acknowledged. However, much of this range lies outside wildlife areas and represents areas of concern both for conservation and human livelihoods illustrating the challenges of conserving large carnivores across their range.</span></p>
Data on the abundance of wild ungulates and large carnivores in the Roztocze National Park (south-east Poland), 2007-2022
<p>Data on the abundance of wild ungulates and large carnivores in the Roztocze National Park (south-east Poland), 2007-2022</p>
Fig. 5 in Morphological disparity in Plio-Pleistocene large carnivore guilds from Italian peninsula
Fig. 5. Disparity values computed for morphospace of each extant and Plio−Pleistocene large carnivore guild. Lines define 95% confidence interval under 999 randomizations. Extant is for all living taxa (N = 34) while Plio−Pleistocene stand for all fossil taxa (N = 23). Kruger, Africa is for Africa, Gunung Lensung, Indonesia for Indonesia, Otishi for South America,, Yellowstone for North America, Krokonose for Czech Republic. Fossil communities are ordered from the youngest to the oldest: Aurelian, 0.3 Ma; Galerian 3, 0.45 Ma; Galerian 2, 0.6 Ma; Galerian 1, 0.8 Ma; Pirro, 1.1 Ma; Valdi− Chiana, 1.5 Ma; Up Valdarno, 1.9 Ma; Montopoli, 2.6 Ma; Triversa, 3.2 Ma.
Fig. 3 in Morphological disparity in Plio-Pleistocene large carnivore guilds from Italian peninsula
Fig. 3. Scatter plots of RW1 (X axis, scale −0.40 / +0.40) versus RW2 (Y axis, scale −0.40 / +0.40). Each extant large carnivore guild is highlighted by closed circles. The Kruger, Africa guild represents Africa, Krokonose is for Czech Republic, Gunung Lensung, Indonesia Lensung for Indonesia, Otishi for South America and Yellowstone for North America.
Fig. 4 in Morphological disparity in Plio-Pleistocene large carnivore guilds from Italian peninsula
Fig. 4. Scatter plots of RW1 (X axis, scale −0.40 / +0.40) versus RW2 (Y axis, scale −0.40 / +0.40). Each Plio−Pleistocene carnivore guild is highlighted by closed circles. Guild are representative of distinct Paleo−Communities trough time: Triversa, 3.2 Ma; Montopoli, 2.6 Ma; Up Valdarno, 1.9 Ma; ValdiChiana, 1.5 Ma; Pirro, 1.1 Ma; Galerian 1, 0.8 Ma; Galerian 2, 0.6 Ma; Galerian 3, 0.45 Ma; and Aurelian, 0.3 Ma.
Fig. 6 in Morphological disparity in Plio-Pleistocene large carnivore guilds from Italian peninsula
Fig. 6. Scatter plot of log number of artiodactyls vs. large carnivore disparity values. Open circles, extant ecosystems; closed, fossil ecosystems. A linear trendline is placed on extant data points. Open circles represent extant ecosystem including Kruger, Africa, Africa; Gunung Lensung, Indonesia Lensung, Indonesia; Otishi, South America; Yellowstone, North America; Krokonose, Czech Republic. Closed circles are fossil communities: Triversa, 3.2 Ma; Montopoli, 2.6 Ma; Up Valdarno, 1.9 Ma; Valdi− Chiana, 1.5 Ma; Pirro, 1.1 Ma; Galerian 1, 0.8 Ma; Galerian 2, 0.6 Ma; Galerian 3, 0.45 Ma; and Aurelian, 0.3 Ma.
Spatial partial identity model for spatial capture-recapture analysis of large carnivores in Kasungu National Park, Malawi
<p>Overview:</p> <p>Decline in global carnivore populations has led to increased demand for assessment of carnivore densities in understudied habitats. Spatial capture-recapture is used increasingly to estimate species densities, where individuals are often identified from their unique pelage patterns. However, uncertainty in bilateral individual identification can lead to the omission of capture data and reduce the precision of results. The recent development of the two-flank spatial partial identity model (SPIM), offers a cost-effective approach which can reduce uncertainty in individual identity assignment and provide robust density estimates. We conducted camera trap surveys annually between 2016 and 2018 in Kasungu National Park, Malawi, a primary miombo woodland and a habitat lacking baseline data on carnivore densities. We used SPIM to estimate density for leopard (<em>Panthera pardus</em>) and spotted hyaena (<em>Crocuta crocuta</em>), and report on the status of other large carnivores.</p> <p>Usage notes:</p> <p>These data are to estimate density for leopard and spotted hyaena in KNP, Malawi. They are provided as an example for using the spatial partial identity model for spatial capture-recapture analysis in populations where individuals are partially identified.</p> <p>Methods:</p> <p>Individual leopards and spotted hyaena were identified from photographs using their unique pelage patterns (Henschel & Ray, 2003). A database was maintained of identified individuals, with partial (single flank) or complete (two flank) identities, to build capture histories for SCR analysis. We identified individuals from left flank captures for both species, due to higher numbers of identified left flank individuals recorded during preliminary surveys. Complete identities were added where flanks were certain to come from the same individual (from baited stations outside of survey time, live captures, dual camera trap stations and multiple passes of a single camera trap). Leopards were sexed by visual determination of external genitalia, presence of the dewlap, frontal bossing and overall body size (Henschel & Ray, 2003; Devens <em>et al</em>. 2018). Sexing was not possible for spotted hyaena due to difficulties in determining sex from external genitalia and body size. Capture histories were developed for spatial captures and trap effort, with each day (24 hours) treated as a separate sampling occasion (Goldberg <em>et al</em>. 2015). Trap effort was measured through a binary matrix of active-inactive days, to improve estimates of detection probability, and included the spatial location of each camera location.</p> <p>Density was modelled using the package <em>SPIM </em>(Augustine, 2018) in R v.3.5.2<em> </em>(R Development Core Team, 2018) to resolve the complete identity of individuals from single-flank samples probabilistically (see Augustine <em>et al</em>. 2018 for complete description of spatial partial identity model), and a Bernoulli observation model fitted, whereby an individual may be captured in each trap only once during each sampling occasion (Royle <em>et al</em>. 2013; Augustine <em>et al</em>. 2018). For Markov Chain Monte Carlo simulations, a single chain of 50,000 iterations per single session analysis was undertaken, with a burn-in of 500 iterations and data augmentation of 100-130 individuals for leopard and 125-250 for spotted hyaena. Analysis was conducted with an increasing buffer width from 10,000 to 25,000 metres (leopard) and 10,000 to 40,000 metres (spotted hyaena), using 5,000 metre increments, until density estimates stabilised (Chase-Grey <em>et al</em>. 2013; Devens <em>et al</em>. 2018).</p>
Figure 9 in Human-wildlife conflict as a barrier to large carnivore management and conservation in Turkey
Figure 9. Responses by occupation for the question "Does the wild animal you see harm you?" as part of a human opinion survey conducted in 2010 and 2014 in villages surrounding the Sarıkamış-Allahuekber Mountains National Park in eastern Turkey. Results are pooled across years.
Figure 12 in Human-wildlife conflict as a barrier to large carnivore management and conservation in Turkey
Figure 12. Previous knowledge of wildlife ecotourism and desire to participate in future opportunities of survey respondents to a human opinion survey conducted in 2010 and 2014 in villages surrounding the Sarıkamış-Allahuekber Mountains National Park in eastern Turkey. Results are pooled across the two survey years.
Figure 6 in Human-wildlife conflict as a barrier to large carnivore management and conservation in Turkey
Figure 6. Responses by survey year for the question "Does the wild animal you see harm you?" as part of a human opinion survey conducted in 2006, 2010, and 2014 in villages surrounding the Sarıkamış-Allahuekber Mountains National Park in eastern Turkey.
Figure 7 in Human-wildlife conflict as a barrier to large carnivore management and conservation in Turkey
Figure 7. Property damage from wildlife experienced by survey respondents to a human opinion survey conducted in 2006, 2010, and 2014 in villages surrounding the Sarıkamış-Allahuekber Mountains National Park in eastern Turkey. Results are pooled across years.
FIGURE 14 in Large mammals (carnivores, artiodactyls) from Solna Jama Cave (Bystrzyckie Mts, Southwestern Poland) in the context of faunal changes in the postglacial period of Central Europe
FIGURE 14. Scatter diagram showing the ratio of total length to proximal epiphysis breadth in late pleistocene-holocene Capreolus capreolus phalanx II. Middle Pleistocene locality: Kozi Grzbiet and Miesenheim 1. Late middle and late Pleistocene locality: Weimar Ehringsdorf, Biśnik Cave, Chlupáč Cave and Deszczowa Cave. Postglacial and Holocene locality: Biśnik Cave (uppermost layers), Jasna Strzegowska Cave and Poland in general. Data from Stefaniak (2015) and references therein.
FIGURE 13 in Large mammals (carnivores, artiodactyls) from Solna Jama Cave (Bystrzyckie Mts, Southwestern Poland) in the context of faunal changes in the postglacial period of Central Europe
FIGURE 13. Scatter diagram showing the ratio of lower carnassial (m1) length (Lm1) and breadth (B m1) in late Pleistocene and Recent Mustela nivalis from Poland. The Solna Jama Cave specimen displays a moderately large size, with the length of m1 less than 4 mm, typical of the late Pleistocene and postglacial period.
FIGURE 12 in Large mammals (carnivores, artiodactyls) from Solna Jama Cave (Bystrzyckie Mts, Southwestern Poland) in the context of faunal changes in the postglacial period of Central Europe
FIGURE 12. Scatter diagram showing the ratio of total calvarium length to zygomatic breadth in extant Mustela nivalis from Poland, compared with the fossil specimen from Solna Jama Cave.
FIGURE 11 in Large mammals (carnivores, artiodactyls) from Solna Jama Cave (Bystrzyckie Mts, Southwestern Poland) in the context of faunal changes in the postglacial period of Central Europe
FIGURE 11. Skulls of Mustela nivalis from Poland: recent specimens (1-3) and the fossil from Solna Jama Cave (4). 1, robust, adult male; 2, adult female; 3, young, adult female; and 4, adult female. Note fully developed sagittal crest in individual from Solna Jama Cave, indicating fully mature age. Scale bar equals 10 mm.
FIGURE 7 in Large mammals (carnivores, artiodactyls) from Solna Jama Cave (Bystrzyckie Mts, Southwestern Poland) in the context of faunal changes in the postglacial period of Central Europe
FIGURE 7. Scatter diagram showing the ratio of m1 trigonid breadth (B tri) to m1 talonid breadth (B tal) in the forms of Gulo: G. schlosseri and G. gulo. Data from Döppes (2001): late Pleistocene G. gulo and recent G. gulo; data from Marciszak (2012): G. schlosseri and late Pleistocene G. gulo (part).
FIGURE 4 in Large mammals (carnivores, artiodactyls) from Solna Jama Cave (Bystrzyckie Mts, Southwestern Poland) in the context of faunal changes in the postglacial period of Central Europe
FIGURE 4. Gulo gulo cranium from Solna Jama Cave (JSJ/Gg/1-1) in dorsal view (left) compared with a cranium from a large modern male from Scandinavia (right) from collection of Natural History Museum University of Wrocław (coll. no. M/500328). Scale bar equals 10 mm.
FIGURE 10 in Large mammals (carnivores, artiodactyls) from Solna Jama Cave (Bystrzyckie Mts, Southwestern Poland) in the context of faunal changes in the postglacial period of Central Europe
FIGURE 10. Scatter diagram showing the ratio of mandiblar height (measured after m1) to m1 length in fossil and extant Mustela eversmanii and Mu. putorius. Data from Marciszak (2012) and references therein.
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