Ecology

Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
Tomas Roslin

University of Helsinki, Helsinki, Finland
Tomas Roslin, Laura Antão, Maria Hällfors, Coong Lo, Juri Kurhinen & Otso Ovaskainen

EarthCape OY, Helsinki, Finland
Evgeniy Meyke

Department of Computer Science, Aalto University, Espoo, Finland
Gleb Tikhonov

Research Unit of Biodiversity (UMIB, UO-CSIC-PA), Oviedo University, Mieres, Spain
Maria del Mar Delgado

3237 Biology-Psychology Building, University of Maryland, College Park, MD, USA
Eliezer Gurarie

National Park Orlovskoe Polesie, Oryol, Russian Federation
Marina Abadonova

Institute of Botany, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
Ozodbek Abduraimov, Azizbek Mahmudov & Mirabdulla Turgunov

Kostomuksha Nature Reserve, Kostomuksha, Russian Federation
Olga Adrianova, Irina Gaydysh & Natalia Sikkila

Altai State Nature Biosphere Reserve, Gorno-Altaysk, Russian Federation
Tatiana Akimova, Svetlana Chuhontseva, Elena Gorbunova, Yury Kalinkin, Helen Korolyova, Oleg Mitrofanov, Miroslava Sahnevich, Vladimir Yakovlev & Tatyana Zubina

Kabardino-Balkarski Nature Reserve, Kashkhatau, Russian Federation
Muzhigit Akkiev

FSE Zapovednoe Podlemorye, Ust-Bargizin, Russian Federation
Aleksandr Ananin, Evgeniya Bukharova & Natalia Luzhkova

Institute of General and Experimental Biology, Siberian Branch, Russian Academy of Sciences, Ulan-Ude, Russian Federation
Aleksandr Ananin

State Nature Reserve Stolby, Krasnoyarsk, Russian Federation
Elena Andreeva, Nadezhda Goncharova, Alexander Hritankov, Anastasia Knorre, Vladimir Kozsheechkin & Vladislav Timoshkin

Carpathian Biosphere Reserve, Rakhiv, Ukraine
Natalia Andriychuk, Alla Kozurak & Anatoliy Vekliuk

Nizhne-Svirsky State Nature Reserve, Lodeinoe Pole, Russian Federation
Maxim Antipin

State Nature Reserve Prisursky, Cheboksary, Russian Federation
Konstantin Arzamascev

Zapovednoe Pribajkalje (Bajkalo-Lensky State Nature Reserve, Pribajkalsky National Park), Irkutsk, Russian Federation
Svetlana Babina

Darwin Nature Biosphere Reserve, Borok, Russian Federation
Miroslav Babushkin, Andrey Kuznetsov, Natalia Nemtseva, Irina Rybnikova & Nicolay Zelenetskiy

Volzhsko-Kamsky National Nature Biosphere Rezerve, Sadovy, Russian Federation
Oleg Bakin, Elena Chakhireva & Alexey Pavlov

FGBU National Park Shushenskiy Bor, Shushenskoe, Russian Federation
Anna Barabancova & Andrej Tolmachev

Voronezhsky Nature Biosphere Reserve, Voronezh, Russian Federation
Inna Basilskaja & Inna Sapelnikova

Baikalsky State Nature Biosphere Reserve, Tankhoy, Russian Federation
Nina Belova, Olga Ermakova, Irina Kozyr, Aleksandra Krasnopevtseva & Nikolay Volodchenkov

Visimsky Nature Biosphere Reserve, Kirovgrad, Russian Federation
Natalia Belyaeva & Rustam Sibgatullin

Kondinskie Lakes National Park named after L. F. Stashkevich, Sovietsky, Russian Federation
Tatjana Bespalova, Alena Butunina, Aleksandra Esengeldenova, Natalia Korotkikh & Evgeniy Larin

FSBI United Administration of the Kedrovaya Pad’ State Biosphere Nature Reserve and Leopard’s Land National Park, Vladivostok, Russian Federation
Evgeniya Bisikalova

Pechoro-Ilych State Nature Reserve, Yaksha, Russian Federation
Anatoly Bobretsov, Murad Kurbanbagamaev, Irina Megalinskaja, Viktor Teplov, Valentina Teplova & Tatiana Tertitsa

A. N. Severtsov Institute of Ecology and Evolution, Moscow, Russian Federation
Vladimir Bobrov & Igor Pospelov

Komsomolskiy Department, FGBU Zapovednoye Priamurye, Komsomolsk-on-Amur, Russian Federation
Vadim Bobrovskyi, Olga Kuberskaya, Polina Van & Vladimir Van

Tigirek State Nature Reserve, Barnaul, Russian Federation
Elena Bochkareva & Evgeniy A. Davydov

Institute of Systematics and Ecology of Animals, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russian Federation
Elena Bochkareva

State Nature Reserve Bolshaya Kokshaga, Yoshkar-Ola, Russian Federation
Gennady Bogdanov

Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences, Ekaterinburg, Russian Federation
Vladimir Bolshakov

Sikhote-Alin State Nature Biosphere Reserve named after K. G. Abramov, Terney, Russian Federation
Svetlana Bondarchuk, Sergey Elsukov, Ludmila Gromyko, Irina Nesterova & Elena Smirnova

FSBI Prioksko-Terrasniy State Reserve, Danky, Russian Federation
Yuri Buyvolov & Galina Sokolova

Lomonosov Moscow State University, Moscow, Russian Federation
Anna Buyvolova & Ilya Prokhorov

National Park Meshchera, Gus-Hrustalnyi, Russian Federation
Yuri Bykov, Zoya Drozdova & Svetlana Mayorova

South Urals Federal Research Center of Mineralogy and Geoecology, Ilmeny State Reserve, Ural Branch, Russian Academy of Sciences, Miass, Russian Federation
Olga Chashchina, Nadezhda Kuyantseva & Valery Zakharov

FGBU National Park Kenozersky, Arkhangelsk, Russian Federation
Nadezhda Cherenkova, Svetlana Drovnina & Alexander Samoylov

FGBU GPZ Kologrivskij les im. M.G. Sinicina, Kologriv, Russian Federation
Sergej Chistjakov

Altai State University, Barnaul, Russian Federation
Evgeniy A. Davydov

Pryazovskyi National Nature Park, Melitopol’, Ukraine
Viktor Demchenko, Elena Diadicheva & Valeri Sanko

State Nature Reserve Privolzhskaya Lesostep, Penza, Russian Federation
Aleksandr Dobrolyubov & Aleksey Kudryavtsev

Komarov Botanical Institute, Russian Academy of Sciences, Saint Petersburg, Russian Federation
Ludmila Dostoyevskaya, Violetta Fedotova & Pavel Lebedev

Sary-Chelek State Nature Reserve, Aksu, Kyrgyzstan
Akynaly Dubanaev

Institute for Evolutionary Ecology NAS Ukraine, Kiev, Ukraine
Yuriy Dubrovsky

FGBU State Nature Reserve Kuznetsk Alatau, Mezhdurechensk, Russian Federation
Lidia Epova

Kerzhenskiy State Nature Biosphere Reserve, Nizhny Novgorod, Russian Federation
Olga S. Ermakova

FSBI United Administration of the Mordovia State Nature Reserve and National Park Smolny, Republic of Mordovia, Saransk, Russian Federation
Elena Ershkova

Ogarev Mordovia State University, Saransk, Russian Federation
Elena Ershkova

Bryansk Forest Nature Reserve, Nerussa, Russian Federation
Oleg Evstigneev, Evgeniya Kaygorodova, Sergey Kossenko, Sergey Kruglikov & Elena Sitnikova

Pinezhsky State Nature Reserve, Pinega, Russian Federation
Irina Fedchenko, Lyudmila Puchnina, Svetlana Rykova & Andrei Sivkov

The Central Chernozem State Biosphere Nature Reserve named after Professor V.V. Alyokhin, Kurskiy, Russian Federation
Tatiana Filatova

Tyumen State University, Tyumen, Russian Federation
Sergey Gashev

Reserves of Taimyr, Norilsk, Russian Federation
Anatoliy Gavrilov, Leonid Kolpashikov, Elena Pospelova & Violetta Strekalovskaya

Chatkalski National Park, Toshkent, Uzbekistan
Dmitrij Golovcov

National Park Ugra, Kaluga, Russian Federation
Tatyana Gordeeva & Viktorija Teleganova

Kaniv Nature Reserve, Kaniv, Ukraine
Vitaly Grishchenko, Yuliia Kulsha, Vasyl Shevchyk & Eugenia Yablonovska-Grishchenko

Smolenskoe Poozerje National Park, Przhevalskoe, Russian Federation
Vladimir Hohryakov, Gennadiy Kosenkov & Ksenia Shalaeva

FSBI Zeya State Nature Reserve, Zeya, Russian Federation
Elena Ignatenko, Klara Pavlova & Sergei Podolski

Polistovsky State Nature Reserve, Pskov, Russian Federation
Svetlana Igosheva & Tatiana Novikova

Ural State Pedagogical University, Yekaterinburg, Russian Federation
Uliya Ivanova, Margarita Kupriyanova, Tamara Nezdoliy, Nataliya Skok & Oksana Yantser

Institute of Mathematical Problems of Biology RAS—the Branch of the Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Pushchino, Russian Federation
Natalya Ivanova & Maksim Shashkov

Kronotsky Federal Nature Biosphere Reserve, Yelizovo, Russian Federation
Fedor Kazansky & Darya Panicheva

Zhiguli Nature Reserve, P. Bakhilova Polyana, Russian Federation
Darya Kiseleva

Institute for Ecology and Geography, Siberian Federal University, Krasnoyarsk, Russian Federation
Anastasia Knorre

Central Forest State Nature Biosphere Reserve, Tver, Russian Federation
Evgenii Korobov, Elena Shujskaja, Sergei Stepanov & Anatolii Zheltukhin

National Park Bashkirija, Nurgush, Russian Federation
Elvira Kotlugalyamova & Lilija Sultangareeva

State Nature Reserve Kurilsky, Juzhno-Kurilsk, Russian Federation
Evgeny Kozlovsky

Vodlozersky National Park, Karelia, Petrozavodsk, Russian Federation
Elena Kulebyakina & Viktor Mamontov

State Nature Reserve Kivach, Kondopoga, Russian Federation
Anatoliy Kutenkov, Nadezhda Kutenkova, Anatoliy Shcherbakov, Svetlana Skorokhodova, Alexander Sukhov & Marina Yakovleva

South-Ural Federal University, Miass, Russian Federation
Nadezhda Kuyantseva

Saint-Petersburg State Forest Technical University, St. Petersburg, Russian Federation
Pavel Lebedev

Astrakhan Biosphere Reserve, Astrakhan, Russian Federation
Kirill Litvinov

FSBI United Administration of the Lazovsky State Reserve and National Park Zov Tigra, Lazo, Russian Federation
Lidiya Makovkina, Aleksandr Myslenkov & Inna Voloshina

State Nature Reserve Tungusskiy, Krasnoyarsk, Russian Federation
Artur Meydus, Julia Raiskaya & Vladimir Sopin

Krasnoyarsk State Pedagogical University named after V.P. Astafyev, Krasnoyarsk, Russian Federation
Artur Meydus

Institute of Geography, Russian Academy of Sciences, Moscow, Russian Federation
Aleksandr Minin

Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow, Russian Federation
Aleksandr Minin

Carpathian National Nature Park, Yaremche, Ukraine
Mykhailo Motruk

State Environmental Institution National Park Braslav lakes, Braslav, Belarus
Nina Nasonova

National Park Synevyr, Synevyr-Ostriki, Ukraine
Tatyana Niroda, Ivan Putrashyk, Yurij Tyukh & Yurij Yarema

Pasvik State Nature Reserve, Nikel, Russian Federation
Natalja Polikarpova

Mari Chodra National Park, Krasnogorsky, Russian Federation
Tatiana Polyanskaya

State Nature Reserve Vishersky, Krasnovishersk, Russian Federation
Irina Prokosheva

State Nature Reserve Olekminsky, Olekminsk, Russian Federation
Yuri Rozhkov, Olga Rozhkova & Dmitry Tirski

Crimea Nature Reserve, Alushta, Republic of Crimea
Marina Rudenko

Forest Research Institute Karelian Research Centre, Russian Academy of Sciences, Petrozavodsk, Russian Federation
Sergei Sazonov, Lidia Vetchinnikova & Juri Kurhinen

Black Sea Biosphere Reserve, Hola Prystan’, Ukraine
Zoya Selyunina

Institute of Physicochemical and Biological Problems in Soil Sciences, Russian Academy of Sciences, Pushchino, Russian Federation
Maksim Shashkov

State Nature Reserve Nurgush, Kirov, Russian Federation
Sergej Shubin & Ludmila Tselishcheva

Caucasian State Biosphere Reserve of the Ministry of Natural Resources, Maykop, Russian Federation
Yurii Spasovski

National Nature Park Vyzhnytskiy, Berehomet, Ukraine
Vitalіy Stratiy

National Park Khvalynsky, Khvalynsk, Russian Federation
Guzalya Suleymanova

State Research Center Arctic and Antarctic Research Institute, Saint Petersburg, Russian Federation
Aleksey Tomilin

Information-Analytical Centre for Protected Areas, Moscow, Russian Federation
Aleksey Tomilin

State Nature Reserve Malaya Sosva, Sovetskiy, Russian Federation
Aleksander Vasin & Aleksandra Vasina

Krasnoyarsk State Medical University named after Prof. V.F.Voino-Yasenetsky, Krasnoyarsk, Russian Federation
Vladislav Vinogradov

Surhanskiy State Nature Reserve, Sherabad, Uzbekistan
Tura Xoliqov

Mordovia State Nature Reserve, Pushta, Russian Federation
Andrey Zahvatov

Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
Otso Ovaskainen

The data were collected by the 195 authors starting from M.A. and ending with T.Z. in the author list. J.K., E.M., C.L., G.T. and E.G. contributed to the establishment and coordination of the collaborative network and to the compilation and curation of the resulting dataset. T.R., O.O., L.A., M.H. and M.d.M.D. conceived the idea behind the current study and wrote the first draft of the paper, with O.O. conducting the analyses. All authors provided useful comments on earlier drafts. More

Samples
We sampled 73 adult fox skulls (Vulpes vulpes) from three separate sample groups: wild (8 F, 12 M), unselected (15 F, 8 M), and domesticated (15 F, 15 M). Domesticated and unselected skulls from the RFF experiment were generously provided by Dr. Trut and transported to Harvard in 2004. Unfortunately, we do not know how these foxes were chosen, but have no reason not to assume that they were selected randomly from both populations. Wild fox skulls in the study were sampled from the collections of the Museum of Comparative Zoology, Harvard University. All but two wild foxes were trapped in Canada east of Quebec between 1894 and 1952, with the majority (70%) between 1894 and 1900 (Table S1). We excluded from the study sample all juvenile skulls, as determined through lower third molar eruption and fusion of the cranial suture between the basioccipital and basisphenoid16, and those skulls that had evidence of damage or disease. After applying these exclusion criteria, we arrived at our final sample of 73 skulls.
3D landmarks
To prevent movement during measurement, each skull was embedded in styrofoam and secured to the workspace desk before 3D coordinates of 29 landmarks, listed, defined and displayed in Table S2 and Fig S1, were collected on the left half of each skull by a single analyst (TMK). Of these 29 landmarks, 17% are on the cranial base, 27% are on the neurocranium, and 55% are on the face. 3D landmark coordinates were measured with a Microscribe G2 (Positional Accuracy ± 0.38 mm, Revware, Inc.). This machine consists of a mobile robotic arm tipped with a probe. After calibration, the probe tip is placed on each landmark to record its XYZ coordinates. To avoid having to move the skulls during measuring and to limit the number of variables in our final GM analysis (too high a number may be a problem given our small sample size), we restricted landmark measurements to one half of each skull. We assume that the fluctuating asymmetry between each fox population is negligible and stable as has previously been shown in comparisons across a domestic-wild hybridization zone in mice17. In most cases, landmark positions were lightly marked with pencil to ensure proper probe placement.
Linear and endocranial volume measurements
Six linear measurements were taken on each skull using digital calipers (Fowler High Precision, Positional Accuracy ± 0.03 mm): total skull length, snout length, cranial vault height, cranial vault width, bi-zygomatic width and upper jaw width (Fig. 1). Endocranial volume was measured using plastic beads. Each cranium was filled up to the level of the foramen magnum and repeatedly shaken and tamped down until no more beads could be added. The beads were then funneled into a graduated cylinder to obtain a volumetric measurement.
Figure 1

Schematic diagram of the six linear measurements taken on the fox skulls. 1: Bi-zygomatic width. 2: Cranial vault width. 3: Upper jaw width. 4: Total skull length. 5: Snout length. 6: Cranial vault height.

Full size image

Geometric morphometrics
Generalized Procrustes analysis was conducted in R v. 4.0.218 using the geomorph v. 3.3.1 package19. Landmark configurations from each specimen were translated to the origin, rescaled to centroid size, and optimally rotated (using a least-squares criterion) until the coordinates of homologous landmarks aligned as closely as possible. These steps place all specimens in the same shape space, centered on the mean shape. An orthogonal projection into a linear tangent space was applied so statistical analyses could be performed on the resulting tangent space coordinates. For Procrustes superimposition, we used the default parameters of the gpagen function in geomorph.
Repeatability analyses
Landmark measurement repeatability was evaluated through repeated measurements of three fox skulls (domesticated male ID# TM23, domesticated female ID# TF476, unselected female ID# UF1058) on ten separate occasions. In this case, repeatability encompasses both Microscribe and operator error. Generalized Procrustes Analysis (GPA) was used on these landmark coordinates to ensure that they were in the same 3D location relative to one another. The average Procrustes distance (PD) between all ten iterations of the same specimen was then compared to the average Procrustes distance within the population-sex grouping to which the skull belonged. To do this, we calculated a sensitivity ratio based on the formula: (Mean Inter-specimen PD – Mean Intra-specimen PD)/Mean Intra-specimen PD. This created a sensitivity ratio that reflects how sensitive the Microscribe measurements are with respect to the average difference among foxes of the same population-sex category. Averaging the sensitivity ratios for our three skulls, we find that the difference between replicates is roughly 3.7 times smaller than the differences within population-sex groupings. This indicates that the Microscribe G2 is robust enough to detect subtle individual differences in measured landmark coordinates. Linear and endocranial measurement repeatability was quantified through a similar method where repeat measurements were taken on 3 domesticated female fox skulls on 15 separate occasions. Sensitivity ratios were deteremined for each measurement (i.e. total skull length, snout length etc.) by calculating the standard deviation of each repeated measurement on a single specimen, averaging the three specimens’ standard deviations for that measurement, and then comparing that value to the population (domesticated female) standard deviation for that measurement. With the exception of cranial vault height (see limitations), the replicate standard deviations of each measurement were roughly a third (or less) of the population standard deviations (Table S3).
Statistical analyses
All statistical analyses were performed in R18. For all parametric inferences, we report point and interval (95% confidence) estimates of effect sizes, while for permutation-based inferences we report point estimates and p-values. All p-values involving multiple comparisons were adjusted for family-wise error using the sequential Bonferroni method.
3D shape comparisons
To test hypotheses about shape differences among the three populations of foxes, we used a permutation-based Procrustes MANOVA to regress tangent space coordinates on population identity and sex in the geomorph v. 3.3.1 package. Because we are unable to detect significant differences in allometry among populations with a permutation-based Procrustes MANOVA of tangent space coordinates on the interaction term of population identity and centroid size, the tangent space coordinates were not corrected for any scaling effects (see Supplemental information and Fig S2). Given this result, we control only for isometric size in geometric morphometric analyses (i.e. no correction for scaling in tangent space coordinates) as well as in our linear measurements. To determine how skull shape differed between fox populations, we performed pairwise comparisons of shape using Procrustes distances. We additionally performed pairwise comparisons between groups of the shape variance within a group (as assessed by the dispersion of residuals around the mean shape for a given population)20. All pairwise comparisons were made using the RRPP v. 0.6.1 package21,22. Permutation-based p-values for the pairwise comparisons were corrected for family-wise error using the sequential Bonferroni method. To visualize changes in skull shape between populations, a principal components analysis (PCA) was performed on the tangent space coordinates. Skull warp changes along the first principal component were graphed to visualize shape changes along this axis. Size differences between populations were assessed via a linear model using a weighted least squares (WLS) estimator, where centroid size was regressed on population identity and sex. The WLS estimator allowed for separate residual variances for each combination of population and sex, so that heteroskedasticity across these groups could be accounted for in the model. Variance in centroid size was assessed with a Levene’s test based on absolute deviations from the median and was performed using the car package v. 3.0-1023.
Linear and endocranial volume comparisons
Prior to modeling linear and volumetric data, we created size-adjusted versions of our variables to account for a difference in isometric size between wild and RFF populations. Normalizing to size allows us to parse out the effects of size selection from those of selection for docility as they likely have overlapping effects on craniofacial shape. We adjusted for size by normalizing each linear measurement and the cube root of endocranial volume by centroid size. We used centroid size rather than the geometric mean of the six linear measurements because centroid size was calculated using a larger sample of craniometric landmarks and is therefore the better proxy of overall cranial size. We performed size corrections on the raw measurements instead of including a size variable in the models because it allows the size-correction to be intrinsic to each fox rather than depending on the size of every fox in the model.
To determine if there were population-level differences in size-corrected linear and volumetric variables, we used a linear model with a generalized least squares (GLS) estimator from the nlme v. 3.1-150 package24 to regress all 7 skull variables simultaneously as correlated responses on population identity and sex (see Supplementary Methods for details of estimation strategy and model specification and Figs. S3, S4). We report estimates of pairwise percent differences between population means for each skull variable. We use this method because the 7 linear and volumetric skull variables were correlated in two ways (see Fig. S3). First, they were measured on the same specimens, and second, they represent non-independent aspects of shape variation. Modeling these response variables in 7 separate general linear models (e.g., ANOVA) would result in biologically unrealistic inferences because these correlations would be artificially fixed at zero. In addition, since skull variables exhibited varying amounts of dispersion, the GLS model allowed for different residual variances for each response variable.
Sexual dimorphism comparisons
Sexual dimorphism within a species is often represented as dimorphism in size as well as shape25. Therefore, in contrast to the previous analyses, we assess the degree of sexual dimorphism in both size and shape. We used a similar GLS model to determine the degree of sexual dimorphism of the raw (non-size corrected) variables within each population. To estimate sex-specific effects, we added an additional interaction term between sex and population identity in this model. We report the degree of dimorphism using estimates of mean differences between males and females for a given skull variable, within a population. For both models using linear and volumetric data, we performed model selection for variance components and correlation structures using the Bayesian information criterion, since this has been shown to provide a good balance between parsimony and over-fitting for explanatory models26. Linear model (GLS) assumptions were checked using diagnostic plots of standardized residuals and fitted values (see Fig. S5). More

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