Philippa Kaur

We set out to disentangle the manifold and interacting drivers of fruit production of large, long-lived tropical canopy trees. We used two B. excelsa populations as models given the critical importance of this single species to ecosystem processes, Amazonian livelihoods, and tropical biodiversity conservation. Our findings uncovered that over 10 years, one site (Cachoeira) consistently generated production levels that were threefold higher than that of the other site (Filipinas). Fruit production variation at Cachoeira was also relatively constant at both individual and population levels compared to Filipinas. Yet as anticipated in the tropics (versus temperate regions) where low climate variability minimizes resource variation18, neither population exhibited masting behavior as indicated by synchrony (S).
Given that we hypothesized that fruit production would show similar patterns over time, and common driving variables, we expected weather and weather cues to play important roles in fruit production. Because our research sites are only ~ 30 km apart, we assumed that each population and individual tree experienced approximately the same weather and climatic cues. Our climate model indicated that more wet days during the narrow 3-month dry season prior to flowering resulted in increased fruit production. Furthermore, the model also indicated that when drier atmospheric conditions (represented by VAP) were present and extended beyond the dry season into the flowering period, fruit production tended to be reduced. Still, models that used the simple “year” variable to explain fruit production variation (versus multiple specific, albeit remote climate variables) had better statistical fit. This leads us to question what overall weather conditions might have caused the extremely low and highly variable production levels of 2017; in Filipinas, more than half of the trees did not produce any fruits (Fig. 1). Local Brazil nut harvesters also characterized 2017 as an exceptional nadir in production – a sentiment echoed in popular media across the Amazon basin19.
The year 2015 was a “Very Strong” El Niño year, which followed immediately on a “Weak” one (2014)20. These years relate to our 2017 production because of  > 15-month fruit maturation lag times. Such El Niño events yield sunny, dry conditions in our study region. Over the 10-year study, VAP for 2017 production was the lowest ranked (26.27 hPa), and 2016 was the second lowest (25.37 hPa) (SI Table S2), signaling back-to-back years of persistent low atmospheric moisture. While increases in solar radiation can boost forest productivity21,22, persistent dry conditions and higher accompanying temperatures induce tree stress23, and ultimately higher mortality24. As a canopy emergent, B. excelsa crowns are exposed to greater radiation levels and higher evaporative demand. Hence, they are predicted to be particularly sensitive to drought due to hydraulic stress25, potentially exacerbated by increased water column tension in such exceptionally tall trees23. Still, such large trees access stored groundwater via deep roots more than previously assumed26, and fluctuations in water supply can be moderated by internal storage in stems, roots and leaves27. It is unknown, however, the extent to which two successive El Niño years may have impacted groundwater recharge and storage, and aggravated overall tree stress. There is evidence that canopy trees are resilient to normal Amazonian dry seasons due to deep roots that access water stored from wet season precipitation3,28; yet they are more vulnerable to extended tropical droughts, as demonstrated by the higher rates of large tree, drought-related mortality29. Corlett23 suggested that this tall tree vulnerability can be attributed to the physiological challenges of transporting water from drying soil through lengthy water conduits to exposed leaves. B. excelsa demonstrates drought avoidance by losing leaves during the dry period, but only for a few days in our study region30, where deciduousness is unexceptional and average rainfall falls short of ~ 2000 mm expected for evergreen tropical forests31. Finally, drought inducement experiments have demonstrated that lower rainfall levels over time negatively affect tropical tree fruit production. Throughfall exclusion over a 4-year period had a cumulative negative effect on fruit production (− 12%) of a sub-canopy tropical Rubiaceae, but differences were only significant in 1 year32.
Delayed rainy season onset also may have influenced the extremely low 2017 fruit production. In our region, the rainy season typically begins in September, yet the key 6-month rainfall (DTF; June through November) period that influenced 2017 production was the lowest in our 10-year data set. Moreover, of the entire 117-year CRU data set, the 2017 DTF period was the 16th lowest on record (SI Table S2), indicating that rainy season onset was delayed beyond norms. Since 1979, there has been a delay in dry season end dates (or rainy season onset) and an increase in dry season length for southern Amazonia33. Grogan and Schulze34 reported that delayed rainy season onset had a negative effect on tropical canopy tree growth, but they did not track fecundity. Finally, negative correlations between fruit production and minimum temperatures during both DPF and DTF (dry season prior to, and through flowering, respectively), particularly in Cachoeira, are consistent with other tropical studies that have showed clear negative effects of high nighttime temperatures on tropical tree growth22. In sum, evidence suggests that dry, and perhaps warming, conditions may have produced cascading effects that compromised 2017 fruit production at both sites (Table S2). Still, Cachoeira responded better than Filipinas not only in 2017, but across all years, as indicated by highly significant site effects across models.
Given these results, we explored the role that site differences might play in fruit production. Previous studies have detected subtle differences in demographic structures at our sites, indicating the presence of smaller B. excelsa individuals in the Filipinas population, but without a clear attribution to ecological or socioeconomic factors9. While Cachoeira has a longer history of disturbance (i.e., low-intensity timber harvest), which could influence the dominance of B. excelsa, we lack evidence that this disturbance influences production. Despite close proximity, our sites are located in different watersheds, and are characterized by slightly different forest types and soil characteristics. Specifically, Cachoeira’s significantly higher levels of P and K (Table 1) are informative, as soil P has been positively linked to higher levels of B. excelsa production11,17. Costa35 showed that B. excelsa can be productive in acidic, less fertile soils, while suggesting that Ca is a key macronutrient for this species.
Site quality has been used extensively to explain and predict productivity across diverse forest types for decades36, and inclusion of more site variables (such as depth to water table) would likely yield improved explanations for Cachoeira’s comparatively superior production. Notwithstanding, individual tree differences, regardless of site, offer further fruit production insights. As with almost all trees, B. excelsa reproductive status and fruit production levels are explained by DBH12,16,37,38,39, with the most productive trees in the 100–150 cm DBH range11. Moreover, DBH for these trees is correlated with crown size17, which was a significant and positive explanatory variable for all our production models, although less so for large trees (≥ 100 cm DBH) in Cachoeira versus Filipinas (Table 2, Models 4a & b). Large crowns of individual trees imply greater photosynthetic capacity and sturdy physical structures that support carbohydrate and nutrient demands of the large B. excelsa fruits. Large-diameter trees with big crowns produce more fruits. Furthermore, these trees are tall; all exhibit dominant or co-dominant canopy positions, suggesting fairly unlimited access to light. Notably, while basal area growth was a significant predictor of fruit production in trees More

Given that elasmobranchs are well known for the rate at which they replace their teeth, it is perhaps surprising that anterior teeth are retained long enough for dietarily informative microwear textures to develop. Yet our results demonstrate that tooth microwear textures vary with diet in C. taurus, and show that DMTA can provide an additional, potentially powerful tool for dietary discrimination in elasmobranchs. Furthermore, recent analysis indicates that C. taurus mostly consume prey in one piece30, implying less interaction of teeth with prey than would the case in animals that process their food before swallowing. We predict that for elasmobranchs that bite their prey the relationship between diet and microwear texture will be even stronger than that reported here.
Sampling individuals with different diets reveals increases in PC 1 values that in turn correspond to changes in a number of different ISO texture parameters. In general terms, as noted above, there is a trend towards ‘rougher’ surfaces with increases in the proportion of elasmobranchs in C. taurus diets, and with increasing consumption of benthic elasmobranchs30,31,32 (which may be associated with an increase in the amount of sediment consumed with prey). The increase in variance of PC1 values may also reflect increased diversity of prey types30,31,32 in larger individuals. To a degree, the greater variance might reflect the greater difference between maximum development of ‘rough’ microwear texture in a tooth near the end of its functional life compared to a smooth, recently erupted tooth. Either way, our results indicate that microwear texture tracks diet, but more work will be required to tease apart these additional factors.
Our analyses indicate that the tooth microwear textures of Specimen 5, from a different geographic area to other specimens, and for which we have no dietary data, are closely comparable to those of samples 1, 2 and 3, in terms of both values and variances. On this basis we interpret specimen 5 to have had a diet dominated by fish. The larger size of this specimen (at ca. 335 cm, larger than any other specimens analysed) lends further support to the hypothesis that microwear texture is tracking diet, and not size. Our dietary predictions regarding C. taurus from this area could be tested using traditional stomach contents, or stable isotope analyses, but this is outside the scope of the present study.
Our results also suggest that application of DMTA to analysis of the diet of individual sharks will produce more reliable results if multiple teeth are sampled rather than a single tooth. Comparing the six teeth of the aquarium individuals (fed only fish) with six teeth sampled randomly from the wild individuals (which had more varied diets) revealed significant differences in every sub-sampling (Supplementary Table S5). However the number of parameters displaying a significant difference between wild and aquarium teeth varied, and fewer significant differences than were found than analyses comparing the aquarium teeth to multiple teeth from each wild individual. This suggests that analyses based on single isolated teeth rather than those from jaws, a situation that would commonly arise in analyses of fossil teeth, have the potential to detect differences between populations and species with different diets, but will be less sensitive than analyses based on multiple teeth per individual. To a certain extent, this will be offset in collections of isolated fossil teeth because the vast majority are teeth that were shed at the end of the functional cycle, so there will be much less sampling of recently erupted teeth with less well-developed microwear textures. (Due to the rate of tooth replacement in elasmobranchs, the number of teeth shed by an individual in its lifetime outnumber the number of teeth in the individuals jaw at time of death by several orders of magnitude).
Drawing wider comparisons with microwear texture analyses in other groups of vertebrates, of the relationship between diet and 3D microwear texture based on ISO parameters, the number of parameters that differ between samples of C. taurus is larger than most previous studies, probably due to greater differences in material properties of food between the samples compared. Wild C. taurus consume a wider variety of prey than aquarium fed C. taurus. Wild individuals consume ‘harder’ prey items, whilst interacting with the natural environment. A wild individual consuming a benthic elasmobranch will have to bite through dermal denticles, a larger cartilage skeleton and inevitably will ingest some sediment during the process. In contrast aquarium individuals are largely fed whole and partial fish within the water column, a much ‘softer’ diet. Comparison of this study to others analysing vertebrate diet, repeatedly display significant differences in certain parameters when comparing groups with harder/softer diets. Purnell and Darras23 found that Sdq, Sdr, Vmc, Vvv, Sk and Sa discriminated best between the specialist durophagous and more opportunist durophagous fish in their study (based on ANOVA and PCA), with these parameters also differing between populations of the opportunist durophage Archosargus probatocephalus with different proportions of hard prey in their diets. Of these parameters, Sk, Sa, Vmc, and Vvv produce pairwise differences between C. taurus samples (between 1 and 4). These parameters capture aspects of surface heights and the volumes of material within the core and voids in valleys, respectively (Supplementary Table S1 online). All increase in value as the proportion of elasmobranchs in the diet increases, the same as the pattern of increase with durophagy seen in Archosargus probatocephalus and Anarhichas lupus23. Vmc, Vvv, and Sk were also found to increase with the amount of hard-shelled prey in the diet of cichlids24. This means that ‘harder’ diets produce tooth surface textures with greater core depth and an increase in the volumes of core material and valleys. In short ‘harder’ diets produce rougher tooth surfaces.
This conclusion is also supported by a recent DMTA study on reptiles29, which exhibit significant overlap with sharks in the parameter trends correlating with ‘harder’ diets. Of the parameters correlating with increasing PC 1 values in sharks, parameters correlated with increasing dietary ‘hardness’ in reptiles include those capturing aspects of texture height (Sa, Sq, S5z), the number of peaks (Spk), and the depth, void volume and material volume of the core (Sk, Vvc, Vmc). Once again ‘harder’ diets produce rougher tooth surfaces.
Other studies, although focussed on terrestrial rather than aquatic vertebrates, have found similar patterns. Vmc, Vvc, Vvv, and Sa increase with more abrasive diets in grazing ungulate mammals34; Vmc, Vvv and Sk increase with increasingly ‘hard’ prey in insectivorous bats21. Unlike other studies, the latter found Sa (the average surface height) to decrease with harder diets26. A recent study of bats and moles35 found that, like sharks, increasing the ‘hardness’ of the prey creates rougher tooth surfaces that can be defined by increases in Sa, Vmc, VVc values (amongst others) and a decrease in Sds values (amongst others). More

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In vivo study: embryotoxicity test
Zebrafish embryos exposed to tested compounds developed lethal and sub-lethal alterations including different abnormalities and unhatching events. LC50 (for mortality rate) and EC50 (for abnormality and unhatching rate) values were extrapolated from concentration–response curves shown in Fig. 1. The rate of dead, abnormal, and/or unhatched specimens was concentration-dependent for all tested compounds (Fig. 1a–c). The lethality of the negative control group was less than 5%. Compounds 4NC and CAT showed the highest toxicity with LC50 values of 8.16 and 10.95 mg/L, respectively, followed by 4,6DNG  > 5NG  > GUA. Experimental LC50/EC50 values and the predicted ones obtained by ECOlogical Structure Activity Relationship (ECOSAR) v2.0 software (https://www.epa.gov/tsca-screening-tools/ecological-structure-activity-relationships-ecosar-predictive-model) based on Quantitative Structure Activity Relationships (QSAR) models showed 4NC and CAT as the most toxic chemicals (Table 2). However, it is important to notice that experimental values for both compounds were approximately two times lower than the predicted ones. This led to the classification of 4NC into the group of molecules toxic to fish (1  GUA).
Figure 3

Recorded sublethal morphological effects in D. rerio embryos/larvae after 48, 72, and 96 h of exposure to CAT, 4NC, GUA, 5NG, and 4,6DNG. Negative control: normally developed embryo at (a) 48, (b) 72, and (c) 96 hpf. During exposure period alterations were manifested as: (d) yolk sac edema (arrow); (e) pericardial edema (asterisk), undeveloped tail region (arrow); (f) hatched fish with malformed spine (arrow); (g) underdeveloped tail and necrosis of its apical part (dashed arrow), rare pigments; (h) pericardial edema (asterisk), scoliosis (arrow), necrosis of the apical part of the tail (dashed arrow), rare pigments, not hatched; (i) scoliosis (arrows), blood accumulation in the brain region (dashed arrow); (j) pericardial edema (asterisk), yolk sac edema (arrow), scoliosis (dashed arrow); (k, l) pericardial edema (asterisk); (m) underdeveloped embryo: underdeveloped head (arrow), tail not detached (asterisk), delay or anomaly in the absorption of the yolk sac; (n) pericardial edema (asterisk), blood accumulation (arrow), not hatched; (o) pericardial edema (asterisk), blood clotting (arrow), not hatched; (p) blood accumulation at the yolk sac (arrow); (r) hatched fish with malformed spine; (s) pericardial edema (black asterisk), blood accumulation above the yolk sac (arrow), swelling of the yolk sac (white asterisk), yolk sac edema (dashed arrow), mild scoliosis. Developmental abnormalities were recorded using LAS EZ 3.2.0 digitizing software (https://www.leica-microsystems.com/products/microscope-software/p/leica-las-ez/).

Full size image

The morphometric measurements (Fig. 4) showed that all tested samples significantly affected sensorial (eye area), skeletal (head height), and physiological (yolk and pericardial sac area) parameters in zebrafish. Significant differences among all treatments with exact p values are presented in Table S2.
Figure 4

Morphometric measurements of D. rerio larvae after 96-h exposure to tested compounds (CAT, 4NC, GUA, 5NG, and 4,6DNG) and control (C). (a) Lateral view showing eye area (EA), head height (HH), yolk sac area (YSA), and pericardial sac area (PSA). Scale bar = 1000 µm. Morphometric parameters are presented by their mean value (b–e; n = 15). The symbol * indicates a significant difference between tested samples and negative control (*p  More

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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