Philippa Kaur

Disturbance detectionWe used a well-established spectral-temporal segmentation method, Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr), to detect disturbances within the Google Earth Engine (GEE) cloud-computing platform57,58. The core of the LandTrendr is to extract a set of disturbance-related metrics by breaking pixel-level annual time-series spectral trajectories into linear features using Landsat observations. The LandTrendr has been widely used for change detection in various forest settings, and details about the algorithms can be found in previous publications57. Here we briefly described the key steps in generating the year and type of disturbances in China’s forests using the LandTrendr within the GEE platform. The overall analytic flow can be found in Supplementary Fig. 10.First, we generated annual spectrally consistent time-series data by using all available, good quality (cloud cover ≤ 20) Tier 1 Landsat 5 (Thematic Mapper), Landsat 7 (Enhanced Thematic Mapper Plus), and Landsat 8 (Operational Land Imager) images acquired during the peak growing seasons (June 1—September 30) from 1986 to 2020. The peak growing seasons were selected to exclude compounding influences from ice, snow, and soil, and to maximize the spectral changes after forest disturbances. To tackle the spectral inconsistency among Landsat sensors, we harmonized spectral values via linear transformations according to band-respective coefficients presented in59. Clouds, cloud shadows, snow, and water were masked out using the Fmask algorithm60. The annual band composites at 30-meter spatial resolution during 1986–2020 were computed using the Medoid method61.Secondly, we ran the LandTrendr using five spectral indices, including two spectral bands (shortwave infrared I and II that were B5 and B7), tasseled cap wetness (TCW), normalized burn ratio (NBR), and normalized difference vegetation index. These five indices were effective indictors to represent vegetation greenness and structures, and were commonly used for detecting changes in forest disturbance and recovery62. For each spectral index, the LandTrendr produced a set of parameters to describe a possible disturbance event at the pixel level, including spectral values at pre-disturbance level (preval), magnitude of change (mag), duration (dur) and rate of change (rate), and the signal-to-noise ratio (dsnr) (n = 5). Using these five spectral indices, we generated a stack of disturbance-related parameter layers (n = 25, 5 spectral indices × 5 parameters), which were later used to detect and classify disturbances using machine learning models derived from reference data (described below).Disturbance classificationReference dataHigh-quality consistent reference data is key to train and classify disturbance types. To do so, we generated a total of 31225 reference points using a hierarchical approach. We first generated a large number of potential disturbance points using forest loss data from 2001 to 20203. Then we separated fire disturbances from non-fire disturbances by overlaying MODIS burned area (BA) with potential disturbance points following the procedure used by63. Specifically, fire disturbances were determined if the MODIS BA data coincided with the Landsat-derived forest loss for the fire year and 2 years postfire (i.e., t + 0, t + 1, t + 2) to account for delayed post-fire tree mortality. Following this step, we derived points as potential disturbances that consisted of fires and non-fire disturbances (including forest conversion to other land use types and silvicultural practices at various intensities). We also generated roughly the same number of points that experienced no disturbances (e.g., persistent forests), which were determined by selecting pixels with very few changes in spectral indices. These reference points, including fire, non-fire disturbances, and persistent forests, were then used to sample the time-series spectral data from 1986 to 2020. Finally, time-series spectral data from each reference point were visually checked to make sure they accurately represented disturbance events. This process resulted into a total of 31225 reference data points, including 2356 fire disturbance points, 13,242 non-fire disturbance points, and 15,627 no disturbance points (persistent forests) (Supplementary Fig. 2).Random forest classificationWe used machine learning modeling to classify each pixel into fire disturbance, non-fire disturbance, or no disturbance. The reference data points were used to sample the LandTrendr-derived disturbance-related parameter layers described above, which resulted into a dataset consisting of disturbance types. We divided the dataset into 70% of training data, and 30% as validation data. Using the training data, a Random Forest (RF) model was trained to classify each reference point into fire, non-fire disturbance, or no disturbance. Our RF approach showed that short-wave infrared (SWIR)-based moisture indices (e.g., B7, TCW) were strong predictors for detecting forest disturbances (Supplementary Fig. 11) likely because of their sensitivity to vegetation water content and canopy structure64. Finally, we applied the trained RF model to the full classification stack to consistently map the disturbance types from 1986 to 2020 across China’s forests, assuming that the spectral trajectories derived from reference data period 2001–2020 can be extrapolated to the whole mapping period 1986–2020. However, note that our approach was meant to detect relatively acuate and discrete disturbances that caused canopy opening, rather than subtle changes of forest structure or composition resulted from low intensive silvicultural practices and chronic disturbances.Year of disturbanceWe used the LandTrendr to determine the year of disturbance as the onset of magnitude of spectral change. Since we ran LandTrendr on five spectral indices, there were five possible years of disturbance for each pixel. Thus, we determined the year of disturbance using the median value from at least three different indices (i.e., NDVI, NBR, TCW, B5, B7). In this way, we only kept pixels that were detected as disturbances using at least three indices, thus reducing commission errors. The year with the greatest spectral changes generated by the LandTrendr often had an accuracy within 3 years11. A confidence level was also assigned to each disturbed pixel based on numbers of indices which showed possible disturbance events. Specially, low, medium, and high confidence were assigned if the disturbance was detected by three, four, or five spectral indices, respectively.ValidationsWe validated the disturbance map at the pixel and national levels. At the pixel level, we validated the final map using the validation sub-sample described in the previous section. We derived a confusion matrix to report user’s and producer’s accuracy (Supplementary Table 1) as the main accuracy assessment metrics. At the national level, we compared forest disturbance detected in this study to available existing dataset. Specifically, we compared the area of forest fire disturbance between our study and the national fire records during 2003–2009 (Supplementary Fig. 5). We compared the disturbance rates between our study and Landsat-derived global forest cover changes from 2001 to 20193 (Supplementary Fig. 4).Post-processingWe applied a series of spatial filters to minimize the unrealistic outliers from two potential sources of uncertainty, including speckle in time-series spectral trajectories or misregistration among images. This may lead to individual pixel or small patches including only a few pixels, which were (a) detected as disturbances, thus increasing the commission errors, or (b) not detected as disturbances, while their surrounding pixels were mostly disturbed, thereby increasing the omission errors. To address the issue (a), we removed all single-pixel disturbance patches through setting the minimum mapping unit as two 30 × 30 m2 pixels (0.18 ha). To address the issue (b), we applied a 3 by 3 moving window to fill holes through assigning the year of disturbance based on the years in the surrounding pixels. Finally, we smoothed the year of disturbance by assigning the center pixel using majority rules from surrounding pixels within the 3 by 3 windows, thus accounting for artefacts associated with uncertainties in the correct identification of the disturbance year.Characterizing disturbance regimes and their trendsWe characterized the disturbance regime using five indicators within each 0.5° grid cell (n = 1946) across China’s forests based on annual forest disturbance maps generated from the previous step. Within each grid cell, we calculated (1) total annually disturbed forest area (km2 yr−1), (2) percentage of forest disturbed annually (% yr−1), as annual disturbed forest area divided by the total forested area, (3) disturbance size (ha), as the number of disturbed pixels for each individual patch using an eight-neighbor rule, (4) disturbance frequency (# of patches per 1000 km2 forested area each year), as the number of disturbance patches per year divided by the total forested area, (5) disturbance severity (ΔNDVI = NDVIt−1 − NDVIt+1), as magnitude of NDVI change 1 year before and 1 year after disturbance, obtained from the LandTrendr analysis. We used (1) and (2) to characterize the disturbance rate, and (3)–(5) to describe the patch characteristics. The (2) and (4) were normalized by forest area within each grid cell, thus making them comparable among grid cells. For (3)–(5), we only calculated the patch size >0.45 ha (five 30 × 30-m2 pixels), because patches  TC2000), and the expansion of forested area from 1986 to 2000 (e.g., TC1986  20% following Liu et al., (2019). We should note that our study area did not include the newly afforested area after 2000. All analyses were performed within the forest mask, thus excluding the potential confounding factors from other land cover types. The description of TC1986 and TC2000 can be found in3,32. More

Morphometric measurements are subjective and used to assess the carcass development and quantitatively measure the muscular distribution in the carcass with estimates of its conformation. In the present study there were not significative differences observed for these parameters or for carcass compactness index (CCI), inferring that the use of cactus pear silage as well as intermittent water supply combined or alone did not alter animal growth and/or carcass conformation, maintaining the muscle pattern achieved by the control diet (usual) and demonstrating body and carcass uniformity. Since animals used in this study were homogeneous and had similar age and body performance, as indicated by the carcass morphometric measurements and by the difference between the empty carcass and hot carcass weights, which resulted in the sum of head + limb with an average of 8.2 ± 0.13 kg between treatments, giving an idea that the animals were similar in chronological age, since the allometric growth of the body occurs from the extremities to the interior of the body.The significant difference between treatments with inclusion of cactus pear silage for hot carcass yield (HCY) and cold carcass yield (CCY) may be related to the weight of the full gastrointestinal tract, which showed higher values for animals fed with a higher proportion of Tifton 85 grass hay in the diet (0% CPS). Increasing the NDF content of the diet reduces the passage rate of digesta, and the emptying of the gastrointestinal tract (GT) that cause a distension of the rumen-reticulum and increase the weight of the gastrointestinal tract, resulting in lower HCY and consequently lower CCY. While the diets with inclusion of CPS increase NFC content, such as pectin, which have higher rates of rumen degradability and, higher rates of passage7,8,9.Measurements and evaluations carried out on the carcass, such as the carcass compactness index and loin eye area (LEA), are parameters that quantitatively measure the muscle distribution in the carcass, an edible part of greater financial return, which indicates the conformation of these animals3, while the body condition score (BCS) and the measure C, which are highly correlated, measure the distribution of fat on the carcass, giving an idea of the carcass finish, in which the higher these variables, the greater the proportion of fat that allows for less water loss due to carcass cooling10. These variables in the present study were also not influenced by the levels of cactus pear silage and water restrictions, presenting an overall mean of 0.17 kg/cm, 7.68 cm, 2.42 points and 0.7 mm respectively, and consequently did not influence the losses due to cooling, which presented an average loss of 1.48%.The main cuts of the goat carcass are the neck, leg, shoulder, loin, and rib. Their economic values differ, and their proportions become an important index to evaluate the carcass quality9. The cuts of greatest importance and commercial values are the leg and the loin, called noble cuts because they present greater yield and muscle tenderness, being interesting that they present a good proportion in the carcass, for providing greater edible tissue content, mainly muscle.Carcasses with similar weight tend to have equivalent proportions of cuts, as they exhibit isogonic growth. As the cold carcass weight (CCW) and the conformation of the animals were similar, with similar morphometric measurements, they had a direct relationship in the absence of an effect on commercial cuts.The commercial value of the carcass, whether through carcass yield and/or the proportions of the cuts, is also linked to tissue composition, thus the dissection of the leg represents an estimate of measuring the tissue composition of the carcass, in which is sought a greater proportion of muscle, intermediate proportion of fat and less bone in carcasses11. In this way, diets with cactus pear silage and the different levels of intermittent water supply resulted in the constancy in the amount of muscle, fat, and bone in legs of goats. The similarity in muscle proportion is related to the lack of effects on slaughter weight and CCW, as the weight of muscles is highly correlated to carcass weight. The average muscle yield was above 60% in all treatments, confirming that the animals showed good efficiency to the diets and adapted well to the water supply levels. Although the diets with cactus silage had high amounts of metabolizable energy (ME) and no difference in DM intake, the energy input was similar that not influencing carcass weights and carcass compactness index. That is, it did not influence muscle deposition in the carcass, probably due to synchronicity of energy and protein.As for the weight and proportion of bone tissue, it is believed that because this is a tissue with early development in relation to muscle and fat2, diets in the final stages of growth (average of 8 months) would hardly change their participation in the tissue composition, where the relationship of this tissue with the others is usually only increased when there are changes in the proportion of muscle and/or fat.Water restriction, as long as it is moderate and acute, mainly affects the loss of body water and not tissues, which does not cause deleterious effects on animal productivity and growth.The muscle:fat ratio indicates the state of leg fattening, while the muscle:bone ratio estimates the carcass muscularity, both being attributes of quality3. The similarity previously reported in the weight of fat, bone and muscle corroborates that these relationships also do not have differences. The same occurs for the leg muscularity index (LMI), due to the weight of the five muscles used to determine the index and the length of the femur which had been similar between the animals.Nevertheless, when considering fat as a percentage of participation in leg weight, it is possible to observe that the intermittency in water supply in both intervals (24 and 48 h) reduced the proportion of fat in the leg. Although in this research, the water supply levels did not affect the daily intake of dry matter from animals, with average intake of 650.67 g/kg DM, ranging from 599 to 682 g/kg DM between treatments7, during days of water deprivation, fat mobilization for energy availability may occur, possibly offsetting water stress and influencing not only feed intake, on these days of deprivation but also affecting energy metabolism, which results in the mobilization of energy reserves2.When the physicochemical composition of the meat was evaluated, it was observed that the diets and water supply levels probably did not affect the reserves of muscle glycogen during the pre-slaughter management as can be seen through pHinitial and pHfinal. The pHinitial right after slaughter should be close to neutrality, as well as in the live animal, indicating that the animal did not suffer from stress during the pre-slaughter period. The pHfinal, on the other hand, is expected to show a considerable variation, between 5.55 and 6.2 for goat meat; and due be inversely proportional to the concentration of muscle glycogen at the time of slaughter, that is, a more intense expenditure of glycogen stores results in less lactic acid production and higher pHfinal10,12,13. In this research, the pHfinal had an average of 5.74, a pH higher than the isoelectric point of muscle proteins (5.2–5.3). This result is favorable, since it is above the neutral charge and presenting an excessive negative charge that provides the repulsion of filaments, which allows water molecules to bind and improve the organoleptic characteristics of the meat, through succulence and texture of meat13 evaluated by cooking loss, moisture, and shear force, principally. The cooking loss (CL), moisture and shear force (SF) were within the values recommended (20–35% CL, moisture above 70% and SF up to 44.13 Newton (N) for goat meat) to classify the meat as soft and tender14. Statistically, interactions were found between the supply of silage and intermittent water supply, in which goats on a diet without cactus pear silage and without intermittent water supply showed higher values of cooking losses and shear force.Higher concentrations of collagen content and/or greater activities of calpastatin (which inhibit the action of calpains), as well as larger fascicles and greater number of fibers present in each muscle fascicle, as was visually observed in the meat of the animals in this research, can lead to reductions in meat tenderness15. Because goat carcasses are generally small, with low marbling degree and a thin layer of subcutaneous fat, there is rapid heat dissipation at the beginning of the post-mortem period, which can lead to cold shortening, muscle hardening, and less tender meats16.pHfinal of the meat has a high correlation with color parameters (L*—lightness, a*—redness, b*—yellowness and Chroma), as the pHfinal can affect the reaction of myoglobin to oxymyoglobin. The b* index in meat, on the other hand, may be related to the concentration of fat and/or the presence of carotenoids in the diet which can be affected by forage preservation processes, such as silage and hay, which significantly reduces by up to 80% carotenoids levels13. It is believed that the carotenoid concentrations in the diet of this study were similar between treatments and consequently in values of b* of meat. Values of a* and Chroma directly depend on the content and state of the heme pigments in the muscle, due to the chemical state of iron (Fe), playing an important role in meat color10. These parameters showed no significant difference between treatments, however, higher values of a* and Chroma in meat are desired, as a result of the increase in oxymyoglobin and decrease in metmyoglobin that provides the meat’s “bloom”. According to Dawson et al.17, the minimum critical value for meat luminosity (L*) is 34. Lower values of L are related to elevating pHfinal, which results in the high concentration of metmyoglobin, making the meat darker, which causes rejection by consumers for associating dark meat to as old meat.The meat’s presentation and more precisely its color is an important factor that can influence a consumer’s purchase decision, as it gives us the idea of freshness and meat’ quality. The L* and a* color parameters are the most representative for these characteristics18. Although in our research it did not have a significant effect on the color parameters, we can indicate that the meat obtained in this research would be well accepted by consumers, because Hopkins19 suggests that consumers will consider meat color acceptable when the L* value is equal to or exceeds 34, and a* value below 19 or equal to or exceeds 9.5 according to Khliji et al.18. In the present study, all values for L* remained above this aforementioned threshold and the values of a* remained within these values which suggests that meats from all diets and water supply levels had an acceptable color for consumers.When evaluating the chemical composition of meat, no significant differences were observed between treatments, except for the ash content, that remained above the average values found in the literature, which is 0.99–1.10%16. It is believed that because cactus pear is a rich source of Ca, Mg, K and with increasing level of cactus pear silage in the diet31, these minerals were consumed in larger amounts, which could have resulted in a higher proportion of minerals in the meat of animals that received 42% cactus pear silage.The lipid fatty acid profile in meat has a major impact on sensory properties and nutritional quality, influencing acceptance and health for consumers20,21. Intermittent water supply, cactus pear silage, and interaction between water supply and cactus pear silage did not influence most fatty acids present in the Longissimus lumborum muscle of the animals under study, except only a few saturated fatty acids e.g. docosanoic acid (C22:0), tricosanoic acid (C23:0), BCFA, anteiso-tridecanoic acid (C13:0 anteiso) and anteiso-pentadecanoic acid (C15:0 anteiso).Biohydrogenation of ruminal bacteria results in a circumstantial variety of fatty acids (FA), which will be absorbed in the intestine and later incorporated into the meat of goats. In addition to the diet and the biohydrogenation, the meat lipid profile can vary due to de novo synthesis, desaturation, duration of the feeding period and differences in pathways of various FA by the animal organism22.A high concentration of saturated fatty acids present in meat is not desirable, as there is evidence that saturated fatty acids, mainly C16:0, as well as myristic (C14:0) and lauric (C12:0) increase the blood cholesterol and low-density lipoproteins (LDL) concentration, due to interferences with hepatic LDL receptors23, however, in the studied treatments, there were no significant differences for these fatty acids. On the other hand, C18:0 has no impact on cholesterol levels, due to being poorly digested and easily desaturated to C18:1 by Δ9-desaturase24, present in the cell endoplasmic reticulum. This fatty acid is not harmful to health and is considered the only desirable SFA. As the levels of C18:0 in diets tend to be minimal, their main origin is the biohydrogenation of PUFA and de novo syntheses in diets with a high energy pattern25.In addition to carrying out the biohydrogenation process, ruminal bacteria synthesize a series of FA, mainly those of odd and branched chain, that comprise mainly the lipids of the bacterial membrane26,27, to maintain membrane fluidity. Linear odd-chains fatty acids are formed when propionyl-CoA, instead of acetyl-CoA, is used as a de novo synthesis initiator25. On the other hand, iso and anteiso FA are synthesized by the precursors branched-chain amino acids (valine, leucine, and isoleucine) and their corresponding branched- short-chain carboxylic acids (isobutyric, isovaleric and 2-methyl butyric acids)28.There is an increasing interest to study odd-and branched-chain fatty acids (OBCFAs) from animal products, mainly in milk due to its higher concentration compared to meat. Researchers reported that several OBCFAs have potential health benefits in humans29 as improved gut health30 and presenting anti-cancer activity31, as well as improve the sensory characteristics of the meat, providing a greater sensation of tenderness and juiciness, because BCFA content are associated with a less consistent fat in meat from lambs due to its lower melting point and its chain structure32.The FAs profile in the ruminal bacteria is largely composed by OBCFAs (C15:0; anteiso C15:0; iso C15:0; C17:0; iso C17:0; C17:1 and anteiso C17:0) in the bacteria membrane lipids24. Thus, the higher concentration of OBCFAs might be the result of the difference in the rumen bacterial populations induced by variation in the dietary carbohydrate, that is, a higher concentration of cellulolytic bacteria in relation to amylolytic bacteria, due to the high neutral detergent fiber (NDF) content in the diet with 0% cactus forage silage. It is also known that amylolytic bacteria produce more linear odd chain and anteiso FAs than iso FAs, whereas cellulolytic bacteria produce more iso FAs28,32. As the Tifton 85 grass hay-based diet had the highest neutral detergent fiber corrected for ash and protein (NDFap) and starch content (highest % of ground corn), the meat of those animals had higher concentrations of anteiso C15:0 and anteiso C13:0 compared to animals fed diets with the inclusion of cactus pear silage, also influencing the total sum of branched chain fatty acids.Although levels of intermittent water supply have generated punctual changes in tricosanoic acid (C23:0) SFA, the same was not observed for MUFA and PUFA, due to changes in the rumen environment, promoted by water restrictions, which were not sufficient to circumstantially modify biohydrogenation, resulting in similarities in concentrations of unsaturated fatty acids in goat meat.The animals subjected to 24 h of intermittent water supply (IWS) presented the highest concentration of C23:0 in relation to other treatments, which is interesting because it is involved in the synthesis of ceramide and reduces the risk of diabetes in humans33.The cactus pear has high non-fibrous carbohydrate (NFC) content (mainly pectin), having 59.5% high and medium rumen degradation carbohydrates which provide a higher production rate and removal of short-chain fatty acids and changes in rumen bacterial populations34. The inclusion of CPS resulted in a higher passage rate of digesta, affected biohydrogenation, and resulted in the escape of intermediate fatty acids isomers that are absorbed in the small intestine. Consequently, there was changing composition of fatty acids in the muscle of these animals, with a significant effect being observed only in the cis-13 C18:1. Furthermore, diets with high proportions of cactus pear silage (CPS), such as 42% CPS diet, can decrease ruminal pH and affect the final stages of biohydrogenation, resulting in the escape of intermediate fatty acids isomers, that are absorbed in the small intestine, which can explain the similarity of the C20:1 in 42% CPS diet from the Tifton hay-based diet, with differences between goat meat from 21% CPS diet and Tifton hay-based diet.Oleic acid (c9-C18:1) was the MUFA with the highest participation in the lipid profile of goat meat, which is interesting because it has a hypocholesterolemic effect, being a desirable fatty acid (DFA) for not reducing the serum high density lipoproteins (HDL) levels and thus prevent cardiovascular disease by reducing LDL levels35. The high concentrations of c9-C18:1 in ruminant meat come from the food intake, the effect of biohydrogenation, and mainly of the high activity of Δ9-desaturase, necessary for animal biosynthesis through desaturation of C18:0 to c9-C18:127. This fatty acid in the lipid profile of red meat varies between 30 and 43%36, confirming that the meat in the present study had a good concentration of this fatty acid.Much of unsaturated fatty acids, which have 18 carbons or 16 carbons, are largely converted to C18:0 and C16:0 through biohydrogenation, and when this process is not 100% completed, in addition to the PUFA that pass through this process intact, some product intermediates are formed, reaching the duodenum and are absorbed by the animal, in which significant amounts of cis and trans-monounsaturated, such as vaccenic fatty acid (t11-C18:1), reach the duodenum and are absorbed, later composing the muscle tissue22.The literature indicates that the precursor of conjugated linoleic acid (CLA) in the meat of animals is trans vaccenic acid (t11-C18:1), so the enzyme ∆9-desaturase, besides acting in the conversion of stearic into oleic fatty acid, also converts the trans-vaccenic acid to its corresponding CLA isomer, c9t11-C18:236. This pathway is more expressive in the mammary gland, and as the concentration of vaccenic acid (t11-C18:1) was not different, the concentration of CLA was not affected by the supply of silage and intermittent water supply, in the same way, that there are also no differences in the activity of ∆9-desaturase. Nevertheless, it is worth noting that in the human adipose tissue there is also the presence of ∆9-desaturase, and therefore, increased intake of vaccenic fatty acid could have the same beneficial effects associated with the intake of CLA, where the dietary vaccenic fatty acid shows 19–30% conversion rate37.Tifton hay is a natural source of n-3 fatty acids, mainly C18:3 n-3 with up to 20% participation in the lipid profile2, allowing a certain part of these PUFAs to be absorbed and increased in the tissue muscle, with 10 to 30% PUFAs in the diet generally escaping from biohydrogenation.Linoleic fatty acid (c9c12 C18:2) and α-linolenic acid (C18:3 n-3) are essential fatty acids for humans, that serve as precursors of the n-3 and n-6 pathways, distinct families, but synthesized by some of the same enzymes (∆4-desaturase, ∆5-desaturase, and ∆6-desaturase)25. Arachidonic fatty acid (C20:4 n-6) comes from elongation and desaturation of linoleic acid, where its concentrations, even close to that of its precursor, may indicate that there was a high activity of ∆6-desaturase (desaturation to γ-linolenic), elongase (elongation of γ-linolenic to dihomo-gamma-linolenic) and ∆5-desaturase. This fatty acid was influenced by the diets, presenting lower concentrations in the meat of animals fed the 42% cactus pear silage when compared to the Tifton hay diet (0% cactus pear silage).A higher concentration of long-chain PUFA n-3, docosahexaenoic (C22:6 n-3), was observed in the muscle of animals fed on Tifton hay. This was probably due to the high concentration of C18:3 n-3, precursor of C22:6 n-3, that the hay presents in relation to the cactus pear silage.The ratios and proportions of fatty acids are used to determine nutritional and nutraceutical values of the product or diet, and mainly, to indicate the cholesterolemic potential4. It is interesting that the n-6/n-3 ratio is low due to the pro-inflammatory properties of n-6; it is recommended to decrease its intake to assist in disease prevention38, while n-3 fatty acids are anti-inflammatory, antithrombotic, antiarrhythmic and reduce blood lipids, with vasodilating properties, being interesting that they present a higher proportion24. n-6 fatty acids tend to have a higher percentage in meat, and this directly influences the formation of n-3 isomers, since linoleic acid, when in excess, can reduce the synthesis of linolenic acid metabolites. The percentage of FA in one group can interfere with the metabolism of the other, reducing its incorporation into tissue lipids and altering its general biological effects38. Therefore, it is not recommended that the n-6/n-3 ratio be kept above 5 or 639, demonstrating that the averages of the current research remained acceptable.In relation to atherogenicity index (AI) and thrombogenicity index (TI), Ulbricht and Southgate39 proposed that sheep meat should have values of up to 1.0 and 1.58, respectively, and the lower the values for these indices in the lipid fraction, the greater the prevention of early stages of cardiovascular diseases. In the present study, the general averages observed were 0.29 for the AI, and 0.81 for the TI, although there were no significant differences, all treatments are within the recommended range, despite having been used as comparative standard to sheep, due to the absence of the proposed standard for goat meat.The h:H ratio did not differ for diets and water supply levels, but had an average of 1.90, below the reference value for meat products, which is 2.0. Values above 2.0 are recommended and favorable40, as it indicates a higher proportion of hypocholesterolemic fatty acids, that are beneficial to human health.The ∆9-desaturase enzyme that acts on both the mammary gland and adipose tissue, responsible for the transformation of SFA into unsaturated fatty acids (UFA), as well as in the endogenous conversion of CLA37 did not differ between treatments. On the other hand, the elongase showed less activity. Probably there was a greater “de novo” synthesis which resulted in a greater accumulation of palmitic fatty acid, and a reduction in the activity of the elongase enzyme.The crossbred goats demonstrated to present efficient mechanisms for adapting to water restrictions, especially when receiving feed with higher water content, such as cactus pear silage, being able to replace Tifton hay with 42% cactus pear silage in the diet for goats in confinement without negatively affecting the carcass traits and meat quality. Because, although these animals have shown some differences in the indices of tenderness and juiciness of their meats, however, all presented values of juiciness and tenderness compatible with meat extremely appreciated by the consumer market, and even goat meat showing some fatty acids with different concentrations induced by the supply of silage and water intermittence, the final lipid profile was appropriate to the health of consumers, observed by the absence of differences in the total concentrations of PUFA and in the main nutraceutical parameters (DFA, n-6/n-3; h:H; AI and TI).These results are relevant, indicating that goat feedlots in regions with low water availability may adopt strategies of lesser demand for drinking water and considerable concentrations of cactus pear silage in the diet, can reduce production costs without considerably affecting the product to be marketed, and therefore, provide higher profitability of the system. More

We acknowledge members of the EDGE lab at the University of Gothenburg for stimulating discussions that inspired this comment. Special thanks to A. Bjorkman and R. Bjork for their feedback on the manuscript and to K. Happonen for help with data collection. More

Wilkinson, D. M. Dispersal biogeography. Encyclopedia of Life Science. (Nature Publishing Group, 2001).Jan, P.L. et al. Range expansion is associated with increased survival and fecundity in a long-lived bat species. Proc. R. Soc. B. 286, 1–9. https://doi.org/10.1098/rspb.2019.0384 (2019).Article 

Google Scholar 
IUCN. IUCN Guidelines for the Prevention of Biodiversity Loss Caused by Alien Invasive Species. https://portals.iucn.org/library/node/12413(2000).McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biological Conservation 127, 247–260. https://doi.org/10.1016/j.biocon.2005.09.005 (2006).
Google Scholar 
Galko, J. et al. Invázne a nepôvodné druhy v lesoch Slovenska: hmyz—huby—rastliny. (Národné lesnícke centrum, 2018).Lockwood, J. L., Hoopes, M. F. & Marchetti, M. P. Invasion ecology_draft_2ed. (John Wiley & Sons, 2013).Colautti, R. I. & MacIsaac, H. J. A neutral terminology to define ‘invasive’ species: Defining invasive species. Divers. Distrib. 10, 135–141. https://doi.org/10.1111/j.1366-9516.2004.00061.x (2004).Article 

Google Scholar 
Ambros, M., Dudich, A., MIKLÓS, P., Stollmann, A. & Žiak, D. Ryšavka tmavopása (Apodemus agrarius)–novỳ druh cicavca Podunajskej roviny (Rodentia: Muridae). Lynx, series nova 41, (2010).Bradley, B. A. et al. Disentangling the abundance—impact relationship for invasive species. Proc. Natl. Acad. Sci. 116, 9919–9924. https://doi.org/10.5281/zenodo.2605254 (2019).Article 
ADS 
CAS 

Google Scholar 
Nackley, L. L., West, A. G., Skowno, A. L. & Bond, W. J. The nebulous ecology of native invasions. Trends Ecol. Evol. 32, 814–824. https://doi.org/10.1016/j.tree.2017.08.003 (2017).Article 

Google Scholar 
Archer, S. Tree-grass dynamics in a Prosopis-thornscrub savanna parkland: reconstructing the past and predicting the future. Ecoscience 2, 83–99. https://doi.org/10.1080/11956860.1995.11682272 (1995).Article 

Google Scholar 
Wigley, B. J., Bond, W. J. & Hoffman, M. T. Thicket expansion in a South African savanna under divergent land use: local vs. global drivers? Global Change Biol. 16, 964–976. https://doi.org/10.1111/j.1365-2486.2009.02030.x (2010).Spiegel, C. S., Hart, P. J., Woodworth, B. L., Tweed, E. J. & LeBrun, J. J. Distribution and abundance of forest birds in low-altitude habitat on Hawai’i Island: Evidence for range expansion of native species. Bird Conserv. Int. 16, 175–185. https://doi.org/10.1017/S0959270906000244 (2006).Article 

Google Scholar 
Livezey, K. B. Range expansion of Barred Owls, part II: facilitating ecological changes. Am. Midland Nat. 323–349 (2009).Veech, J. A., Small, M. F. & Baccus, J. T. The effect of habitat on the range expansion of a native and an introduced bird species. J. Biogeogr. 38, 69–77. https://doi.org/10.1111/j.1365-2699.2010.02397.x (2011).Article 

Google Scholar 
Krupa, J. J. & Haskins, K. E. Invasion of the meadow vole (Microtus pennsylvanicus) in southeastern Kentucky and its possible impact on the southern bog lemming (Synaptomys cooperi). American Midland Naturalist 14–22 (1996).Jareño, D. et al. Factors associated with the colonization of agricultural areas by common voles Microtus arvalis in NW Spain. Biol. Invasions 17, 2315–2327. https://doi.org/10.1007/s10530-015-08774 (2015).Article 

Google Scholar 
Malygin, V. M., Baskevich, M. I. & Khlyap, L. A. Invasions of the common vole sibling species. Russ. J. Biol. Invas. 11, 47–65 (2020).Article 

Google Scholar 
Tong, X., Wang, R. & Chen, X.-Y. Expansion or invasion? A Response to Nackley et al. Trends Ecol. Evol. 33, 234–235. https://doi.org/10.1016/j.tree.2018.01.008 (2018).Thompson, K. & Davis, M. A. Why research on traits of invasive plants tell us very little. Trends Ecol. Evol. 24,115–116. https://doi.org/10.1016/j.tree.2011.01.007 (2011).Davis, M. A. & Thompson, K. Eight ways to be a colonizer; two ways to be an invader: A proposed nomenclature scheme for invasion ecology. Bull. Ecol. Soc. Am. 81, 226–230 (2000).
Google Scholar 
Davis, M. A., Thompson, K., Grime, J. P. & Charles, S. Elton and the dissociation of invasion ecology from the rest of ecology. Diversity and Distribution 7, 97–102. https://doi.org/10.1046/j.1472-4642.2001.00099.x (2001).Article 

Google Scholar 
Davis, M. A. Invasion biology. (Oxford University Press on Demand, 2009).Davis, M. A. et al. Don’t judge species on their origins. Nature 474, 153–154 (2011).Article 
CAS 

Google Scholar 
Klempa, B. et al. Complex evolution and epidemiology of Dobrava-Belgrade hantavirus: Definition of genotypes and their characteristics. Adv. Virol. 158, 521–529. https://doi.org/10.1007/s00705-012-1514-5 (2013).CAS 

Google Scholar 
Kraljik, J. et al. Genetic diversity of Bartonella genotypes found in the striped field mouse (Apodemus agrarius) in Central Europe. Parasitology 143, 1437–1442. https://doi.org/10.1017/S0031182016000962 (2016).Article 

Google Scholar 
Latinne, A. et al. Phylogeography of the striped field mouse, Apodemus agrarius (Rodentia: Muridae), throughout its distribution range in the Palaearctic region. Mamm. Biol. 100, 19–31. https://doi.org/10.1007/s42991-019-00001-0 (2020).Article 

Google Scholar 
Dudich, A. & Szabó, I. Über die Verbreitung der Hystrichopsylla Taschenberg, 1880 (Siphonaptera) in Ungarn. Folia Entomol. Hung 45, 27–32 (1984).
Google Scholar 
Dudich, A. Dynamika areálu ryšavky tmavopásej (Apodemus agrarius Pall.)–expanzia či invázia. Pp.: 53–62. Invázie a invázne organizmy. SEKOS pre SNK SCOPE, Nitra (1997).Karaseva, E. V., Tikhonova, G. N. & Bogomolov, P. L. Distribution of the Striped field mouse (Apodemus agrarius) and peculiarities of its ecology in different parts of its range. Zoologičeskij žurnal 71, 106–115 (1992).
Google Scholar 
Polechová, J. & Graciasová, R. Návrat myšice temnopásé, Apodemus agrarius (Rodentia: Muridae) na jižní Moravu. Lynx (Praha), ns 31, 153–155 (2000).Bryja, J. & Řehák, Z. Další doklady současné expanze areálu myšice temnopásé (Apodemus agrarius) na Moravě. Lynx (Praha), ns (2002).Herzig-Straschil, B., Bihari, Z. & Spitzenberger, F. Recent changes in the distribution of the field mouse (Apodemus agrarius) in the western part of the Carpathian basin. Annalen des Naturhistorischen Museums in Wien. Serie B für Botanik und Zoologie 421–428 (2003).Dudich, A., Ambros, M., Stollmann, A., Uhrin, M. & Urban, P. Ryšavka tmavopása Apodemus agrarius (Pallas) v Novohrade. Príroda okresu Veľký Krtíš – 15 rokov od celoslovenského tábora ochrancov prírody 110–115 (2003).Horáček, I. & Ložek, V. Biostratigraphic investigation in the Hámorská cave (Slovak karst). Pp.: 49–60. Krasové sedimenty. Fosilní záznam klimatickỳch oscilací a změn prostředí. Knihovna České speleologické společnosti, Svazek 21, (1993).Pazonyi, P. & Kordos, L. Late Eemian (Late Pleistocene) vertebrate fauna from the Horváti-lik (Uppony, NE Hungary). Fragmenta Palaeontologica Hungarica 22, 107–117 (2004).
Google Scholar 
Obuch, J., Danko, S. & Noga, M. Recent and subrecent diet of the barn owl (Tyto alba) in Slovakia. Slovak Raptor J. 10, 1–50 . https://doi.org/10.1515/srj-2016-0003 (2016).Article 

Google Scholar 
Obuch, J. Temporal changes in proportions of small mammals in the diet of the mammalian and avian predators in Slovakia. Lynx, n. s 55, 86–106. https://doi.org/10.37520/lynx.2021.007 (2022).Niethammer, J. Die Verbreitung der Brandmaus (Apodemus agrarius) in der Bundesrepublik Deutschland. Acta Sc. Nat. Brno 10, 43–55 (1976).
Google Scholar 
von Lehmann, E. Die Brandmaus in Hessen als Beispiel für die Problematik der Verbreitungsgrenzen vieler Säugetierarten. Natur und Museum 106, 112–117.Farský, O. Úlovky myšice temnopásé, Apodemus agrarius (Pallas), na Moravě a ve Slezsku v letech 1920 až 1940 [Caughts of the striped field mouse, Apodemus agrarius (Pallas), in Moravia and Silesia in the years 1920–1940]. Lynx, n. s 5, 11–18 (1965).Heroldová, M., Homolka, M. & Zejda, J. Některé nepublikované nálezy Apodemus agrarius v Čechách a na Moravě v návaznosti na současnỳ stav znalostí o jejím rozšíření (Rodentia: Muridae). Lynx, series nova 44, (2013).Ambros, M., Dudich, A. & Stollmann, A. Fauna drobných hmyzožravcov a hlodavcov (Insectivora, Rodentia) vybraných mokraďných biotopov južného Slovenska. Rosalia (Nitra) 14, 195–202 (1999).
Google Scholar 
Balát, F. Potrava sovy pálené na jižní Moravě a na jižním Slovensku [Food of Barn owl on the south Moravia and south Slovakia]. Zoologické listy 5, 237–256 (1956).
Google Scholar 
Baláž, I., Stollmann, A., Ambros, M. & Dudich, A. Drobné cicavce rezervácie Lohótsky močiar. Chránené územia Slovenska 58, 27–29 (2003).
Google Scholar 
Bridišová, Z., Baláž, I. & Ambros, M. Drobné cicavce prírodnej rezervácie Alúvium Žitavy [Small mammals of Alúvium Žitavy natural reservation]. Chránené územia Slovenska 69, 7–9 (2006).
Google Scholar 
Dudich, A., Lysỳ, J. & Štollmann, A. Súčasné poznatky o rozšírení drobnỳch zemnỳch cicavcov (Insectivora, Rodentia) južnej časti Podunajskej nížiny. Spravodaj Oblastného múzea v Komárne, Prírodné vedy 5, 157–186 (1985).
Google Scholar 
Kristofik, J. Small mammals in floodplain forests. Folia Zoologica (Czech Republic) (1999).Méhely, L. Két új poczokfaj a magyar faunában. Állattani közlemények 7, 3–14 (1908).
Google Scholar 
Noga, M. The wintering and food ecology of Long-eared Owl in South-Western part of Slovakia (Comenius University in Bratislava, 2007).
Google Scholar 
Pachinger, K., Novackỳ, M., Facuna, V. & Ambruš, B. Dynamika a zloženie synúzií mikromammálií na izolovanỳch ostrovoch vnútozemskej delty Dunaja v oblasti vodného diela Gabčíkovo. Acta Environ. Universitatis Comenianae 9, 71–77 (1997).
Google Scholar 
Poláčiková, Z. Small terrestrial mammals’ (Eulipotyphla, Rodentia) synusia of selected localities in western Slovakia. Ekológia (Bratislava) 29, 131–139. https://doi.org/10.4149/ekol_2010_02_131 (2010).
Google Scholar 
Reiterová, K. et al. Úloha drobnỳch cicavcov–dôležitỳch rezervoárov v cirkulácii larválnej toxoplazmózy. Slovenskỳ Veterinárny Časopis 4, 217–222 (2010).
Google Scholar 
Stanko, M., Mošanský, L. & Fričová, J. Small mammal communities (Eulipotyphla, Rodentia) of the middle part of alluvium Ipeľ river (Lučenská and Ipeľská basins). Ochrana prírody 26, 43–52 (2010).
Google Scholar 
Spitzenberger, F. & Engelberg, S. A new look at the dynamic western distribution border of Apodemus agrarius in Central Europe (Rodentia: Muridae). Lynx, series nova 45, (2014).Sládkovičová, V. H., Žiak, D. & Miklós, P. Synúzie drobných zemných cicavccov mokradných biotopov Podunajskej roviny. Folia faunistica Slovaca 18, 13–19 (2013).
Google Scholar 
Tulis, F. et al. Expansion of the Striped field mouse (Apodemus agrarius) in the south-western Slovakia during 2010–2015. Folia Oecologica 43, 64–73 (2016).
Google Scholar 
Ambros, M. et al. Zmeny v rozšírení ryšavky tmavopásej (Apodemus agrarius) na Slovensku. in Zborník príspevkov z vedeckého kongresu ‘Zoológia 2022’ 10 (2022).Dudich, A. Ektoparazitofauna cicavcov a vtákov južnej časti Podunajskej nížiny so zreteľom na Žitný ostrov. 1. Siphonaptera. Žitnoostrovské múzeum Dunajská Streda—Spravodaj múzea 9, 61–96 (1986).Dudich, A. Príspevok k poznaniu drobných zemných cicavcov (Insectivora, Rodentia) a ich ektoparazitov (Acarina, Anoplura, Siphonaptera) okolia ŠPR Čenkovská lesostep (Podunajská nížina). Iuxta Danubium (Komárno) 10, 186–191 (1993).
Google Scholar 
Cyprich, D., Krumpál, M. & Dúha, J. Blchy (Siphonaptera) cicavcov (Mammalia) Štátnej prírodnej rezervácii Šúr. Ochrana prírody 8, 241–289 (1987).
Google Scholar 
Lapin, M., Faško, P., Melo, M., Štastný, P. & Tomlain, J. Climate zones. in Landscape Atlas of the Slovak Republic (Harmanec: VKÚ, 2002).Faško, P. & Štastný, P. Average annual precipitation. in Landscape Atlas of the Slovak Republic (2002).Russell, J. C., Stjernman, M., Lindström, Å. & Smith, H. G. Community occupancy before-after-control-impact (CO-BACI) analysis of Hurricane Gudrun on Swedish forest birds. Ecol. Appl. 25, 685–694.  https://doi.org/10.1890/14-0645.1 (2015).Article 

Google Scholar 
Desrosiers, M., Planas, D. & Mucci, A. Short-term responses to watershed logging on biomass mercury and methylmercury accumulation by periphyton in boreal lakes. Can. J. Fish. Aquat. Sci. 63, 1734–1745. https://doi.org/10.1139/f06-077 (2006).Article 
CAS 

Google Scholar 
Hanisch, J. R., Tonn, W. M., Paszkoswki, C. A. & Scrimgeour, G. J. Stocked trout have minimal effects on littoral invertebrate assemblages of productive fish-bearing lakes: A whole-lake BACI study: Stocked trout have minimal effects on littoral invertebrates. Freshw. Biol. 58, 895–907. https://doi.org/10.1111/fwb.12095 (2013).Article 

Google Scholar 
Louhi, P., Mäki-Petäys, A., Erkinaro, J., Paasivaara, A. & Muotka, T. Impacts of forest drainage improvement on stream biota: A multisite BACI-experiment. For. Ecol. Manage. 260, 1315–1323. https://doi.org/10.1016/j.foreco.2010.07.024 (2010).Article 

Google Scholar 
Conner, M. M., Saunders, W. C., Bouwes, N. & Jordan, C. Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration. Environ Monit Assess 188, 555. https://doi.org/10.1007/s10661-016-5526-6
(2016).Article 

Google Scholar 
Popescu, V. D., de Valpine, P., Tempel, D. & Peery, M. Z. Estimating population impacts via dynamic occupancy analysis of Before-After Control–Impact studies. Ecol. Appl. 22, 1389–1404. https://doi.org/10.1890/11-1669.1 (2012).Article 

Google Scholar 
Horváth, G. F. & Herczeg, R. Site occupancy response to natural and anthropogenic disturbances of root vole: Conservation problem of a vulnerable relict subspecies. J. Nat. Conserv. 21, 350–358. https://doi.org/10.1016/j.jnc.2013.03.004 (2013).Article 

Google Scholar 
Pounder, K. C. et al. Novel Hantavirus in Wildlife, United Kingdom. Emerg. Infect. Dis. 19, 673–675. https://doi.org/10.3201/eid1904.121057 (2013).Article 

Google Scholar 
Kreisinger, J., Bastien, G., Hauffe, H. C., Marchesi, J. & Perkins, S. E. Interactions between multiple helminths and the gut microbiota in wild rodents. Phil. Trans. R. Soc. B 370, 20140295. https://doi.org/10.1098/rstb.2014.0295 (2015).Article 

Google Scholar 
Kim, H.-C. et al. Hantavirus surveillance and genetic diversity targeting small mammals at Camp Humphreys, a US military installation and new expansion site Republic of Korea. PLoS ONE 12, e0176514. https://doi.org/10.1371/journal.pone.0176514 (2017).Article 

Google Scholar 
Burnham, K. P., Anderson, D. R. & Burnham, K. P. Model selection and multimodel inference: a practical information-theoretic approach. (Springer, 2002).Sugiura, N. Further analysis of the data by Akaike’s information criterion and the finite corrections: Further analysis of the data by akaike’ s. Commun. Stat. Theory Methods 7, 13–26 (1978).Article 
MATH 

Google Scholar 
Hurvich, C. M. & Tsai, C.-L. Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).Article 
MathSciNet 
MATH 

Google Scholar 
Morris, E. K. et al. Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecol. Evol. 4, 3514–3524. https://doi.org/10.1002/ece3.1155 (2014).Article 

Google Scholar 
Ingram, J. C. Berger–Parker Index. in Encyclopedia of Ecology 332–334 (Elsevier, 2008). https://doi.org/10.1016/B978-008045405-4.00091-4.Bürkner, P.-C. brms : An R package for bayesian multilevel models using Stan. J. Stat. Soft. 80, (2017).Wang, Y., Naumann, U., Eddelbuettel, D., Wilshire, J. & Warton, D. mvabund: Statistical Methods for Analysing Multivariate Abundance Data. (2022).Warton, D. I., Thibaut, L. & Wang, Y. A. The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses. PLoS ONE 12, e0181790. https://doi.org/10.1371/journal.pone.0181790 (2017).Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. (2022).R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2022).Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).Szacki, J. & Liro, A. Movements of small mammals in the heterogeneous landscape. Landsc. Ecol. 5, 219–224 (1991).Article 

Google Scholar 
Szacki, J., Babinska-Werka, J. & Liro, A. The influence of landscape spatial structure on small mammal movements. Acta Theriol. 2, (1993).Pimentel, D., Pimentel, M. & Wilson, A. Plant, animal, and microbe invasive species in the United States and World. Biol. Invasions 193 (2007).Valéry, L., Hervé, F., Lefeuvre, J. C. & Simberloff, D. Invasive species can also be native. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2009.07.003 (2009).Article 

Google Scholar 
Deinet, S. et al. Wildlife comeback in Europe: The recovery of selected mammal and bird species. (2013).Gompper, M. Top Carnivores in the Suburbs? Ecological and Conservation Issues Raised by Colonization of North-eastern North America by Coyotes: The expansion of the coyote’s geographical range may broadly influence community structure, and rising coyote densities in the suburbs may alter how the general public views wildlife. BioScience 52, 185–190. https://doi.org/10.1641/0006-3568 (2002).Dalecky, A. et al. Range expansion of the invasive house mouse Mus musculus domesticus in Senegal, West Africa: A synthesis of trapping data over three decades, 1983–2014. Mammal. Rev. 45, 176–190. https://doi.org/10.1111/mam.12043 (2015).Article 

Google Scholar 
Konečnỳ, A. et al. Invasion genetics of the introduced black rat (Rattus rattus) in Senegal West Africa. Mol. Ecol. 22, 286–300. https://doi.org/10.1111/mec.12112 (2013).Article 

Google Scholar 
Bramley, G. N. Home ranges and interactions of kiore (Rattus exulans) and Norway rats (R. norvegicus) on Kapiti Island, New Zealand. New Zeal. J. Ecol. 328–334 (2014).O’Rourke, R. L., Anson, J. R., Saul, A. M. & Banks, P. B. Limits to alien black rats (Rattus rattus) acting as equivalent pollinators to extinct native small mammals: The influence of stem width on mammal activity at native Banksia ericifolia inflorescences. Biol. Invasions 22, 329–338. https://doi.org/10.1007/s10530-019-02090-x (2020).Article 

Google Scholar 
White, T. A. et al. Range expansion in an invasive small mammal: influence of life-history and habitat quality. Biol. Invasions 14, 2203–2215. https://doi.org/10.1007/s10530-012-0225-x (2012).Article 

Google Scholar 
McDevitt, A. D. et al. Invading and expanding: Range dynamics and ecological consequences of the greater white-toothed shrew (Crocidura russula) invasion in Ireland. PLoS ONE 9, e100403. https://doi.org/10.1371/journal.pone.0100403 (2014).Article 
ADS 
CAS 

Google Scholar 
Aguilar, J.-P., Pélissié, T., Sigé, B. & Michaux, J. Occurrence of the stripe field mouse lineage (Apodemus agrarius Pallas 1771; Rodentia; Mammalia) in the Late Pleistocene of southwestern France. C.R. Palevol 7, 217–225 (2008).Article 

Google Scholar 
Kordos, L. Historico-zoogeographical and ecological investigation of the subfossil vertebrate fauna of the Aggtelek Karst. Vert. Hung. 18, 85–100 (1978).
Google Scholar 
Hairston, N. G., Smith, F. E. & Slobodkin, L. B. Community structure, population control, and competition. Am. Nat. 94, 421–425 (1960).Article 

Google Scholar 
Agnew, P., Hide, M., Sidobre, C. & Michalakis, Y. A minimalist approach to the effects of density-dependent competition on insect life-history traits. Ecol. Entomol. 27, 396–402 (2002).Article 

Google Scholar 
Krebs, C. J. Beyond population regulation and limitation. Wildl. Res. 29, 1–10 (2002).Article 

Google Scholar 
Huitu, O., Norrdahl, K. & Korpimäki, E. Competition, predation and interspecific synchrony in cyclic small mammal communities. Ecography 27, 197–206.  https://doi.org/10.1111/j.0906-7590.2003.03684.x (2004).Article 

Google Scholar 
Lofgren, O. Niche expansion and increased maturation rate of Clethrionomys glareolus in the absence of competitors. J. Mammal. 76, 1100–1112 (1995).Article 

Google Scholar 
Hansson, L. Competition between Rodents in Successional Stages of Taiga Forests: Microtus agrestis vs. Clethrionomys glareolus. Oikos 40, 258 (1983).Article 

Google Scholar 
Eccard, J. A. & Ylönen, H. Direct interference or indirect exploitation? An experimental study of fitness costs of interspecific competition in voles. Oikos 99, 580–590. https://doi.org/10.1034/j.1600-0706.2002.11833.x (2002).Article 

Google Scholar 
Gliwicz, J. Competition among forest rodents: Effects of Apodemus flavicollis and Clethrionomys glareolus on A. agrarius. Acta Zool. Fennica 1984. (1984).Neet, C. R. & Hausser, J. Habitat selection in zones of parapatric contact between the common shrew Sorex araneus and Millet’s shrew S. coronatus. J. Anim. Ecol. 235–250 (1990).Zub, K., Jędrzejewska, B., Jędrzejewski, W. & Bartoń, K. A. Cyclic voles and shrews and non-cyclic mice in a marginal grassland within European temperate forest. Acta Theriol. 57, 205–216 (2012).Article 
CAS 

Google Scholar 
Henttonen, H. et al. Long-term population dynamics of the common shrew Sorex araneus in Finland. in Annales Zoologici Fennici 349–355 (JSTOR, 1989).Gębczyńska, Z., Gębczyński, M., Morzuch, K. & Zielińska, D. M. Food eaten by four species of rodents in polluted forests. Acta Theriol. 34, 465–477 (1989).Article 

Google Scholar 
Babinska-Werka, J. Response of rodents to an increased and quantitatively diverse food base. Acta theriologica 35, (1990).Margaletic, J., Glavaš, M. & Bäumler, W. The development of mice and voles in an oak forest with a surplus of acorns. J. Pest Sci. 75, 95–98 (2002).Holisova, V. The food of Apodemus agrarius (Pall.). Zoologické listy 16, 1–14 (1967).Obrtel, R. & Hološová, V. The trophic niche of Apodemus agrarius in northern Moravia. Folia Zool. 30, 125–138 (1981).
Google Scholar 
Grant, P. R. Interspecific competition among rodents. Annu. Rev. Ecol. Syst. 3, 79–106 (1972).Article 

Google Scholar 
Redfield, J. A., Krebs, C. J. & Taitt, M. J. Competition between Peromyscus maniculatus and Microtus townsendii in grasslands of coastal British Columbia. J. Anim. Ecol. 607–616 (1977).Kincaid, W. B. & Cameron, G. N. Effects of species removal on resource utilization in a Texas rodent community. J. Mammal. 63, 229–235 (1982).Article 

Google Scholar 
Yurkonis, K. A., Meiners, S. J. & Wachholder, B. E. Invasion impacts diversity through altered community dynamics. J. Ecol. 93, 1053–1061 (2005).Article 

Google Scholar 
Powell, K. I., Chase, J. M. & Knight, T. M. A synthesis of plant invasion effects on biodiversity across spatial scales. Am. J. Bot. 98, 539–548. https://doi.org/10.3732/ajb.1000402 (2011).Article 

Google Scholar 
Jaksic, F. M. Vertebrate invaders and their ecological impacts in Chile. Biodivers. Conserv. 7, 1427–1445 (1998).Article 

Google Scholar 
Richter-Boix, A. et al. Effects of the non-native amphibian species Discoglossus pictus on the recipient amphibian community: Niche overlap, competition and community organization. Biol. Invasions 15, 799–815. https://doi.org/10.1007/s10530-012-0328-4 (2013).Article 

Google Scholar 
Kumschick, S., Bacher, S. & Blackburn, T. M. What determines the impact of alien birds and mammals in Europe? Biol. Invasions 15, 785–797. https://doi.org/10.1007/s10530-012-0326-6 (2013).Article 

Google Scholar 
Tedeschi, L., Biancolini, D., Capinha, C., Rondinini, C. & Essl, F. Introduction, spread, and impacts of invasive alien mammal species in Europe. Mam. Rev. Mam. 12277. https://doi.org/10.1111/mam.12277 (2021).Harris, D. B. Review of negative effects of introduced rodents on small mammals on islands. Biol. Invasions 11, 1611–1630. https://doi.org/10.1007/s10530-008-9393-0 (2009).Article 

Google Scholar 
Traveset, A. et al. A review on the effects of alien rodents in the Balearic (Western Mediterranean Sea) and Canary Islands (Eastern Atlantic Ocean). Biol. Invasions 11, 1653–1670. https://doi.org/10.1007/s10530-008-9395-y (2009).Article 

Google Scholar 
Jung, T. S., Nagorsen, D. W., Kukka, P. M. & Barker, O. E. Alien invaders: recent establishment of an urban population of house mice (Mus musculus) in the Yukon. Northwest. Nat. 93, 240–242 (2012).Article 

Google Scholar 
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: A few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453. https://doi.org/10.1016/S0169-5347(99)01679-1 (1999).Article 
CAS 

Google Scholar 
MacGregor-Fors, I., Morales-Pérez, L., Quesada, J. & Schondube, J. E. Relationship between the presence of House Sparrows (Passer domesticus) and Neotropical bird community structure and diversity. Biol. Invasions 12, 87–96. https://doi.org/10.1007/s10530-009-9432-5 (2010).Article 

Google Scholar 
Phillips, B. L. Behaviour on Invasion Fronts, and the Behaviour of Invasion Fronts. in Biological Invasions and Animal Behaviour (eds. Weis, J. S. & Sol, D.) 82–95 (Cambridge University Press, 2016). https://doi.org/10.1017/CBO9781139939492.007.Pietrek, A. G., Goheen, J. R., Riginos, C., Maiyo, N. J. & Palmer, T. M. Density dependence and the spread of invasive big-headed ants (Pheidole megacephala) in an East African savanna. Oecologia 195, 667–676. https://doi.org/10.1007/s00442-021-04859-1 (2021).Article 
ADS 

Google Scholar 
Stanko, M. Ryšavka tmavopása (Apodemus agrarius, Rodentia) na Slovensku. (Parazitologický ústav SAV, 2014).Thompson, K., Hodgson, J. G. & Rich, T. C. Native and alien invasive plants: more of the same?. Ecography 18, 390–402 (1995).Article 

Google Scholar  More

Cavanaugh CM, McKiness ZP, Newton ILG, Stewart FJ. Marine chemosynthetic symbioses. Prokaryotes. 2006;1:475–507.Article 

Google Scholar 
Beinart RA, Luo C, Konstantinidis KT, Stewart FJ, Girguis PR. The bacterial symbionts of closely related hydrothermal vent snails with distinct geochemical habitats show broad similarity in chemoautotrophic gene content. Front Microbiol. 2019;10:1818.Article 

Google Scholar 
Robidart JC, Bench SR, Feldman RA, Novoradovsky A, Podell SB, Gaasterland T, et al. Metabolic versatility of the Riftia pachyptila endosymbiont revealed through metagenomics. Environ Microbiol. 2008;10:727–37.Article 
CAS 

Google Scholar 
Ponnudurai R, Sayavedra L, Kleiner M, Heiden SE, Thürmer A, Felbeck H, et al. Genome sequence of the sulfur-oxidizing Bathymodiolus thermophilus gill endosymbiont. Stand Genom Sci. 2017;12:50.Article 

Google Scholar 
Duperron S, Bergin C, Zielinski F, Blazejak A, Pernthaler A, McKiness ZP, et al. A dual symbiosis shared by two mussel species, Bathymodiolus azoricus and Bathymodiolus puteoserpentis (Bivalvia: Mytilidae), from hydrothermal vents along the northern Mid-Atlantic Ridge. Environ Microbiol. 2006;8:1441–7.Article 
CAS 

Google Scholar 
Dubilier N, Bergin C, Lott C. Symbiotic diversity in marine animals: the art of harnessing chemosynthesis. Nat Rev Microbiol. 2008;6:725–40.Article 
CAS 

Google Scholar 
Sogin EM, Leisch N, Dubilier N. Chemosynthetic symbioses. Curr Biol. 2020;30:R1137–R1142.Article 
CAS 

Google Scholar 
Roeselers G, Newton ILG. On the evolutionary ecology of symbioses between chemosynthetic bacteria and bivalves. Appl Microbiol Biotechnol. 2012;94:1–10.Article 
CAS 

Google Scholar 
Moran NA. Symbiosis as an adaptive process and source of phenotypic complexity. Proc Natl Acad Sci USA. 2007;104 Suppl 1:8627–33.Article 
CAS 

Google Scholar 
McMullen JG, Peterson BF, Forst S, Blair HG, Patricia Stock S. Fitness costs of symbiont switching using entomopathogenic nematodes as a model. BMC Evol Biol. 2017;17. https://doi.org/10.1186/s12862-017-0939-6.Taylor JD, Glover E. Biology, evolution and generic review of the chemosymbiotic bivalve family Lucinidae. London, UK: Ray Society; 2021.Osvatic JT, Wilkins LGE, Leibrecht L, Leray M, Zauner S, Polzin J, et al. Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups. Proc Natl Acad Sci USA. 2021;118. https://doi.org/10.1073/pnas.2104378118.Petersen JM, Kemper A, Gruber-Vodicka H, Cardini U, van der Geest M, Kleiner M, et al. Chemosynthetic symbionts of marine invertebrate animals are capable of nitrogen fixation. Nat Microbiol. 2016;2:16195.Article 
CAS 

Google Scholar 
Lim SJ, Davis B, Gill D, Swetenburg J, Anderson LC, Engel AS, et al. Gill microbiome structure and function in the chemosymbiotic coastal lucinid Stewartia floridana. FEMS Microbiol Ecol. 2021;97. https://doi.org/10.1093/femsec/fiab042.Lim SJ, Davis BG, Gill DE, Walton J, Nachman E, Engel AS, et al. Taxonomic and functional heterogeneity of the gill microbiome in a symbiotic coastal mangrove lucinid species. ISME J. 2019;13:902–20.Article 
CAS 

Google Scholar 
Gros O, Liberge M, Felbeck H. Interspecific infection of aposymbiotic juveniles of Codakia orbicularis by various tropical lucinid gill-endosymbionts. Mar Biol. 2003;142:57–66.Article 

Google Scholar 
Gros O, Elisabeth NH, Gustave SDD, Caro A, Dubilier N. Plasticity of symbiont acquisition throughout the life cycle of the shallow-water tropical lucinid Codakia orbiculata (Mollusca: Bivalvia). Environ Microbiol. 2012;14:1584–95.Article 
CAS 

Google Scholar 
Gros O, Frenkiel L, Mouëza M. Embryonic, larval, and post-larval development in the symbiotic clam Codakia orbicularis (Bivalvia: Lucinidae). Invertebr Biol. 1997;116:86–101.Article 

Google Scholar 
König S, Gros O, Heiden SE, Hinzke T, Thürmer A, Poehlein A, et al. Nitrogen fixation in a chemoautotrophic lucinid symbiosis. Nat Microbiol. 2016;2:16193.Article 

Google Scholar 
Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol. 2010;18:455–63.Article 
CAS 

Google Scholar 
Cardini U, Bednarz VN, Foster RA, Wild C. Benthic N2 fixation in coral reefs and the potential effects of human-induced environmental change. Ecol Evol. 2014;4:1706–27.Article 

Google Scholar 
Glover EA, Taylor JD. Lucinidae of the Philippines: highest known diversity and ubiquity of chemosymbiotic bivalves from intertidal to bathyal depths (Mollusca: Bivalvia). mém Mus Natl Hist Nat. 2016;208:65–234.
Google Scholar 
Taylor JD, Glover EA, Williams ST. Diversification of chemosymbiotic bivalves: origins and relationships of deeper water Lucinidae. Biol J Linn Soc Lond. 2014;111:401–20.Article 

Google Scholar 
von Cosel R. Taxonomy of tropical West African bivalves. VI. Remarks on Lucinidae (Mollusca, Bivalvia), with description of six new genera and eight new species. Zoosystema. 2006;28:805.
Google Scholar 
Glover EA, Taylor JD, Rowden AA. Bathyaustriella thionipta, a new lucinid bivalve from a hydrothermal vent on the Kermadec Ridge, New Zealand and its relationship to shallow-water taxa (Bivalvia: Lucinidae). J Mollusca Stud. 2004;70:283–95.Article 

Google Scholar 
Paulus E Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front Mar Sci. 2021;8. https://doi.org/10.3389/fmars.2021.667048.Brown A, Thatje S. Explaining bathymetric diversity patterns in marine benthic invertebrates and demersal fishes: physiological contributions to adaptation of life at depth. Biol Rev Camb Philos Soc. 2014;89:406–26.Article 

Google Scholar 
Smith CR, De Leo FC, Bernardino AF, Sweetman AK, Arbizu PM. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol Evol. 2008;23:518–28.Article 

Google Scholar 
Gage JD, Tyler PA. Deep-sea biology: a natural history of organisms at the deep-sea floor. Cambridge, UK: Cambridge University Press; 1991.Iken K, Brey T, Wand U, Voigt J, Junghans P. Food web structure of the benthic community at the Porcupine Abyssal Plain (NE Atlantic): a stable isotope analysis. Prog Oceanogr. 2001;50:383–405.Article 

Google Scholar 
von Cosel R, Bouchet P. Tropical deep-water lucinids (Mollusca: Bivalvia) from the Indo-Pacific: essentially unknown, but diverse and occasionally gigantic. mém Mus Natl Hist Nat. 2008;196:115–213.
Google Scholar 
Stearns REC Scientific results of explorations by the US Fish Commission steamer Albatross. No. XVII. Descriptions of new West American land, fresh-water, and marine shells, with notes and comments. Proceedings of the United States National Museum. 1890. https://repository.si.edu/bitstream/handle/10088/13174/1/USNMP-13_813_1890.pdf.Taylor JD, Glover EA. The lucinid bivalve genus Cardiolucina (Mollusca, Bivalvia, Lucinidae): systematics, anatomy and relationships. Bull Br Mus Nat Hist Zoo. 1997;63:93–122.
Google Scholar 
Coan EV, Valentich-Scott P, Sadeghian PS. Bivalve seashells of tropical West America: marine bivalve mollusks from Baja California to Northern Peru. Santa Barbara, USA: Museum of Natural History; 2012.von Cosel R, Gofas S. Marine bivalves of tropical West Africa: from Rio de Oro to southern Angola. Marseille, France: Muséum national d’Histoire naturelle, Paris; 2019. p 1104.Atkinson L, Sink K. Field guide to the offshore marine invertebrates of South Africa. 2018. https://doi.org/10.15493/SAEON.PUB.10000001.Montagu G. Testacea Britannica, or natural history of British shells. London, UK: JS Hollis; 1803.Taylor J, Glover E. New lucinid bivalves from shallow and deeper water of the Indian and West Pacific Oceans (Mollusca, Bivalvia, Lucinidae). ZooKeys. 2013;326:69–90.Article 

Google Scholar 
Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.Article 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.Article 
CAS 

Google Scholar 
Pjevac P, Hausmann B, Schwarz J, Kohl G, Herbold CW, Loy A, et al. An economical and flexible dual barcoding, two-step PCR approach for highly multiplexed amplicon sequencing. Front Microbiol. 2021;12:669776.Article 

Google Scholar 
McLaren MR, Callahan BJ. Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4587955.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.Article 
CAS 

Google Scholar 
Andersen KS, Kirkegaard RH, Karst SM, Albertsen M. ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. 2018. https://www.biorxiv.org/content/10.1101/299537v1.Bushnell B. BBMap: a fast, accurate, splice-aware aligner. Berkeley, CA, USA: Lawrence Berkeley National Lab. (LBNL); 2014.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.Article 
CAS 

Google Scholar 
Nurk S, Bankevich A, Antipov D, Gurevich A, Korobeynikov A, Lapidus A, et al. Assembling genomes and mini-metagenomes from highly chimeric reads. In: Research in Computational Molecular Biology. Springer Berlin Heidelberg; 2013. p. 158–70.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.Article 

Google Scholar 
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.Article 

Google Scholar 
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.Article 
CAS 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.Article 

Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
CAS 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019. https://doi.org/10.1093/bioinformatics/btz848.Parks DH, Chuvochina M, Chaumeil P-A, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.Article 
CAS 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.Article 
CAS 

Google Scholar 
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.Article 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.Article 

Google Scholar 
Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.Article 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.Article 

Google Scholar 
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.Article 
CAS 

Google Scholar 
Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.Article 

Google Scholar 
Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–5.Article 
CAS 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–W296.Article 
CAS 

Google Scholar 
Varghese NJ, Mukherjee S, Ivanova N, Konstantinidis KT, Mavrommatis K, Kyrpides NC, et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 2015;43:6761–71.Article 
CAS 

Google Scholar 
Qin Q-L, Xie B-B, Zhang X-Y, Chen X-L, Zhou B-C, Zhou J, et al. A proposed genus boundary for the prokaryotes based on genomic insights. J Bacteriol. 2014;196:2210–5.Article 

Google Scholar 
Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–D314.Article 
CAS 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol Biol Evol. 2017;34:2115–22.Article 
CAS 

Google Scholar 
Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, Olsen GJ, et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep. 2015;5:8365.Article 

Google Scholar 
Mahram A, Herbordt MC. NCBI BLASTP on high-performance reconfigurable computing systems. ACM Trans Reconfigurable Technol Syst. 2015;7:1–20.Article 

Google Scholar 
Yang Z. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 1997;13:555–6.CAS 

Google Scholar 
Osvatic J, Wilkins L. Strength of selection scripts. FigShare. 2022;8. https://doi.org/10.6084/m9.figshare.20626746.v1.Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56:1919–25.Article 
CAS 

Google Scholar 
Lan Y, Sun J, Chen C, Sun Y, Zhou Y, Yang Y, et al. Hologenome analysis reveals dual symbiosis in the deep-sea hydrothermal vent snail Gigantopelta aegis. Nat Commun. 2021;12:1165.Article 
CAS 

Google Scholar 
Leray M, Wilkins LGE, Apprill A, Bik HM, Clever F, Connolly SR, et al. Natural experiments and long-term monitoring are critical to understand and predict marine host-microbe ecology and evolution. PLoS Biol. 2021;19:e3001322.Article 
CAS 

Google Scholar 
Petersen Jillian M, Yuen B, Alexandre G. The symbiotic ‘all-rounders’: partnerships between marine animals and chemosynthetic nitrogen-fixing bacteria. Appl Environ Microbiol 2020;87:e02129–20.Johnson KS, Childress JJ, Hessler RR, Sakamoto-Arnold CM, Beehler CL. Chemical and biological interactions in the Rose Garden hydrothermal vent field, Galapagos spreading center. Deep Sea Res A. 1988;35:1723–44.Article 

Google Scholar 
Kennicutt ME II, Brooks JM, Burke RA Jr. Hydrocarbon seepage, gas hydrates, and authigenic carbonate in the northwestern Gulf of Mexico. Offshore Technology Conference; 1989. https://doi.org/10.4043/5952-ms.Lilley MD, Butterfield DA, Olson EJ, Lupton JE, Macko SA, McDuff RE. Anomalous CH4 and NH4+ concentrations at an unsedimented mid-ocean-ridge hydrothermal system. Nature. 1993;364:45–47.Article 
CAS 

Google Scholar 
Von Damm KL, Edmond JM, Measures CI, Grant B. Chemistry of submarine hydrothermal solutions at Guaymas Basin, Gulf of California. Geochim Cosmochim Acta. 1985;49:2221–37.Article 

Google Scholar 
Lee RW, Thuesen EV, Childress JJ. Ammonium and free amino acids as nitrogen sources for the chemoautotrophic symbiosis Solemya reidi Bernard (Bivalvia: Protobranchia). J Exp Mar Bio Ecol. 1992;158:75–91.Article 
CAS 

Google Scholar 
Sanders JG, Beinart RA, Stewart FJ, Delong EF, Girguis PR. Metatranscriptomics reveal differences in in situ energy and nitrogen metabolism among hydrothermal vent snail symbionts. ISME J. 2013;7:1556–67.Article 
CAS 

Google Scholar 
Touchette BW, Burkholder JM. Review of nitrogen and phosphorus metabolism in seagrasses. J Exp Mar Bio Ecol. 2000;250:133–67.Article 
CAS 

Google Scholar 
Fourqurean JW, Zieman JC, Powell GVN. Relationships between porewater nutrients and seagrasses in a subtropical carbonate environment. Mar Biol. 1992;114:57–65.Article 
CAS 

Google Scholar 
Williams SL. Experimental studies of Caribbean seagrass bed development. Ecol Monogr. 1990;60:449–69.Article 

Google Scholar 
Herbert RA. Nitrogen cycling in coastal marine ecosystems. FEMS Microbiol Rev. 1999;23:563–90.Article 
CAS 

Google Scholar 
Risgaard-Petersen N, Dalsgaard T, Rysgaard S, Christensen PB, Borum J, McGlathery K, et al. Nitrogen balance of a temperate eelgrass Zostera marina bed. Mar Ecol Prog Ser. 1998;174:281–91.Article 
CAS 

Google Scholar 
Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.Article 
CAS 

Google Scholar 
Karthäuser C, Ahmerkamp S, Marchant HK, Bristow LA, Hauss H, Iversen MH, et al. Small sinking particles control anammox rates in the Peruvian oxygen minimum zone. Nat Commun. 2021;12:3235.Article 

Google Scholar 
Kuypers MMM, Lavik G, Woebken D, Schmid M, Fuchs BM, Amann R, et al. Massive nitrogen loss from the Benguela upwelling system through anaerobic ammonium oxidation. Proc Natl Acad Sci USA. 2005;102:6478–83.Article 
CAS 

Google Scholar 
Johnson KS, Beehler CL, Sakamoto-Arnold CM, Childress JJ. In situ measurements of chemical distributions in a deep-sea hydrothermal vent field. Science. 1986;231:1139–41.Article 
CAS 

Google Scholar 
Childress JJ, Girguis PR. The metabolic demands of endosymbiotic chemoautotrophic metabolism on host physiological capacities. J Exp Biol. 2011;214:312–25.Article 
CAS 

Google Scholar 
Hentschel U, Hand S, Felbeck H. The contribution of nitrate respiration to the energy budget of the symbiont-containing clam Lucinoma aequizonata: a calorimetric study. J Exp Biol. 1996;199:427–33.Article 
CAS 

Google Scholar 
Breusing C, Mitchell J, Delaney J, Sylva SP, Seewald JS, Girguis PR, et al. Physiological dynamics of chemosynthetic symbionts in hydrothermal vent snails. ISME J. 2020;14:2568–79.Article 
CAS 

Google Scholar 
Amorim K, Loick-Wilde N, Yuen B, Osvatic JT, Wäge-Recchioni J, Hausmann B, et al. Chemoautotrophy, symbiosis and sedimented diatoms support high biomass of benthic molluscs in the Namibian shelf. Sci Rep. 2022;12:9731.Article 
CAS 

Google Scholar 
Breusing C, Johnson SB, Tunnicliffe V, Clague DA, Vrijenhoek RC, Beinart RA. Allopatric and sympatric drivers of speciation in Alviniconcha hydrothermal vent snails. Mol Biol Evol. 2020;37:3469–84.Article 
CAS 

Google Scholar 
Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.Article 

Google Scholar 
Brissac T, Gros O, Merçot H. Lack of endosymbiont release by two Lucinidae (Bivalvia) of the genus Codakia: consequences for symbiotic relationships. FEMS Microbiol Ecol. 2009;67:261–7.Article 
CAS 

Google Scholar 
Werner GDA, Cornelissen JHC, Cornwell WK, Soudzilovskaia NA, Kattge J, West SA, et al. Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown. Proc Natl Acad Sci USA. 2018;115:5229–34.Article 
CAS 

Google Scholar 
Sudakaran S, Kost C, Kaltenpoth M. Symbiont acquisition and replacement as a source of ecological innovation. Trends Microbiol. 2017;25:375–90.Article 
CAS 

Google Scholar 
Li Y, Liles MR, Halanych KM. Endosymbiont genomes yield clues of tubeworm success. ISME J. 2018;12:2785–95.Article 
CAS 

Google Scholar 
Moran NA, Yun Y. Experimental replacement of an obligate insect symbiont. Proc Natl Acad Sci USA. 2015;112:2093–6.Article 
CAS 

Google Scholar 
Sørensen MES, Wood AJ, Cameron DD, Brockhurst MA. Rapid compensatory evolution can rescue low fitness symbioses following partner switching. Curr Biol. 2021;31:3721–3728.e4.Article 

Google Scholar 
Taylor JD, Glover EA, Smith L, Ikebe C, Williams ST. New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia). Zootaxa. 2016;4196:zootaxa.4196.3.2.Article 

Google Scholar 
Osvatic J. Fig1 gtdb tree and alignment. figshare. 2021. https://doi.org/10.6084/m9.figshare.16837216.v1.Osvatic J. Figure 2: GTDB alignment and phylogeny. 2021. https://doi.org/10.6084/m9.figshare.16837237. More

Alphan, H. Land use change and urbanisation of Adana, Turkey. Land Degrad. Dev. 14, 575–586 (2003).Article 

Google Scholar 
Catalàn, B., Sauri, D. & Serra, P. Urban sprawl in the Mediterranean? Patterns of growth and change in the Barcelona Metropolitan Region 1993–2000. Landsc. Urban Plan. 85(3–4), 174–184 (2008).
Google Scholar 
Chen, K., Long, H., Liao, L., Tu, S. & Li, T. Land use transitions and urban-rural integrated development: Theoretical framework and China’s evidence. Land Use Policy 92, 104465 (2020).Article 

Google Scholar 
Bianchini, L. et al. Forest transition and metropolitan transformations in developed countries: Interpreting apparent and latent dynamics with local regression models. Land 11(1), 12 (2021).Article 

Google Scholar 
Angel, S., Parent, J., Civco, D. L., Blei, A. & Potere, D. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plan. 75(2), 53–107 (2011).Article 

Google Scholar 
Fischer, A. P. Forest landscapes as social-ecological systems and implications for management. Landsc. Urban Plan. 177, 138–147 (2018).Article 

Google Scholar 
Darvishi, A., Yousefi, M. & Marull, J. Modelling landscape ecological assessments of land use and cover change scenarios. Application to the Bojnourd Metropolitan Area (NE Iran). Land Use Policy 99, 105098 (2020).Article 

Google Scholar 
Cheng, L. L., Tian, C. & Yin, T. T. Identifying driving factors of urban land expansion using Google earth engine and machine-learning approaches in Mentougou District, China. Sci. Rep. 12(1), 1–13 (2022).Article 
CAS 

Google Scholar 
Kasanko, M. et al. Are European Cities becoming dispersed? A comparative analysis of fifteen European urban areas. Landsc. Urban Plan. 77(1–2), 111–130 (2006).Article 

Google Scholar 
Terzi, F. & Bolen, F. Urban sprawl measurement of Istanbul. Eur. Plan. Stud. 17(10), 1559–1570 (2009).Article 

Google Scholar 
Angel, S., Parent, J. & Civco, D. L. Ten compactness properties of circles: measuring shape in geography. Can. Geogr. 54, 441–461 (2010).Article 

Google Scholar 
Salvati, L., Gemmiti, R. & Perini, L. Land degradation in Mediterranean urban areas: An unexplored link with planning?. Area 44(3), 317–325 (2012).Article 

Google Scholar 
Attorre, F., Bruno, M., Francesconi, F., Valenti, R. & Bruno, F. Landscape changes of Rome through tree-lined roads. Landsc. Urban Plan. 49, 115–128 (2000).Article 

Google Scholar 
Turok, I. & Mykhnenko, V. The trajectories of European cities, 1960–2005. Cities 24(3), 165–182 (2007).Article 

Google Scholar 
Ioannidis, C., Psaltis, C. & Potsiou, C. Towards a strategy for control of suburban informal buildings through automatic change detection. Comput. Environ. Urban Syst. 33, 64–74 (2009).Article 

Google Scholar 
Grekousis, G., Manetos, P. & Photis, Y. N. Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the athens metropolitan area. Cities 30, 193–203 (2013).Article 

Google Scholar 
Salvati, L. Towards a polycentric region? The socioeconomic trajectory of Rome, an ‘Eternally Mediterranean’ city. Tijdschr. Econ. Soc. Geogr. 105(3), 268–284 (2014).Article 

Google Scholar 
Chorianopoulos, I., Pagonis, T., Koukoulas, S. & Drymoniti, S. Planning, competitiveness and sprawl in the Mediterranean city: The case of Athens. Cities 27, 249–259 (2010).Article 

Google Scholar 
Munafò, M., Salvati, L. & Zitti, M. Estimating soil sealing rate at national level—Italy as a case study. Ecol. Ind. 26, 137–140 (2013).Article 

Google Scholar 
Morelli, V. G., Rontos, K. & Salvati, L. Between suburbanisation and re-urbanisation: Revisiting the urban life cycle in a Mediterranean compact city. Urban Res. Pract. 7(1), 74–88 (2014).Article 

Google Scholar 
Basem Ajjur, S. & Al-Ghamdi, S. G. Exploring urban growth–climate change–flood risk nexus in fast growing cities. Sci. Rep. 12, 12265 (2022).Article 
ADS 

Google Scholar 
Li, H. & Wu, J. Use and misuse of landscape indices. Landsc. Ecol. 19, 389–399 (2004).Article 

Google Scholar 
Salvati, L. Agro-forest landscape and the ‘fringe’city: A multivariate assessment of land-use changes in a sprawling region and implications for planning. Sci. Total Environ. 490, 715–723 (2014).Article 
ADS 
CAS 

Google Scholar 
Sang, X. et al. Intensity and stationarity analysis of land use change based on CART algorithm. Sci. Rep. 9(1), 1–12 (2019).Article 
ADS 
CAS 

Google Scholar 
Ettehadi Osgouei, P., Sertel, E. & Kabadayı, M. E. Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020. Sci. Rep. 12(1), 1–17 (2022).Article 

Google Scholar 
He, S., Yu, S., Li, G. & Zhang, J. Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities. Land Use Policy 95, 104576 (2020).Article 

Google Scholar 
Bockarjova, M., Wouter Botzen, W. J., Bulkeley, H. A. & Toxopeus, H. Estimating the social value of nature-based solutions in European cities. Sci. Rep. 12, 19833 (2022).Article 
ADS 
CAS 

Google Scholar 
Liu, J. & Niyogi, D. Meta-analysis of urbanisation impact on rainfall modification. Sci. Rep. 9(1), 1–14 (2019).ADS 

Google Scholar 
Holland, J. H. Studying complex adaptive systems. J. Syst. Sci. Complex. 19(1), 1–8 (2006).Article 
MathSciNet 
MATH 

Google Scholar 
Salvati, L. & Serra, P. Estimating rapidity of change in complex urban systems: A multidimensional, local-scale approach. Geogr. Anal. 48(2), 132–156 (2016).Article 

Google Scholar 
Bura, S., Guerin-Pace, F., Mathian, H., Pumain, D. & Sanders, L. Multi-agents systems and the dynamics of a settlement system. Geogr. Anal. 28(2), 161–178 (1996).Article 

Google Scholar 
Hasse, J. E. & Lathrop, R. G. Land resource impact indicators of urban sprawl. Appl. Geogr. 23, 159–175 (2003).Article 

Google Scholar 
Grafius, D. R., Corstanje, R. & Harris, J. A. Linking ecosystem services, urban form and green space configuration using multivariate landscape metric analysis. Landsc. Ecol. 33(4), 557–573 (2018).Article 

Google Scholar 
Pumain, D. Hierarchy in Natural and Social Sciences (Kluwer-Springer, 2005).
Google Scholar 
Cabral, P., Augusto, G., Tewolde, M. & Araya, Y. Entropy in urban systems. Entropy 15(12), 5223–5236 (2013).Article 
ADS 

Google Scholar 
Salvati, L. & Carlucci, M. In-between stability and subtle changes: Urban growth, population structure, and the city life cycle in Rome. Popul. Space Place 22(3), 216–227 (2016).Article 

Google Scholar 
Batty, M. & Longley, P. Fractal Cities (Academic Press, 1994).MATH 

Google Scholar 
Berry, B. J. L. Cities as systems within systems of cities. Pap. Reg. Sci. 13, 147–163 (2005).Article 

Google Scholar 
Petrosillo, I. et al. The resilient recurrent behavior of mediterranean semi-arid complex adaptive landscapes. Land 10(3), 296 (2021).Article 

Google Scholar 
Portugali, J. Complexity, Cognition and the City, Understanding Complex Systems (Springer, 2011).Book 

Google Scholar 
Wu, J., Jenerette, G. D., Buyantuyev, A. & Redman, C. L. Quantifying spatiotemporal patterns of urbanisation: The case of the two fastest growing metropolitan regions in the United States. Ecol. Complex. 8(1), 1–8 (2011).Article 

Google Scholar 
Sun, Y., Gao, C., Li, J., Li, W. & Ma, R. Examining urban thermal environment dynamics and relations to biophysical composition and configuration and socioeconomic factors: A case study of the Shanghai metropolitan region. Sustain. Cities Soc. 40, 284–295 (2018).Article 

Google Scholar 
Phillips, M. A. & Ritala, P. A complex adaptive systems agenda for ecosystem research methodology. Technol. Forecast. Soc. Change 148, 119739 (2019).Article 

Google Scholar 
Walker, B., Holling, C. S., Carpenter, S. R. & Kinzig, A. Resilience, adaptability and transformability in social-ecological systems. Ecol. Soc. 9(2), 5 (2004).Article 

Google Scholar 
Kelly, C. et al. Community resilience and land degradation in forest and shrublandsocio-ecological systems: A case study in Gorgoglione, Basilicata regionn, Italy. Land Use Policy 46, 11–20 (2015).Article 

Google Scholar 
Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems. Ecol. Soc. 23(4), 46 (2018).Article 

Google Scholar 
Ferrara, A. et al. Shaping the role of ‘fast’ and ‘slow’ drivers of change in forest-shrubland socio-ecological systems. J. Environ. Manag. 169, 155–166 (2016).Article 

Google Scholar 
Lamy, T., Liss, K. N., Gonzalez, A. & Bennett, E. M. Landscape structure affects the provision of multiple ecosystem services. Environ. Res. Lett. 11(12), 124017 (2016).Article 
ADS 

Google Scholar 
Riitters, K. H., Vogt, P., Soille, P., Kozak, J. & Estreguil, C. Neutral model analysis of landscape patterns from mathematical morphology. Landsc. Ecol. 22(7), 1033–1043 (2007).Article 

Google Scholar 
Riitters, K., Vogt, P., Soille, P. & Estreguil, C. Landscape patterns from mathematical morphology on maps with contagion. Landsc. Ecol. 24(5), 699–709 (2009).Article 

Google Scholar 
Anas, A., Arnott, R. & Small, K. Urban spatial structure. J. Econ. Lit. 36(3), 1426–1464 (1998).
Google Scholar 
Arroyo-Mora, J. P., Sánchez-Azofeifa, G. A., Rivard, B., Calvo, J. C. & Janzen, D. H. Dynamics in landscape structure and composition for the Chorotega region, Costa Rica from 1960 to 2000. Agr. Ecosyst. Environ. 106(1), 27–39 (2005).Article 

Google Scholar 
Siles, G., Charland, A., Voirin, Y. & Bénié, G. B. Integration of landscape and structure indicators into a web-based geoinformation system for assessing wetlands status. Eco. Inform. 52, 166–176 (2019).Article 

Google Scholar 
Soille, P. Morphological Image Analysis: Principles and Applications (Springer, 2003).MATH 

Google Scholar 
Soille, P. & Vogt, P. Morphological segmentation of binary patterns. Pattern Recogn. Lett. 30, 456–459 (2009).Article 
ADS 

Google Scholar 
Vogt, P. et al. Mapping spatial patterns with morphological image processing. Landsc. Ecol. 22(2), 171–177 (2007).Article 

Google Scholar 
Bajocco, S., Ceccarelli, T., Smiraglia, D., Salvati, L. & Ricotta, C. Modeling the ecological niche of long-term land use changes: The role of biophysical factors. Ecol. Ind. 60, 231–236 (2016).Article 

Google Scholar 
Yin, Y., Zhou, K. & Chen, Y. Deconstructing the driving factors of land development intensity from multi-scale in differentiated functional zones. Sci. Rep. 12(1), 1–13 (2022).Article 

Google Scholar 
Duvernoy, I., Zambon, I., Sateriano, A. & Salvati, L. Pictures from the other side of the fringe: Urban growth and peri-urban agriculture in a post-industrial city (Toulouse, France). J. Rural. Stud. 57, 25–35 (2018).Article 

Google Scholar 
Smiraglia, D., Ceccarelli, T., Bajocco, S., Salvati, L. & Perini, L. Linking trajectories of land change, land degradation processes and ecosystem services. Environ. Res. 147, 590–600 (2016).Article 
CAS 

Google Scholar 
Shaker, R. R. Examining sustainable landscape function across the Republic of Moldova. Habitat Int. 72, 77–91 (2018).Article 
ADS 

Google Scholar 
Zheng, H. & Li, H. Spatial–temporal evolution characteristics of land use and habitat quality in Shandong Province, China. Sci. Rep. 12(1), 1–12 (2022).Article 

Google Scholar 
Tombolini, I., Munafò, M. & Salvati, L. Soil sealing footprint as an indicator of dispersed urban growth: A multivariate statistics approach. Urban Res. Pract. 9(1), 1–15 (2016).Article 

Google Scholar 
Salvati, L., Sateriano, A., Grigoriadis, E. & Carlucci, M. New wine in old bottles: The (changing) socioeconomic attributes of sprawl during building boom and stagnation. Ecol. Econ. 131, 361–372 (2017).Article 

Google Scholar 
Zambon, I., Benedetti, A., Ferrara, C. & Salvati, L. Soil matters? A multivariate analysis of socioeconomic constraints to urban expansion in Mediterranean Europe. Ecol. Econ. 146, 173–183 (2018).Article 

Google Scholar 
Paul, V. & Tonts, M. Containing urban sprawl: Trends in land use and spatial planning in the Metropolitan Region of Barcelona. J. Environ. Plann. Manag. 48(1), 7–35 (2005).Article 

Google Scholar 
Serra, P., Vera, A., Tulla, A. F. & Salvati, L. Beyond urban–rural dichotomy: Exploring socioeconomic and land-use processes of change in Spain (1991–2011). Appl. Geogr. 55, 71–81 (2014).Article 

Google Scholar 
Seifollahi-Aghmiuni, S., Kalantari, Z., Egidi, G., Gaburova, L. & Salvati, L. Urbanisation-driven land degradation and socioeconomic challenges in peri-urban areas: Insights from Southern Europe. Ambio 51(6), 1446–1458 (2022).Article 

Google Scholar 
Pili, S., Grigoriadis, E., Carlucci, M., Clemente, M. & Salvati, L. Towards sustainable growth? A multi-criteria assessment of (changing) urban forms. Ecol. Ind. 76, 71–80 (2017).Article 

Google Scholar 
Salvati, L., Sateriano, A. & Grigoriadis, E. Crisis and the city: Profiling urban growth under economic expansion and stagnation. Lett. Spat. Resour. Sci. 9(3), 329–342 (2016).Article 

Google Scholar 
Champion, T. & Hugo, G. New Forms of Urbanisation: Beyond the Urban-Rural Dichotomy (Ashgate, 2004).
Google Scholar 
Frondoni, R., Mollo, B. & Capotorti, G. A landscape analysis of land cover change in the municipality of Rome (Italy): Spatio-temporal characteristics and ecological implications of land cover transitions from 1954 to 2001. Landsc. Urban Plan. 100(1–2), 117–128 (2011).Article 

Google Scholar 
Perrin, C., Nougarèdes, B., Sini, L., Branduini, P. & Salvati, L. Governance changes in peri-urban farmland protection following decentralisation: A comparison between Montpellier (France) and Rome (Italy). Land Use Policy 70, 535–546 (2018).Article 

Google Scholar 
Salvati, L. Monitoring high-quality soil consumption driven by urban pressure in a growing city (Rome, Italy). Cities 31, 349–356 (2013).Article 

Google Scholar 
Salvati, L., Ciommi, M. T., Serra, P. & Chelli, F. M. Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy. Land Use Policy 81, 143–152 (2019).Article 

Google Scholar 
Ferrara, C., Salvati, L. & Tombolini, I. An integrated evaluation of soil resource depletion from diachronic settlement maps and soil cartography in peri-urban Rome, Italy. Geoderma 232, 394–405 (2014).Article 
ADS 

Google Scholar 
Egidi, G. & Salvati, L. Beyond the suburban-urban divide: Convergence in age structures in metropolitan Rome, Italy. J. Popul. Soc. Stud. 28(2), 130–142 (2020).Article 

Google Scholar 
Pili, S., Serra, P. & Salvati, L. Landscape and the city: Agro-forest systems, land fragmentation and the ecological network in Rome, Italy. Urban For. Urban Green. 41, 230–237 (2019).Article 

Google Scholar 
European Environment Agency. Urban Sprawl in Europe – The Ignored Challenge. Copenhagen: EEA Report no. 10 (2006).Park, S., Hepcan, Ç. C., Hepcan, Ş & Cook, E. A. Influence of urban form on landscape pattern and connectivity in metropolitan regions: a comparative case study of Phoenix, AZ, USA, and Izmir, Turkey. Environ. Monit. Assess. 186(10), 6301–6318 (2014).Article 

Google Scholar 
Luo, F., Liu, Y., Peng, J. & Wu, J. Assessing urban landscape ecological risk through an adaptive cycle framework. Landsc. Urban Plan. 180, 125–134 (2018).Article 

Google Scholar 
Ortega, M., Pascual, S., Elena-Rosselló, R. & Rescia, A. J. Land-use and spatial resilience changes in the Spanish olive socio-ecological landscape. Appl. Geogr. 117, 102171 (2020).Article 

Google Scholar 
Parcerisas, L. et al. Land use changes, landscape ecology and their socioeconomic driving forces in the Spanish Mediterranean coast (El Maresme County, 1850–2005). Environ. Sci. Policy 23, 120–132 (2012).Article 

Google Scholar 
Masini, E. et al. Urban growth, land-use efficiency and local socioeconomic context: A comparative analysis of 417 metropolitan regions in Europe. Environ. Manag. 63(3), 322–337 (2019).Article 
ADS 

Google Scholar 
Luck, M. & Wu, J. A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landsc. Ecol. 17(4), 327–339 (2002).Article 

Google Scholar 
Pesaresi, M. & Bianchin, A. Recognising settlement structure using mathematical morphology and image texture. Remote Sensing Urban Anal. GISDATA 9, 46–60 (2003).
Google Scholar 
Schneider, A. & Woodcock, C. E. Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Stud. 45(3), 659–692 (2008).Article 

Google Scholar 
Mubareka, S., Koomen, E., Estreguil, C. & Lavalle, C. Development of a composite index of urban compactness for land use modelling applications. Landsc. Urban Plan. 103(3–4), 303–317 (2011).Article 

Google Scholar 
Vogt, P. et al. Mapping landscape corridors. Ecol. Ind. 7(2), 481–488 (2007).Article 

Google Scholar 
Daya Sagar, B. S. & Murthy, K. S. R. Generation of a fractal landscape using nonlinear mathematical morphological transformations. Fractals 8(03), 267–272 (2000).Article 

Google Scholar 
Scott, A. J., Carter, C., Reed, M. R., Stonyer, B. & Coles, R. Disintegrated development at the rural-urban fringe: Re-connecting spatial planning theory and practice. Prog. Plan. 83, 1–52 (2013).Article 

Google Scholar 
Zhao, Q., Wen, Z., Chen, S., Ding, S. & Zhang, M. Quantifying land use/land cover and landscape pattern changes and impacts on ecosystem services. Int. J. Environ. Res. Public Health 17(1), 126 (2020).Article 

Google Scholar 
Parr, J. The regional economy, spatial structure and regional urban systems. Reg. Stud. 48(12), 1926–1938 (2014).Article 

Google Scholar 
Salvati, L., Zambon, I., Chelli, F. M. & Serra, P. Do spatial patterns of urbanisation and land consumption reflect different socioeconomic contexts in Europe?. Sci. Total Environ. 625, 722–730 (2018).Article 
ADS 
CAS 

Google Scholar 
Coppi, R. & Bolasco, S. Multiway Data Analysis (Elsevier, 1988).MATH 

Google Scholar 
Kroonenberg, P. M. Applied Multiway Data Analysis (Wiley, 2008).Book 
MATH 

Google Scholar 
Escofier, B. & Pages, J. Multiple factor analysis (AFMULT Package). Comput. Stat. Data Anal. 18, 121–140 (1994).Article 
MATH 

Google Scholar 
De Rosa, S. & Salvati, L. Beyond a ‘side street story’? Naples from spontaneous centrality to entropic polycentricism, towards a ‘crisis city’. Cities 51, 74–83 (2016).Article 

Google Scholar 
Favaro, J.-M. & Pumain, D. Gibrat revisited: An urban growth model incorporating spatial interaction and innovation cycles. Geogr. Anal. 43(3), 261–286 (2011).Article 

Google Scholar 
Walker, B. H., Carpenter, S. R., Rockstrom, J., Crepin, A.-S. & Peterson, G. D. “Drivers, “slow” variables, “fast” variables, shocks, and resilience. Ecol. Soc. 17(3), 30 (2012).Article 

Google Scholar 
Zhang, Z., Su, S., Xiao, R., Jiang, D. & Wu, J. Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay, China. Appl. Geogr. 45, 193–202 (2013).Article 

Google Scholar 
Fratarcangeli, C., Fanelli, G., Franceschini, S., De Sanctis, M. & Travaglini, A. Beyond the urban-rural gradient: Self-organising map detects the nine landscape types of the city of Rome. Urban For. Urban Green. 38, 354–370 (2019).Article 

Google Scholar 
Crisci, M., Benassi, F., Rabiei-Dastjerdi, H., McArdle, G. Spatio-temporal variations and contextual factors of the supply of Airbnb in Rome. An initial investigation. Lett. Spat. Resour. Sci. 1–17 (2022).Lelo, K., Monni, S. & Tomassi, F. Socio-spatial inequalities and urban transformation. The case of Rome districts. Socio-Econ. Plann. Sci. 68, 100696 (2019).Article 

Google Scholar 
Crisci, M. The impact of the real estate crisis on a south european metropolis: From urban diffusion to Reurbanisation. Appl. Spat. Anal. Policy 15(3), 797–820 (2022).Article 

Google Scholar 
Wang, Y. & Zhang, X. A dynamic modeling approach to simulating socioeconomic effects on landscape changes. Ecol. Model. 140(1–2), 141–162 (2001).Article 

Google Scholar 
Voghera, A. The River agreement in Italy. Resilient planning for the co-evolution of communities and landscapes. Land Use Policy 91, 104377 (2020).Article 

Google Scholar 
Chen, A. & Partridge, M. D. When are cities engines of growth in China? Spread and backwash effects across the urban hierarchy. Reg. Stud. 47(8), 1313–1331 (2013).Article 

Google Scholar 
Ciommi, M., Chelli, F. M., Carlucci, M. & Salvati, L. Urban growth and demographic dynamics in southern Europe: Toward a new statistical approach to regional science. Sustainability 10(8), 2765 (2018).Article 

Google Scholar 
Jacobs-Crisioni, C., Rietveld, P. & Koomen, E. The impact of spatial aggregation on urban development analyses. Appl. Geogr. 47, 46–56 (2014).Article 

Google Scholar 
Kourtit, K., Nijkamp, P. & Reid, N. The new urban world: Challenges and policy. Appl. Geogr. 49, 1–3 (2014).Article 

Google Scholar 
Bruegmann, R. Sprawl: A Compact History (University of Chicago Press, 2005).Book 

Google Scholar 
Neuman, M. & Hull, A. The Futures of the City Region. Reg. Stud. 43(6), 777–787 (2009).Article 

Google Scholar 
Couch, C., Petschel-held, G. & Leontidou, L. Urban Sprawl In Europe: Landscapes, Land-use Change and Policy (Blackwell, 2007).Book 

Google Scholar 
Longhi, C. & Musolesi, A. European cities in the process of economic integration: towards structural convergence. Ann. Reg. Sci. 41, 333–351 (2007).Article 

Google Scholar 
Tian, G., Ouyang, Y., Quan, Q. & Wu, J. Simulating spatiotemporal dynamics of urbanisation with multi-agent systems—A case study of the Phoenix metropolitan region, USA. Ecol. Model. 222(5), 1129–1138 (2011).Article 

Google Scholar 
Tian, L., Chen, J. & Yu, S. X. Coupled dynamics of urban landscape pattern and socioeconomic drivers in Shenzhen, China. Landsc. Ecol. 29(4), 715–727 (2014).Article 

Google Scholar 
Fielding, A. J. Counterurbanization in Western Europe. Prog. Plan. 17, 1–52 (1982).Article 

Google Scholar 
Oueslati, W., Alvanides, S. & Garrod, G. Determinants of urban sprawl in European cities. Urban Stud. 52(9), 1594–1614 (2015).Article 

Google Scholar 
Tress, B., Tress, G., Décamps, H. & d’Hauteserre, A. M. Bridging human and natural sciences in landscape research. Landsc. Urban Plan. 57(3–4), 137–141 (2001).Article 

Google Scholar 
Xu, Z., Lv, Z., Li, J., Sun, H. & Sheng, Z. A Novel perspective on travel demand prediction considering natural environmental and socioeconomic factors. IEEE Intell. Transp. Syst. Mag. https://doi.org/10.1109/MITS.2022.3162901 (2022).Article 

Google Scholar 
Xu, Z., Lv, Z., Li, J. & Shi, A. A novel approach for predicting water demand with complex patterns based on ensemble learning. Water Resour. Manag. 36(11), 4293–4312 (2022).Article 

Google Scholar 
Lv, Z., Li, J., Dong, C., Li, H. & Xu, Z. Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalisation index. Data Knowl. Eng. 135, 101912 (2021).Article 

Google Scholar  More

Ai H, Fang X, Yang B, Huang Z, Chen H, Mao L et al. (2015) Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing. Nat Genet 47:217–225Article 
CAS 

Google Scholar 
Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664Article 
CAS 

Google Scholar 
Alexandri P, Megens HJ, Crooijmans RPMA, Groenen MAM, Goedbloed DJ, Herrero-Medrano JM et al. (2017) Distinguishing migration events of different timing for wild boar in the Balkans. J Biogeogr 44:259–270Article 

Google Scholar 
Alexandri P, Triantafyllidis A, Papakostas S, Chatzinikos E, Platis P, Papageorgiou N et al. (2012) The Balkans and the colonization of Europe: the post-glacial range expansion of the wild boar, Sus scrofa. J Biogeogr 39:713–723Article 

Google Scholar 
Alves PC, Pinheiro I, Godinho R, Vicente JJ, Gortázar C, Scandura M et al. (2010) Genetic diversity of wild boar populations and domestic pig breeds (Sus scrofa) in South-western Europe. Biol J Linn Soc 101:797–822Article 

Google Scholar 
Apollonio M, Andersen R, Putman R (2010) European ungulates and their management in the 21st century (M Apollonio, R Andersen, and R Putman, Eds.) Cambridge University Press: Cambridge, UKAzzaroli A, De Giuli C, Ficcarelli G, Torre D (1988) Late pliocene to early mid-pleistocene mammals in Eurasia: Faunal succession and dispersal events. Palaeogeogr Palaeoclimatol Palaeoecol 66:77–100Article 

Google Scholar 
Bérénos C, Ellis PA, Pilkington JG, Pemberton JM (2016) Genomic analysis reveals depression due to both individual and maternal inbreeding in a free‐living mammal population. Mol Ecol 25:3152–3168Article 

Google Scholar 
Braga RT, Rodrigues JFM, Diniz-Filho JAF, Rangel TF (2019) Genetic population structure and allele surfing during range expansion in dynamic habitats. An da Academia Brasileira de Ciências 91:e20180179Article 

Google Scholar 
Bragina EV, Ives AR, Pidgeon AM, Kuemmerle T, Baskin LM, Gubar YP, Piquer-Rodríguez M, Keuler NS, Petrosyan VG, Radeloff VC (2015) Rapid Declines of Large Mammal Populations after the Collapse of the Soviet Union. Cons Biol 29:844–853Article 

Google Scholar 
Brewer S, Cheddadi R, de Beaulieu JL, Reille M, Allen J, Almqvist-Jacobson H et al. (2002) The spread of deciduous Quercus throughout Europe since the last glacial period. For Ecol Manag 156:27–48Article 

Google Scholar 
Cahill S, Llimona F, Cabañeros L, Calomardo F (2012) Characteristics of wild boar (Sus scrofa) habituation to urban areas in the Collserola Natural Park (Barcelona) and comparison with other locations. Anim Biodivers Conserv 35:221–233Article 

Google Scholar 
Canu A, Costa S, Iacolina L, Piatti P, Apollonio M, Scandura M (2014) Are captive wild boar more introgressed than free-ranging wild boar? Two case studies in Italy. Eur J Wildl Res 60:459–467Article 

Google Scholar 
Canu A, Vilaça STT, Iacolina L, Apollonio M, Bertorelle G, Scandura M (2016) Lack of polymorphism at the MC1R wild-type allele and evidence of domestic allele introgression across European wild boar populations. Mamm Biol 81:477–479Article 

Google Scholar 
Carranza J, Salinas M, de Andrés D, Pérez-González J (2016) Iberian red deer: paraphyletic nature at mtDNA but nuclear markers support its genetic identity. Ecol Evol 6:905–922Article 

Google Scholar 
Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 4:1–16Article 

Google Scholar 
Cheddadi R, Bar-Hen A (2009) Spatial gradient of temperature and potential vegetation feedback across Europe during the late Quaternary. Clim Dyn 32:371–379Article 

Google Scholar 
Clark PU, Dyke AS, Shakun JD, Carlson AE, Clark J, Wohlfarth B et al. (2009) The Last Glacial Maximum. Science 325:710–714Article 
CAS 

Google Scholar 
DeGiorgio M, Rosenberg NA (2013) Geographic sampling scheme as a determinant of the major axis of genetic variation in principal components analysis. Mol Biol Evol 30:480–488Article 
CAS 

Google Scholar 
Deinet S, Ieronymidou C, McRae L, Burfield IJ, Foppen RP, Collen B, et al. (2013) Wildlife comeback in Europe. The recovery of selected mammal and bird species. London, UKEckert CG, Samis KE, Lougheed SC (2008) Genetic variation across species’ geographical ranges: the central–marginal hypothesis and beyond. Mol Ecol 17:1170–1188Article 
CAS 

Google Scholar 
Fang M, Berg F, Ducos A, Andersson L (2006) Mitochondrial haplotypes of European wild boars with 2n = 36 are closely related to those of European domestic pigs with 2n = 38. Anim Genet 37:459–464Article 
CAS 

Google Scholar 
Ferenčaković M, Sölkner J, Curik I (2013) Estimating autozygosity from high-throughput information: Effects of SNP density and genotyping errors. Genet Sel Evol 45:42Article 

Google Scholar 
Ferreira E, Souto L, Soares AMVM, Fonseca C (2009) Genetic structure of the wild boar population in Portugal: Evidence of a recent bottleneck. Mamm Biol 74:274–285Article 

Google Scholar 
Franois O, Currat M, Ray N, Han E, Excoffier L, Novembre J (2010) Principal component analysis under population genetic models of range expansion and admixture. Mol Biol Evol 27:1257–1268Article 

Google Scholar 
Frantz AC, Bertouille S, Eloy MC, Licoppe A, Chaumont F, Flamand MC (2012) Comparative landscape genetic analyses show a Belgian motorway to be a gene flow barrier for red deer (Cervus elaphus), but not wild boars (Sus scrofa). Mol Ecol 21:3445–3457Article 
CAS 

Google Scholar 
Fulgione D, Rippa D, Buglione M, Trapanese M, Petrelli S, Maselli V (2016) Unexpected but welcome. Artificially selected traits may increase fitness in wild boar. Evol Appl 9:769–776Article 
CAS 

Google Scholar 
Goedbloed DJ, Megens HJ, van Hooft P, Herrero-Medrano JM, Lutz W, Alexandri P et al. (2013a) Genome-wide single nucleotide polymorphism analysis reveals recent genetic introgression from domestic pigs into Northwest European wild boar populations. Mol Ecol 22:856–866Article 
CAS 

Google Scholar 
Goedbloed DJ, van Hooft P, Megens HJ, Langenbeck K, Lutz W, Crooijmans RPMA et al. (2013b) Reintroductions and genetic introgression from domestic pigs have shaped the genetic population structure of Northwest European wild boar. BMC Genet 14:2–10Article 

Google Scholar 
Groenen MAM, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF et al. (2012) Analyses of pig genomes provide insight into porcine demography and evolution. Nature 491:393–398Article 
CAS 

Google Scholar 
Herrero-Medrano JM, Megens H-J, Groenen MAM, Ramis G, Bosse M, Pérez-Enciso M et al. (2013) Conservation genomic analysis of domestic and wild pig populations from the Iberian Peninsula. BMC Genet 14:1–13Article 

Google Scholar 
Hewitt GM (1999) Post-glacial re-colonization of European biota. Biol J Linn Soc 68:87–112Article 

Google Scholar 
Hewitt GM (2004) Genetic consequences of climatic oscillations in the Quaternary. Philos Trans R Soc Lond Ser B Biol Sci 359:183–195Article 
CAS 

Google Scholar 
Hiemstra PH, Pebesma EJ, Twenhöfel CJW, Heuvelink GBM (2009) Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Comput Geosci 35:1711–1721Article 
CAS 

Google Scholar 
Howrigan DP, Simonson MA, Keller MC (2011) Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms. BMC Genomics 12:460Article 
CAS 

Google Scholar 
Huisman J, Kruuk LEB, Ellis PA, Clutton-Brock T, Pemberton JM (2016) Inbreeding depression across the lifespan in a wild mammal population. Proc Natl Acad Sci 113:3585–3590Article 
CAS 

Google Scholar 
Iacolina L, Corlatti L, Buzan E, Safner T, Šprem N (2019) Hybridisation in European ungulates: an overview of the current status, causes, and consequences. Mamm Rev 49:45–59Article 

Google Scholar 
Iacolina L, Pertoldi C, Amills M, Kusza S, Megens H-J, Bâlteanu VA et al. (2018) Hotspots of recent hybridization between pigs and wild boars in Europe. Sci Rep. 8:17372Article 
CAS 

Google Scholar 
Iacolina L, Scandura M, Goedbloed DJ, Alexandri P, Crooijmans RPMA, Larson G et al. (2016) Genomic diversity and differentiation of a managed island wild boar population. Heredity 116:60–67Article 
CAS 

Google Scholar 
Jombart T, Ahmed I (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27:1–2Article 

Google Scholar 
de Jong JF, Hooft van P, Megens HJ, Crooijmans RPMA, Groot de GA, Pemberton JM, Huisman J et al. (2020) Fragmentation and translocation distort the genetic landscape of ungulates: red deer in the Netherlands. Front Ecol Evol 8:535715Article 

Google Scholar 
Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281Article 

Google Scholar 
Kardos M, Åkesson M, Fountain T, Flagstad Ø, Liberg O, Olason P et al. (2018) Genomic consequences of intensive inbreeding in an isolated wolf population. Nat Ecol Evol 2:124–131Article 

Google Scholar 
Kaplan JO, Krumhardt KM, Zimmermann N (2009) The prehistoric and preindustrial deforestation of Europe. Quat Sci Rev 28:3016–3034. https://doi.org/10.1016/j.quascirev.2009.09.028Koemle D, Zinngrebe Y, Yu X (2018) Highway construction and wildlife populations: Evidence from Austria. Land use policy 73:447–457Article 

Google Scholar 
Krže B (1982) Divji prašič: biologija, gojitev, ekologija. Lovska zveza Slovenije, Ljubljana
Google Scholar 
Kusza S, Podgórski T, Scandura M, Borowik T, Jávor A, Sidorovich VE et al. (2014) Contemporary genetic structure, phylogeography and past demographic processes of wild boar Sus scrofa population in central and eastern Europe. PLoS One 9:e91401Article 

Google Scholar 
Lorenzini R, Lovari S, Masseti M (2002) The rediscovery of the Italian roe deer: Genetic differentiation and management implications. Ital J Zool 69(4):367–379Article 

Google Scholar 
Lorenzini R, San José C, Braza F, Aragón S (2003) Genetic differentiation and phylogeography of roe deer in Spain, as suggested by mitochondrial DNA and microsatellite analysis. Ital J Zool 70(1):89–99Article 
CAS 

Google Scholar 
Magri D (2013) Early to Middle Pleistocene dynamics of plant and mammal communities in South West Europe. Quat Int 288:63–72Article 

Google Scholar 
Manunza A, Zidi A, Yeghoyan S, Balteanu VA, Carsai TC, Scherbakov O et al. (2013) A high throughput genotyping approach reveals distinctive autosomal genetic signatures for European and Near Eastern wild boar. PLoS One 8:e55891Article 
CAS 

Google Scholar 
Maselli V, Rippa D, De Luca A, Larson G, Wilkens B, Linderholm A et al. (2016) Southern Italian wild boar population, hotspot of genetic diversity. Hystrix 27:137–144
Google Scholar 
McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5:e1000686Article 

Google Scholar 
Megens H-J, Crooijmans RP, Cristobal M, Hui X, Li N, Groenen MA (2008) Biodiversity of pig breeds from China and Europe estimated from pooled DNA samples: differences in microsatellite variation between two areas of domestication. Genet Sel Evol 40:103
Google Scholar 
Melis C, Szafrańska PA, Jȩdrzejewska B, Bartoń K (2006) Biogeographical variation in the population density of wild boar (Sus scrofa) in western Eurasia. J Biogeogr 33:803–811Article 

Google Scholar 
Mihalik B, Stéger V, Frank K, Szendrei L, Kusza S (2018) Barrier effect of the M3 highway in Hungary on the genetic diversity of wild boar (Sus scrofa) population. Res J Biotechnol 13:32–38
Google Scholar 
NCBI (2018) Genome Organism Overview: Sus scrofa (pig). https://www.ncbi.nlm.nih.gov/genome?term=sus%20scrofa%20%5BOrganism%5D&cmd=DetailsSearch&report=OverviewNikolov IS, Gum B, Markov G, Kuehn R (2009) Population genetic structure of wild boar Sus scrofa in Bulgaria as revealed by microsatellite analysis. Acta Theriol (Warsz) 54:193–205Article 

Google Scholar 
Nykänen M, Rogan E, Foote AD, Kaschner K, Dabin W, Louis M et al. (2019) Postglacial colonization of northern coastal habitat by bottlenose dolphins: a marine leading-edge expansion? J Hered 110:662–674Article 

Google Scholar 
Palombo M, Romana AV-G (2003) Remarks on the biochronology of mammalian faunal complexes from the Pliocene to the Middle Pleistocene in France. Geol Rom: 145–163Paradis E, Claude J, Strimmer K (2004) APE: analysis of phylogenetics and evolution in R language. Bioinformatics 20:289–290Article 
CAS 

Google Scholar 
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D et al (2007) PLINK: A tool Set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. www.cog-genomics.org/plink/1.9/Putman R, Apollonio M, Andersen R (2011) Ungulate management in Europe: problems and practices. Cambridge University Press, Cambridge, UKBook 

Google Scholar 
R Core Team (2018) R: A language and environment for statistical computing. Vienna, AustriaRejduch B, Sota E, Ró M, Ko M (2003) Chromosome number polymorphism in a litter of European wild boar (Sus scrofa scrofa L.). Anim Sci Pap Rep. 21:57–62
Google Scholar 
Scandura M, Iacolina L, Apollonio M (2011a) Genetic diversity in the European wild boar Sus scrofa: phylogeography, population structure and wild x domestic hybridization: Genetic variation in European wild boar. Mamm Rev 41:125–137Article 

Google Scholar 
Scandura M, Iacolina L, Cossu A, Apollonio M (2011b) Effects of human perturbation on the genetic make-up of an island population: The case of the Sardinian wild boar. Heredity 106:1012–1020Article 
CAS 

Google Scholar 
Scandura M, Iacolina L, Crestanello B, Pecchioli E, Di Benedetto MF, Russo V et al. (2008) Ancient vs. recent processes as factors shaping the genetic variation of the European wild boar: Are the effects of the last glaciation still detectable? Mol Ecol 17:1745–1762Article 
CAS 

Google Scholar 
Scandura M, Fabbri G, Caniglia R, Iacolina L, Mattucci F, Mengoni C, Pante G, Apollonio M, Mucci N (2022) Resilience to Historical Human Manipulations in the Genomic Variation of Italian Wild Boar Populations. Front Ecol Evol 10:833081Article 

Google Scholar 
Schmitt T, Varga Z (2012) Extra-Mediterranean refugia: the rule and not the exception. Front Zool 9:22Article 

Google Scholar 
Sommer RS, Fahlke JM, Schmölcke U, Benecke N, Zachos FE (2009) Quaternary history of the European roe deer Capreolus capreolus. Mamm Rev 39:1–16Article 

Google Scholar 
Sommer RS, Nadachowski A (2006) Glacial refugia of mammals in Europe: evidence from fossil records. Mamm Rev 36:251–265Article 

Google Scholar 
Sommer RS, Zachos FE (2009) Fossil evidence and phylogeography of temperate species: ‘glacial refugia’ and post-glacial recolonization. J Biogeogr 36:2013–2020Article 

Google Scholar 
Sommer RS, Zachos FE, Street M, Jöris O, Skog A, Benecke N (2008) Late Quaternary distribution dynamics and phylogeography of the red deer (Cervus elaphus) in Europe. Quat Sci Rev 27:714–733Article 

Google Scholar 
Stillfried M, Fickel J, Börner K, Wittstatt U, Heddergott M, Ortmann S et al. (2017) Do cities represent sources, sinks or isolated islands for urban wild boar population structure? J Appl Ecol 54:272–281Article 

Google Scholar 
Taberlet P, Fumagalli L, Wust-Saucy AG, Cossons JF (1998) Comparative phylogeography and post-glacial colonization routes in Europe. Mol Ecol 7:453–461.Article 
CAS 

Google Scholar 
Veličković N, Djan M, Ferreira E, Stergar M, Obreht D, Maletić V et al. (2015) From north to south and back: the role of the Balkans and other southern peninsulas in the recolonization of Europe by wild boar. J Biogeogr 42:716–728Article 

Google Scholar 
Veličković N, Ferreira E, Djan M, Ernst M, Obreht Vidaković D, Monaco A et al. (2016) Demographic history, current expansion and future management challenges of wild boar populations in the Balkans and Europe. Heredity 117:348–357Article 

Google Scholar 
Vernesi C, Crestanello B, Pecchioli E, Tartari D, Caramelli D, Hauffe H et al. (2003) The genetic impact of demographic decline and reintroduction in the wild boar (Sus scrofa): A microsatellite analysis. Mol Ecol 12:585–595Article 
CAS 

Google Scholar 
Vilaça ST, Biosa D, Zachos F, Iacolina L, Kirschning J, Alves PC et al. (2014) Mitochondrial phylogeography of the European wild boar: The effect of climate on genetic diversity and spatial lineage sorting across Europe. J Biogeogr 41:987–998Article 

Google Scholar 
Zachos FE, Frantz AC, Kuehn R, Bertouille S, Colyn M, Niedziałkowska M et al. (2016) Genetic structure and effective population sizes in European red deer (Cervus elaphus) at a continental scale: insights from microsatellite DNA. J Hered 107:318–326 More

Study areaWe focused on hiking activity in the four main islands of Japan (Honshu, Hokkaido, Kyushu, and Shikoku) and nearby small islands connected to the main islands by a bridge (Fig. 1a). These islands lie between latitudes 31.0° and 45.5°N, and the total area is 361,000 km2. The islands are generally mountainous and tallest mountains in central Honshu exceed 3000 m a.s.l. (Fig. 1c). In Tokyo, mean monthly temperatures range between 5.2 °C in January and 26.4 °C in August, while they range between − 18.4 °C in January and 6.2 °C in August at the summit of the highest mountain, Mt. Fuji (3776 m a.s.l., Japan Meteorological Agency). In northern Honshu and Hokkaido, snow depth can exceed 1 m even at low elevations and high mountains are covered with snow even in southern Japan.Vegetation excluding farmland and pasture covers 70.9% of the study area and the 93.9% is forest. Plantations of mostly evergreen conifers such as Japanese cedar (Cryptomeria japonica) occupy 37.6% of the vegetation area (National Surveys on the Natural Environment by the Biodiversity Center of Japan 2nd–7th; http://www.biodic.go.jp/trialSystem/top_en.html). Secondary vegetation after past human disturbances occupies 39.4% of the total vegetation and the remaining 23.0% is primary vegetation. The typical primary vegetation types are, from north to south, boreal mixed forest, deciduous broad leaved forest, and evergreen broad leaved forest.Grid squaresRecords of hiking activity were summarized for 4244 secondary grid squares based on Standard Grid Square System, which was defined by the Minister’s Order of Administrative Management Agency in 1973. In the system, the secondary grid was defined as a grid of 5′ in latitude and 7′ 30″ in longitude, which roughly corresponds to a 10 km grid in the study area. This is the standard grid system of the government and we adopted the system for convenience in future application uses and communication with practitioners. The grids, which are defined by latitude and longitude, are different in the area up to 22% between the north and south ends. Therefore, area of each grid was included in a model as an offset term.Hiking activityAccording to a government survey in 2016, (the Survey on Time Use and Leisure Activities by the Statistics Bureau of Japan, http://www.stat.go.jp/english/data/shakai/index.htm), 10.0% (about 10.7 million people) of Japan’s population age 15 or over enjoyed hiking/mountaineering in the last year. The census showed also that hiking is more popular among urban residents in the metropolitan areas. Both multi-day expedition to high mountains and day trek to low mountains in suburban areas are popular. Because of the severe winter climate, unskilled hikers use the high mountains in summer and early autumn only. During a summer vacation, whose peak time in Japan is August, many hikers enjoy multi-day trips to distant mountains. Spring and autumn are also popular seasons because of the mild weather and the scenic beauty of the fresh green or autumn colors.Data collectionIn this study, we used number of hiking records accumulated on the most popular social networking service for hikers in Japan (Yamareco; https://www.yamareco.com) as a surrogate for flow of recreation service. For all the registered destinations in the study area, the number of hiking records for each month and the latitude and longitude of the destination were collected from the service in September 2016 with the rvest28 package in R software29. This service launched in October 2005 hosts records of the hiking route, photos, participants, and impressions of a hiking trip and facilitates communication among users. Although monthly number of records for each destination is always available on the site, the exact date of each hiking record is not always public information for privacy reasons; therefore, all of the records from the almost 11 years since the start of the service were lumped together in our analysis. Hikers may record multiple places in a single trip, so the total number of records must be larger than the number of unique trips. Users of the service sometime record a place that is not a destination, e.g. start points and stations of trails, parking areas, stations of transports, and bus stops. Such records were excluded before analyses as far as it can be judged from the name of the place. As a result, the total number of hiking records was 4,708,229 records for 16,179 destinations. Finally, these records were assigned to the 4244 grids based on the latitude and longitude of each destination and then total number of records for each grid was used as a surrogate of the recreation service flow in our analysis. Not only total number but also monthly number was used in our analysis to examine seasonal changes in associations between the service and vegetation. Total record number of the grids was strongly right-skewed; no record (handled as 0 in our analysis) was found in 2036 grids while mean and maximum record number were 1109 and 350,384, respectively.Explanation variablesFifty ecological, environmental, and social/infrastructural variables (Table S1) were prepared for each grid by using ArcGIS version 10.5 (ESRI, Redlands, CA, USA). For vegetation and land-use attributes, National Surveys on the Natural Environment by the Biodiversity Center of Japan (2nd–7th; http://www.biodic.go.jp/trialSystem/top_en.html) and National Land Numerical Information (http://nlftp.mlit.go.jp/ksj-e/index.html) were used. The proportion of sea, that of total vegetation cover (excluding agricultural land and pasture) to land area, that of agricultural land (including pasture) to land area, that of natural vegetation (vegetation excluding plantations) to total vegetated area, and that of primary vegetation (vegetation with no record or evidence of a disturbance) to natural vegetation were summarized at four spatial scales: a radius of 10 km, 20 km, 50 km, and 100 km from the center of each grid. Spatial patterns of the three vegetation variables in 10 km radius were summarized in Fig. 1d–f.Maximum elevation, minimum elevation, and ruggedness (index of topographic heterogeneity30) were summarized at the four spatial scales based on a digital elevation model (10-m resolution) provided by the Geospatial Information Authority of Japan (https://fgd.gsi.go.jp/download/menu.php). For climatic variables (annual and monthly mean temperature, annual and monthly precipitation, annual and monthly hours of sunshine, and annual maximum snow depth), the National Land Numeric Information provided by the Ministry of Land, Infrastructure, Transport and Tourism of Japan (http://nlftp.mlit.go.jp/ksj-e/index.html) was referenced. Densities of population and roads at the four spatial scales were prepared from population census data from the Statistics Bureau of Japan (http://e-stat.go.jp/SG2/eStatGIS/page/download.html) and the National Land Numeric Information. For calculation of these densities, the sea surface was excluded. In addition, latitude and longitude of center of each grid were also used as explanatory variables to average effects of spatial coordinates.Statistical analysisIn this study, we employed BRT, a machine-learning method based on regression trees31 for modeling the complex relationship between a CES flow and landscape attributes12. BRT is an ensemble learning method where multiple regression trees are sequentially combined to minimize the loss function by means of gradient descent. This technique has advantage in the development of a model with a high predictive performance, in which high-dimensional interactions among explanatory variables and nonlinear responses are fully accounted for. In ecology, BRT has been frequently used for modeling of a species distribution32.Total and monthly numbers of hiking records were modeled as a function of the 50 variables described above under the assumption of a Poisson response. For temperature, precipitation, and hours of sunshine, annual and monthly average were used for the analysis of total and monthly records, respectively. In modeling by BRT, parameters for building of each learner and assembly of the learners must be carefully chosen to maximize generalization ability of a model31. In our case, candidate parameters were 2, 5, and 10 for the maximum depth of variable interactions for each learner; 2, 5, 10, and 20 for the minimum number of observations in the terminal nodes for each learner; 0.5 and 0.75 for the proportion of training data used for building each learner; and 1000, 2000, 4000, 6000, 8000 and 10,000 for the total number of learners (Table S2). In the model assembling process, the value of 0.01 was used as a shrinkage parameter. Ten-fold cross validation was used to obtain the best suites of parameters. R2 based on sum of squares:$${R}^{2}=1-frac{{sum ({y}_{i}-widehat{{y}_{i}})}^{2}}{{sum ({y}_{i}-overline{{y }_{i}})}^{2}}$$
was used for evaluation of the model’s prediction performance. The importance of explanatory variables was evaluated as an increase of mean absolute error after 100-times permutation of a variable33.Effects of each explanatory variable (a landscape attribute) on the response variable (record number) and the context dependence were visually inspected by individual conditional expectation (ICE) plot34. ICE plot visualizes the effect of a given explanatory variable for each observation by connecting outcome of a model for shifting values of the focal explanatory variable throughout the range while keeping other explanatory variables as the original value. Predictions were performed in log-scale and each line was centered to be zero at the left end of the x-axis to show relative effects of explanatory variables (c-ICE plot sensu Goltstein et al.34). Each line in ICE plot can be colored based on value of the second explanatory variable to assist assessment of the interactive effects of the two predictors. Friedman’s H statistic35 was used to detect explanatory variables whose interaction with the vegetation variables are important and therefore should be used for color-coding of an ICE plot. Friedman’s H is defined as a proportion of variance of partial dependence estimates explained by interactive effects for arbitrary suites of explanatory variables.Then, expected impacts of 0.1 decrease in the three local vegetation variables were assessed by the trained model and mapped. Although vegetation variables were sometimes more important at larger spatial scales (see “Results”), we focused on vegetation at a local (10 km radius) scale because most changes in vegetation occur at the scale in Japan (National Surveys on the Natural Environment by the Biodiversity Center of Japan, https://www.biodic.go.jp/kiso/fnd_list_h.html).All statistical analyses were performed using the R software packag29. The gbm36 package was used for BRT, the iml37 package was used for calculation of Friedman’s H statistic, and the cv.models (Oguro, https://github.com/Marchen/cv.models) packages was used for cross validation and parameter tuning. More