Philippa Kaur

Abram, D. The spell of the sensuous: Perception and language in a more-than-human world. Vintage (2012).Ingold, T. Being alive: Essays on movement, knowledge and description. Routledgehttps://doi.org/10.4324/9780203818336 (2011).Article 

Google Scholar 
Kimmerer, R.W. Braiding sweetgrass: Indigenous wisdom, scientific knowledge and the teachings of plants (Milkweed editions, 2013).Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359(6374), 466–469 (2018).ADS 
CAS 
Article 

Google Scholar 
Gibbs, J.P. Amphibian movements in response to forest edges, roads, and streambeds in southern New England. in The Journal of Wildlife Management (1998), pp. 584–589. https://doi.org/10.2307/3802333.Moller, H., Berkes, F., O’Brian Lyver, P., & Kislalioglu, M. Combining science and traditional ecological knowledge: Monitoring populations for co-management. in Ecology and society (2004).Lorimer, J. Wildlife in the Anthropocene: conservation after nature. (U of Minnesota Press, 2015).Wiens, J. A. Spatial scaling in ecology. Funct. Ecol. 3(4), 385–397 (1989).Article 

Google Scholar 
Abram, D. Becoming animal: An earthly cosmology. Vintage (2010).Nathan, R. et al. A movement ecology paradigm for unifying organismal movement research. Proc. Natl. Acad. Sci. 105(49), 19052–19059 (2008).ADS 
CAS 
Article 

Google Scholar 
Tischendorf, L. & Fahrig, L. On the usage and measurement of landscape connectivity. Oikos 90(1), 7–19. https://doi.org/10.1034/j.1600-0706.2000.900102.x (2000).Article 

Google Scholar 
Rudnick, D., Ryan, S.J., Beier, P., Cushman, S.A., Dieffenbach, F., Epps, C., Gerber, L.R., Hartter, J.N., Jenness, J.S., & Kintsch, J. et al. The role of landscape connectivity in planning and implementing conservation and restoration priorities. Issues in Ecology (2012).Hilty, J.A., Lidicker, W.Z., & Merenlender, A.M. Corridor Ecology: The Science and Practice of Linking Landscapes for Biodiversity Conservation (Island Press, 2012).Cushman, S.A., McRae, B.H., Adriaensen, F., Beier, P., Shirley, M., & Zeller, K. Biological corridors and connectivity [Chapter 21]. in Key Topics in Conservation Biology 2nd ed. (eds Macdonald, D.W., Willis, K.J.) pp. 384–404 (Hoboken, NJ: Wiley-Blackwell, 2013).Unnithan Kumar, S., Turnbull, J., Hartman Davies, O., Hodgetts, T., & Cushman, S.A. Moving beyond landscape resistance: Considerations for the future of connectivity modelling and conservation science. in Landscape Ecology (2022).Zeller, K. A., McGarigal, K. & Whiteley, A. R. Estimating landscape resistance to movement: a review. Landscape Ecol. 27(6), 777–797 (2012).Article 

Google Scholar 
Adriaensen, F. et al. The application of ‘least-cost’ modelling as a functional landscape model. Landsc. Urban Plan. 64(4), 233–247 (2003).Article 

Google Scholar 
Cushman, S. A. & McKelvey, K. S. Use of empirically derived source-destination models to map regional conservation corridors. Conserv. Biol. 23(2), 368–376. https://doi.org/10.1111/j.1523-1739.2008.01111.x (2009).Article 
PubMed 

Google Scholar 
Moilanen, A. On the limitations of graph-theoretic connectivity in spatial ecology and conservation. J. Appl. Ecol. pp. 1543–1547 (2011).Compton, B. W., McGarigal, K., Cushman, S. A. & Gamble, L. R. A resistant kernel model of connectivity for amphibians that breed in vernal pools. Conserv. Biol. 21(3), 788–799. https://doi.org/10.1111/j.1523-1739.2007.00674.x (2007).Article 
PubMed 

Google Scholar 
McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89(10), 2712–2724. https://doi.org/10.1890/07-1861.1. (2008).Article 
PubMed 

Google Scholar 
Zeller, K. A. et al. Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Divers. Distrib. 24(7), 868–879. https://doi.org/10.1111/ddi.12742. (2018).Article 

Google Scholar 
Pullinger, M. G. & Johnson, C. J. Maintaining or restoring connectivity of modified landscapes: evaluating the least-cost path model with multiple sources of ecological information. Landscape Ecol. 25(10), 1547–1560 (2010).Article 

Google Scholar 
Sawyer, S. C., Clinton, W. E. & Brashares, J. S. Placing linkages among fragmented habitats: do least-cost models reflect how animals use landscapes?. J. Appl. Ecol. 48(3), 668–678 (2011).Article 

Google Scholar 
Laliberté, J. & St-Laurent, M.-H. Validation of functional connectivity modeling: The Achilles’ heel of landscape connectivity mapping. Landsc. Urban Plan. 202, 103878 (2020).Article 

Google Scholar 
Landguth, E. L. & Cushman, S. A. CDPOP: A spatially explicit cost distance popula tion genetics program. Mol. Ecol. Resour. 10(1), 156–161. https://doi.org/10.1111/j.1755-0998.2009.02719.x. (2010).CAS 
Article 
PubMed 

Google Scholar 
Landguth, E. L. et al. Quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19(19), 4179–4191. https://doi.org/10.1111/j.1365-294X.2010.04808.x (2010).CAS 
Article 
PubMed 

Google Scholar 
Cushman, S. A. & Landguth, E. L. Scale dependent inference in landscape genetics. Landsc. Ecol. 25(6), 967–979 (2010).Article 

Google Scholar 
Cushman, S. A., Shirk, A. J. & Landguth, E. L. Separating the effects of habitat area, fragmentation and matrix resistance on genetic differentiation in complex landscapes. Landscape Ecol. 27(3), 369–380. https://doi.org/10.1007/s10980-011-9693-0 (2012).Article 

Google Scholar 
Macdonald, E. A. et al. Simulating impacts of rapid forest loss on population size, connectivity and genetic diversity of Sunda clouded leopards (Neofelis diardi) in Borneo. PLoS ONE 13(9), e0196974 (2018).Article 

Google Scholar 
Schumaker, N. H. et al. Mapping sources, sinks, and connectivity using a simulation model of northern spotted owls. Landscape Ecol. 29(4), 579–592 (2014).Article 

Google Scholar 
Unnithan Kumar, S., Kaszta, Ż & Cushman, S. A. Pathwalker: A new individual-based movement model for conservation science and connectivity modelling. ISPRS Int. J. Geo Inf. 11(6), 329 (2022).Article 

Google Scholar 
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17(3), 261–272 (2020).CAS 
Article 

Google Scholar 
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14(6), 927–930 (2003).Article 

Google Scholar 
Dray, S., Royer-Carenzi, M. & Calenge, C. The exploratory analysis of autocorrelation in animal-movement studies. Ecol. Res. 25(3), 673–681. https://doi.org/10.1007/s11284-010-0701-7 (2010).Article 

Google Scholar 
Cushman, S.A. Animal movement data: GPS telemetry, autocorrelation and the need for path-level analysis. in Spatial Complexity, Informatics, and Wildlife Conservation (Springer, 2010), pp. 131-149.Zeller, K. A. et al. Sensitivity of landscape resistance estimates based on point selection functions to scale and behavioral state: pumas as a case study. Landscape Ecol. 29(3), 541–557 (2014).Article 

Google Scholar 
Kareiva, P. M. & Shigesada, N. Analyzing insect movement as a correlated random walk. Oecologia 56(2), 234–238 (1983).ADS 
CAS 
Article 

Google Scholar 
Schumaker, N.H. Using landscape indices to predict habitat connectivity. Ecology (1996), pp. 1210–1225.Schumaker, N. H. & Brookes, A. HexSim: A modeling environment for ecology and conservation. Landscape Ecol. 33(2), 197–211 (2018).Article 

Google Scholar 
Bocedi, G., Palmer, S. C. F., Malchow, A.-K., Zurell, D. & Watts, K. RangeShifter 2.0: An extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes. Ecography 44(10), 1453–1462 (2021).Article 

Google Scholar 
Kaszta, Ż, Cushman, S. A. & Slotow, R. Temporal non-stationarity of path- selection movement models and connectivity: An example of African elephants in Kruger national park. Front. Ecol. Evol. 9, 207 (2021).Article 

Google Scholar 
Osipova, L. et al. Using step-selection functions to model landscape connectivity for African elephants: Accounting for variability across individuals and seasons. Anim. Conserv. 22(1), 35–48 (2019).Article 

Google Scholar 
Vergara, M., Cushman, S. A. & Ruiz-González, A. Ecological differences and limiting factors in different regional contexts: landscape genetics of the stone marten in the Iberian Peninsula. Landscape Ecol. 32(6), 1269–1283 (2017).Article 

Google Scholar 
Reddy, P. A., Puyravaud, J.-P., Cushman, S. A. & Segu, H. Spatial variation in the response of tiger gene ow to landscape features and limiting factors. Anim. Conserv. 22(5), 472–480 (2019).Article 

Google Scholar 
Zeller, K. A., Lewsion, R., Fletcher, R. J., Tulbure, M. G. & Jennings, M. K. Understanding the importance of dynamic landscape connectivity. Land 9(9), 303. https://doi.org/10.3390/land9090303 (2020).Article 

Google Scholar 
Cronon, W. The trouble with wilderness: or, getting back to the wrong nature. Environ. Hist. 1(1), 7–28 (1996).Article 

Google Scholar 
Ingold, T. The Perception of the Environment: Essays on Livelihood, Dwelling and Skill (Routledge, 2021).Boettiger, A. N. et al. Inferring ecological and behavioral drivers of African elephant movement using a linear filtering approach. Ecology 92(8), 1648–1657 (2011).Article 

Google Scholar 
Pooley, S. et al. An interdisciplinary review of current and future approaches to improving human-predator relations. Conserv. Biol. 31(3), 513–523. https://doi.org/10.1111/cobi.12859 (2017).CAS 
Article 
PubMed 

Google Scholar 
Benson, E. S. Minimal animal: Surveillance, simulation, and stochasticity in wildlife biology. Antennae 30, 39 (2014).
Google Scholar 
Kaszta, Ż et al. Integrating Sunda clouded leopard (Neofelis diardi) conservation into development and restoration planning in Sabah (Borneo). Biol. Cons. 235, 63–76 (2019).Article 

Google Scholar 
Penjor, U., Astaras, C., Cushman, S. A., Kaszta, Ż & Macdonald, D. W. Contrasting effects of human settlement on the interaction among sympatric apex carnivores. Proc. R. Soc. B 289(1973), 20212681 (2022).Article 

Google Scholar 
Barua, M. Bio-geo-graphy: Landscape, dwelling, and the political ecology of human-elephant relations. Environ. Plann. D Soc. Space 32(5), 915–934 (2014).Article 

Google Scholar 
Elliot, N. B., Cushman, S. A., Macdonald, D. W. & Loveridge, A. J. The devil is in the dispersers: Predictions of landscape connectivity change with demography. J. Appl. Ecol. 51(5), 1169–1178 (2014).Article 

Google Scholar 
Kareiva, P. & Marvier, M. What is conservation science?. Bioscience 62(11), 962–969 (2012).Article 

Google Scholar 
Bennett, N. J. et al. Conservation social science: Understanding and integrating human dimensions to improve conservation. Biol. Conserv. 205, 93–108 (2017).Article 

Google Scholar 
Bunnefeld, N., Nicholson, E., & Milner-Gulland, E.J. Decision-Making in Conservation and Natural Resource Management: Models for Interdisciplinary Approaches. (Vol. 22, Cambridge University Press, 2017).Parathian, H. E., McLennan, M. R., Hill, C. M., Fraza o-Moreira, A. & Hockings, K. J. Breaking through disciplinary barriers: Human-wildlife interactions and multispecies ethnography. Int. J. Primatol. 39(5), 749–775 (2018).Article 

Google Scholar 
Hodgetts, T. Connectivity as a multiple: In with and as “nature’’. Area 50(1), 83–90. https://doi.org/10.1111/area.12353 (2018).Article 
PubMed 

Google Scholar 
Berkes, F. Sacred ecology (Routledge, 2017). https://doi.org/10.4324/9781315114644.Parrenas, J.S. Decolonizing Extinction: The Work of Care in Orangutan Rehabilitation (Duke University Press, 2018).Bill Adams, W., & Mulligan, M. Decolonizing Nature: Strategies for Conservation in a Post-Colonial Era (Routledge, 2012). More

Protein domains show a Gompertzian growthThe protein domain RSA distributions of 3368 bacterial genomes were obtained as detailed in the “Materials and methods” section. Briefly, for each bacterial genome we retrieved all the identifiable protein domains. Then, we computed the RSA by counting the number of protein domains belonging to each protein domain family.Three evolutionary hypotheses were tested by fitting the empirical RSAs with the Log-Series [Eq. (7)], the Negative Binomial (Eq. (6)) and the Poisson Log-Normal (Eq. (4)) distribution (Fig. 1a). According to the Akaike Information Criterion (AIC)30, in (99.97%) of bacteria the selected model was the Poisson Log-Normal (Fig. 1b). This model performed better than both the Log-Series and the Negative Binomial and described the data well, with an average (R^2) of 0.97 (minimum (R^2)=0.86). The selection of the Poisson Log-Normal model instead of the Negative Binomial or the Log-Series, implies that the protein domains evolution process is characterized by a Gompertzian density regulation function ((g(x)=gamma ln (x+epsilon ))) rather than a linear one ((g(x)=eta x)). This suggests an asymmetric process in which the proliferation rate for low abundant protein domains is faster than for the high abundant ones.Figure 1Fit of protein domains RSA. (a) Example of protein domains Preston plot fitted with three different distributions: the Poisson Log-Normal, the Negative Binomial and the Log-Series. Results refer to the bacterial genome (text {GCA}_000717515). The Negative Binomial and the Log-Series fit overlap. This implies that the dispersion parameter (alpha) of the Negative Binomial distribution (see Eq. (6)) is close to zero. The mean and the median of the dispersion parameter obtained for the 3368 bacterial genomes are ({2.67times 10^{-4}}) and ({2.62times 10^{-7}}), in agreement with the observed overlap. (b) Distribution of the difference between the AIC obtained with the Poisson Log-Normal model (PL) and the Log-Series (LS) or the Negative Binomial (NB) model, considering all the 3368 bacterial genomes.Full size imageProtein domains deactivation is faster than duplicationThe examination of the Poisson Log-Normal scale ((mu)) and location ((sigma ^2)) parameters (see Eq. (4) and Supplementary Material) estimated by the fitting procedure for each bacterial genome, allows us to reveal further features of the evolutionary process of protein domains.First of all, Fig. 2 shows that (mu) has negative values in all bacterial genomes. Recalling that (mu =r/gamma), where r is the growth rate and (gamma) is the multiplicative constant of the Gompertzian function, which must be positive, this implies that the growth rate of protein domains, r, is also negative. Notice that the growth rate can be expressed as the difference between the birth and the death rate, (r=b-d). Hence, a negative r means that the death rate is greater than the birth rate ((d > b)). In the evolutionary model of protein domains, the birth rate b has the meaning of duplication rate, while the death rate d is the rate at which protein domains are deactivated. A negative r hence implies that protein domain deactivation, which is related to the accumulation of events which disrupt the coding sequence of protein domains, happens at a faster rate than the duplication of the whole protein domain sequence within the genome.Figure 2Distribution of species according to the model parameters. Scatter plot of Poisson Log-Normal parameters (mu) versus (sigma ^2) obtained fitting the protein domains RSAs. Only species represented by at least 10 different strains were included in the plot, for a total of 1173 bacterial genomes which belong to 48 different species. Different colors represent different species as indicated in the legend.Full size imageFurthermore, the plot of (mu) as a function of (sigma ^2) (Fig. 2) highlights the negative linear relationship between (mu) and (sigma ^2). Such relationship can also be deduced mathematically.Starting from the expressions (mu =r/gamma) and (sigma ^2=sigma _e^2 / 2gamma), and after simple algebraic manipulation, we can in fact obtain that (mu = 2rsigma ^2 / sigma _e^2), which explains the negative linear relationship between the two parameters.Besides the negative relationship, the plot of (mu) versus (sigma ^2) also highlights the presence of clusters of bacterial genomes with similar ecological features, which are pictured in the plot as roughly parallel stripes (Fig. 2). When we depict bacterial strains belonging to the same species using the same color, it emerges that the stripes are related to the bacterial taxonomy. This result motivates us to introduce a new approach to bacterial phylogeny based on the ecological modeling of protein domains and the consequent estimation of the parameters (mu) and (sigma ^2).Protein domain RSA and evolutionary distanceWe propose to calculate the pairwise evolutionary distances between bacteria based on three parameters: the Poisson Log-Normal scale and location parameters discussed above ((mu) and (sigma)), and the density of protein domains in the bacterial genome. Such density describes to which extend the whole bacterial genome is populated with protein domains and it hence constitutes an additional feature of the protein domain ecological dynamics. As detailed in the Materials and Methods, the distance between bacteria is specifically computed as the 3D euclidean distance in the scaled space of (mu), (sigma), and protein domain density. In the following, we refer to such distance as ‘RSA distance’.To evaluate the bacterial interrelationships derived from the RSA distances, we compared our results with both the bacterial taxonomic classification and the 16S rRNA gene-based phylogeny. Specifically, starting from the RSA distance matrix we computed a hierarchical clustering of bacteria and we compared the resulting clusters with those obtained from the 16S rRNA gene-based distance matrix. Both clustering results were then compared with the bacterial taxonomic classification.Notice that the usage of both 16S rRNA phylogeny and bacterial taxonomic classification allows us to exploit the complementary information that these two approaches provide, despite their intrinsic connection. Namely, modern microbial taxonomy is mostly based on 16S rRNA gene6 and, on the other hand, the cutoffs commonly used in 16S rRNA phylogeny originated from phenotype-based taxonomy31. However, while taxonomy allows us to assign human interpretable names to bacteria, to associate such names with phenotypic properties, and to classify bacteria into a predefined hierarchy, 16S rRNA phylogeny provides a quantitative measurement of the evolutionary distance between bacteria that can be compared with the RSA distance without setting any pre-defined threshold. Moreover, the usage of 16S rRNA phylogeny allows us to investigate the bacterial relationships at the intraspecies level, for which the taxonomic classification is not available.As detailed in the Materials and Methods, 16S rRNA distances were calculated based on the bacterial 16S rRNA gene reference sequences, following the standard procedure32. Taxonomic classification, instead, was retrieved from NCBI and included the following levels: phylum, class, order, family, genus and species. In order to obtain a comparable number of clusters from all three methods, we considered separately each taxonomic level and we cut the 16S rRNA and the RSA -based hierarchical trees so as to get a number of clusters equivalent to the number of taxa available at the selected taxonomic level.At each taxonomic level, the Normalized Mutual Information (NMI) was used as a measurement of agreement between different clustering solutions33. Notice that, while the theoretical range of the NMI score is the interval (left[ 0,1right]), NMI is biased towards clustering solutions with more clusters and fewer data points34. Consequently, the baseline of NMI score in practise is not zero and relatively high NMI scores can be an artifact caused by the low ratio between number of bacteria and number of taxonomic groups. To make the comparison fair, we used simulations to calculate the baseline NMI for each taxonomic level (box plots of Fig. 3).As expected by their intrinsic relationship, taxonomy and 16S rRNA phylogeny show high agreement (red dots in Fig. 3). RSA-based clusters, instead, show a certain deviation from both taxonomy (blue dots in Fig. 3) and phylogeny (green dots in Fig. 3). For both comparisons, however, the NMI scores are still evidently higher than the baseline, signifying that the RSA model captures phylogenetic signals to a certain degree. Comparing the obtained NMI scores with the baseline, we notice that the agreement between RSA-based clusters and both taxonomy and phylogeny increases at lower taxonomic levels, reaching the maximum at species level. Taking as ground truth the taxonomic classification, the total purity of the RSA-based clusters at species level is 0.65, signifying that 65% of bacteria are correctly classified.Figure 3Comparison between the three clustering results at different taxonomic levels. NMI scores (y-axis) are calculated as a measurement of agreement between clusters based on: RSA method and taxonomy (blue), 16S rRNA gene and taxonomy (red), RSA method and 16S rRNA gene (green). Different taxonomic levels are considered for the comparison: phylum, class, order, family, genus and species (x-axis). The box plots represent the baselines of NMI score and are based on simulations.Full size imageTo assess the robustness and stability of the RSA-based phylogeny, with regard to the choice of protein domains, we randomly selected subsamples of protein domains in different proportions (from (10%) to 90% of all protein domains). The reconstructed phylogenetic trees were then compared with the phylogenetic tree obtained using all protein domains (see Materials and Methods for details), and the correlation between the trees was calculated (see Supplementary Fig. S6). As expected, with larger proportions of protein domains taken into account, the correlation between subsample-based phylogeny and base phylogeny increases. For larger subsampling proportions, the compared phylogenetic trees are in good agreement: for a subsample with 90% of protein domains, the mean cophenetic correlation is equal to 0.74, and the mean common-nodes-correlation is equal to 0.68. We notice that the common-nodes-correlation is more stable compared to the cophenetic correlation, as expected since cophenetic correlation is affected by the height of the phylogenetic trees. The results suggest that the overall structure of the phylogenies is stable even for smaller subsampling proportions, while subsampling height of the branches correlates with the full-data height only at larger subsampling proportions.To evaluate the intraspecies composition obtained from the RSA-based clustering, we selected the subset of species for which at least 10 different strains were present in our data (48 species). Among them, we selected the species where hierarchical clustering showed a clear separation of clusters (including outliers) and for which published literature characterizing at least some of the strains was available (6 out of 48 species). For these 6 species, we again assessed the robustness and stability of RSA phylogenies, as detailed in the “Materials and methods” section. Our results suggest (see Supplementary Fig. S7) that the subsample-based phylogenies are in good agreement with the full-data phylogenies, especially for larger subsampling proportions. We notice the correlations is larger than in the case of phylogenetic trees for randomly selected 100 bacteria (Supplementary Fig. S6), especially for certain species (i.e., Xanthomonas citri). This could be attributed to the smaller size of the phylogenetic tree. However, the species with similar phylogenetic tree size still show differences in correlation (i.e., Xanthomonas citri and Francisella tularensis), suggesting that the RSA-based distance matrix between the strains of Xanthomonas citri carries stronger phylogenetic signal. Comparing 6 observed species with the randomly sampled subsets of 100 bacteria, we can analogously conclude that the RSA-captured phylogenetic signal is stronger within the species. In the following, we discuss the results obtained for the 6 selected bacterial species in more details.Figure 4(Previous page.) Hierarchical clustering of bacteria at the intraspecies level, comparing solutions obtained by RSA and 16S rRNA method. Each subplot shows a tanglegram with RSA-based dendrogram on the left and 16S rRNA-based dendrogram on the right. Lines connect the same bacteria from two dendrograms. The color/type of the line represents the feature of the bacterium it connects. (a) 22 strains of Xanthomonas citri belong to two different pathovars: A (orange) and (hbox {A}^{mathrm{W}}) (purple). (b) 10 strains of Chlamydia pneumoniae are isolated from different tissues: conjuctival (yellow), respiratory (magenta) and vascular (violet). 9 strains represented with solid line are human (Homo sapiens) pathogens while the one strain represented by dashed line is koala (Phascolarctos cinereus) pathogen. (c) 14 strains of Vibrio cholerae are colored based on their karyotype. 11 strains have two circular chromosomes Chr1 ((sim 3) Mb) and Chr2 ((sim 1) Mb) (magenta). 2 strains have one (sim)4 Mb long circular chromosome (yellow). One strain has three chromosomes Chr1 ((sim)3 Mb), Chr2 ((sim)1 Mb) and Chr3 ((sim)1 Mb) (violet).Full size imageRSA-based method distinguishes subspecies infecting different hostsXanthomonas citri subsp. citri (XCC) and Chlamydia pneumoniae (Cpn) are two species whose subspecies can infect different hosts. Here we show that the RSA-based method correctly discriminates such subspecies even when their divergence is not detected comparing the 16S rRNA gene sequences.Xanthomonas citri subsp. citri (XCC) is a causal agent of citrus canker type A, a bacterial disease affecting different plants from the genus Citrus. While citrus canker A infects most citrus species, two of its variants, A* and (hbox {A}^{mathrm{W}}), have a much more limited host range with XCC pathotype (hbox {A}^{mathrm{W}}) infecting only Key lime (C. aurantifolia) and alemow (C. macrophylla)2. Our data set includes 17 strains of XCC pathotype A and 5 strains of XCC pathotype (hbox {A}^{mathrm{W}})2. RSA-based clustering of the 22 XCC strains identifies two separated clusters (Fig. 4a, left) which coincide with the two XCC pathotypes. Concurrently, clustering based on 16S rRNA gene fails to identify the two pathotypes of XCC (Fig. 4a, right). This suggests that even though pathotypes A and (hbox {A}^{mathrm{W}}) have different hosts, their diversification is not reflected in the variability of the 16S rRNA gene. On the other hand, modeling the protein domain RSA of the two pathotypes succesfully captures the different functions of their proteomes.Another important aspect of the citrus canker is the geographical spread of the disease. The 22 strains of XCC included in our data set have diverse geographical origin. While all (hbox {A}^{mathrm{W}}) strains were sampled from USA, strains of pathotype A originate from USA, Brazil and China. RSA clustering of 17 A-type strains colored by their sampling location shows a geographical pattern (Supplementary Fig. S2) similar to the one obtained by Patané et al.2 using a maximum likelihood tree based on 1785 concatenated unicopy genes, with the only exception of strain jx-6 ((text {GCA}_001028285)) coming from China.For what concerns Chlamydia pneumoniae (Cpn), this is an obligate intercellular parasite which is widespread in human population and causes acute respiratory disease. Besides humans, different animal species can be infected with Chlamydia pneumoniae. Our data set includes 9 strains which infect humans (Homo sapiens) and 1 strain isolated from koala (Phascolarctos cinereus). RSA-based clustering clearly separates such isolate from the group of highly similar human isolates (Fig. 4b, left). This result is confirmed by 16S rRNA-based clustering (Fig. 4b, right) and is in agreeement with previous results in which the comparison of four human-derived isolates and the koala strain LPCoLN ((text {GCA}_000024145)) through whole-genome sequencing showed a much higher variation between human and koala-derived strains than within the human-derived strains35.Another peculiarity of Chlamydia pneumoniae is tissue tropism. The human-derived strains of Chlamydia pneumoniae can in fact be divided into conjuctival, raspiratory and vascular based on their tissue of origin. Cpn tissue tropism was the focus of the study conducted by Weinmaier et al., where whole-genome sequences of multiple Cpn strains isolated from different human anatomical sites were compared and animal isolates were used as outgroup3. Weinmaier et al. found a good agreement between the anatomical origin of strains and the maximum likelihood phylogenetic tree based on all SNPs. However, they could not obtain a clear separation between anatomical subgroups of Cpn. Our results show that the RSA-based method partially succeeds in separating subspecies related to different tissues (Fig. 4b, left). The RSA-based dendrogram, in fact, shows a cluster of four respiratory bacteria. However, it does not separate the other subspecies by infection site, suggesting that tissue tropism is not entirely captured by our method.RSA-based method discriminates subspecies with different genome compositionIn some cases, subspecies of the same species are characterized by global differences in the genome composition. This is, for example, the case of Vibrio cholerae and Buchnera aphidicola. Here, we show that the RSA-based model is able to capture such differences and to discriminate subspecies with known different genomic peculiarities.Vibrio cholerae is the causative agent of cholera disease. Its genome is normally composed of two chromosomes: Chr1 ((sim 3) Mb) and Chr2 ((sim 1) Mb). However, some strains show a different karyotype. The two strains (1154text {-}74) ((text {GCA}_000969235)) and (10432text {-}62) ((text {GCA}_000969265)), for instance, underwent the process of chromosomal fusion and possess only one (sim 4) Mb long circular chromosome, which shows a high degree of synteny with the two chromosomes of the more common strains36. The strain (text {TSY}216) ((text {GCA}001045415)), on the other hand, besides having the original two chromosomes, also contains an additional (sim 1) Mb long replicon, which does not share any conserved region with Chr1 and Chr237. For these reasons, we expect the single- and two-chromosome strains to be phylogenetically closer to each other than to the three-chromosome strain, which contains the extra replicon. The 16S rRNA gene-based clustering, however, does not identify any clear separation between the three types of strains (Fig. 4c, right). As a matter of fact, all the 16S rRNA gene copies of all the Vibrio cholerae strains included in our data set are located on (sim 3) Mb long chromosome, which shows high synteny across all strains. It is therefore not surprising that the comparison of the 16S rRNA genes does not capture the global genomic differences that exist between the considered strains. On the other hand, the results obtained with the RSA-based clustering show a clear distinction of the strains with different genomic structure (Fig. 4c, left). The reason for the success of the RSA-based method lies in the theoretical definition of RSA-based distance. In fact, the RSA-based distance depends on the Poisson Log-Normal location parameter (sigma ^2), which increases with the genome length (Supplementary Fig. S1): by definition, (sigma ^2 = sigma _e^2 / 2gamma), and, while the environmental noise (sigma _e^2) can be reasonably considered independent of the genome length, the density regulation (gamma) is expected to be stronger for smaller genomes, which repesent a scarcer environment with less resources.Buchnera aphidicola is a bacterial species in mutualistic endosymbiotic relationship with different aphids (members of superfamily Aphidoidea). As many endosymbionts, Buchnera aphidicola underwent the process of genome reduction as an adaptation to the host-associated lifestyle and has a genome with length ( More

National Center for Theoretical Sciences, Taipei, 10617, TaiwanChun-Wei Chang & Chih-hao HsiehResearch Center for Environmental Changes, Academia Sinica, Taipei, 11529, TaiwanChun-Wei Chang, Fuh-Kwo Shiah & Chih-hao HsiehFaculty of Advanced Science and Technology, Ryukoku University, Otsu, Shiga, 520-2194, JapanTakeshi MikiInstitute of Oceanography, National Taiwan University, Taipei, 10617, TaiwanTakeshi Miki, Fuh-Kwo Shiah & Chih-hao HsiehCenter for Biodiversity Science, Ryukoku University, Otsu, Shiga, 520-2194, JapanTakeshi MikiHealth Science Center Libraries, University of Florida, Gainesville, FL, 32611, USAHao YeUniv. Lille, CNRS, Univ, Littoral Côte D’Opale, IRD, UMR 8187, LOG— Laboratoire D’Océanologie et de Géosciences, Station Marine de Wimereux, F- 59000, Lille, FranceSami SouissiLeibniz Institute of Freshwater Ecology and Inland Fisheries, IGB, 12587, Berlin, GermanyRita AdrianFreie Universität Berlin, Department of Biology, Chemistry and Pharmacy, 14195, Berlin, GermanyRita AdrianNational Research Institute for Agriculture, Food and Environment (INRAE), CARRTEL, Université Savoie Mont Blanc, 74200, Thonon les Bains, FranceOrlane AnnevilleCentre for Limnology, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5D, 51014, Tartu, EstoniaHelen Agasild & Peeter NõgesDepartment of Ecosystem Studies, School of Environmental Science, The University of Shiga Prefecture, Hikone, 522-8533, Shiga, JapanSyuhei Ban & Xin LiuKinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, P.O. Box 447, 14950, Migdal, IsraelYaron Be’eri-Shlevin, Gideon Gal & Tamar ZoharyBiodiversity Research Center, Academia Sinica, Taipei, 11529, TaiwanYin-Ru Chiang & Jiunn-Tzong WuUK Centre for Ecology & Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster, Lancashire, LA1 4AP, UKHeidrun Feuchtmayr & Stephen J. ThackerayLake Biwa Environmental Research Institute, Otsu, 520-0022, JapanSatoshi Ichise & Michio KumagaiFaculty of Environment and Information Sciences, Yokohama National University, Yokohama, 240-8502, Kanagawa, JapanMaiko KagamiDepartment of Environmental Science, Faculty of Science, Toho University, Funabashi, Chiba, 274-8510, JapanMaiko KagamiResearch Center for Lake Biwa & Environmental Innovation, Ritsumeikan University, Kusatsu, 525-0058, Shiga, JapanMichio KumagaiBiodiversity Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, JapanShin-Ichiro S. MatsuzakiCNR Water Research Institute (IRSA), L.go Tonolli 50, 28922, Verbania, Pallanza, ItalyMarina M. Manca, Roberta Piscia & Michela RogoraPlymouth Marine Laboratory, Prospect Place, West Hoe, Plymouth, PL1 3DH, UKClaire E. WiddicombeInstitute of Ecology and Evolutionary Biology, Department of Life Science, National Taiwan University, Taipei, 10617, TaiwanChih-hao Hsieh More

Bauer, T., Bäte, D. A., Kempfer, F. & Schirmel, J. Differing impacts of two major plant invaders on urban plant-dwelling spiders (Araneae) during flowering season. Biol. Invasions 23(5), 1473–1485. https://doi.org/10.1007/s10530-020-02452-w (2021).Article 

Google Scholar 
Ustinova, E. N., Schepetov, D. M., Lysenkov, S. N. & Tiunov, A. V. Soil arthropod communities are not affected by invasive Solidago gigantea Aiton (Asteraceae), based on morphology and metabarcoding analyses. Soil Biol. Biochem. 159, 108288. https://doi.org/10.1016/j.soilbio.2021.108288 (2021).CAS 
Article 

Google Scholar 
Tanner, R. A. et al. Impacts of an Invasive Non-Native Annual Weed, Impatiens glandulifera, on Above- and Below-Ground Invertebrate Communities in the United Kingdom. PLoS ONE 8(6), e67271. https://doi.org/10.1371/journal.pone.0067271 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wei, Q. et al. The diversity of soil mesofauna decline after bamboo invasion in subtropical China. Sci. Total Environ. 789, 147982. https://doi.org/10.1016/j.scitotenv.2021.147982 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Szymura, M. & Szymura, T. H. Growth, phenology, and biomass allocation of alien Solidago species in central Europe. Plant Species Biol. 30(4), 245–256. https://doi.org/10.1111/1442-1984.12059 (2015).Article 

Google Scholar 
Bobuľská, L., Demková, L., Čerevková, A. & Renčo, M. Invasive goldenrod (Solidago gigantea) influences soil microbial activities in forest and grassland ecosystems in central Europe. Diversity 11(8), 134. https://doi.org/10.3390/d11080134 (2019).CAS 
Article 

Google Scholar 
Sterzyńska, M., Shrubovych, J. & Nicia, P. Impact of plant invasion (Solidago gigantea L.) on soil mesofauna in a riparian wet meadows. Pedobiologia 64, 1–7. https://doi.org/10.1016/j.pedobi.2017.07.004 (2017).Article 

Google Scholar 
Zubek, S. et al. Solidago canadensis invasion in abandoned arable fields induces minor changes in soil properties and does not affect the performance of subsequent crops. Land Degrad. Dev. 31(3), 1–12. https://doi.org/10.1002/ldr.3452 (2019).Article 

Google Scholar 
Čerevková, A., Miklisová, D., Bobul’ská, L. & Renčo, M. Impact of the invasive plant Solidago gigantea on soil nematodes in a semi-natural grassland and a temperate broadleaved mixed forest. J. Helminthol. 94, 1–14. https://doi.org/10.1017/S0022149X19000324 (2020).Article 

Google Scholar 
de Groot, M., Kleijn, D. & Jogan, N. Species groups occupying different trophic levels respond differently to the invasion of semi-natural vegetation by Solidago canadensis. Biol. Conserv. 136(4), 612–617. https://doi.org/10.1016/j.biocon.2007.01.005 (2007).Article 

Google Scholar 
Baranová, B., Manko, P. & Jászay, T. Differences in surface-dwelling beetles of grasslands invaded and non-invaded by goldenrods (Solidago canadensis, S. gigantea) with special reference to Carabidae. J. Insect. Conserv. 18(4), 623–635. https://doi.org/10.1007/s10841-014-9666-0 (2014).Article 

Google Scholar 
Lenda, M., Witek, M., Skórka, P., Moroń, D. & Woyciechowski, M. Invasive alien plants affect grassland ant communities, colony size and foraging behaviour. Biol. Invasions 15(11), 2403–2414. https://doi.org/10.1007/s10530-013-0461-8 (2013).Article 

Google Scholar 
Kajzer-Bonk, J., Szpiłyk, D. & Woyciechowski, M. Invasive goldenrods affect abundance and diversity of grassland ant communities (Hymenoptera: Formicidae). J. Insect Conserv. 20(1), 99–105. https://doi.org/10.1007/s10841-016-9843-4 (2016).Article 

Google Scholar 
Trigos-Peral, G. et al. Ant communities and Solidago plant invasion: Environmental properties and food sources. Entomol. Sci. 21(3), 270–278. https://doi.org/10.1111/ens.12304 (2018).Article 

Google Scholar 
Fenesi, A. et al. Solidago canadensis impacts on native plant and pollinator communities in different-aged old fields. Basic Appl. Ecol. 16(4), 335–346. https://doi.org/10.1016/j.baae.2015.03.003 (2015).Article 

Google Scholar 
Sheley, R. L., Mangold, J. M. & Anderson, J. L. Potential for successional theory to guide restoration of invasive-plant-dominated rangeland. Ecol. Monogr. 76(3), 365–379. https://doi.org/10.1890/0012-9615(2006)076[0365:PFSTTG]2.0.CO;2 (2006).Article 

Google Scholar 
Byun, C., de Blois, S. & Brisson, J. Management of invasive plants through ecological resistance. Biol. Invasions 20(1), 13–27. https://doi.org/10.1007/s10530-017-1529-7 (2018).Article 

Google Scholar 
Weidlich, E. W. A., Flórido, F. G., Sorrini, T. B. & Brancalion, P. H. S. Controlling invasive plant species in ecological restoration: A global review. J. Appl. Ecol. 57(9), 1806–1817. https://doi.org/10.1111/1365-2664.13656 (2020).Article 

Google Scholar 
Zaller, J. G. et al. Effects of glyphosate-based herbicides and their active ingredients on earthworms, water infiltration and glyphosate leaching are influenced by soil properties. Environ. Sci. Eur. 33(1), 1–16. https://doi.org/10.1186/s12302-021-00492-0 (2021).CAS 
Article 

Google Scholar 
Szymura, M., Świerszcz, S. & Szymura, T. H. Restoration of ecologically valuable grassland on sites degraded by invasive Solidago: Lessons from a six year experiment. Land Degrad. Dev. https://doi.org/10.1002/ldr.4278 (2022).Article 

Google Scholar 
Świerszcz, S., Szymura, M., Wolski, K. & Szymura, T. H. Comparison of methods for restoring meadows invaded by Solidago species. Pol. J. Environ. Stud. 26(3), 1251–1258. https://doi.org/10.15244/pjoes/67338 (2017).Article 

Google Scholar 
Nagy, D. U. et al. The more we do, the less we gain? Balancing effort and efficacy in managing the Solidago gigantea invasion. Weed Res. 60(3), 232–240. https://doi.org/10.1111/wre.12417 (2020).Article 

Google Scholar 
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511. https://doi.org/10.1038/nature13855 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bardgett, R. D. & Wardle, D. A. Aboveground-Belowground Linkages: Biotic Interactions, Ecosystem Processes, and Global Change (Oxford University Press, Oxford, 2010).
Google Scholar 
Gruss, I. et al. Microarthropods and vegetation as biological indicators of soil quality studied in poor sandy sites at former military facilities. Land Degrad. Dev. 33(2), 358–367. https://doi.org/10.1002/ldr.4157 (2022).Article 

Google Scholar 
Sabais, A. C. W., Scheu, S. & Eisenhauer, N. Plant species richness drives the density and diversity of Collembola in temperate grassland. Acta Oecol. 37(3), 195–202. https://doi.org/10.1016/j.actao.2011.02.002 (2011).ADS 
Article 

Google Scholar 
Kardol, P. & Wardle, D. A. How understanding aboveground-belowground linkages can assist restoration ecology. Trends Ecol. Evol. 25(11), 670–679. https://doi.org/10.1016/j.tree.2010.09.001 (2010).Article 
PubMed 

Google Scholar 
Eviner, V. T. & Hawkes, C. V. Embracing variability in the application of plant-soil interactions to the restoration of communities and ecosystems. Restor. Ecol. 16(4), 713–729. https://doi.org/10.1111/j.1526-100X.2008.00482.x (2008).Article 

Google Scholar 
Zhao, J., Chen, J., Wu, H., Li, L. & Pan, F. Effects of mowing frequency on soil nematode diversity and community structure in a chinese meadow steppe. Sustainability 13, 5555. https://doi.org/10.3390/su13105555 (2021).Article 

Google Scholar 
Hyvönen, T. et al. Aboveground and belowground biodiversity responses to seed mixtures and mowing in a long-term set-aside experiment. Agric. Ecosyst. Environ. https://doi.org/10.1016/j.agee.2021.107656 (2021).Article 

Google Scholar 
Gilmullina, A., Rumpel, C., Blagodatskaya, E. & Chabbi, A. Management of grasslands by mowing versus grazing – impacts on soil organic matter quality and microbial functioning. Appl. Soil Ecol. https://doi.org/10.1016/j.apsoil.2020.103701 (2020).Article 

Google Scholar 
Kladivko, E. J. Tillage systems and soil ecology. Soil Tillage Res. 61(1–2), 61–76. https://doi.org/10.1016/S0167-1987(01)00179-9 (2001).Article 

Google Scholar 
Bispo, A. et al. Indicators for monitoring soil biodiversity. Integr. Environ. Assess. Manag. 5(4), 717–719 (2009).CAS 
Article 

Google Scholar 
Santorufo, L., van Gestel, C. A. M., Rocco, A. & Maisto, G. Soil invertebrates as bioindicators of urban soil quality. Environ. Pollut. 161, 57–63. https://doi.org/10.1016/j.envpol.2011.09.042 (2012).CAS 
Article 
PubMed 

Google Scholar 
Boyce R. L. Life Under Your Feet: Measuring soil invertebrate diversity. Teaching Issues and Experiments in Ecology, Ecological Society of America, 3: Experiment #1. https://tiee.esa.org/vol/v3/experiments/soil/downloads.html (2005).Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–656 (1948).MathSciNet 
Article 

Google Scholar 
Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144. https://doi.org/10.1016/0022-5193(66)90013-0 (1966).ADS 
Article 

Google Scholar 
Margalef, R. Information theory in ecology. Gen. Syst. 3, 36–71 (1958).
Google Scholar 
Jones, H. P. Impact of ecological restoration on ecosystem services. In Encyclopedia of Biodiversity (ed. Levin, S. A.) 199–208 (Academic Press, New York, 2013).Chapter 

Google Scholar 
Menta, C. Soil fauna diversity – function, soil degradation, biological indices, soil restoration. In Biodiversity Conservation and Utilization in a Diverse World (ed. Lameed, G. A.) (IntechOpen, London, 2012).
Google Scholar 
Hoffland, E., Kuyper, T. W., Comans, R. N. & Creamer, R. E. Eco-functionality of organic matter in soils. Plant Soil 455(1), 1–22. https://doi.org/10.1007/s11104-020-04651-9 (2020).CAS 
Article 

Google Scholar 
Huera-Lucero, T., Labrador-Moreno, J., Blanco-Salas, J. & Ruiz-Téllez, T. A framework to incorporate biological soil quality indicators into assessing the sustainability of territories in the Ecuadorian Amazon. Sustainability 12(7), 3007. https://doi.org/10.3390/su12073007 (2020).Article 

Google Scholar 
van Eekeren, N. et al. Microarthropod communities and their ecosystem services restore when permanent grassland with mowing or low-intensity grazing is installed. Agric. Ecosyst. Environ. 323, 107682. https://doi.org/10.1016/j.agee.2021.107682 (2022).Article 

Google Scholar 
Humbert, J. Y., Ghazoul, J., Sauter, G. J. & Walter, T. Impact of different meadow mowing techniques on field invertebrates. J. Appl. Entomol. 134(7), 592–599. https://doi.org/10.1111/j.1439-0418.2009.01503.x (2010).Article 

Google Scholar 
Steidle, J. L. M., Kimmich, T., Csader, M. & Betz, O. Negative impact of roadside mowing on arthropod fauna and its reduction with ‘arthropod-friendly’ mowing technique. J. Appl. Entomol. https://doi.org/10.1111/jen.12976 (2022).Article 

Google Scholar 
Briones, M. J. Soil fauna and soil functions: a jigsaw puzzle. Front. Environ. Sci. 2, 7. https://doi.org/10.3389/fenvs.2014.00007 (2014).Article 

Google Scholar 
Shao, C., Chen, J., Li, L. & Zhang, L. Ecosystem responses to mowing manipulations in an arid Inner Mongolia steppe: An energy perspective. J. Arid Environ. 82, 1–10. https://doi.org/10.1016/j.jaridenv.2012.02.019 (2012).ADS 
Article 

Google Scholar 
de Almeida, T., Forey, E. & Chauvat, M. Alien invasive plant effect on soil fauna is habitat dependent. Diversity 14(2), 61. https://doi.org/10.3390/d14020061 (2022).CAS 
Article 

Google Scholar 
Wissuwa, J., Salamon, J. A. & Frank, T. Effects of habitat age and plant species on predatory mites (Acari, Mesostigmata) in grassy arable fallows in Eastern Austria. Soil Biol. Biochem. 50, 96–107. https://doi.org/10.1016/j.soilbio.2012.02.025 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Petersen, H. Collembolan communities in shrublands along climatic gradients in Europe and the effects of experimental warming and drought on population density, biomass and diversity. Soil Org. 83(3), 463–488 (2011).
Google Scholar 
Eisenhauer, N. et al. Plant community impacts on the structure of earthworm communities depend on season and change with time. Soil Biol. Biochem. 41(12), 2430–2443. https://doi.org/10.1016/j.soilbio.2009.09.001 (2009).CAS 
Article 

Google Scholar 
Eisenhauer, N. et al. Plant diversity surpasses plant functional groups and plant productivity as driver of soil biota in the long term. PLoS ONE 6(1), 15–18. https://doi.org/10.1371/journal.pone.0016055 (2011).CAS 
Article 

Google Scholar 
Gao, D., Wang, X., Fu, S. & Zhao, J. Legume plants enhance the resistance of soil to ecosystem disturbance. Front. Plant Sci. 8, 1295. https://doi.org/10.3389/fpls.2017.01295 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Yang, G., Roy, J., Veresoglou, S. D. & Rillig, M. C. Soil biodiversity enhances the persistence of legumes under climate change. New Phytol. 229(5), 2945–2956. https://doi.org/10.1111/nph.17065 (2021).CAS 
Article 
PubMed 

Google Scholar 
Zhao, J., Zeng, Z., He, X., Chen, H. & Wang, K. Effects of monoculture and mixed culture of grass and legume forage species on soil microbial community structure under different levels of nitrogen fertilization. Eur. J. Soil Biol. 68, 61–68. https://doi.org/10.1016/j.ejsobi.2015.03.008 (2015).CAS 
Article 

Google Scholar 
Zhao, J., Wang, X., Wang, X. & Fu, S. Legume-soil interactions: legume addition enhances the complexity of the soil food web. Plant Soil 385(1), 273–286. https://doi.org/10.1007/s11104-014-2234-2 (2014).CAS 
Article 

Google Scholar 
Bonkowski, M., Villenave, C. & Griffiths, B. Rhizosphere fauna: the functional and structural diversity of intimate interactions of soil fauna with plant roots. Plant Soil 321, 213–233. https://doi.org/10.1007/s11104-009-0013-2 (2009).CAS 
Article 

Google Scholar 
Hector, A., Dobson, K., Minns, A., Bazeley-White, E. & Hartley Lawton, J. Community diversity and invasion resistance: an experimental test in a grassland ecosystem and a review of comparable studies. Ecol. Res. 16(5), 819–83. https://doi.org/10.1046/j.1440-1703.2001.00443.x (2001).Article 

Google Scholar 
Gastine, A., Scherer-Lorenzen, M. & Leadley, P. W. No consistent effects of plant diversity on root biomass, soil biota and soil abiotic conditions in temperate grassland communities. Appl. Ecol. 24, 101–111. https://doi.org/10.1016/S0929-1393(02)00137-3 (2003).Article 

Google Scholar 
Scherber, C. et al. Effects of plant diversity on invertebrate herbivory in experimental grassland. Oecologia 147(3), 489–500. https://doi.org/10.1007/s00442-005-0281-3 (2006).ADS 
Article 
PubMed 

Google Scholar 
Viketoft, M., Palmborg, C., Sohlenius, B., Huss-Danell, K. & Bengtsson, J. Plant species effects on soil nematode communities in experimental grasslands. Appl. Soil Ecol. 30(2), 90–103. https://doi.org/10.1016/j.apsoil.2005.02.007 (2005).Article 

Google Scholar 
Viketoft, M. et al. Long-term effects of plant diversity and composition on soil nematode communities in model grasslands. Ecology 90(1), 90–99. https://doi.org/10.1890/08-0382.1 (2009).Article 
PubMed 

Google Scholar  More

Wilson, E. O. Sociobiology: The New Synthesis (Harvard University Press, 1975).
Google Scholar 
Székely, T., Remeš, V., Freckleton, R. P. & Liker, A. Why care? Inferring the evolution of complex social behaviour. J. Evol. Biol. 26, 1381–1391 (2013).PubMed 
Article 

Google Scholar 
Royle, N. J., Smiseth, P. T. & Kölliker, M. The Evolution of Parental Care (Oxford University Press, 2012).Book 

Google Scholar 
Clutton-Brock, T. H. The Evolution of Parental Care (Princeton University Press, 1991).Book 

Google Scholar 
Székely, T., Webb, J. N., Houston, A. I. & McNamara, J. M. An evolutionary approach to offspring desertion in birds. In Current Ornithology Vol. 13 (eds Nolan, V. & Ketterson, E. D.) (Springer, 1996).
Google Scholar 
McGraw, L., Székely, T. & Young, L. J. Pair bonds and parental behaviour. In Social Behaviour: Genes, Ecology and Evolution (eds Székely, T. et al.) (Cambridge University Press, 2010).
Google Scholar 
Smiseth, P. T., Kölliker, M. & Royle, N. J. What is parental care? In The Evolution of Parental Care (eds Royle, N. J. et al.) 1–17 (Oxford Univ. Press, 2012).
Google Scholar 
Mank, J. E., Promislow, D. E. L. & Avise, J. C. Phylogenetic perspectives in the evolution of parental care in ray-finned fishes. Evolution 59, 1570–1578 (2005).PubMed 
Article 

Google Scholar 
Benun Sutton, F. & Wilson, A. B. Where are all the moms? External fertilization predicts the rise of male parental care in bony fishes. Evolution 73, 2451–2460 (2019).PubMed 
Article 

Google Scholar 
Furness, A. I. & Capellini, I. The evolution of parental care diversity in amphibians. Nat. Commun. 10, 4709 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vági, B., Végvári, Z., Liker, A., Freckleton, R. P. & Székely, T. Terrestriality and the evolution of parental care in frogs. Proc. R. Soc. Lond. B. 286, 20182737 (2019).
Google Scholar 
Vági, B., Végvári, Z., Liker, A., Freckleton, R. P. & Székely, T. Climate and mating systems as drivers of global diversity of parental care in frogs. Glob. Ecol. Biogeogr. 29, 1373–1386 (2020).Article 

Google Scholar 
Gilbert, J. D. J. & Manica, A. Parental care trade-offs and life-history relationships in insects. Am. Nat. 176, 212–226 (2010).PubMed 
Article 

Google Scholar 
Gilbert, J. D. & Manica, A. The evolution of parental care in insects: A test of current hypotheses. Evolution 69, 1255–1270 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Reynolds, J. D., Goodwin, N. B. & Freckleton, R. P. Evolutionary transitions in parental care and live bearing in vertebrates. Philos. Trans. R. Soc. Lond. B. 357, 269–281 (2002).Article 

Google Scholar 
AlRashidi, M., Kosztolányi, A., Shobrak, M., Küpper, C. & Székely, T. Parental cooperation in an extreme hot environment: Natural behaviour and experimental evidence. Anim. Behav. 82, 235–243 (2011).Article 

Google Scholar 
Vincze, O. et al. Parental cooperation in a changing climate: Fluctuating environments predict shifts in care division. Glob. Ecol. Biogeogr. 26, 347–385 (2017).Article 

Google Scholar 
Martin, K. L. & Carter, A. L. Brave new propagules: Terrestrial embryos in anamniotic eggs. Integr. Comp. Biol. 53, 233–247 (2013).CAS 
PubMed 
Article 

Google Scholar 
Ishimatsu, A., Mai, H. V. & Martin, K. L. Patterns of fish reproduction at the interface between air and water. Integr. Comp. Biol. 58, 1064–1085 (2018).CAS 
PubMed 

Google Scholar 
Bickford, D. P. Differential parental care behaviors of arboreal and terrestrial microhylid frogs from Papua New Guinea. Behav. Ecol. Sociobiol. 55, 402–409 (2004).Article 

Google Scholar 
Poo, S. & Bickford, D. P. The adaptive significance of egg attendance in a South-East Asian tree frog. Ethology 119, 1–9 (2013).Article 

Google Scholar 
Gomez-Mestre, I., Pyron, R. A. & Wiens, J. J. Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs. Evolution 66, 3687–3700 (2012).PubMed 
Article 

Google Scholar 
Wells, K. D. The Ecology and Behaviour of Amphibians (University of Chicago Press, 2007).Salthe, S. N. Reproductive modes and the number and sizes of ova in Urodeles. Am. Midl. Nat. 81, 467–490 (1969).Article 

Google Scholar 
Nussbaum, R. A. The Evolution of Parental Care in Salamanders. (University of Michigan Press, 1985).Nussbaum, R. A. Parental care and egg size in salamanders: An examination of the safe harbor hypothesis. Res. Popul. Ecol. 29, 27–44 (1987).Article 

Google Scholar 
Furness, A. I., Venditti, C. & Capellini, I. Terrestrial reproduction and parental care drive rapid evolution in the trade-off between offspring size and numbers across amphibians. PLoS Biol. 20, e3001495 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beck, C. W. Mode of fertilization and parental care in anurans. Anim. Behav. 55, 439–449 (1998).CAS 
PubMed 
Article 

Google Scholar 
Kahn, A. T., Schwanz, L. E. & Kokko, H. Paternity protection can provide a kick-start for the evolution of male-only parental care. Evolution 67, 2207–2217 (2013).PubMed 
Article 

Google Scholar 
Summers, K., McKeon, C. S. & Heying, H. The evolution of parental care and egg size: A comparative analysis in frogs. Proc. R. Soc. B 273, 687–692 (2006).PubMed 
Article 

Google Scholar 
Lack, D. L. Ecological Adaptations for Breeding in Birds (Methuen, 1968).Suski, C. D. & Ridgway, M. S. Climate and body size influence nest survival in a fish with parental care. J. Anim. Ecol. 76, 730–739 (2007).CAS 
PubMed 
Article 

Google Scholar 
Oneto, F., Ottonello, D., Pastorino, M. V. & Salvidio, S. Posthatching parental care in salamanders revealed by infrared video surveillance. J. Herpetol. 44, 649–653 (2010).Article 

Google Scholar 
Reinhard, S., Voitel, S. & Kupfer, A. External fertilisation and paternal care in the paedomorphic salamander Siren intermedia Barnes, 1826. Zool. Anz. 253, 1–5 (2013).Article 

Google Scholar 
Amphibiaweb. University of California. https://amphibiaweb.org (2021).Vial, J. L. The ecology of the tropical salamander, Bolitoglossa pesrubra Costa Rica. Rev. Biol. Trop. 15, 13–115 (1968).
Google Scholar 
Han, X. & Fu, J. Does life history shape sexual size dimorphism in anurans? A comparative analysis. BMC Evol. Biol. 13, 27 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Prado, C. P. A. & Haddad, C. F. B. Size-fecundity relationships and reproductive investment in female frogs in the Pantanal, South-Western Brasil. Herpetol. J. 15, 181–189 (2005).
Google Scholar 
Kupfer, A., Maxwell, E., Reinhard, S. & Kuehnel, S. The evolution of parental investment in caecilian amphibians: A comparative approach. Biol. J. Linn. Soc. 119, 4–14 (2016).Article 

Google Scholar 
Smith, R. J. Statistics of sexual size dimorphism. J. Hum. Evol. 36, 423–458 (1999).CAS 
PubMed 
Article 

Google Scholar 
Fairbairn, D. J. Introduction: The enigma of sexual size dimorphism. In Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (eds Fairbairn, D. J. et al.) (Oxford University Press, 2007).Chapter 

Google Scholar 
Lhotka, O., Kyselý, J. & Farda, A. Climate change scenarios of heat waves in Central Europe and their uncertainties. Theor. Appl. Climatol. 131, 1043–1054 (2018).ADS 
Article 

Google Scholar 
Lion, M. B. et al. Global patterns of terrestriality in amphibian reproduction. Glob. Ecol. Biogeogr. 28, 744–756 (2019).Article 

Google Scholar 
Bivand, R. & Lewin-Koh, N. maptools: Tools for Handling Spatial Objects. R Package Version 0.9-9. https://CRAN.R-project.org/package=maptools (2019).Hijmans, R. J. raster: Geographic Data Analysis and Modelling. R Package Version 3.0-7. R package. https://CRAN.R-project.org/package=raster (2015).Bivand, R. et al. Package ‘rgdal’. Bindings for the Geospatial Data Abstraction Library. https://cran.r-project.org/web/packages/rgdal/index.html (2017).IUCN. The IUCN Red List of threatened species. https://www.iucnredlist.org (2021).WorldClim. Maps, Graphs, Tables and Data of the Global Climate. https://www.worldclim.org (2021).Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).PubMed 
Article 

Google Scholar 
Boyko, J. D. & Beaulieu, J. M. Generalized hidden Markov models for phylogenetic comparative datasets. Methods Ecol. Evol. 12, 468–478 (2021).Article 

Google Scholar 
Ho, L. S. T. et al. Package ‘Phylolm’. https://cran.r-project.org/web/packages/phylolm (2018).Jetz, W. et al. VertLife. https://vertlife.org (2021).R-Core-Team R. Version 4.0.4. A Language and Environment for Statistical Computing. http://www.r-project.org/ (2021).Gross, M. R. & Shine, R. Parental care and mode of fertilization in ectothermic vertebrates. Evolution 35, 775–793 (1981).PubMed 
Article 

Google Scholar 
Ridley, M. & Rechten, C. Female sticklebacks prefer to spawn with males whose nests contain eggs. Behaviour 76, 152–161 (1981).Article 

Google Scholar 
Jackson, M. E., Scott, D. E. & Estes, R. A. Determinants of nest success in the marbled salamander (Ambystoma opacum). Can. J. Zool. 67, 2277–2281 (1989).Article 

Google Scholar 
Petranka, J. W. Observations on nest site selection, nest desertion and embryonic survival in marbled salamanders. J. Herpetol. 24, 229–234 (1990).Article 

Google Scholar 
Croshaw, A. & Scott, D. E. Experimental evidence that nest attendance benefits female marbled salamanders (Ambystoma opacum) by reducing egg mortality. Am. Midl. Nat. 154, 398–411 (2005).Article 

Google Scholar 
Knapp, R. A. & Sargent, R. C. Egg mimicry as a mating strategy in the fantail darter, Ethiostoma flabellare: Females prefer males with eggs. Behav. Ecol. Sociobiol. 25, 321–326 (1989).Article 

Google Scholar 
Okada, S., Fukuda, Y. & Takahashi, M. K. Paternal care behaviors of Japanese giant salamander Andrias japonicus in natural populations. J. Ethol. 33, 1–7 (2015).Article 

Google Scholar 
Browne, R. K. et al. The giant salamanders (Cryptobranchidae): Part B. Biogeography, ecology and reproduction. Amphib. Reptile Conserv. 5, 30–50 (2014).
Google Scholar 
Taborsky, M. Sperm competition in fish: ‘Bourgeois’ males and parasitic spawning. Trends Ecol. Evol. 13, 222–227 (1998).CAS 
PubMed 
Article 

Google Scholar 
Vieites, D. R. et al. Post-mating clutch-piracy in an amphibian. Nature 431, 305–308 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Balshine, S. & Abate, M. E. Parental care in cichlid fishes. In The Behavior, Ecology and Evolution of Cichlid Fishes (eds Abate, M. E. & Noakes, D. L. G.) (Springer, 2021).
Google Scholar 
Ota, K., Kohda, M. & Sato, T. Unusual allometry of sexual size dimorphism in a cichlid where males are extremely larger than females. J. Biosci. 35, 257–265 (2010).PubMed 
Article 

Google Scholar 
Mokos, J., Scheuring, I., Liker, A., Freckleton, R. P. & Székely, T. Degree of anisogamy is unrelated to the intensity of sexual selection. Sci. Rep. 11, 19424 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bourne, G. R. Amphisexual parental behaviour of a terrestrial breeding frog Eleutherodactylus johnstonei in Guyana. Behav. Ecol. 9, 1–7 (1998).Article 

Google Scholar 
Beal, C. A. & Tallamy, D. W. A new record of amphisexual care in an insect with extensive parental care: Rhynocoris tristis (Heteroptera: Reduviidae). J. Ethol. 24, 305–307 (2006).Article 

Google Scholar 
Ringler, E. et al. Flexible compensation of uniparental care: Female poison frogs take over when males disappear. Behav. Ecol. 26, 1219–1225 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Tumulty, J., Morales, V. & Summers, K. The biparental care hypothesis for the evolution of monogamy: Experimental evidence in an amphibian. Behav. Ecol. 25, 262–270 (2014).Article 

Google Scholar 
Remeš, V., Freckleton, R. P., Tökölyi, J., Liker, A. & Székely, T. The evolution of parental cooperation in birds. Proc. Natl. Acad. Sci. USA 112, 12603–13608 (2015).Article 

Google Scholar 
Guex, G.-D. & Chen, P. S. Epitheliophagy: Intrauterine cell nourishment in the viviparous alpine salamander, Salamandra atra (Laur.). Experientia 42, 1205–1218 (1986).CAS 
PubMed 
Article 

Google Scholar 
Goycoechea, O., Garrido, O. & Jorquera, B. Evidence for a trophic paternal-larval relationship in the frog Rhinoderma darwinii. J. Herpetol. 20, 168–178 (1986).Article 

Google Scholar 
Hansen, R. W. About our cover: Ecnomyohyla rabborum. Herpetol. Rev. 42, 3 (2012).
Google Scholar 
Brown, J. L., Morales, V. & Summers, K. A key ecological trait drove the evolution of biparental care and monogamy in an amphibian. Am. Nat. 175, 436–446 (2010).PubMed 
Article 

Google Scholar 
Dugas, M. B., Moore, M. P., Martin, R. A., Richards-Zawacki, C. L. & Sprehn, Z. G. The pay-offs of maternal care increase as offspring develop, favouring extended provisioning in an egg-feeding frog. J. Evol. Biol. 29, 1977–1985 (2016).CAS 
PubMed 
Article 

Google Scholar 
Kupfer, A. et al. Parental investment by skin feeding in a caecilian amphibian. Nature 440, 926–929 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Shine, R. Propagule size and parental care: The “safe harbour” hypothesis. J. Theor. Biol. 75, 417–424 (1978).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Székely, T., Webb, J. N. & Cuthill, I. C. Mating patterns, sexual selection and parental care: An integrative approach. In Vertebrate Mating Systems (eds Apollonio, M. et al.) (World Scientific Press, 2000).
Google Scholar 
Ah-King, M., Kvarnemo, C. & Tullberg, B. S. The influence of territoriality and mating system on the evolution of parental care: A phylogenetic study on fish. J. Evol. Biol. 18, 371–382 (2005).CAS 
PubMed 
Article 

Google Scholar 
Zamudio, K. R., Bell, R. C., Nali, R. C., Haddad, C. F. B. & Prado, C. P. A. Polyandry, predation and the evolution of frog reproductive modes. Am. Nat. 188, S41–S61 (2016).PubMed 
Article 

Google Scholar 
Graham, S. P., Kline, R., Steen, D. A. & Kelehear, C. Description of an extant salamander from the Gulf Coastal Plain of North America: The Reticulated Siren, Siren reticulata. PLoS ONE 13, e0207460 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yan, F. et al. The Chinese giant salamander exemplifies the hidden extinction of cryptic species. Curr. Biol. 28, R590–R592 (2018).CAS 
PubMed 
Article 

Google Scholar 
Jaramillo, A. F. et al. Vastly underestimated species richness of Amazonian salamanders (Plethodontidae: Bolitoglossa) and implications about plethodontid diversification. Mol. Phylogenet. Evol. 149, 106841 (2020).PubMed 
Article 

Google Scholar 
Parra-Olea, G. et al. Biology of tiny animals: Three new species of minute salamanders (Plethodontidae: Thorius) from Oaxaca, Mexico. PeerJ 4, e2694 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Balázs, G., Lewarne, B. & Herczeg, G. Extreme site fidelity of the olm (Proteus anguinus) revealed by a long-term capture-mark-recapture study. J. Zool. 311, 99–105 (2020).Article 

Google Scholar  More

Ives, C. D. et al. Cities are hotspots for threatened species. Glob. Ecol. Biogeogr. 25, 117–126 (2016).Article 

Google Scholar 
Planchuelo, G., von Der Lippe, M. & Kowarik, I. Untangling the role of urban ecosystems as habitats for endangered plant species. Landsc. Urban Plan. 189, 320–334 (2019).Article 

Google Scholar 
Soanes, K. & Lentini, P. E. When cities are the last chance for saving species. Front. Ecol. Environ. 17, 225–231 (2019).Article 

Google Scholar 
Ducatez, S., Sayol, F., Sol, D. & Lefebvre, L. Are urban vertebrates city specialists, artificial habitat exploiters, or environmental generalists? Integr. Comp. Biol. 58, 929–938 (2018).PubMed 
Article 

Google Scholar 
Hegglin, D. et al. Baiting red foxes in an urban area: A camera trap study. J. Wildl. Manag. 68, 1010–1017 (2004).Article 

Google Scholar 
Møller, A. P. Successful city dwellers: A comparative study of the ecological characteristics of urban birds in the Western Palearctic. Oecologia 159, 849–858 (2009).ADS 
PubMed 
Article 

Google Scholar 
Castillo-Contreras, R. et al. Wild boar in the city: Phenotypic responses to urbanisation. Sci. Total Environ. 773, 145593 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Barrios-Garcia, M. N. & Ballari, S. A. Impact of wild boar (Sus scrofa) in its introduced and native range: A review. Biol. Invasions 14, 2283–2300 (2012).Article 

Google Scholar 
Cahill, S., Llimona, F., Cabaneros, L. & Calomardo, F. 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, 221–233 (2012).Article 

Google Scholar 
Csokas, A. et al. Space use of wild boar (Sus Scrofa) in Budapest: Are they resident or transient city dwellers? Biol. Futura 71, 39–51 (2020).CAS 
Article 

Google Scholar 
Stillfried, M. et al. Do cities represent sources, sinks or isolated islands for urban wild boar population structure? J. Appl. Ecol. 54, 272–281 (2017).Article 

Google Scholar 
Stillfried, M. et al. Secrets of success in a landscape of fear: Urban wild boar adjust risk perception and tolerate disturbance. Front. Ecol. Evol. 5, 440 (2017).Article 

Google Scholar 
Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. Oikos 69, 373–386 (1994).Article 

Google Scholar 
Herrero, J., Garcia-Serrano, A., Couto, S., Ortuno, V. M. & Garcia-Gonzalez, R. Diet of wild boar Sus scrofa L. and crop damage in an intensive agroecosystem. Eur. J. Wildl. Res. 52, 245–250 (2006).Article 

Google Scholar 
Schley, L. & Roper, T. J. Diet of wild boar Sus scrofa in Western Europe, with particular reference to consumption of agricultural crops. Mamm. Rev. 33, 43–56 (2003).Article 

Google Scholar 
Horčičková, E., Brůna, J. & Vojta, J. Wild boar (Sus scrofa) increases species diversity of semidry grassland: Field experiment with simulated soil disturbances. Ecol. Evol. 9, 2765–2774 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Massei, G. & Genov, P. V. The environmental impact of wild boar. Galemys Bol. Inf. Soc. Esp. Para Conserv. Estud. Los Mamíferos 16, 135–145 (2004).
Google Scholar 
Sandom, C. J., Hughes, J. & Macdonald, D. W. Rewilding the scottish highlands: Do wild boar, Sus scrofa, use a suitable foraging strategy to be effective ecosystem engineers? Restor. Ecol. 21, 336–343 (2013).Article 

Google Scholar 
Wirthner, S. et al. Do changes in soil properties after rooting by wild boars (Sus scrofa) affect understory vegetation in Swiss hardwood forests? Can. J. For. Res.-Rev. Can. Rech. For. 42, 585–592 (2012).CAS 
Article 

Google Scholar 
Bankovich, B., Boughton, E., Boughton, R., Avery, M. L. & Wisely, S. M. Plant community shifts caused by feral swine rooting devalue Florida rangeland. Agric. Ecosyst. Environ. 220, 45–54 (2016).Article 

Google Scholar 
Felix, R. K., Orzell, S. L., Tillman, E. A., Engeman, R. M. & Avery, M. L. Fine-scale, spatial and temporal assessment methods for feral swine disturbances to sensitive plant communities in south-central Florida. Environ. Sci. Pollut. Res. 21, 10399–10406 (2014).Article 

Google Scholar 
Boonman-Berson, S., Driessen, C. & Turnhout, E. Managing wild minds: From control by numbers to a multinatural approach in wild boar management in the Veluwe, the Netherlands. Trans. Inst. Br. Geogr. 44, 2–15 (2019).Article 

Google Scholar 
Keuling, O., Strauß, E. & Siebert, U. Regulating wild boar populations is ‘somebody else’s problem’!-Human dimension in wild boar management. Sci. Total Environ. 554–555, 311–319 (2016).ADS 
PubMed 
Article 

Google Scholar 
Brunet, J., Hedwall, P. O., Holmstrom, E. & Wahlgren, E. Disturbance of the herbaceous layer after invasion of an eutrophic temperate forest by wild boar. Nord. J. Bot. 34, 120–128 (2016).Article 

Google Scholar 
Burrascano, S. et al. Wild boar rooting intensity determines shifts in understorey composition and functional traits. Community Ecol. 16, 244–253 (2015).Article 

Google Scholar 
Fagiani, S. et al. Monitoring protocols for the evaluation of the impact of wild boar (Sus scrofa) rooting on plants and animals in forest ecosystems. Hystrix Ital. J. Mamm. 25, 31–38 (2014).
Google Scholar 
Bruinderink, G. W. T. A. G. & Hazebroek, E. Wild boar (Sus scrofa scrofa L.) rooting and forest regeneration on podzolic soils in the Netherlands. For. Ecol. Manag. 88, 71–80 (1996).Article 

Google Scholar 
Pankova, N. L., Markov, N. I. & Vasina, A. L. Effect of the rooting activity of wild boar Sus scrofa on plant communities in the middle Taiga of Western Siberia. Russ. J. Biol. Invasions 11, 363–371 (2020).Article 

Google Scholar 
Carpio, A. J. et al. Effect of wild ungulate density on invertebrates in a Mediterranean ecosystem. Anim. Biodivers. Conserv. 37, 115–125 (2014).Article 

Google Scholar 
Cuevas, M. F., Novillo, A., Campos, C., Dacar, M. A. & Ojeda, R. A. Food habits and impact of rooting behaviour of the invasive wild boar, Sus scrofa, in a protected area of the Monte Desert, Argentina. J. Arid Environ. 74, 1582–1585 (2010).ADS 
Article 

Google Scholar 
Kenyeres, Z., Szabo, S. & Bauer, N. Conservation possibilities of the rare grasshopper Stenobothrus eurasius Zubovski, 1898 are hampered by wild game in its fragmented western outposts. J. Insect Conserv. 24, 115–124 (2020).Article 

Google Scholar 
Reading, C. J. & Jofre, G. M. Habitat use by grass snakes and three sympatric lizard species on lowland heath managed using ‘conservation grazing’. Herpetol. J. 26, 131–138 (2016).
Google Scholar 
de Schaetzen, F., van Langevelde, F. & WallisDeVries, M. F. The influence of wild boar (Sus scrofa) on microhabitat quality for the endangered butterfly Pyrgus malvae in the Netherlands. J. Insect Conserv. 22, 51–59 (2018).Article 

Google Scholar 
Albrecht, H. & Haider, S. Species diversity and life history traits in calcareous grasslands vary along an urbanization gradient. Biodivers. Conserv. 22, 2243–2267 (2013).Article 

Google Scholar 
Cilliers, S. S., Müller, N. & Drewes, E. Overview on urban nature conservation: Situation in the western-grassland biome of South Africa. Urban For. Urban Green. 3, 49–62 (2004).Article 

Google Scholar 
Becker, M. & Buchholz, S. The sand lizard moves downtown-habitat analogues for an endangered species in a metropolitan area. Urban Ecosyst. 19, 361–372 (2016).Article 

Google Scholar 
Senate Department for Urban Development and Housing. Impervious Soil Coverage (Sealing of Soil Surface). (2016).Fischer, L. K., von der Lippe, M., Rillig, M. C. & Kowarik, I. Creating novel urban grasslands by reintroducing native species in wasteland vegetation. Biol. Conserv. 159, 119–126 (2013).Article 

Google Scholar 
von der Lippe, M., Buchholz, S., Hiller, A., Seitz, B. & Kowarik, I. CityScapeLab Berlin: A research platform for untangling urbanization effects on biodiversity. Sustainability 12, 30 (2020).
Google Scholar 
LUA. Brandenburg State Environmental Office. Brandenburg State Environmental Office. Catalogue of Natural Habitats and Species of Appendices I and II of the Habitats Directive in Brandenburg: German Institute for Standardization. (2002).Leuschner, C. & Ellenberg, H. Ecology of central European non-forest vegetation: Coastal to alpine, natural to man-made habitats: vegetation ecology of Central Europe. Volume II. (Springer, 2017).Kotanen, P. M. Responses of vegetation to a changing regime of disturbance-effects of feral pigs in a Californian Coastal Prairie. Ecography 18, 190–199 (1995).Article 

Google Scholar 
Dovrat, G., Perevolotsky, A. & Ne’eman, G. The response of mediterranean herbaceous community to soil disturbance by native wild boars. Plant Ecol. 215, 531–541 (2014).Article 

Google Scholar 
Haaverstad, O., Hjeljord, O. & Wam, H. K. Wild boar rooting in a northern coniferous forest-minor silviculture impact. Scand. J. For. Res. 29, 90–95 (2014).Article 

Google Scholar 
van der Maarel, E. & Franklin, J. (Eds. ). Vegetation Ecology. (2nd edition. Wiley, 2012).Hennekens, S. M. & Schaminee, J. H. J. TURBOVEG, a comprehensive data base management system for vegetation data. J. Veg. Sci. 12, 589–591 (2001).Article 

Google Scholar 
Seitz, B., Ristow, M., Meißner, J., Machatzi, B. & Sukopp, H. Rote Liste und Gesamtartenliste der etablierten Farn- und Blütenpflanzen von Berlin. in Der Landesbeauftragte für Naturschutzt und Landschaftspflege, Senatsverwaltung für Umwelt, Klima und Verkehr (Hrsg): Rote Listen der gefährdeten Pflanzen, Pilze und Tiere von 118 (2018). doi:https://doi.org/10.14279/depositonce-6689.Jäger, E. J. Exkursionsflora von Deutschland. Gefäßpflanzen: Grundband (W. Rothmaler, founder). (Spektrum, 2011).Landeck, I. Kartieranleitung Heuschrecken für das Naturschutzfachliche Monitoring im Naturparadies Grünhaus und im “Revier 55”. (Forschungsinstitut für Bergbaufolgelandschaften, Finsterwalde, 2007).Fischer, J. et al. Die Heuschrecken Deutschlands und Nordtirols-Bestimmen-Beobachten-Schützen. (Quelle & Meyer, 2020).Machatzi, B., Ratsch, A., Prasse, R. & Ristow, M. Rote Liste und Gesamtartenliste der Heuschrecken und Grillen (Saltatoria: Ensifera et Caelifera) von Berlin. (2005).Doerpinghaus, A. et al. Methoden zur Erfassung von Arten der Anhänge IV und V der FFH-Richtlinie. Naturschutz Biol. Vielfalt 20, 454 (2005).
Google Scholar 
Beery, S., Morris, D. & Yang, S. Efficient Pipeline for Camera Trap Image Review. ArXiv Prepr. arXiv:190706772 (2019).Greco, I. et al. Guest or pest? Spatio-temporal occurrence and effects on soil and vegetation of the wild boar on Elba island. Mamm. Biol. https://doi.org/10.1007/s42991-020-00083-1 (2020).Article 

Google Scholar 
Dufrêne, M. & Legendre, P. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 
De Caceres, M. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).PubMed 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (2020).Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Zhang, D. Coefficients of Determination for Mixed-Effects Models. arXiv:200708675 (2021).Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.5-6. Retrieved from https://CRAN.R-project.org/package=vegan (2019).Massei, G., Roy, S. & Bunting, R. Too many hogs? A review of methods to mitigate impact by wild boar and feral hogs. Human-Wildlife Interact. 5, 5008 (2011).
Google Scholar 
Bueno, C. G., Alados, C. L., Gomez-Garcia, D., Barrio, I. C. & Garcia-Gonzalez, R. Understanding the main factors in the extent and distribution of wild boar rooting on alpine grasslands. J. Zool. 279, 195–202 (2009).Article 

Google Scholar 
Cuevas, M. F., Mastrantonio, L., Ojeda, R. A. & Jaksic, F. M. Effects of wild boar disturbance on vegetation and soil properties in the Monte Desert. Argentina. Mamm. Biol. 77, 299–306 (2012).Article 

Google Scholar 
Cushman, J. H., Tierney, T. A. & Hinds, J. M. Variable effects of feral pig disturbances on native and exotic plants in a California grassland. Ecol. Appl. 14, 1746–1756 (2004).Article 

Google Scholar 
Cuevas, M. F., Campos, C. M., Ojeda, R. A. & Jaksic, F. M. Vegetation recovery after 11 years of wild boar exclusion in the Monte Desert, Argentina. Biol. Invasions 22, 1607–1621 (2020).Article 

Google Scholar 
Oldfield, C. A. & Evans, J. P. Twelve years of repeated wild hog activity promotes population maintenance of an invasive clonal plant in a coastal dune ecosystem. Ecol. Evol. 6, 2569–2578 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Tierney, T. A. & Cushman, J. H. Temporal changes in native and exotic vegetation and soil characteristics following disturbances by feral pigs in a California grassland. Biol. Invasions 8, 1073–1089 (2006).Article 

Google Scholar 
Buchholz, S., Seitz, B., Hiller, A., von der Lippe, M. & Kowarik, I. Impacts of dogs on urban grassland ecosystems. Landsc. Urban Plan. 215, 104201 (2021).Article 

Google Scholar 
Heinken, T., Schmidt, M., von Oheimb, G., Kriebitzsch, W. U. & Ellenberg, H. Soil seed banks near rubbing trees indicate dispersal of plant species into forests by wild boar. Basic Appl. Ecol. 7, 31–44 (2006).Article 

Google Scholar 
Heinken, T. Dispersal of plants by a dog in a deciduous forest. Bot. Jahrb Syst. 122, 449–467 (2000).
Google Scholar 
Planchuelo, G., Kowarik, I. & von der Lippe, M. Plant traits, biotopes and urbanization dynamics explain the survival of endangered urban plant populations. J. Appl. Ecol. 57, 1581–1592 (2020).Article 

Google Scholar 
Gardiner, T. & Hassall, M. Does microclimate affect grasshopper populations after cutting of hay in improved grassland? J. Insect Conserv. 13, 97–102 (2009).Article 

Google Scholar 
Willott, S. J. Thermoregulation in four species of British grasshoppers (Orthoptera: Acrididae). Funct. Ecol. 11, 705–713 (1997).Article 

Google Scholar 
Wouters, B. et al. The effects of shifting vegetation mosaics on habitat suitability for coastal dune fauna-a case study on sand lizards (Lacerta agilis). J. Coast. Conserv. 16, 89–99 (2012).Article 

Google Scholar 
De Bruyn, GJ. Animal communities in Dutch dunes. in Van der Maarel E (ed) Dry coastal ecosystems: General aspects. (ed. Elsevier, A.) 361–386 (1997).Seidling, W. Recent changes in forest vegetation in an area on the outskirts of Berlin. in H. Sukopp, S. Hejny, & I. Kowarik (Eds.), Plants and plant communities in the urban environment 223 (1990). More

As the Italian probe LICIACube whizzed past asteroids Didymos (bottom) and Dimorphos (top), it captured a debris plume spraying out from the DART spacecraft as it smashed into Dimorphos.Credit: ASI/NASA

Astronomers see fireworks as spacecraft ploughs into asteroidTelescopes in space and across Earth captured the spectacular aftermath of NASA’s Double Asteroid Redirection Test (DART) spacecraft crashing into the asteroid Dimorphos on 26 September.The goal was to knock the harmless space rock into a slightly different orbit to test whether humanity could do such a thing if a dangerous asteroid were ever detected heading for Earth. The smash-up was “the first human experiment to deflect a celestial body”, says Thomas Zurbuchen, NASA’s associate administrator for science, and “an enormous success”.A ringside view came from LICIACube, a tiny Italian spacecraft that flew along with DART and photographed the impact, which took place 11 million kilometres from Earth. LICIACube’s first images, released by the Italian Space Agency on 27 September, show a large fireworks-like plume of rocks and other debris coming off Dimorphos (pictured, top) after DART had ploughed into it.It will take days to weeks before mission scientists can confirm whether the test worked, and did in fact cut the time it takes Dimorphos to orbit its partner asteroid, Didymos (pictured, bottom), by 10–15 minutes.

The shell of the endangered hawksbill sea turtle (pictured) is prized for trinkets and jewellery.Credit: Reinhard Dirscherl/SPL

Sea turtles swim more freely as poaching declinesPoaching is less of a threat to the survival of sea turtles than it once was, an analysis suggests (J. F. Senko et al. Glob. Change Biol. https://doi.org/gqrzzn; 2022). Illegal sea-turtle catch has dropped sharply since 2000, and most current exploitation occurs in areas with relatively healthy turtle populations.The analysis is the first worldwide estimate of the number of adult sea turtles that are moved on the black market. The authors surveyed sea-turtle specialists and sifted through documents to derive an estimate that around 1.1 million sea turtles were illegally harvested between 1990 and 2020. Nearly 90% of them were funnelled into China and Japan. Of the species that could be identified, the critically endangered hawksbill turtle (Eretmochelys imbricata; pictured), prized for its beautiful shell, was among the most frequently exploited.But the team also found that the illegal catch from 2010 to 2020 was nearly 30% lower than in the previous decade. “The silver lining is that, despite the seemingly large illegal take, exploitation is not having a negative impact on sea-turtle populations on a global scale,” says co-author Jesse Senko, a marine-conservation scientist at Arizona State University in Tempe. More

One thing that the team at Los Amigos did not do is peer deeper into the reserve to try to determine where the Mashco Piro are camped out. Gutiérrez says the decision on whether to establish some kind of monitoring system for isolated communities rests with governments and Indigenous groups, but few doubt that it is possible.
Some researchers worry about the implications of this kind of work. Greg Asner, an ecologist at Arizona State University in Tempe, regularly captured evidence of encampments of isolated groups more than a decade ago, when his team was surveying the Peruvian Amazon in a plane equipped with a powerful laser-based system that provides 3D images of the forest. He flagged the images to his sources at Peru’s environment ministry, but never saw the groups themselves as a legitimate research topic. Even today, he doesn’t see the value in actively tracking them.
“It’s creepy, like describing the home range of jaguars, but human rights are different than jaguar rights,” says Asner. “If we know they are in there, why do we need to know exactly where they are sleeping at night?”
Despite the ethical worries about monitoring, some Indigenous leaders are open to the idea. Knowing where isolated groups are could help surrounding Indigenous communities to prevent unintended and dangerous contact, but “it is the Indigenous organizations that should implement and execute any system of control and surveillance of the Indigenous peoples in isolation,” says Julio Cusurichi, president of FENAMAD, which has worked with the Peruvian government to prevent contact and conflict since the Mashco Piro began to emerge.
FENAMAD was also instrumental in pushing for the creation of the Madre de Dios reserve in 2002. Twenty years later, however, the reserve’s borders have yet to be finalized, and the Indigenous organization is still pushing to expand the eastern boundary to cover areas where the Mashco Piro are known to roam. The problem is that these same areas are currently occupied by logging concessions, which would be costly for the government to cancel.

Julio Cusurichi, president of the Native Federation of the Madre de Dios River and Tributaries (FENAMAD).

Julio Cusurichi, president of the Native Federation of the Madre de Dios River and Tributaries (FENAMAD).

For Cusurichi, the killing of the logger in August is yet another reminder of the precarious situation along the border of the reserve and the risks to both outsiders and the Mashco Piro. Too often, he contends, the government is more concerned with protecting economic interests than the rights of isolated peoples.
Tauli-Corpuz, the former UN rapporteur, has little doubt that scientists mean well, but she worries about any efforts to document the precise location of isolated groups. “If this information falls into the wrong hands, these people will be disturbed in ways they could never imagine,” she says.
Officials from the culture ministry acknowledged these dangers in discussions with Nature, and said they were looking at potential regulations to control the flow of information and restrict who can peer into the reserves.
Although Forsyth says the ministry is full of people who want to do the right thing, he is wary of assuming that government officials always mean well. In Brazil, critics have accused President Bolsonaro, a right-wing populist, of sidelining scientists at FUNAI and attempting to appoint a former Christian missionary to head the division that handles isolated peoples. In the Madre de Dios region, the former governor, Luis Hidalgo Okimura, disappeared in February just before he was to be jailed in connection with an investigation into an illegal logging ring.
“In some cases, the government may not be trustworthy,” Forsyth warns. He places more faith in Indigenous organizations and their advocates. “Giving them access to whatever information they would like or can’t generate themselves ought to be the priority.” More