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    Tropical tree mortality has increased with rising atmospheric water stress

    Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zuleta, D., Duque, A., Cardenas, D., Muller-Landau, H. C. & Davies, S. J. Drought-induced mortality patterns and rapid biomass recovery in a terra firme forest in the Colombian Amazon. Ecology 98, 2538–2546 (2017).PubMed 
    Article 

    Google Scholar 
    Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Powers, J. S. et al. A catastrophic tropical drought kills hydraulically vulnerable tree species. Glob. Chang. Biol. 26, 3122–3133 (2020).PubMed 
    Article 

    Google Scholar 
    Bennett, A. C. et al. Resistance of African tropical forests to an extreme climate anomaly. Proc. Natl Acad. Sci. USA 118, e2003169118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, (2020).Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Matthews, H. D. et al. An integrated approach to quantifying uncertainties in the remaining carbon budget. Commun. Earth Environ. 2, 7 (2021).Article 

    Google Scholar 
    Girardin, C. A. J. et al. Nature-based solutions can help cool the planet—if we act now. Nature 593, 191–194 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Friedlingstein, P. et al. Earth Syst. Sci. Data 14, 1917–2005 (2022)
    Google Scholar 
    Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rowland, L. et al. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 528, 119–122 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lloyd, J. & Farquhar, G. D. Effects of rising temperatures and [CO2] on the physiology of tropical forest trees. Phil. Trans. R. Soc. B 363, 1811–1817 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    O’Sullivan, O. S. et al. Thermal limits of leaf metabolism across biomes. Glob. Chang. Biol. 23, 209–223 (2017).PubMed 
    Article 

    Google Scholar 
    Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).PubMed 
    Article 

    Google Scholar 
    Rifai, S. W., Li, S. & Malhi, Y. Coupling of El Niño events and long-term warming leads to pervasive climate extremes in the terrestrial tropics. Environ. Res. Lett. 14, 105002 (2019).CAS 
    Article 

    Google Scholar 
    Rifai, S. W. et al. ENSO drives interannual variation of forest woody growth across the tropics. Phil. Trans. R. Soc. B 373, 20170410 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Smith, M. N. et al. Empirical evidence for resilience of tropical forest photosynthesis in a warmer world. Nat. Plants 6, 1225–1230 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106, 20610–20615 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McDowell, N., Allen, C. D. & Anderson‐Teixeira, K. Drivers and mechanisms of tree mortality in moist tropical forests. New Phytol. 219, 851–869 (2018).PubMed 
    Article 

    Google Scholar 
    McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178, 719–739 (2008).PubMed 
    Article 

    Google Scholar 
    Bauman, D. et al. Tropical tree growth sensitivity to climate is driven by species intrinsic growth rate and leaf traits. Glob. Chang. Biol. 28, 1414–1432 (2022).PubMed 
    Article 

    Google Scholar 
    Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11, 5515 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Anderegg, W. R. L., Anderegg, L. D. L., Kerr, K. L. & Trugman, A. T. Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Chang. Biol. 25, 3793–3802 (2019).PubMed 
    Article 

    Google Scholar 
    Aguirre-Gutiérrez, J. et al. Drier tropical forests are susceptible to functional changes in response to a long-term drought. Ecol. Lett. 22, 855–865 (2019).PubMed 
    Article 

    Google Scholar 
    Aguirre-Gutiérrez, J. et al. Long-term droughts may drive drier tropical forests towards increased functional, taxonomic and phylogenetic homogeneity. Nat. Comm. 11, 3346 (2020).Article 

    Google Scholar 
    Meir, P., Mencuccini, M. & Dewar, R. C. Drought-related tree mortality: addressing the gaps in understanding and prediction. New Phytol. 207, 28–33 (2015).PubMed 
    Article 

    Google Scholar 
    Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McMahon, S. M., Arellano, G. & Davies, S. J. The importance and challenges of detecting changes in forest mortality rates. Ecosphere 10, e02615 (2019).Article 

    Google Scholar 
    Trugman, A. T. et al. Tree carbon allocation explains forest drought-kill and recovery patterns. Ecol. Lett. 21, 1552–1560 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is tree drought mortality so hard to predict? Trends Ecol. Evol. 36, 520–532 (2021).PubMed 
    Article 

    Google Scholar 
    Phillips, O. L. et al. Drought–mortality relationships for tropical forests. New Phytol. 187, 631–646 (2010).PubMed 
    Article 

    Google Scholar 
    Aleixo, I. et al. Amazonian rainforest tree mortality driven by climate and functional traits. Nat. Clim. Change 9, 384–388 (2019).Article 

    Google Scholar 
    Lingenfelder, M. & Newbery, D. M. On the detection of dynamic responses in a drought-perturbed tropical rainforest in Borneo. Plant Ecol. 201, 267–290 (2009).Article 

    Google Scholar 
    McDowell, N. G. et al. The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends Ecol. Evol. 26, 523–532 (2011).PubMed 
    Article 

    Google Scholar 
    Zuleta, D. et al. Individual tree damage dominates mortality risk factors across six tropical forests. New Phytol. 233, 705–721 (2022).PubMed 
    Article 

    Google Scholar 
    Fontes, C. G. et al. Dry and hot: the hydraulic consequences of a climate change-type drought for Amazonian trees. Phil. Trans. R. Soc. B 373, 20180209 (2018).Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).PubMed 
    Article 

    Google Scholar 
    Peters, J. M. R. et al. Living on the edge: a continental-scale assessment of forest vulnerability to drought. Glob. Chang. Biol. 27, 3620–3641 (2021).PubMed 
    Article 

    Google Scholar 
    Yang, J., Cao, M. & Swenson, N. G. Why functional traits do not predict tree demographic rates. Trends Ecol. Evol. 33, 326–336 (2018).PubMed 
    Article 

    Google Scholar 
    Espírito-Santo, F. D. B. et al. Size and frequency of natural forest disturbances and the Amazon forest carbon balance. Nat. Commun. 5, 3434 (2014).PubMed 
    Article 

    Google Scholar 
    Chambers, J. Q. et al. The steady-state mosaic of disturbance and succession across an old-growth Central Amazon forest landscape. Proc. Natl Acad. Sci. USA 110, 3949–3954 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rifai, S. W. et al. Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon. Ecol. Appl. 26, 2225–2237 (2016).PubMed 
    Article 

    Google Scholar 
    López, J., Way, D. A. & Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Glob. Chang. Biol. 27, 1704–1720 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to droughttextendashfire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Phillips, O. L. et al. Pattern and process in Amazon tree turnover, 1976–2001. Phil. Trans. R. Soc. Lond. B 359, 381–407 (2004).CAS 
    Article 

    Google Scholar 
    Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).Article 

    Google Scholar 
    Andrus, R. A., Chai, R. K., Harvey, B. J., Rodman, K. C. & Veblen, T. T. Increasing rates of subalpine tree mortality linked to warmer and drier summers. J. Ecol. 109, 2203–2218 (2021).Article 

    Google Scholar 
    Murphy, H. T., Bradford, M. G., Dalongeville, A., Ford, A. J. & Metcalfe, D. J. No evidence for long-term increases in biomass and stem density in the tropical rain forests of Australia. J. Ecol. 101, 1589–1597 (2013).Article 

    Google Scholar 
    Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 1, 15139 (2015).PubMed 
    Article 

    Google Scholar 
    Chitra-Tarak, R. et al. Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytol. 231, 1798–1813 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Anderegg, W. R. L. et al. Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. Proc. Natl Acad. Sci. USA 113, 5024–5029 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Taylor, T. C., Smith, M. N., Slot, M. & Feeley, K. J. The capacity to emit isoprene differentiates the photosynthetic temperature responses of tropical plant species. Plant Cell Environ. 42, 2448–2457 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Arellano, G., Zuleta, D. & Davies, S. J. Tree death and damage: a standardized protocol for frequent surveys in tropical forests. J. Veg. Sci. 32, e12981 (2021).Article 

    Google Scholar 
    Bradford, M. G., Murphy, H. T., Ford, A. J., Hogan, D. L. & Metcalfe, D. J. Long-term stem inventory data from tropical rain forest plots in Australia. Ecology 95, 2362 (2014).Article 

    Google Scholar 
    Johnson, D. J. et al. Climate sensitive size-dependent survival in tropical trees. Nat. Ecol. Evol. 2, 1436–1442 (2018).PubMed 
    Article 

    Google Scholar 
    Needham, J., Merow, C., Chang-Yang, C.-H., Caswell, H. & McMahon, S. M. Inferring forest fate from demographic data: from vital rates to population dynamic models. Proc. Biol. Sci. 285, 20172050 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Lewis, S. L. et al. Tropical forest tree mortality, recruitment and turnover rates: calculation, interpretation and comparison when census intervals vary. J. Ecol. 92, 929–944 (2004).Article 

    Google Scholar 
    Reeves, J., Chen, J., Wang, X. L., Lund, R. & Lu, Q. Q. A review and comparison of changepoint detection techniques for climate data. J. Appl. Meteorol. Climatol. 46, 900–915 (2007).Article 

    Google Scholar 
    Clark, J. S., Bell, D. M., Kwit, M. C. & Zhu, K. Competition-interaction landscapes for the joint response of forests to climate change. Glob. Chang. Biol. 20, 1979–1991 (2014).PubMed 
    Article 

    Google Scholar 
    Oliva, J., Stenlid, J. & Martínez-Vilalta, J. The effect of fungal pathogens on the water and carbon economy of trees: implications for drought-induced mortality. New Phytol. 203, 1028–1035 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Franklin, J. F., Shugart, H. H. & Harmon, M. E. Tree death as an ecological process. Bioscience 37, 550–556 (1987).Article 

    Google Scholar 
    Yanoviak, S. P. et al. Lightning is a major cause of large tree mortality in a lowland neotropical forest. New Phytol. 225, 1936–1944 (2020).PubMed 
    Article 

    Google Scholar 
    Preisler, Y., Tatarinov, F., Grünzweig, J. M. & Yakir, D. Seeking the ‘point of no return’ in the sequence of events leading to mortality of mature trees. Plant Cell Environ. 44, 1315–1328 (2020).PubMed 
    Article 

    Google Scholar 
    Aragão, L. E. O. C. et al. Spatial patterns and fire response of recent Amazonian droughts. Geophys. Res. Lett. 34, L07701 (2007).Article 

    Google Scholar 
    Malhi, Y. et al. The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests. Glob. Chang. Biol. 21, 2283–2295 (2015).PubMed 
    Article 

    Google Scholar 
    Hutchinson, M. F., Xu, T., Kesteven, J. L., Marang, I. J. & Evans, B. J.ANUClimate v2.0, NCI Australia. https://doi.org/10.25914/60a10aa56dd1b (2021).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Carscadden, K. A. et al. Niche breadth: causes and consequences for ecology, evolution, and conservation. Q. Rev. Biol. 95, 179–214 (2020).Article 

    Google Scholar 
    Swenson, N. G. et al. A reframing of trait–demographic rate analyses for ecology and evolutionary biology. Int. J. Plant Sci. 181, 33–43 (2020).Article 

    Google Scholar 
    Morueta-Holme, N. et al. Habitat area and climate stability determine geographical variation in plant species range sizes. Ecol. Lett. 16, 1446–1454 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brum, M. et al. Hydrological niche segregation defines forest structure and drought tolerance strategies in a seasonal Amazon forest. J. Ecol. 107, 318–333 (2019).Article 

    Google Scholar 
    Chitra-Tarak, R. et al. The roots of the drought: hydrology and water uptake strategies mediate forest-wide demographic response to precipitation. J. Ecol. 106, 1495–1507 (2018).Article 

    Google Scholar 
    Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Modell. 275, 73–77 (2014).Article 

    Google Scholar 
    Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).CAS 
    PubMed 
    Article 

    Google Scholar 
    Duursma, R. A. Plantecophys—an R package for analysing and modelling leaf gas exchange data. PLoS ONE 10, e0143346 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    De Kauwe, M. G. et al. A test of the ‘one-point method’ for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytol. 210, 1130–1144 (2016).PubMed 
    Article 

    Google Scholar 
    Bloomfield, K. J. et al. The validity of optimal leaf traits modelled on environmental conditions. New Phytol. 221, 1409–1423 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and STAN (CRC Press, 2020).“RStan: the R interface to Stan.” R package version 2.21.2. http://mc-stan.org/ (Stan Development Team, 2020).Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

    Google Scholar 
    R Core Team. R: a language and environment for statistical computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2021).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

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    Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques

    Our study combining field data and aerial imagery analysis clearly showed that the spotted souslik avoids close coexistence with another burrowing species, i.e. the European mole, in the period of low population abundance. This is the first study on this subject described in the available literature, as attention has been paid mainly to other parameters of the habitat so far14,18,20. The present results can (1) make a new contribution to the knowledge of the ecology of burrowing mammals and their interspecies relationships, (2) contribute to better designs of conservation and assessment of the quality of habitats of endangered burrowing mammals, and (3) indicate new possibilities of using remote sensing and deep learning methods in ecology and conservation. Below we will try to address each of these issues.The interaction between underground animals is not a new idea in ecology (e.g.22); however, this issue has not been analyzed for the mole and the souslik so far. This was probably related to the fact that the potential negative or positive relationships between these species are not intuitively obvious. The spatial distribution of underground tunnels of these animals is completely different: the mole builds an extensive network of horizontal tunnels close to the ground surface, while the souslik usually builds one deep nest burrow with a vertical entrance and possibly a small number of shallow safety burrows near the nest burrow. Moreover, the food preferences of the souslik and the mole differ, i.e. the former is mainly a herbivore, while the latter is an obligatory predator. There are also clear differences in the annual cycle: the mole is active all year round, and the souslik hibernates in an underground nest for about half a year from October to March. Thus, it seems that the emergence of competitive relationships between these two species is unlikely. Our study shows, however, that these species avoid each other in space, which raises the question of the mechanism of this relationship. Based on the knowledge of the biology of both species, some hypothetical mechanisms can be proposed.Although they are colonial animals, sousliks inhabit burrows alone (except for mother and offspring) and they have a strong behavioural trait of a negative reaction to the presence of other animals in their burrows and their close vicinity14,23. The negative reaction to other sousliks is a reflection of the intraspecific competition in the population and the territoriality of individuals. It is regulated by odour signals and the social structure of the population30,31. Koshev32 described aggressive reactions of free-ranging European sousliks to other vertebrate species that appeared near burrows: towards the reptile Lacerta trilineata, the bird Corvus frugilegus, and the mammal Mustela nivalis. Theoretically, the mole can get into the souslik’s burrow unintentionally when digging new tunnels. For souslik, the presence of moles in their nest burrow means a violation of its strictly defended territory and is probably a highly stressful episode. It can therefore be assumed that sousliks should choose places outside areas of frequent occurrence of other burrowing mammals to set up a nest burrow.It remains an open question whether avoidance of areas where the mole is often present may be important for the souslik during winter hibernation. Theoretically, the presence of moles in souslik burrows during hibernation may disturb this process and cause waking up and energy-consuming increases in metabolism, which may reduce winter survival. It is also unknown whether the mole can be a predator for the souslik during winter hibernation. Remains of rodent species were found in the digestive tracts of moles33; therefore, at least theoretically, the mole may use such a food source. On the other hand, remains of vertebrates, including the remains of moles, were sometimes found in the stomachs of sousliks18. The relationship between the souslik and the mole may therefore be more complex and require further research focused on this issue. It is possible that the moles can avoid the souslik colonies as well. This scenario seems also realistic, since the moles home ranges are likely much more dynamic than that of sousliks, that likely benefit from dwelling within an existing colony of the conspecifics.The spotted souslik protection requires the designation of special areas of conservation16. A number of various conservation activities are also routinely undertaken for this species, including regular monitoring of the population size, habitat monitoring, mowing, reduction of predation risk, and application of more invasive methods such as reintroduction. Similar activities are also performed for a closely related species, i.e. the European souslik Spermophilus citellus, in Europe. Importantly, in the current guidelines of souslik conservation, the issue of the competition with other species and its impact on spatial distribution is not considered. In turn, there is evidence in the literature that interspecies interactions may be important for the souslik population21. In periods of low abundance, when the survival of the population is at risk, the sousliks may have different habitat preferences than in periods of the abundant population20. It seems, therefore, that nowadays, when the souslik most often forms small populations, more attention should be paid to a wider range of factors and threats that may determine longer term population trends or the health condition, survival, and abundance of their colonies.Our study indicates that, in the period of low population abundance, the presence of other burrowing species may be an important factor determining the distribution of sousliks. This observation shows that in addition to the assessment of the area and condition of the habitat the presence of other potentially competitive species should also be taken into account in the analysis of population survival. In such a case, the actual area of habitats suitable for sousliks in a given location may turn out to be much lower than assumed. In our study area, the habitat suitable for the souslik was reduced from 105 ha to approx. 65 ha, i.e. by nearly 38%, but it probably is even smaller (compare Fig. 8). This observation has consequences for improvement of the reintroduction methods of sousliks (or other burrowing mammals), which are constantly of scientific interest20,34,35. Our results indicate that the reintroduction of sousliks should be carried out in places where there is the lowest probability of competition for resources including even shelter or space with other burrowing species and where adequate space for the settlement of the population is ensured.So far, investigations of the distribution of small burrowing mammals have been based on laborious field studies involving site inspections by trained observers (e.g.36,37,38). Our results show that, in certain conditions, high-resolution imagery can be successfully used to support studies of the distribution of such animals. As reported by other authors (e.g.7,10,12), however, such animals must produce clear signs of their presence in the environment. Evidence of the presence of the European mole, i.e. mounds of soil, in short vegetation habitats has shown that remote sensing can detect moles and their area of occupancy successfully. The advantage of these markers of the presence of moles is that the mounds are redundant and quite durable and can be visible in the environment for up to several months.By combining field research and remote sensing, it is also possible to study more sophisticated ecological issues, e.g. interspecies interactions. In this work, the remote estimation of the distribution of moles facilitated estimation of the actual habitat available to the souslik and excluded areas with the lowest probability of its occurrence. As a result, the population may be monitored more economically. Since the conservation guidelines recommend monitoring souslik populations by means of laborious inspections of transects, the indication of areas with no burrows may significantly reduce the amount of fieldwork without negative consequences for the accuracy of results. Some areas of the souslik occurrence are large, e.g. Świdnik (105 ha) or Pastwiska nad Huczwą (150 ha), and every 10 ha to be monitored means one day’s work for one observer (according to the calculations presented in the results). Our study showed that when the area of the occurrence of moles is excluded from the monitoring (Fig. 8), the error in estimating the size of the souslik population will be relatively small (0.9–8.7%). At the same time, the time devoted to the research can be limited by 14% or 38%, respectively. This suggests that our method can contribute to improved monitoring and management of these protected species, especially that souslik monitoring requires considerable research effort and has to be carried out twice a year.However, mole mounds may be underestimated by remote sensing, which can be seen in Fig. 7. Small mole mounds that are easily identified during field research may not be noticed by remote sensing. Such underestimation does not constitute a critical threat to the determination of the mole area according to the scheme shown in Fig. 8, since its marks are highly redundant. However, since there is currently little research on this subject, we recommend combining field research and remote sensing in assessments similar to ours. Finally, it is worth noting that, for a better understanding of the issue of the interactions between souslik and other burrowing species, it is advisable to use another remote sensing technique—telemetry. Telemetry studies are successfully conducted in Bulgarian souslik populations34 and their combination with studies of habitat selectivity dependent on other burrowing species may provide new and valuable insight into this issue. More

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    Distance to public transit predicts spatial distribution of dengue virus incidence in Medellín, Colombia

    DataAll data was processed and analyzed using R (R Core Team, Version 4.0.3).Dengue case data were collected and shared by the Alcaldía de Medellín, Secretaría de Salud. In Medellin, dengue case surveillance is conducted by public health institutions that classify and report all cases that meet the WHO clinical dengue case criteria for a probable case to Medellin’s Secretaría de Salud through SIVIGILA (“el Sistema Nacional de Vigilancia en Salud Publica). All case data were de-identified and aggregated to the SIT Zone level.Human public transit usage and movement data were collected and shared by the Área Metropolitana del Valle de Aburrá for 50–200 respondents per SIT Zone. The “Encuestas Origen Destino” (Origen Destination Surveys) were conducted in 2005, 2011, and 2016 and published in 2006, 2012, and 2017, with survey methods described by the Área Metropolitana del Valle de Aburrá25. Survey respondents include a randomly selected subset of all Medellin residents in each SIT zone regardless of whether they use public transit or not. Survey respondents reported the start and end locations, purpose for travel, and mode of travel for all movement over the last 24 h from the time the survey was administered. Respondents reported all modes of movement, including public transit, private transit, and movement on foot. The results of the survey published in 2017 are published online by the Área Metropolitana del Valle de Aburrá26, and select data are available through the geodata-Medellin open data portal27. The results and data of the survey published in 2012 are not publicly available and were obtained directly from the Área Metropolitana del Valle de Aburrá.The public transit usage survey data were also used to extract socioeconomic data to the SIT zone; surveyors also reported basic demographic data including household Estrato, which was averaged per SIT zone to estimate zone socioeconomic status. “Estrato” measures socioeconomic status on a scale from 1 (lowest) to 6 (highest). This system is used by the government of Colombia to allocate public services and subsidies (Law 142, 1994). Data from the public transit usage survey were used to extract socioeconomic status data because it is the only location available where the spatial scale of the data matched the spatial scale of the SIT zone.Data on the location of Medellín public transit lines was downloaded as shape files from the geodata-Medellín open data portal27 and subset for each year to the set of transit lines that was available in that year. Data on the opening date of each Medellín public transit line was taken from the Medellín metro website28.Because census data at the zone level were not available for this study and only exists for 2005 and 2018, we used population estimates for each year downloaded from the WorldPop project29 and aggregated by SIT zone. The accuracy of WorldPop estimates were checked against available census data for 2005 and 2018 at the comuna level, accessed via the geodata- Medellín open data portal27.Ethical considerationsNo human subjects research was conducted. All data used was de-identified, and the analysis was conducted on a database of cases meeting the clinical criteria for dengue with no intervention or modification of biological, physical, psychological, or social variables. All methods were performed in accordance with the relevant guidelines and regulations.Data analysisQuantifying public transit usage and distance from nearest transit lineTo quantify public transit usage, we determined if each respondent reported using the metro, metroplus, or ruta alimentadora (supplementary bus route system integrated with the metro system) in the last 24 h. We then calculated the percent of respondents using the public transit system at least once for each SIT zone.To quantify the distance to the nearest public transit line, we calculated the distance from the center point of each zone to the closest metro, metroplus, tranvía, metrocable, ruta alimentadora, or escalera eléctrica. This was recalculated for each year, including new transit lines that were added within that year.Spatial autoregressive models of dengue incidenceDengue incidence per year at the level of the SIT zone was modeled using a fixed effects spatial panel model by maximum likelihood (R package splm30) as described in31. Our fixed effects were socioeconomic status, distance from public transit, a two-way interaction between these factors, and year. To weight dengue cases by population per SIT zone, the model contained a log offset of population per zone per year. Dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year. Year was analyzed as a categorical variable to avoid smoothing epidemic years. All continuous variables were scaled to enable comparison of effect size. Because these panel models require balanced data across time, data was truncated to SIT zones that had data for all years available (247 remaining of 291). Spatial dependency was evaluated, and the model was selected using the Hausman specification test and locally robust panel Lagrange Multiplier tests for spatial dependence. Based on a significant Hausman specification test result, which indicates a poor specification of the random effect model, a fixed effect model was chosen. This result is supported by the fact that we had a nearly exhaustive sample of SIT zones in the Medellin metro area. Lagrange multiplier tests were used to determine the most appropriate spatial dependency specifications. Based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was the most appropriate to incorporate spatial dependency; a SAR model considers that the number of dengue cases in a SIT zone depends on the number in neighboring zones.Because public transit usage was a measurement taken during just two of the study years, we constructed an additional fixed effects spatial panel model by maximum likelihood model of dengue incidence in just 2011 and 2016 that included ridership as an additional predictor variable. Our fixed effects were year, socioeconomic status, distance from public transit, a two-way interaction between socioeconomic status and distance from public transit, percent utilizing public transit, and a two-way interaction between socioeconomic status and percent utilizing public transit. As in our model of all years, the model contained a log offset of population per zone per year and dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year, year was analyzed as a categorical variable, and all continuous variables were scaled to enable comparison of effect size. The data was truncated to SIT zones that had data for all years available (251 remaining of 291). We used the same model selection process, and again a fixed effect model was chosen, and based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was determined the most appropriate to incorporate spatial dependency. More

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    Parasite names, mouse rejuvenation and toxic sunscreen

    Young cerebrospinal fluid probably improves the conductivity of the neurons in ageing mice.Credit: Qilai Shen/Bloomberg/Getty

    Young brain fluid improves memory in old miceCerebrospinal fluid (CSF) from young mice can improve memory function in older mice, researchers report in Nature (T. Iram et al. Nature 605, 509–515; 2022).A direct brain infusion of young CSF probably improves the conductivity of the neurons in ageing mice, which improves the process of making and recalling memories.CSF is a cocktail of essential ions and nutrients that cushions the brain and spinal cord and is essential for normal brain development. But as mammals age, CSF loses some of its punch. Those changes might affect cells related to memory, says co-author Tal Iram, a neuroscientist at Stanford University in California.The researchers found that young CSF helps ageing mice to generate more early-stage oligodendrocytes, cells in the brain that produce the insulating sheath around nerve projections and help to maintain brain function.The team suggest that the improvements are largely due to a specific protein in the fluid.“This is super exciting from the perspective of basic science, but also looking towards therapeutic applications,” says Maria Lehtinen, a neurobiologist at Boston Children’s Hospital in Massachusetts.Gender bias worms its way into parasite namingA study examining the names of nearly 3,000 species of parasitic worm discovered in the past 20 years reveals a markedly higher proportion named after male scientists than after female scientists — and a growing appetite for immortalizing friends and family members in scientific names.Robert Poulin, an ecological parasitologist at the University of Otago in Dunedin, New Zealand, and his colleagues combed through papers published between 2000 and 2020 that describe roughly 2,900 new species of parasitic worm (R. Poulin et al. Proc. R. Soc. B https://doi.org/htqn; 2022). The team found that well over 1,500 species were named after their host organism, where they were found or a prominent feature of their anatomy.

    Source: R. Poulin et al. Proc. R. Soc. B https://doi.org/htqn (2022)

    Many others were named after people, ranging from technical assistants to prominent politicians. But just 19% of the 596 species named after eminent scientists were named after women, a percentage that barely changed over the decades (see ‘Parasite name game’). Poulin and his colleagues also noticed an upward trend in the number of parasites named after friends, family members and even pets of the scientists who formally described them. This practice should be discouraged, Poulin argues.

    Sea anemones turn oxybenzone into a light-activated agent that can bleach and kill corals.Credit: Georgette Douwma/Getty

    Anemones suggest why sunscreen turns toxic in seaA common but controversial sunscreen ingredient that is thought to harm corals might do so because of a chemical reaction that causes it to damage cells in the presence of ultraviolet light.Researchers have discovered that sea anemones, which are similar to corals, make the sun-blocking molecule oxybenzone water-soluble by tacking a sugar onto it. This inadvertently turns oxybenzone into a molecule that — instead of blocking UV light — is activated by sunlight to produce free radicals that can bleach and kill corals. The animals “convert a sunscreen into something that’s essentially the opposite of a sunscreen”, says Djordje Vuckovic, an environmental engineer at Stanford University in California.It’s not clear how closely these laboratory-based studies mimic the reality of reef ecosystems. The concentration of oxybenzone at a coral reef can vary widely, depending on factors such as tourist activity and water conditions. And other factors threaten the health of coral reefs; these include climate change, ocean acidification, coastal pollution and overfishing. The study, published on 5 May (D. Vuckovic et al. Science 376, 644–648; 2022) does not show where oxybenzone ranks in the list. More

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    Changes in global DNA methylation under climatic stress in two related grasses suggest a possible role of epigenetics in the ecological success of polyploids

    Kelly, A. E. & Goulden, M. L. Rapid shifts in plant distribution with recent climate change. Proc. Natl. Acad. Sci. U.S.A. 105, 11823–11826. https://doi.org/10.1073/pnas.0802891105 (2008).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104. https://doi.org/10.1371/journal.pbio.2001104 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Swinnen, J., Burkitbayeva, S., Schierhorn, F., Prishchepov, A. V. & Müller, D. Production potential in the “bread baskets” of Eastern Europe and Central Asia. Global Food Secur. 14, 38–53. https://doi.org/10.1016/j.gfs.2017.03.005 (2017).Article 

    Google Scholar 
    Henry, R. J. Innovations in plant genetics adapting agriculture to climate change. Curr. Opin. Plant Biol. 56, 168–173. https://doi.org/10.1016/j.pbi.2019.11.004 (2020).Article 
    PubMed 

    Google Scholar 
    Stokes, C. & Howden, M. Adapting Agriculture to Climate Change: Preparing Australian Agriculture, Forestry and Fisheries for the Future (Csiro Publishing, 2010).Book 

    Google Scholar 
    Bräutigam, K. et al. Epigenetic regulation of adaptive responses of forest tree species to the environment. Ecol. Evol. 3, 399–415. https://doi.org/10.1002/ece3.461 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yaish, M. W., Colasanti, J. & Rothstein, S. J. The role of epigenetic processes in controlling flowering time in plants exposed to stress. J. Exp. Bot. 62, 3727–3735. https://doi.org/10.1093/jxb/err177 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yaish, M. W. DNA methylation-associated epigenetic changes in stress tolerance of plants. In Molecular Stress Physiology of Plants (eds Rout, G. R. & Das, A. B.) 427–440 (Springer India, 2013).Chapter 

    Google Scholar 
    Suji, K. K. & Joel, A. J. An epigenetic change in rice cultivars underwater stress conditions. Electron. J. Plant Breed. 1, 1142–1143 (2010).
    Google Scholar 
    Peng, H. & Zhang, J. Plant genomic DNA methylation in response to stresses: Potential applications and challenges in plant breeding. Prog. Nat. Sci. 19, 1037–1045. https://doi.org/10.1016/j.pnsc.2008.10.014 (2009).CAS 
    Article 

    Google Scholar 
    Baduel, P. & Colot, V. The epiallelic potential of transposable elements and its evolutionary significance in plants. Philos. Trans. R. Soc. B 376, 20200123. https://doi.org/10.1098/rstb.2020.0123 (2021).CAS 
    Article 

    Google Scholar 
    Labra, M. et al. Analysis of cytosine methylation pattern in response to water deficit in pea root tips. Plant Biol. 4, 694–699. https://doi.org/10.1055/s-2002-37398 (2002).CAS 
    Article 

    Google Scholar 
    Wang, W.-S. et al. Drought-induced site-specific DNA methylation and its association with drought tolerance in rice (Oryza sativa L.). J. Exp. Bot. 62, 1951–1960. https://doi.org/10.1093/jxb/erq391 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Šmarda, P., Bureš, P., Horová, L., Foggi, B. & Rossi, G. Genome size and GC content evolution of Festuca: Ancestral expansion and subsequent reduction. Ann. Bot. 101, 421–433. https://doi.org/10.1093/aob/mcm307 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Tomczyk, P. P., Kiedrzyński, M., Jedrzejczyk, I., Rewers, M. & Wasowicz, P. The transferability of microsatellite loci from a homoploid to a polyploid hybrid complex: An example from fine-leaved Festuca species (Poaceae). PeerJ 8, e9227. https://doi.org/10.7717/peerj.9227 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Piękoś-Mirkowa, H. & Mirek, Z. Distribution patterns and habitats of endemic vascular plants in the Polish Carpathians. Acta Soc. Bot. Pol. 78, 321–326 (2009).Article 

    Google Scholar 
    Kiedrzyński, M., Zielińska, K. M., Rewicz, A. & Kiedrzyńska, E. Habitat and spatial thinning improve the Maxent models performed with incomplete data. J. Geophys. Res. Biogeosci. 122(6), 1359–1370. https://doi.org/10.1002/2016JG003629 (2017).Article 

    Google Scholar 
    Rewicz, A. et al. Morphometric traits in the fine-leaved fescues depend on ploidy level: The case of Festuca amethystina L. PeerJ 6, e5576. https://doi.org/10.7717/peerj.5576 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kiedrzyński, M. et al. Tetraploids expanded beyond the mountain niche of their diploid ancestors in the mixed-ploidy grass Festuca amethystina L. Sci. Rep. 11, 18735 (2021).ADS 
    Article 

    Google Scholar 
    Mounger, J. et al. Epigenetics and the success of invasive plants. Philos. Trans. R. Soc. B 376, 20200117. https://doi.org/10.1098/rstb.2020.0117 (2021).CAS 
    Article 

    Google Scholar 
    Bewick, A. J. & Schmitz, R. J. Epigenetics in the wild. Elife 4, e07808. https://doi.org/10.7554/eLife.07808 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sahu, P. P. et al. Epigenetic mechanisms of plant stress responses and adaptation. Plant Cell Rep. 32(8), 1151–1159. https://doi.org/10.1007/s00299-013-1462-x (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Alonso, C. et al. Interspecific variation across angiosperms in global DNA methylation: Phylogeny, ecology and plant features in tropical and Mediterranean communities. New Phytol. 224(2), 949–960. https://doi.org/10.1111/nph.16046 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Angers, B., Castonguay, E. & Massicotte, R. Environmentally induced phenotypes and DNA methylation: How to deal with unpredictable conditions until the next generation and after. Mol. Ecol. 19(7), 1283–1295. https://doi.org/10.1111/j.1365-294X.2010.04580.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Batog, J. & Wawro, A. Process of obtaining bioethanol from sorghum biomass using genome shuffling. Cellul. Chem. Technol. 53, 459–467 (2019).CAS 
    Article 

    Google Scholar 
    Richards, C. L., Schrey, A. W. & Pigliucci, M. Invasion of diverse habitats by few Japanese knotweed genotypes is correlated with epigenetic differentiation. Ecol. Lett. 15, 1016–1025. https://doi.org/10.1111/j.1461-0248.2012.01824.x (2012).Article 
    PubMed 

    Google Scholar 
    Li, N. et al. DNA methylation repatterning accompanying hybridization, whole genome doubling and homoeolog exchange in nascent segmental rice allotetraploids. New Phytol. 223(2), 979–992. https://doi.org/10.1111/nph.15820 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Róis, A. S. et al. Epigenetic rather than genetic factors may explain phenotypic divergence between coastal populations of diploid and tetraploid Limonium spp. (Plumbaginaceae) in Portugal. BMC Plant Biol. 13(1), 205. https://doi.org/10.1186/1471-2229-13-205 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, A. et al. DNA methylation in genomes of several annual herbaceous and woody perennial plants of varying ploidy as detected by MSAP. Plant Mol. Biol. Rep. 29, 784–793. https://doi.org/10.1007/s11105-010-0280-3 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    Sokolova, D. A., Vengzhen, G. S. & Kravets, A. P. An Analysis of the correlation between the changes in satellite DNA methylation patterns and plant cell responses to the stress. Cell Bio 2, 163–171. https://doi.org/10.4236/cellbio.2013.23018 (2013).CAS 
    Article 

    Google Scholar 
    Johnson, L. I. & Tricker, P. J. Epigenomic plasticity within populations: Its evolutionary significance and potential. Heredity 105, 113–121. https://doi.org/10.1038/hdy.2010.25 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zheng, X. et al. Transgenerational variations in DNA methylation induced by drought stress in two rice varieties with distinguished difference to drought resistance. PLoS One 8(11), e80253. https://doi.org/10.1371/journal.pone.0080253 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Karan, R., DeLeon, T., Biradar, H. & Subudhi, P. K. Salt Stress induced variation in DNA methylation pattern and its influence on gene expression in contrasting rice genotypes. PLoS One 7(6), e40203. https://doi.org/10.1371/journal.pone.0040203 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Richards, C. L. & Pigliucci, M. Epigenetic inheritance. A decade into the extended evolutionary synthesis. Paradigmi 38, 463–494. https://doi.org/10.30460/99624 (2020).Article 

    Google Scholar 
    Chelaifa, H., Monnier, A. & Ainouche, M. Transcriptomic changes following recent natural hybridization and allopolyploidy in the salt marsh species Spartina × townsendii and Spartina anglica (Poaceae). New Phytol. 186(1), 161–174. https://doi.org/10.1111/j.1469-8137.2010.03179.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Al-Lawati, A., Al-Bahry, S., Victor, R., Al-Lawati, A. H. & Yaish, M. W. Salt stress alters DNA methylation levels in alfalfa (Medicago spp.). Genet. Mol. Res. 15, 15018299. https://doi.org/10.4238/gmr.15018299 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lewandowska-Gnatowska, E. et al. Is DNA methylation modulated by wounding-induced oxidative burst in maize?. Plant Physiol. Biochem. 82, 202–208. https://doi.org/10.1016/j.plaphy.2014.06.003 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Marfil, C. et al. Changes in grapevine DNA methylation and polyphenols content induced by solar ultraviolet-B radiation, water deficit and abscisic acid spray treatments. Plant Physiol. Biochem. 135, 287–294. https://doi.org/10.1016/j.plaphy.2018.12.021 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zedek, F. et al. Endopolyploidy is a common response to UV-B stress in natural plant populations, but its magnitude may be affected by chromosome type. Ann. Bot. 126(5), 883–889. https://doi.org/10.1093/aob/mcaa109 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pandey, N. & Pandey-Rai, S. Deciphering UV-B-induced variation in DNA methylation pattern and its influence on regulation of DBR2 expression in Artemisia annua L. Planta 242(4), 869–879. https://doi.org/10.1007/s00425-015-2323-3 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Molinier, J. Genome and epigenome surveillance processes underlying UV exposure in plants. Genes 8(11), 316. https://doi.org/10.3390/genes8110316 (2017).CAS 
    Article 
    PubMed Central 

    Google Scholar 
    Niederhuth, C. E. et al. Widespread natural variation of DNA methylation within angiosperms. Genome Biol. 17, 194. https://doi.org/10.1186/s13059-016-1059-0 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lira-Medeiros, C. F. et al. Epigenetic variation in mangrove plants occurring in contrasting natural environment. PLoS One 5, e10326. https://doi.org/10.1371/journal.pone.0010326 (2010).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Richards, C. L., Verhoeven, K. J. F. & Bossdorf, O. Evolutionary significance of epigenetic variation. In Plant Genome Diversity Vol. 1 (eds Wendel, J. F. et al.) 257–274 (Springer Vienna, 2012).Chapter 

    Google Scholar 
    Paun, O. et al. Stable epigenetic effects impact adaptation in allopolyploid orchids (Dactylorhiza: Orchidaceae). Mol. Biol. Evol. 27, 2465–2473. https://doi.org/10.1093/molbev/msq150 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xie, H. et al. Global DNA methylation patterns can play a role in defining terroir in grapevine (Vitis vinifera cv. Shiraz). Front. Plant Sci. 8, 130398. https://doi.org/10.3389/fpls.2017.01860 (2017).Article 

    Google Scholar 
    Herrera, C. M. & Bazaga, P. Epigenetic differentiation and relationship to adaptive genetic divergence in discrete populations of the violet Viola cazorlensis. New Phytol. 187(3), 867–876. https://doi.org/10.1111/j.1469-8137.2010.03298.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Portis, E., Acquadro, A., Comino, C. & Lanteri, S. Analysis of DNA methylation during germination of pepper (Capsicum annuum L.) seeds using methylation-sensitive amplification polymorphism (MSAP). Plant Sci. 166, 169–178. https://doi.org/10.1016/j.plantsci.2003.09.004 (2004).CAS 
    Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. http://www.R-project.org (R Foundation for Statistical Computing, 2013).Schloerke, B. et al. GGally: Extension to “ggplot2” R package version 2.1.0. https://CRAN.R-project.org/package=GGally (2021).StatSoft, Inc. STATISTICA (Data Analysis Software System), Version 10. http://www.statsoft.com (2011).Tomczyk, P. Phenotypic measurement of inbreeding depression in grasses—An overview of traits (Fenotypowe miary depresji wsobnej u traw—przegląd cech). Wiad. Bot. https://doi.org/10.5586/wb.2019.005 (2019).Article 

    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315. https://doi.org/10.1002/joc.5086 (2017).Article 

    Google Scholar 
    Fox, J. & Weisberg, S. An {R} Companion to Applied Regression (Sage Publications, 2019).
    Google Scholar  More

  • in

    Human-ignited fires result in more extreme fire behavior and ecosystem impacts

    Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).ADS 
    Article 

    Google Scholar 
    Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    United Nations Environment Programme. Spreading like Wildfire–The Rising Threat of Extraordinary Landscape Fires. A UNEP Rapid Response Assessment. (United Nations Environment Programme, Nairobi, 2022).Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earth’s Future 7, 892–910 (2019).ADS 
    Article 

    Google Scholar 
    Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).ADS 
    Article 

    Google Scholar 
    Westerling, A. L. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. B: Biol. Sci. 371, 20150178 (2016).Article 

    Google Scholar 
    Pyne, S. J. Fire in America: A Cultural History of Wildland and Rural Fire. (University of Washington Press, 2017).Fire and Resource Assessment Program. Fire Perimeters. Available: https://frap.fire.ca.gov/frap-projects/fire-perimeters/. (California Department of Forestry & Fire Protection, 2018).Westerling, A. L., Hidalgo, H. G., Cayan, D. R. & Swetnam, T. W. Warming and earlier spring increase Western U.S. forest wildfire activity. Science 313, 940–943 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    Starrs, C. F., Butsic, V., Stephens, C. & Stewart, W. The impact of land ownership, firefighting, and reserve status on fire probability in California. Environ. Res. Lett. 13, 034025 (2018).ADS 
    Article 

    Google Scholar 
    Lydersen, J. M. et al. Evidence of fuels management and fire weather influencing fire severity in an extreme fire event. Ecol. Appl. 27, 2013–2030 (2017).Article 

    Google Scholar 
    Parsons, D. J. & DeBenedetti, S. H. Impact of fire suppression on a mixed-conifer forest. For. Ecol. Manag. 2, 21–33 (1979).Article 

    Google Scholar 
    Vose, R., Easterling, D. R., Kunkel, K. & Wehner, M. Temperature Changes in the United States. (NASA, 2017).Balch, J. K. et al. Human-started wildfires expand the fire niche across the United States. Proc. Natl Acad. Sci. USA 114, 2946–2951 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Stephens, S. L., Martin, R. E. & Clinton, N. E. Prehistoric fire area and emissions from California’s forests, woodlands, shrublands, and grasslands. For. Ecol. Manag. 251, 205–216 (2007).Article 

    Google Scholar 
    Sugihara, N. G., Van Wagtendonk, J. W., Fites-Kaufman, J., Shaffer, K. E. & Thode, A. E. Fire in California’s Ecosystems. (University of California Press, 2006).Jin, Y. et al. Identification of two distinct fire regimes in Southern California: implications for economic impact and future change. Environ. Res. Lett. 10, 094005 (2015).ADS 
    Article 

    Google Scholar 
    Trollope, W. in Ecological Effects of Fire In South African Ecosystems. 199–217 (Springer, 1984).Byram, G. M. in Forest Fire: Control and Use (ed. Davis, K. P.) 155–182 (McGraw-Hill, 1959).McLauchlan, K. K. et al. Fire as a fundamental ecological process: Research advances and frontiers. J. Ecol. https://doi.org/10.1111/1365-2745.13403 (2020).Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    Schroeder, W., Oliva, P., Giglio, L. & Csiszar, I. A. The New VIIRS 375m active fire detection data product: Algorithm description and initial assessment. Remote Sens. Environ. 143, 85–96 (2014).ADS 
    Article 

    Google Scholar 
    Rothermel, R. C. A Mathematical Model for Predicting Fire Spread in Wildland Fuels (USFS, 1972).Hood, S. M., Varner, J. M., van Mantgem, P. & Cansler, C. A. Fire and tree death: understanding and improving modeling of fire-induced tree mortality. Environ. Res. Lett. 13, 113004 (2018).ADS 
    Article 

    Google Scholar 
    Cattau, M. E., Wessman, C., Mahood, A., Balch, J. K. & Poulter, B. Anthropogenic and lightning‐started fires are becoming larger and more frequent over a longer season length in the USA. Glob. Ecol. Biogeogr. 29, 668–681 (2020).Article 

    Google Scholar 
    Abatzoglou, J. T., Balch, J. K., Bradley, B. A. & Kolden, C. A. Human-related ignitions concurrent with high winds promote large wildfires across the USA. Int. J. Wildland Fire 27, 377–386 (2018).Article 

    Google Scholar 
    Fried, J. S. et al. Predicting the effect of climate change on wildfire behavior and initial attack success. Clim. Change 87, 251–264 (2008).Article 

    Google Scholar 
    van Wagtendonk, J. W. The history and evolution of wildland fire use. Fire Ecol. 3, 3–17 (2007).Article 

    Google Scholar 
    Sullivan, A. L. Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. Int. J. Wildland Fire 18, 369–386 (2009).Article 

    Google Scholar 
    Wang, X. et al. Projected changes in fire size from daily spread potential in Canada over the 21st century. Environ. Res. Lett. 15, 104048 (2020).ADS 
    Article 

    Google Scholar 
    Parks, S. A. et al. High-severity fire: evaluating its key drivers and mapping its probability across western US forests. Environ. Res. Lett. 13, 044037 (2018).ADS 
    Article 

    Google Scholar 
    Hantson, S. et al. The status and challenge of global fire modelling. Biogeosciences 13, 3359–3375 (2016).ADS 
    Article 

    Google Scholar 
    Reinhardt, E. D. First Order Fire Effects Model: FOFEM 4.0, User’s Guide. (Intermountain Forest and Range Experiment Station, Forest Service, US …, 1997).Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 1–11 (2015).CAS 
    Article 

    Google Scholar 
    Pateiro-Lopez, B. & Rodriguez-Casal, A. alphahull: Generalization of the Convex Hull of a Sample of Points in the Plane v. R package version 2.2 (2019).Edelsbrunner, H., Kirkpatrick, D. & Seidel, R. On the shape of a set of points in the plane. IEEE Trans. Inf. theory 29, 551–559 (1983).MathSciNet 
    Article 

    Google Scholar 
    Rodríguez Casal, A. & Pateiro López, B. Generalizing the Convex Hull of A Sample: the R Package alphahull. (2010).Bell, D. M. et al. Multiscale divergence between Landsat-and lidar-based biomass mapping is related to regional variation in canopy cover and composition. Carbon Balance Manag. 13, 15 (2018).Article 

    Google Scholar 
    Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).Article 

    Google Scholar 
    MTBS. Monitoring Trends in Burn Severity Data Access: Fire Level Geospatial Data. (MTBS). (2018).Miller, J. D. et al. Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens. Environ. 113, 645–656 (2009).ADS 
    Article 

    Google Scholar 
    Homer, C. et al. Completion of the 2011 National Land Cover Database for the conterminous United States–representing a decade of land cover change information. Photogrammetric Eng. Remote Sens. 81, 345–354 (2015).
    Google Scholar  More

  • in

    Endocranial volume increases across captive generations in the endangered Mexican wolf

    Sol, D., Bacher, S., Reader, S. M. & Lefebvre, L. Brain size predicts the success of mammal species introduced into novel environments. Am. Nat. 172(Suppl. 1), S63–S71 (2008).PubMed 
    Article 

    Google Scholar 
    González-Lagos, C., Sol, D. & Reader, S. M. Large-brained mammals live longer. J. Evol. Biol. 23, 1064–1074 (2010).PubMed 
    Article 

    Google Scholar 
    Gonda, A., Herczeg, G. & Merilä, J. Evolutionary ecology of intraspecific brain size variation: A review. Ecol. Evol. 3(8), 2751–2764 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. M. & Holekamp, K. E. Brain size predicts problem-solving ability in mammalian carnivores. PNAS 113(9), 2532–2537 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Näslund, J., Aarestrup, K., Thomassen, S. T. & Johnsson, J. I. Early enrichment effects on brain development in hatchery-reared Atlantic salmon (Salmo salar): No evidence for a critical period. Can. J. Fish. Aquat. Sci. 69(9), 1481–1490 (2012).Article 

    Google Scholar 
    Logan, C. J., Kruuk, L. E. B., Stanley, R., Thompson, A. M. & Clutton-Brock, T. H. Endocranial volume is heritable and is associated with longevity and fitness in a wild mammal. R. Soc. Open Sci. 3(12), 160622 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yamaguchi, N., Kitchener, A. C., Gilissen, E. & MacDonald, D. W. Brain size of the lion (Panthera leo) and the tiger (P. tigris): Implications for intrageneric phylogeny, intraspecific differences and the effects of captivity. Biol. J. Linn. Soc. 98, 85–93 (2009).Article 

    Google Scholar 
    Turschwell, M. P. & White, C. R. The effects of laboratory housing and spatial enrichment on brain size and metabolic rate in the eastern mosquitofish Gambusia holbrooki. Biol. Open. 5(3), 205–210 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Welniak-Kaminska, M. et al. Volumes of brain structures in captive wild-type and laboratory rats: 7T magnetic resonance in vivo automatic atlas-based study. PLoS ONE 14(4), e0215348 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guay, P. J., Parrott, M. & Selwood, L. Captive breeding does not alter brain volume in a marsupial over a few generations. Zoo Biol. 31, 82–86 (2012).PubMed 
    Article 

    Google Scholar 
    Isler, K. et al. Endocranial volumes of primate species: Scaling analyses using a comprehensive and reliable data set. J. Hum. Evol. 55(6), 967–978 (2008).PubMed 
    Article 

    Google Scholar 
    Burns, J. G., Saravanan, A. & Rodd, F. H. Rearing environment affects the brain size of guppies: Lab-reared guppies have smaller brains than wild-caught guppies. Ethol. 115(2), 122–133 (2009).Article 

    Google Scholar 
    Kruska, D. On the evolutionary significance of encephalization in some eutherian mammals: Effects of adaptive radiation, domestication, and feralization. Brain Behav. Evol. 65(2), 73–108 (2005).PubMed 
    Article 

    Google Scholar 
    Logan, C. J. & Clutton-Brock, T. H. Validating methods for estimating endocranial volume in individual red deer (Cervus elaphus). Behav. Processes. 92, 143–146 (2013).PubMed 
    Article 

    Google Scholar 
    Colby, A. E., Kimock, C. M. & Higham, J. P. Endocranial volume is variable and heritable, but not related to fitness, in a free-ranging primate. Sci. Rep. 11, 1–11 (2021).Article 
    CAS 

    Google Scholar 
    Stuermer, I. W. & Wetzel, W. Early experience and domestication affect auditory discrimination learning, open field behaviour and brain size in wild Mongolian gerbils and domesticated Laboratory gerbils (Meriones unguiculatus forma domestica). Behav. Brain Res. 173, 11–21 (2006).PubMed 
    Article 

    Google Scholar 
    Agnvall, B., Bélteky, J. & Jensen, P. Brain size is reduced by selection for tameness in red junglefowl-correlated effects in vital organs. Sci. Rep. 7(3306), 1–7 (2017).CAS 

    Google Scholar 
    Röhrs, M. & Ebinger, P. Wild is not really wild: Brain weight of wild and domestic mammals. Berl. Munch. Tierarztliche Wochenschrift. 112(6–7), 234–238 (1999).
    Google Scholar 
    Smith, B. P., Lucas, T. A., Norris, R. M. & Henneberg, M. Brain size/body weight in the dingo (Canis dingo): Comparisons with domestic and wild canids. Aust. J. Zool. 65(5), 292–301 (2017).Article 

    Google Scholar 
    Roberts, T., McGreevy, P. & Valenzuela, M. Human induced rotation and reorganization of the brain of domestic dogs. PLoS ONE 5(7), e11946 (2010).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Pollen, A. A. et al. Environmental complexity and social organization sculpt the brain in Lake Tanganyikan cichlid fish. Brain Behav. Evol. 70, 21–39 (2007).PubMed 
    Article 

    Google Scholar 
    Kihslinger, R. L., Lema, S. C. & Nevitt, G. A. Environmental rearing conditions produce forebrain differences in wild Chinook salmon Oncorhynchus tshawytscha. Comp. Biochem. Physiol. 145(2), 145–151 (2006).CAS 
    Article 

    Google Scholar 
    Guay, P. J. & Iwaniuk, A. N. Captive breeding reduces brain volume in waterfowl (Anseriformes). Condor 110(2), 276–284 (2008).Article 

    Google Scholar 
    Diamond, M. C., Ingham, C. A., Johnson, R. E., Bennett, E. L. & Rosenzweig, M. R. Effects of environment on morphology of rat cerebral cortex and hippocampus. J. Neurobiol. 7, 75–85 (1976).CAS 
    PubMed 
    Article 

    Google Scholar 
    Courtney Jones, S. K., Munn, A. J. & Byrne, P. G. Effect of captivity on morphology: Negligible changes in external morphology mask significant changes in internal morphology. R. Soc. Open Sci. 5(5), 1–13 (2018).Article 

    Google Scholar 
    Kruska, D. & Röhrs, M. Comparative-quantitative investigations on brains of feral pigs from the Galapagos Islands and of European domestic pigs. Z. Anat. Entwicklungsgesch. 144(1), 61–73 (1974).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kruska, D. Changes of brain size in Tylopoda during phylogeny and caused by domestication. Verh. Dtsch. Zool. Ges. 75, 173–183 (1982).
    Google Scholar 
    Groves, C. P. Skull-changes due to captivity in certain Equidae. Z. Säugetierkd. 31, 44–46 (1966).
    Google Scholar 
    Groves, C. P. The skulls of Asian rhinoceroses: Wild and captive. Zoo Biol. 1, 251–261 (1982).Article 

    Google Scholar 
    Hollister, N. Some effects of environment and habit on captive lions. Proc. US. Natl. Mus. 53, 177–193 (1917).Article 

    Google Scholar 
    Price, E. O. Behavioral development in animals undergoing domestication. Appl. Anim. Behav. Sci. 65(3), 245–271 (1999).Article 

    Google Scholar 
    Wolff, J. Das Gesetz der Transformation der Knochen (A. Hirchwild, 1892).
    Google Scholar 
    Herring, S. W. Formation of the vertebrate face: Epigenetic and functional influences. Am. Zool. 33, 472–483 (1993).Article 

    Google Scholar 
    Wroe, S. & Milne, N. Convergence and remarkably consistent constraint in the evolution of carnivore skull shape. Evol. 61(5), 1251–1260 (2007).Article 

    Google Scholar 
    Damasceno, E. M., Hingst-Zaher, E. & Astúa, D. Bite force and encephalization in the Canidae (Mammalia: Carnivora). J. Zool. 290(4), 246–254 (2013).Article 

    Google Scholar 
    Van Valkenburgh, B. Deja vu: the evolution of feeding morphologies in the Carnivora. Integr. Comp. Biol. 47, 147–163 (2007).PubMed 
    Article 

    Google Scholar 
    Van Valkenburgh, B. Carnivore dental adaptations and diet: A study of trophic diversity within guilds in Carnivore behavior, ecology, and evolution (ed. Gittleman, J. L.) 410–436 (Springer Science & Business Media, 1989).Slater, G. J., Dumont, E. R. & Van Valkenburgh, B. Implications of predatory specialization for cranial form and function in canids. J. Zool. 278(3), 181–188 (2009).Article 

    Google Scholar 
    Michaud, M., Veron, G. & Fabre, A. C. Phenotypic integration in feliform carnivores: Covariation patterns and disparity in hypercarnivores versus generalists. Evol. 74(12), 2681–2702 (2020).Article 

    Google Scholar 
    O’Regan, H. J. & Kitchener, A. C. The effects of captivity on the morphology of captive, domesticated and feral mammals. Mamm. Rev. 35, 215–230 (2005).Article 

    Google Scholar 
    Kapoor, V., Antonelli, T., Parkinson, J. A. & Hartstone-Rose, A. Oral health correlates of captivity. Res. Vet. Sci. 107, 213–219 (2016).PubMed 
    Article 

    Google Scholar 
    Mitchell, D. R., Wroe, S., Ravosa, M. J. & Menegaz, R. A. More challenging diets sustain feeding performance: Applications toward the captive rearing of wildlife. Integr. Org. Biol. 3, 1–13 (2021).
    Google Scholar 
    Curtis, A. A., Orke, M., Tetradis, S. & Van Valkenburgh, B. Diet-related differences in craniodental morphology between captive-reared and wild coyotes, Canis latrans (Carnivora: Canidae). Biol. J. Linn. Soc. 123(3), 677–693 (2018).Article 

    Google Scholar 
    Siciliano-Martina, L., Light, J. E. & Lawing, A. M. Cranial morphology of captive mammals: A meta-analysis. Front. Zool. 18(4), 1–13 (2021).
    Google Scholar 
    Corruccini, R. S. & Beecher, R. M. Occlusal variation related to soft diet in a nonhuman primate. Science 218, 74–75 (1982).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Ramirez Rozzi, F. V., González-José, R. & Pucciarelli, H. M. Cranial growth in normal and low-protein-fed Saimiri An environmental heterochrony. J. Hum. Evol. 49(4), 515–535 (2005).PubMed 
    Article 

    Google Scholar 
    Taylor, A. B. & van Schaik, C. P. Variation in brain size and ecology in Pongo. J. Hum. Evol. 52, 59–71 (2007).PubMed 
    Article 

    Google Scholar 
    AZA Canid TAG. Large Canid (Canidae) Care Manual. (Association of Zoos and Aquariums, 2012).Mexican Wolf Species Survival Plan. Mexican Gray Wolf Husbandry Manual: Guidelines for Captive Management (2009 edition). (Mexican Wolf Species Survival Plan and U.S. Fish and Wildlife Service, 2009).Carrera, R. et al. Comparison of Mexican wolf and coyote diets in Arizona and New Mexico. The J. Wildl. Manag. 72(2), 376–381 (2008).Article 

    Google Scholar 
    Reed, J. E. et al. Diets of free-ranging Mexican gray wolves in Arizona and New Mexico. Wildl. Soc. Bull. 34(4), 1127–1133 (2006).Article 

    Google Scholar 
    Kazimierska, K., Biel, W. & Witkowicz, R. Mineral composition of cereal and cereal-free dry dog foods versus nutritional guidelines. Molecules 25(21), 1–24 (2020).Article 
    CAS 

    Google Scholar 
    Pezzali, J. G. & Aldrich, C. G. Effect of ancient grains and grain-free carbohydrate sources on extrusion parameters and nutrient utilization by dogs. J. Anim. Sci. 98(2), 3758–3767 (2019).Article 

    Google Scholar 
    Hartstone-Rose, A., Selvey, H., Villari, J. R., Atwell, M. & Schmidt, T. The three-dimensional morphological effects of captivity. PLoS ONE 9(11), 1–15 (2014).Article 
    CAS 

    Google Scholar 
    Siciliano-Martina, L., Light, J. E. & Lawing, A. M. Changes in canid cranial morphology induced by captivity and conservation implications. Biol. Conserv. 257, 109143 (2021).Article 

    Google Scholar 
    Hedrick, P. W. & Fredrickson, R. Genetic rescue guidelines with examples from Mexican wolves and Florida panthers. Conserv. Genet. 11(2), 615–626 (2010).Article 

    Google Scholar 
    Greely, S. E. Mexican Wolf, Canis lupus baileyi, International Studbook 2018. Palm Desert, California. (2018).Kalinowski, S. T., Hedrick, P. W. & Miller, P. S. No inbreeding depression observed in Mexican and red wolf captive breeding programs. Conserv. Biol. 13(6), 1371–1377 (1999).Article 

    Google Scholar 
    Sakai, S. T., Whitt, B., Arsznov, B. M. & Lundrigan, B. L. Endocranial development in the coyote (Canis latrans) and gray wolf (Canis lupus): A computed tomographic study. Brain Behav. Evol. 91(2), 1–18 (2018).Article 

    Google Scholar 
    Van Valkenburgh, B. Skeletal and dental predictors of body mass in carnivores in Body size in mammalian paleobiology: estimation and biological implications (eds. Damuth, J. & MacFadden, B. J.) (Cambridge University Press, 1990).Rohlf, F. J. TPSDig2: a program for landmark development and analysis (2001).Siciliano-Martina, L., Light, J. E., Riley, D. G. & Lawing, A. M. One of these wolves is not like the other: morphological effects and conservation implications of captivity in Mexican wolves. Anim. Conserv. 25, 77–90 (2021).Article 

    Google Scholar 
    Zelditch, M. L., Donald, L., Swiderski, H., Sheets, D. & Fink, W. L. Geometric morphometrics for biologists: a primer. (Elsevier Academic Press, 2004).Coster, A. pedigree: Pedigree functions. R package version 1.4 (2013).Traylor-Holzer, K. (ed.). PMx user’s manual. Version 1.0. Apple Valley, MN: IUCN SSC Conservation Breeding Specialist Group. (2011).Thomason, J. J. Cranial strength in relation to estimated biting forces in some Mammals. Can. J. Zool. 69, 2326–2333 (1991).Article 

    Google Scholar 
    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods. 9(7), 676–682 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2020).Cofran, Z. Brain size growth in wild and captive chimpanzees (Pan troglodytes). Am. J. Primat. 80(7), 1–8 (2018).Article 

    Google Scholar 
    Witzenberger, K. A. & Hochkirch, A. Ex situ conservation genetics: A review of molecular studies on the genetic consequences of captive breeding programmes for endangered animal species. Biodivers. Conserv. 20(9), 1843–1861 (2011).Article 

    Google Scholar 
    Gómez-Sánchez, D. et al. On the path to extinction: Inbreeding and admixture in a declining grey wolf population. Mole. Ecol. 27(18), 3599–3612 (2018).Article 

    Google Scholar 
    Elbroch, M. Animal skulls: a guide to North American species. (Stackpole Books, 2006).Conde, D. A., Flesness, N., Colchero, F., Jones, O. R. & Scheuerlein, A. An emerging role of zoos to conserve biodiversity. Science 331, 1390–1391 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Prado, E. L. & Dewey, K. G. Nutrition and brain development in early life. Nutr. Rev. 72(4), 267–284 (2014).PubMed 
    Article 

    Google Scholar 
    Hecht, E. E. et al. Neuromorphological changes following selection for tameness and aggression in the Russian farm-fox experiment. J. Neurosci. 41(28), 6144–6156 (2021).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Bennett, E. L., Rosenzweig, M. R. & Diamond, M. C. Rat brain: Effects of environmental enrichment on wet and dry weights. Science 163(3869), 825–826 (1969).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Cummins, R. A., Walsh, R. N., Budtz-Olsen, O. E., Konstantinos, T. & Horsfall, C. R. Environmentally-induced changes in the brains of elderly rats. Nature 243(5409), 516–518 (1973).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Welch, B. L., Brown, D. G., Welch, A. S. & Lin, D. C. Isolation, restrictive confinement or crowding of rats for one year. I. Weight, nucleic acids and protein of brain regions. Brain Res. 75, 71–84 (1974).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Infected food web and ecological stability

    Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: How many parasites? How many hosts?. Proc. Natl. Acad. Sci. 105, 11482–11489 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    Seabloom, E. W. et al. The community ecology of pathogens: Coinfection, coexistence and community composition. Ecol. Lett. 18, 401–415 (2015).Article 

    Google Scholar 
    French, R. K. & Holmes, E. C. An ecosystems perspective on virus evolution and emergence. Trends Microbiol. 28, 165–175 (2020).CAS 
    Article 

    Google Scholar 
    Hudson, P. J., Dobson, A. P. & Lafferty, K. D. Is a healthy ecosystem one that is rich in parasites?. Trends Ecol. Evol. 21, 381–385 (2006).Article 

    Google Scholar 
    Raffel, T. R., Martin, L. B. & Rohr, J. R. Parasites as predators: Unifying natural enemy ecology. Trends Ecol. Evol. 23, 610–618 (2008).Article 

    Google Scholar 
    Johnson, P. T. J. et al. When parasites become prey: Ecological and epidemiological significance of eating parasites. Trends Ecol. Evol. 25, 362–371 (2010).Article 

    Google Scholar 
    Frainer, A., McKie, B. G., Amundsen, P. A., Knudsen, R. & Lafferty, K. D. parasitism and the biodiversity-functioning relationship. Trends Ecol. Evol. 33, 260–268 (2018).Article 

    Google Scholar 
    Jephcott, T. G., Sime-Ngando, T., Gleason, F. H. & Macarthur, D. J. Host-parasite interactions in food webs: Diversity, stability, and coevolution. Food Webs 6, 1–8 (2016).Article 

    Google Scholar 
    Rohr, J. R. et al. Towards common ground in the biodiversity–disease debate. Nat. Ecol. Evol. 4, 24–33 (2020).Article 

    Google Scholar 
    Johnson, P. T. J., De Roode, J. C. & Fenton, A. Why infectious disease research needs community ecology. Science 349, 1259504 (2015).Article 

    Google Scholar 
    Marcogliese, D. J. & Cone, D. K. Food webs: A plea for parasites. Trends Ecol. Evol. 12, 320–325 (1997).CAS 
    Article 

    Google Scholar 
    Chen, H.-W. et al. Network position of hosts in food webs and their parasite diversity. Oikos 117, 1847–1855 (2008).Article 

    Google Scholar 
    Lafferty, K. D., Dobson, A. P. & Kuris, A. M. Parasites dominate food web links. Proc. Natl. Acad. Sci. USA 103, 11211–11216 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    Lafferty, K. D. et al. Parasites in food webs: The ultimate missing links. Ecol. Lett. 11, 533–546 (2008).Article 

    Google Scholar 
    Dunne, J. A. The network structure of food webs. In Ecological Networks: Linking Structure to Dynamics (eds Pascual, M. & Dunne, J. A.) 27–28 (Oxford University Press, 2005).
    Google Scholar 
    Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: Robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

    Google Scholar 
    Hudson, P. J., Rizzoli, A., Grenfell, B. T., Heesterbeek, H. & Dobson, A. P. The Ecology of Wildlife Diseases. (Oxford University Press, Oxford, 2002).
    Google Scholar 
    Anderson, R. M. & May, R. M. Infectious Diseases of Humans: Dynamics and Control (Oxford University Press, 1992).
    Google Scholar 
    McCallum, H. & Dobson, A. Detecting disease and parasite threats to endangered species and ecosystems. Trends Ecol. Evol. 10, 190–194 (1995).CAS 
    Article 

    Google Scholar 
    De Castro, F. & Bolker, B. M. Parasite establishment and host extinction in model communities. Oikos 111, 501–513 (2005).Article 

    Google Scholar 
    McQuaid, C. F. & Britton, N. F. Parasite species richness and its effect on persistence in food webs. J. Theor. Biol. 364, 377–382 (2015).ADS 
    Article 

    Google Scholar 
    Holt, R. D., Dobson, A. P., Begon, M., Bowers, R. G. & Schauber, E. M. Parasite establishment in host communities. Ecol. Lett. 6, 837–842 (2003).
    Article 

    Google Scholar 
    Hatcher, M. J. & Dunn, A. M. Parasites in Ecological Communities: From Interactions to Ecosystems (Cambridge University Press, 2011).Book 

    Google Scholar 
    Dobson, A. Population dynamics of pathogens with multiple host species. Am. Nat. 164, S64–S78 (2004).Article 

    Google Scholar 
    McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794–798 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    Neutel, A. M., Heesterbeek, J. A. P. & de Ruiter, P. C. Stability in real food webs: Weak links in long loops. Science 296, 1120–1123 (2002).ADS 
    CAS 
    Article 

    Google Scholar 
    Chen, X. & Cohen, J. E. Transient dynamics and food–web complexity in the Lotka–Volterra cascade model. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268, 869–877 (2001).CAS 
    Article 

    Google Scholar 
    May, R. M. Stability in multispecies community models. Math. Biosci. 12, 59–79 (1971).MathSciNet 
    Article 

    Google Scholar 
    May, R. M. Will a large complex system be stable?. Nature 238, 413–414 (1972).ADS 
    CAS 
    Article 

    Google Scholar 
    Hilker, F. M. & Schmitz, K. Disease-induced stabilization of predator-prey oscillations. J. Theor. Biol. 255, 299–306 (2008).ADS 
    MathSciNet 
    Article 

    Google Scholar 
    Hethcote, H. W., Wang, W., Han, L. & Ma, Z. A predator–prey model with infected prey. Theor. Popul. Biol. 66, 259–268 (2004).Article 

    Google Scholar 
    Kooi, B. W., van Voorn, G. A. K. & Das, K. P. Stabilization and complex dynamics in a predator-prey model with predator suffering from an infectious disease. Ecol. Complex. 8, 113–122 (2011).Article 

    Google Scholar 
    Winemiller, K. O. Spatial and temporal variation in tropical fish trophic networks. Ecol. Monogr. 60, 331–367 (1990).Article 

    Google Scholar 
    Paine, R. T. Food-web analysis through field measurement of per capita interaction strength. Nature 355, 73–75 (1992).ADS 
    Article 

    Google Scholar 
    Wootton, J. T. Estimates and tests of per capita interaction strength: Diet, abundance, and impact of intertidally foraging birds. Ecol. Monogr. 67, 45–64 (1997).Article 

    Google Scholar 
    Cohen, J. E., Briand, F. & Newman, C. M. Community Food Webs: Data and Theory (Springer, 1990).Book 

    Google Scholar 
    Mougi, A. Diversity of biological rhythm and food web stability. Biol. Lett. 17, 20200673 (2021).Article 

    Google Scholar  More