Philippa Kaur

Shaw, J., Weyrich, L. & Cooper, A. Using environmental (e)DNA sequencing for aquatic biodiversity surveys: A beginner’s guide. Mar. Freshw. Res. 68, 68 (2016).
Google Scholar 
Smith, K. J. et al. Stable isotope analysis of specimens of opportunity reveals ocean-scale site fidelity in an elusive whale species. Front. Conserv. Sci. 2, 1–11 (2021).Article 

Google Scholar 
Coll, M. et al. The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats. PLoS One 5, (2010).Cavanagh, R. D. & Gibson, C. Overview of the conservation status of cartilaginous fishes (Chondrichthyans) in the Mediterranean Sea. https://doi.org/10.2305/iucn.ch.2007.mra.3.en (2007).Pace, D. S., Tizzi, R. & Mussi, B. Cetaceans value and conservation in the Mediterranean Sea. Journal Biodivers. Endanger. Species S1:
S1.004 (2015).Carlucci, R. et al. Modeling the spatial distribution of the striped dolphin (Stenella coeruleoalba) and common bottlenose dolphin (Tursiops truncatus) in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea). Ecol. Indic. 69, 707–721 (2016).Article 

Google Scholar 
Boldrocchi, G. et al. Distribution, ecology, and status of the white shark, Carcharodon carcharias, in the Mediterranean Sea. Rev. Fish Biol. Fish. 27, 515–534 (2017).Article 

Google Scholar 
Karamanlidis, A. A. et al. The Mediterranean monk seal Monachus monachus: Status, biology, threats, and conservation priorities. Mammal Review 46, 92–105. https://doi.org/10.1111/mam.12053 (2016).Article 

Google Scholar 
Johnson, W. M. The role of the Mediterranean monk seal (Monachus monachus) in European history and culture, from the fall of Rome to the 20th century Monk Seals in Post-Classical History. (2004).Johnson, W. M. & Lavigne, D. M. The Mediterranean Monk Seal (Monachus monachus) in Ancient History and Literature Monk Seals in Antiquity. (1999).Israëls, l. D. Thirty Years of Mediterranean Monk Seal Protection – A Review. Netherlands Com- Mission Int. Nat. Prot. Inst. voor Taxon. Zoölogie/Zoölogische Museum, Univ. van Amsterdam, Amsterdam, Netherlands. Meded. No. 281–65. (1992).Stringer, C. B. et al. Neanderthal exploitation of marine mammals in Gibraltar. Proc. Natl. Acad. Sci. U. S. A. 105, 14319–14324 (2008).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
La Mesa, G., Lauriano, G., Mo, G., Paglialonga, A. & Tunesi, L. Assessment of the conservation status of marine species of the Habitats Directive (92/43/EEC) in Italy: results, drawbacks and perspectives of the fourth national report (2013–2018). Biodivers Conserv (2021).Adamantopoulou, S., Karamanlidis, A. A., Dendrinos, P. & Gimenez, O. Citizen science indicates significant range recovery and defines new conservation priorities for Earth’s most endangered pinniped in Greece. Anim. Conserv. https://doi.org/10.1111/acv.12806 (2022).Article 

Google Scholar 
Nicolaou, H., Dendrinos, P., Marcou, M., Michaelides, S. & Karamanlidis, A. A. Re-establishment of the Mediterranean monk seal Monachus monachus in Cyprus: Priorities for conservation. Oryx 55, 526–528 (2021).Article 

Google Scholar 
Tenan, S. et al. Evaluating mortality rates with a novel integrated framework for nonmonogamous species. Conserv. Biol. 30, 1307–1319 (2016).Article 
PubMed 

Google Scholar 
Vanpe, C. et al. Estimating abundance of a recovering transboundary brown bear population with capture- recapture models. Peer Community Journal, 2, e71. (2022).Lecaudey, L. A., Schletterer, M., Kuzovlev, V. V., Hahn, C. & Weiss, S. J. Fish diversity assessment in the headwaters of the Volga River using environmental DNA metabarcoding. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 1785–1800 (2019).Article 

Google Scholar 
Itakura, H. et al. Environmental DNA analysis reveals the spatial distribution, abundance, and biomass of Japanese eels at the river-basin scale. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 361–373 (2019).Article 

Google Scholar 
Closek, C. J. et al. Marine vertebrate biodiversity and distribution within the central California current using environmental DNA (eDNA) metabarcoding and ecosystem surveys. Front. Mar. Sci. Vol. 6. (2019).Boldrocchi, G. & Storai, T. Data-mining social media platforms highlights conservation action for the Mediterranean Critically Endangered blue shark Prionace glauca. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 3087–3099 (2021).Article 

Google Scholar 
Thiel, M. et al. Citizen scientists and marine research: Volunteer participants, their contributions, and projection for the future. Oceanogr. Mar. Biol. An Annu. Rev. 52, 257–314 (2014).
Google Scholar 
Araujo, G. et al. Citizen science sheds light on the cryptic ornate eagle ray Aetomylaeus vespertilio. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 2012–2018 (2020).Article 

Google Scholar 
Silvertown, J. A new dawn for citizen science. Trends Ecol. Evol. 24, 467–471 (2009).Article 
PubMed 

Google Scholar 
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).Article 

Google Scholar 
Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, (2014).Strickler, K. M., Fremier, A. K. & Goldberg, C. S. Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biol. Conserv. 183, 85–92 (2015).Article 

Google Scholar 
Eichmiller, J., Best, S. E. & Sorensen, P. W. Effects of temperature and trophic state on degradation of environmental DNA in lake water. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.5b05672 (2016).Article 
PubMed 

Google Scholar 
Mächler, E., Osathanunkul, M. & Altermatt, F. Shedding light on eDNA: neither natural levels of UV radiation nor the presence of a filter feeder affect eDNA-based detection of aquatic organisms. PLoS ONE 13, 1–15 (2018).Article 

Google Scholar 
Jo, T., Murakami, H., Yamamoto, S., Masuda, R. & Minamoto, T. Effect of water temperature and fish biomass on environmental DNA shedding, degradation, and size distribution. Ecol. Evol. 9, 1135–1146 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mauvisseau, Q. et al. The multiple states of environmental DNA and what is known about their persistence in aquatic environments. Environ. Sci. Technol. 56, 5322–5333 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Valsecchi, E. et al. A species – specific qPCR assay provides novel insight into range expansion of the Mediterranean monk seal (Monachus monachus ) by means of eDNA analysis. Biodivers. Conserv. 31, 1175–1196 (2022).Article 

Google Scholar 
Collins, R. A. et al. Persistence of environmental DNA in marine systems. Commun. Biol. https://doi.org/10.1038/s42003-018-0192-6 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhao, B., P.M., B. & Timbros, K. The particle size distribution of environmental DNA varies with species and degradation. Sci. Total Environ. 797, 149175 (2021).Würtz, M. Mediterranean submarine canyons. in Ecology and Governance (ed. IUCN) 192 (2012).Valsecchi, E. et al. Ferries and environmental DNA: Underway sampling from commercial vessels provides new opportunities for systematic genetic surveys of marine biodiversity. Front. Mar. Sci. 8, 1–17 (2021).Article 

Google Scholar 
Bustin, S. A. et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 622, 611–622 (2009).Article 

Google Scholar 
Klymus, K. E. et al. Reporting the limits of detection and quantification for environmental DNA assays. Environ. DNA 1–12. https://doi.org/10.1002/edn3.29 (2019).Goldberg, G. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 1299–1307. https://doi.org/10.1111/2041-210X.12595 (2016).Farrell, J. A. et al. Detection and population genomics of sea turtle species via noninvasive environmental DNA analysis of nesting beach sand tracks and oceanic water. Mol. Ecol. Resour. (2022).Shamblin, B. M. et al. Loggerhead turtle eggshells as a source of maternal nuclear genomic DNA for population genetic studies. Mol. Ecol. Resour. 11, 110–115 (2011).Article 
PubMed 

Google Scholar 
MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).Article 

Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 37–41 (1999).Akaike, H. Information theory and an extension of the maximum likelihood principle in Breakthroughs in Statistics, Vol.I, Foundations and Basic Theory, (eds. Kotz, S. and Johnson, N.L.) 610–624 (Springer-Verlag, New York, 1992).Adamantopoulou, S. et al. Movements of Mediterranean Monk Seals (Monachus monachus) in the Eastern Mediterranean Sea. Aquat. Mamm. 37, 256–261 (2011).Article 

Google Scholar  More

Forest Inventory dataTree growth, tree survival, and plot-level basal area data were compiled from the Forest Inventory and Analysis (FIA) program database (accessed on January 24, 2017, FIA phase 2 manual version 6.1; http://www.fia.fs.fed.us/). Aboveground tree biomass was estimated from tree diameter measurements44 and then multiplied by 0.5 to estimate aboveground C. Tree growth rates were calculated from the difference in estimated aboveground C between the latest and first live measurement of every tree and divided by the elapsed time between measurements to the day. Tree species that had at least 2000 individual trees after the data filters were applied were retained for further growth and survival evaluation. The probability of tree survival was calculated using the first measurement to the last measurement of a plot. Trees that were alive at both measurements were assigned a value of 1 (survived) and trees alive at the first and dead at the last measurement were assigned a value of 0 (dead). The duration between the first and last measurement was used to determine the annual probability of tree survival. Trees that were recorded as dead at both measurement inventories and trees that were harvested were excluded from the survival analysis.Predictor data: Climate, deposition, size, and competitionThere were six predictors that were related to the response rate of growth or survival for each individual tree: mean annual temperature, mean annual precipitation, mean annual total nitrogen deposition, mean annual total S deposition, tree size, and plot-level competition.To obtain total N and S deposition rates for each tree, we used spatially modeled N and S deposition data from the National Atmospheric Deposition Program’s Total Deposition Science Committee32. Annual N and S deposition rates were then averaged from the first year of measurement to the last year of measurement for every tree so that each tree had an individualized average N deposition based on the remeasurement years, and each species had an individualized range of average N deposition exposure based on its distribution. Monthly mean temperature and precipitation values were obtained in a gridded (4 x 4 km) format from the PRISM Climate Group at Oregon State45 for the contiguous US and averaged between measurement periods for each tree in a similar manner. Tree size was represented by estimated aboveground tree C (previously described). Because the climate and deposition predictors were tailored to each plot, the years assessed varied by plot, but spanned 2000–2016. Thus, the results from the earlier study6 used conditions from the 1980–1990s, whereas the results from this study used more recent environmental and stand conditions. Tree competition was represented by a combination two factors: (1) plot basal area and (2) the basal area of trees larger than the focal tree being modeled. How all six variables were statistically modeled is discussed below.Modeling tree growth and survivalWe developed in ref. 20 multiple models to predict tree growth (G; kg C year−1) and survival (P(s); annual probability of survival). Our growth model (Eq. 1 and 2) assumes that there is a potential maximum growth rate (a) that is modified by up to six predictors in our study (which are multipliers from 0 to 1): temperature (T), precipitation (P), N deposition (N), S deposition (S), tree size (m), and competition. The potential full growth model included all six terms (Eq. 1 for the general form and Eq. 2 for the specific form). The size effect was modeled as a power function (z) based on the aboveground biomass (m). N deposition may affect the allometric relationships between tree diameter and aboveground tree biomass46, but these relationships are not yet accounted for in U.S. inventories44. Competition between trees was modeled as a function of plot basal area (BA) and the basal area of trees larger than that of the tree of interest (BAL) similar to the methods of47. The environmental factors (N deposition, S deposition, temperature, precipitation) were modeled as two-term lognormal functions (e.g., t1 and t2 for temperature effects, n1 and n2 for nitrogen deposition effects). The two-term lognormal functions allowed for flexibility in both the location of the peak (determined by t1 for temperature, for example), and the steepness of the curve (determined by t2 for temperature, for example). Thus, the full growth model is presented in Eq. 2.$$G=potentialgrowthratetimes competitiontimes temperaturetimes precipitationtimes {S}_{dep}times {N}_{dep}$$
(1)
$$G=a* {m}^{z}* {e}^{({c}_{1}* BAL+{c}_{2}* {{{{mathrm{ln}}}}}(BA))}* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}$$
(2)
We examined a total of five different growth models: (1) a full model with the size, competition, climate, S deposition, and N deposition terms (Eq. 2); (2) a model with all terms except the N deposition term; (3) a model with all terms except the S deposition term; (4) a model with all terms but without S and N deposition terms; and (5) a null model that estimated a single parameter for the mean growth parameter (a in Eq. 2).The annual probability of survival (P(s)) was estimated similarly as for growth, except that the probability was a function of time and we explored two different representations for competition. The general form of the model is shown in Eq. 3, and the full survival model in Eqs. 4, 5 for the two competition forms.$$P(s)={[acdot {{{{{rm{size}}}}}}times competitiontimes temperaturetimes precipitationtimes {N}_{dep}times {S}_{dep}]}^{time}$$
(3)
$$P(s)= {left[a* [((1-z{c}_{1}{e}^{-z{c}_{2}* m})* {e}^{-z{c}_{3}* {m}^{z{c}_{4}}})({e}^{-b{r}_{1}* B{A}_{ratio}{,}^{br2}* B{A}^{b{r}_{3}}})]vphantom{{left.* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}right]}}^{time}right.}\ {left.* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}right]}^{time}$$
(4)
$$P(s)= {left[a* left({e}^{-0.5* {left(frac{ln(m/{m}_{1})}{{m}_{2}}right)}^{2}* -0.5* {left(frac{ln(BA/b{a}_{1})}{b{a}_{2}}right)}^{2}* -0.5* {left(frac{ln(BAL+1/b{l}_{1}+1)}{b{l}_{2}}right)}^{2}}right)vphantom{{left.* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}right]}^{time}}right.}\ {left.* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}right]}^{time}$$
(5)
A total of nine survival models were examined: four using the formulation for size and competition in Eq. 4 (with the same combinations of predictors as above for growth), four using formulation for size and competition in Eq. 5, and a null survival model in which a mean annual estimate of survival (a) was raised to the exponent of the elapsed time.Parameters for each of the growth and survival models above were fit for a given species using maximum likelihood estimates through simulated annealing with 100,000 iterations via the likelihood package (v2.1.1) in Program R. Akaike’s Information Criteria (AIC) was estimated for all models. The best model was the model with the lowest AIC, and statistically indistinguishable models are those with a delta AIC  More

Wohlleben, P. The Hidden Life of Trees: What They Feel, How They Communicate—Discoveries From a Secret World Vol. 1 (Greystone Books, 2016).Simard, S. W. Finding the Mother Tree: Discovering the Wisdom of the Forest (Knopf Doubleday Publishing Group, 2022).Powers, R. The Overstory (W. W. Norton & Company, 2018).Jabr, F. The social life of forests. New York Times Magazine https://www.nytimes.com/interactive/2020/12/02/magazine/tree-communication-mycorrhiza.html (2020).Kaplan, S. With forests in peril, she’s on a mission to save ‘mother trees’. Washington Post (27 December 2022).Chung, D. & Williams, R. T. Talking trees. Natl Geogr. 233, 6 (2018).
Google Scholar 
Grant, R. Do trees talk to each other? Smithsonian Magazine https://www.smithsonianmag.com/science-nature/the-whispering-trees-180968084/ (2018).Schwartzberg, L. Fantastic Fungi. Moving Art (2019).Druyan, A. Cosmos: Possible Worlds: the Search for Intelligent Life on Earth (2020).Mills, M. C’mon C’mon (2020).Simard, S. W. How trees talk to each other. YouTube https://www.youtube.com/watch?v=Un2yBgIAxYs (2016).Abumrad J & Krulwich, R. From tree to shining tree. Radiolab https://radiolab.org/episodes/from-tree-to-shining-tree (2016).Geddes, L. Unearthing the secret social lives of trees. The Guardian Science Weekly https://www.theguardian.com/science/audio/2021/apr/29/unearthing-the-secret-social-lives-of-trees-podcast (2021).Davies, D. Trees talk to each other. ‘Mother Tree’ ecologist hears lessons for people, too. National Public Radio https://www.npr.org/sections/health-shots/2021/05/04/993430007/trees-talk-to-each-other-mother-tree-ecologist-hears-lessons-for-people-too (2021).Braff, Z. Midnight train to Royston. Ted Lasso (2021).Murphy, R. Welcome, friends. The Watcher (2022).Milović, M., Kebert, M. & Orlović, S. How mycorrhizas can help forests to cope with ongoing climate change? Pregledni Članci Rev. 5, 279–286 (2021).
Google Scholar 
Simard, S. W. & Austin, M. E. in Climate Change and Variabilty (eds Simard, S. W. & Austin, M. E.) 275–302 (IntechOpen Europe, 2010).Domínguez-Núñez, J. A. in Structure and Functions of the Pedosphere (eds Giri, B. et al.) 365–391 (Springer, 2022).Authier, L., Violle, C. & Richard, F. Ectomycorrhizal networks in the anthropocene: from natural ecosystems to urban planning. Front. Plant Sci. 13, 900231 (2022).Article 

Google Scholar 
Selosse, M.-A., Richard, F., He, X. & Simard, S. W. Mycorrhizal networks: des liaisons dangereuses? Trends Ecol. Evol. 21, 621–628 (2006).Article 

Google Scholar 
Newman, E. Mycorrhizal links between plants—their functioning and ecological significance. Adv. Ecol. Res. 18, 243–270 (1988).Article 

Google Scholar 
Bonello, P., Bruns, T. D. & Gardes, M. Genetic structure of a natural population of the ectomycorrhizal fungus Suillus pungens. New Phytol. 138, 533–542 (1998).Article 
CAS 

Google Scholar 
Dahlberg, A. & Stenlid, J. Size, distribution and biomass of genets in populations of Suillus bovinus (L.: Fr.) Roussel revealed by somatic incompatibility. New Phytol. 128, 225–234 (1994).Article 

Google Scholar 
Kretzer, A. M., Dunham, S., Molina, R. & Spatafora, J. W. Microsatellite markers reveal the below ground distribution of genets in two species of Rhizopogon forming tuberculate ectomycorrhizas on Douglas fir. New Phytol. 161, 313–320 (2004).Article 
CAS 

Google Scholar 
Figueiredo, A. F., Boy, J. & Guggenberger, G. Common mycorrhizae network: a review of the theories and mechanisms behind underground interactions. Front. Fungal Biol. 2, https://doi.org/10.3389/ffunb.2021.735299 (2021).Leake, J. et al. Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning. Can. J. Bot. 82, 1016–1045 (2004).Article 

Google Scholar 
Trappe, J. M. & Fogel, R. in The Belowground Ecosystem: a Synthesis of Plant-Associated Processes (ed. Marshall J. K.) 205–214 (Colorado State Univ., 1977).Beiler, K. J., Durall, D. M., Simard, S. W., Maxwell, S. A. & Kretzer, A. M. Architecture of the wood-wide web: Rhizopogon spp. genets link multiple Douglas-fir cohorts. New Phytol. 185, 543–553 (2010).Article 
CAS 

Google Scholar 
Beiler, K. J., Simard, S. W. & Durall, D. M. Topology of tree–mycorrhizal fungus interaction networks in xeric and mesic Douglas-fir forests. J. Ecol. 103, 616–628 (2015).Article 

Google Scholar 
Beiler, K. J., Simard, S. W., LeMay, V. & Durall, D. M. Vertical partitioning between sister species of Rhizopogon fungi on mesic and xeric sites in an interior Douglas-fir forest. Mol. Ecol. 21, 6163–6174 (2012).Article 

Google Scholar 
Lian, C., Narimatsu, M., Nara, K. & Hogetsu, T. Tricholoma matsutake in a natural Pinus densiflora forest: correspondence between above- and below-ground genets, association with multiple host trees and alteration of existing ectomycorrhizal communities. New Phytol. 171, 825–836 (2006).Article 

Google Scholar 
Van Dorp, C. H., Simard, S. W. & Durall, D. M. Resilience of Rhizopogon–Douglas-fir mycorrhizal networks 25 years after selective logging. Mycorrhiza 30, 467–474 (2020).Article 

Google Scholar 
Cazzolla Gatti, R. et al. The number of tree species on Earth. Proc. Natl Acad. Sci. USA 119, e2115329119 (2022).Article 

Google Scholar 
Tedersoo, L. & Bahram, M. Mycorrhizal types differ in ecophysiology and alter plant nutrition and soil processes. Biol. Rev. 94, 1857–1880 (2019).Article 

Google Scholar 
Setälä, H. Growth of birch and pine seedlings in relation to grazing by soil fauna on ectomycorrhizal fungi. Ecology 76, 1844–1851 (1995).Article 

Google Scholar 
Kanters, C., Anderson, I. C. & Johnson, D. Chewing up the wood-wide web: selective grazing on ectomycorrhizal fungi by collembola. Forests 6, 2560–2570 (2015).Article 

Google Scholar 
Horton, T. R., Bruns, T. D. & Parker, V. T. Ectomycorrhizal fungi associated with Arctostaphylos contribute to Pseudotsuga menziesii establishment. Can. J. Bot. 77, 93–102 (1999).
Google Scholar 
Kennedy, P. G., Izzo, A. D. & Bruns, T. D. There is high potential for the formation of common mycorrhizal networks between understorey and canopy trees in a mixed evergreen forest. J. Ecol. 91, 1071–1080 (2003).Article 

Google Scholar 
Kennedy, P. G., Smith, D. P., Horton, T. R. & Molina, R. J. Arbutus menziesii (Ericaceae) facilitates regeneration dynamics in mixed evergreen forests by promoting mycorrhizal fungal diversity and host connectivity. Am. J. Bot. 99, 1691–1701 (2012).Article 

Google Scholar 
Horton, T. R., Molina, R. & Hood, K. Douglas-fir ectomycorrhizae in 40- and 400-year-old stands: mycobiont availability to late successional western hemlock. Mycorrhiza 15, 393–403 (2005).Article 
CAS 

Google Scholar 
Buscardo, E. et al. Is the potential for the formation of common mycorrhizal networks influenced by fire frequency? Soil Biol. Biochem. 46, 136–144 (2012).Article 
CAS 

Google Scholar 
Hewitt, R. E., Chapin, F. S. III, Hollingsworth, T. N. & Taylor, D. L. The potential for mycobiont sharing between shrubs and seedlings to facilitate tree establishment after wildfire at Alaska arctic treeline. Mol. Ecol. 26, 3826–3838 (2017).Article 

Google Scholar 
Jia, S., Nakano, T., Hattori, M. & Nara, K. Root-associated fungal communities in three Pyroleae species and their mycobiont sharing with surrounding trees in subalpine coniferous forests on Mount Fuji, Japan. Mycorrhiza 27, 733–745 (2017).Article 
CAS 

Google Scholar 
Hortal, S. et al. Beech roots are simultaneously colonized by multiple genets of the ectomycorrhizal fungus Laccaria amethystina clustered in two genetic groups. Mol. Ecol. 21, 2116–2129 (2012).Article 
CAS 

Google Scholar 
Wadud, M. A., Nara, K., Lian, C., Ishida, T. A. & Hogetsu, T. Genet dynamics and ecological functions of the pioneer ectomycorrhizal fungi Laccaria amethystina and Laccaria laccata in a volcanic desert on Mount Fuji. Mycorrhiza 24, 551–563 (2014).Article 

Google Scholar 
Germain, S. J. & Lutz, J. A. Shared friends counterbalance shared enemies in old forests. Ecology 102, e03495 (2021).Article 

Google Scholar 
Simard, S. W. et al. Partial retention of legacy trees protect mycorrhizal inoculum potential, biodiversity, and soil resources while promoting natural regeneration of interior Douglas-fir. Front. For. Glob. Change 3, https://doi.org/10.3389/ffgc.2020.620436 (2021).Björkman, E. Monotropa hypopitys L.—an epiparasite on tree roots. Physiol. Plant. 13, 308–327 (1960).Article 

Google Scholar 
Simard, S. W. et al. Net transfer of carbon between ectomycorrhizal tree species in the field. Nature 388, 579–582 (1997).Article 
CAS 

Google Scholar 
Read, D. The ties that bind. Nature 388, 517–518 (1997).Article 
CAS 

Google Scholar 
Aleklett, K. & Boddy, L. Fungal behaviour: a new frontier in behavioural ecology. Trends Ecol. Evol. 36, 787–796 (2021).Article 

Google Scholar 
Franklin, O., Näsholm, T., Högberg, P. & Högberg, M. N. Forests trapped in nitrogen limitation—an ecological market perspective on ectomycorrhizal symbiosis. New Phytol. 203, 657–666 (2014).Article 
CAS 

Google Scholar 
Hasselquist, N. J. et al. Greater carbon allocation to mycorrhizal fungi reduces tree nitrogen uptake in a boreal forest. Ecology 97, 1012–1022 (2016).
Google Scholar 
Näsholm, T. et al. Are ectomycorrhizal fungi alleviating or aggravating nitrogen limitation of tree growth in boreal forests? New Phytol. 198, 214–221 (2013).Article 

Google Scholar 
Hoeksema, J. D. in Mycorrhizal Networks (ed. Horton, T. R.) 255–277 (Springer Netherlands, 2015).Teste, F. P. & Simard, S. W. Mycorrhizal networks and distance from mature trees alter patterns of competition and facilitation in dry Douglas-fir forests. Oecologia 158, 193–203 (2008).Article 

Google Scholar 
Teste, F. P., Simard, S. W., Durall, D. M., Guy, R. D. & Berch, S. M. Net carbon transfer between Pseudotsuga menziesii var. glauca seedlings in the field is influenced by soil disturbance. J. Ecol. 98, 429–439 (2010).Article 
CAS 

Google Scholar 
Teste, F. P. et al. Access to mycorrhizal networks and roots of trees: importance for seedling survival and resource transfer. Ecology 90, 2808–2822 (2009).Article 

Google Scholar 
Lerat, S. et al. 14C transfer between the spring ephemeral Erythronium americanum and sugar maple saplings via arbuscular mycorrhizal fungi in natural stands. Oecologia 132, 181–187 (2002).Article 

Google Scholar 
Klein, T., Siegwolf, R. T. W. & Korner, C. Belowground carbon trade among tall trees in a temperate forest. Science 352, 342–344 (2016).Article 
CAS 

Google Scholar 
He, X., Bledsoe, C. S., Zasoski, R. J., Southworth, D. & Horwath, W. R. Rapid nitrogen transfer from ectomycorrhizal pines to adjacent ectomycorrhizal and arbuscular mycorrhizal plants in a California oak woodland. New Phytol. 170, 143–151 (2006).Article 
CAS 

Google Scholar 
Schoonmaker, A. L., Teste, F. P., Simard, S. W. & Guy, R. D. Tree proximity, soil pathways and common mycorrhizal networks: their influence on the utilization of redistributed water by understory seedlings. Oecologia 154, 455–466 (2007).Article 

Google Scholar 
Warren, J. M., Brooks, J. R., Meinzer, F. C. & Eberhart, J. L. Hydraulic redistribution of water from Pinus ponderosa trees to seedlings: evidence for an ectomycorrhizal pathway. New Phytol. 178, 382–394 (2008).Article 
CAS 

Google Scholar 
Bingham, M. A. & Simard, S. W. Seedling genetics and life history outweigh mycorrhizal network potential to improve conifer regeneration under drought. For. Ecol. Manag. 287, 132–139 (2013).Article 

Google Scholar 
Kranabetter, J. M. Understory conifer seedling response to a gradient of root and ectomycorrhizal fungal contact. Can. J. Bot. 83, 638–646 (2005).Article 

Google Scholar 
Liang, M. et al. Soil fungal networks maintain local dominance of ectomycorrhizal trees. Nat. Commun. 11, 2636 (2020).Article 
CAS 

Google Scholar 
Liang, M. et al. Soil fungal networks moderate density-dependent survival and growth of seedlings. New Phytol. 230, 2061–2071 (2021).Article 

Google Scholar 
McGuire, K. L. Common ectomycorrhizal networks may maintain monodominance in a tropical rain forest. Ecology 88, 567–574 (2007).Article 

Google Scholar 
Pec, G. J., Simard, S. W., Cahill, J. F. & Karst, J. The effects of ectomycorrhizal fungal networks on seedling establishment are contingent on species and severity of overstorey mortality. Mycorrhiza 30, 173–183 (2020).Article 

Google Scholar 
Corrales, A., Mangan, S. A., Turner, B. L. & Dalling, J. W. An ectomycorrhizal nitrogen economy facilitates monodominance in a neotropical forest. Ecol. Lett. 19, 383–392 (2016).Article 

Google Scholar 
Booth, M. G. Mycorrhizal networks mediate overstorey–understorey competition in a temperate forest. Ecol. Lett. 7, 538–546 (2004).Article 

Google Scholar 
Booth, M. G. & Hoeksema, J. D. Mycorrhizal networks counteract competitive effects of canopy trees on seedling survival. Ecology 91, 2294–2302 (2010).Article 

Google Scholar 
Brearley, F. Q. et al. Testing the importance of a common ectomycorrhizal network for dipterocarp seedling growth and survival in tropical forests of Borneo. Plant Ecol. Divers. 9, 563–576 (2016).Article 

Google Scholar 
Dehlin, H. et al. Tree seedling performance and below-ground properties in stands of invasive and native tree species. N. Z. J. Ecol. 32, 67–79 (2008).
Google Scholar 
Newbery, D. M. & Neba, G. A. Micronutrients may influence the efficacy of ectomycorrhizas to support tree seedlings in a lowland African rain forest. Ecosphere 10, e02686 (2019).Article 

Google Scholar 
Oliveira, I. R. et al. Nutrient deficiency enhances the rate of short-term belowground transfer of nitrogen from Acacia mangium to Eucalyptus trees in mixed-species plantations. For. Ecol. Manag. 491, 119192 (2021).Article 

Google Scholar 
Paula, R. R. et al. Evidence of short-term belowground transfer of nitrogen from Acacia mangium to Eucalyptus grandis trees in a tropical planted forest. Soil Biol. Biochem. 91, 99–108 (2015).Article 
CAS 

Google Scholar 
Nygren, P. & Leblanc, H. A. Dinitrogen fixation by legume shade trees and direct transfer of fixed N to associated cacao in a tropical agroforestry system. Tree Physiol. 35, 134–147 (2015).Article 
CAS 

Google Scholar 
Liu, Y., Chen, H. & Mou, P. Spatial patterns nitrogen transfer models of ectomycorrhizal networks in a Mongolian scotch pine plantation. J. For. Res. 29, 339–346 (2018).Article 
CAS 

Google Scholar 
Bingham, M. A. & Simard, S. Ectomycorrhizal networks of Pseudotsuga menziesii var. glauca trees facilitate establishment of conspecific seedlings under drought. Ecosystems 15, 188–199 (2012).Article 
CAS 

Google Scholar 
Robinson, D. & Fitter, A. The magnitude and control of carbon transfer between plants linked by a common mycorrhizal network. J. Exp. Bot. 50, 9–13 (1999).Article 
CAS 

Google Scholar 
Chen, W., Koide, R. T. & Eissenstat, D. M. Root morphology and mycorrhizal type strongly influence root production in nutrient hot spots of mixed forests. J. Ecol. 106, 148–156 (2018).Article 
CAS 

Google Scholar 
Jones, M. D., Durall, D. M. & Tinker, P. B. A comparison of arbuscular and ectomycorrhizal Eucalyptus coccifera: growth response, phosphorus uptake efficiency and external hyphal production. New Phytol. 140, 125–134 (1998).Article 

Google Scholar 
Pickles, B. J. et al. Transfer of 13C between paired Douglas-fir seedlings reveals plant kinship effects and uptake of exudates by ectomycorrhizas. New Phytol. 214, 400–411 (2017).Article 
CAS 

Google Scholar 
Teste, F. P., Simard, S. W. & Durall, D. M. Role of mycorrhizal networks and tree proximity in ectomycorrhizal colonization of planted seedlings. Fungal Ecol. 2, 21–30 (2009).Article 

Google Scholar 
Bingham, M. A. & Simard, S. W. Mycorrhizal networks affect ectomycorrhizal fungal community similarity between conspecific trees and seedlings. Mycorrhiza 22, 317–326 (2012).Article 

Google Scholar 
Pec, G. J. et al. Change in soil fungal community structure driven by a decline in ectomycorrhizal fungi following a mountain pine beetle (Dendroctonus ponderosae) outbreak. New Phytol. 213, 864–873 (2017).Article 
CAS 

Google Scholar 
Coomes, D. A. & Grubb, P. J. Impacts of root competition in forests and woodlands: a theoretical framework and review of experiments. Ecol. Monogr. 70, 171–207 (2000).Article 

Google Scholar 
Finlay, R. D. & Read, D. J. The structure and function of the vegetative mycelium of ectomycorrhizal plants. New Phytol. 103, 143–156 (1986).Article 

Google Scholar 
Brownlee, C., Duddridge, J. A., Malibari, A. & Read, D. J. The structure and function of mycelial systems of ectomycorrhizal roots with special reference to their role in forming inter-plant connections and providing pathways for assimilate and water transport. Plant Soil 71, 433–443 (1983).Article 

Google Scholar 
Wu, B., Nara, K. & Hogetsu, T. Can 14C-labeled photosynthetic products move between Pinus densiflora seedlings linked by ectomycorrhizal mycelia? New Phytol. 149, 137–146 (2001).Article 
CAS 

Google Scholar 
Anten, N. P. R. & Chen, B. J. W. Detect thy family: mechanisms, ecology and agricultural aspects of kin recognition in plants. Plant Cell Environ. 44, 1059–1071 (2021).Article 
CAS 

Google Scholar 
Dominguez, P. G. & Niittylä, T. Mobile forms of carbon in trees: metabolism and transport. Tree Physiol. 42, 458–487 (2021).Article 

Google Scholar 
Yu, R.-P., Lambers, H., Callaway, R. M., Wright, A. J. & Li, L. Belowground facilitation and trait matching: two or three to tango. Trends Plant Sci. 26, 1227–1235 (2021).Article 
CAS 

Google Scholar 
Simard, S. W. in The Word for World is Still Forest (eds Springer, A. & Turpin, E.) 66–72 (K Verlag and Haus der Kulturen der Welt, 2017).Simard, S. W. in Memory and Learning in Plants (eds Baluska, F. et al.) 191–213 (Springer, 2018).Boyno, G. & Demir, S. Plant–mycorrhiza communication and mycorrhizae in inter-plant communication. Symbiosis 86, 155–168 (2022).Article 

Google Scholar 
Rasheed, M. U., Brosset, A. & Blande, J. D. Tree communication: the effects of “wired” and “wireless” channels on interactions with herbivores. Curr. For. Rep. 9, 33–47 (2023).
Google Scholar 
Song, Y. Y., Simard, S. W., Carroll, A., Mohn, W. W. & Zeng, R. S. Defoliation of interior Douglas-fir elicits carbon transfer and stress signalling to ponderosa pine neighbors through ectomycorrhizal networks. Sci. Rep. 5, 8495 (2015).Article 
CAS 

Google Scholar 
Gorzelak, M. A. Kin-Selected Signal Transfer Through Mycorrhizal Networks in Douglas-Fir. PhD thesis, Univ. British Columbia (2017).Asay, A. K. Mycorrhizal Facilitation of Kin Recognition in Interior Douglas-Fir (Pseudotsuga menziesii var. glauca). MSc thesis, Univ. British Columbia (2013).Orrego, G. Western Hemlock Regeneration on Coarse Woody Debris is Facilitated by Linkage into a Mycorrhizal Network in an Old-Growth Forest. MSc thesis, Univ. British Columbia (2018).Diédhiou, A. G. et al. Multi-host ectomycorrhizal fungi are predominant in a Guinean tropical rainforest and shared between canopy trees and seedlings. Environ. Microbiol. 12, 2219–2232 (2010).
Google Scholar 
Grelet, G.-A. et al. New insights into the mycorrhizal Rhizoscyphus ericae aggregate: spatial structure and co-colonization of ectomycorrhizal and ericoid roots. New Phytol. 188, 210–222 (2010).Article 
CAS 

Google Scholar 
Van der Heijden, M. G. A. & Horton, T. R. Socialism in soil? The importance of mycorrhizal fungal networks for facilitation in natural ecosystems. J. Ecol. 97, 1139–1150 (2009).Article 

Google Scholar 
Babikova, Z., Johnson, D., Bruce, T., Pickett, J. & Gilbert, L. Underground allies: how and why do mycelial networks help plants defend themselves? BioEssays 36, 21–26 (2014).Article 

Google Scholar 
Alaux, P.-L., Zhang, Y., Gilbert, L. & Johnson, D. Can common mycorrhizal fungal networks be managed to enhance ecosystem functionality? Plants People Planet 3, 433–444 (2021).Article 

Google Scholar 
Simard, S. W. et al. Mycorrhizal networks: mechanisms, ecology and modelling. Fungal Biol. Rev. 26, 39–60 (2012).Article 

Google Scholar 
Flinn, K. The idea that trees talk to cooperate is misleading. Scientific American https://www.scientificamerican.com/article/the-idea-that-trees-talk-to-cooperate-is-misleading/ (2021).Högberg, P. & Högberg, M. N. Does successful forest regeneration require the nursing of seedlings by nurse trees through mycorrhizal interconnections. For. Ecol. Manag. 516, 120252 (2022).Article 

Google Scholar 
Teste, F. P., Jones, M. D. & Dickie, I. A. Dual-mycorrhizal plants: their ecology and relevance. New Phytol. 225, 1835–1851 (2020).Article 

Google Scholar 
Toju, H., Guimarães, P. R., Olesen, J. M. & Thompson, J. N. Assembly of complex plant–fungus networks. Nat. Commun. 5, 5273 (2014).Article 
CAS 

Google Scholar 
Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis 3rd edn (Elsevier, 2008).Nara, K. Ectomycorrhizal networks and seedling establishment during early primary succession. New Phytol. 169, 169–178 (2006).Article 
CAS 

Google Scholar 
Arnebrant, K., Ek, H., Finlay, R. D. & Söderström, B. Nitrogen translocation between Alnus glutinosa (L.) Gaertn. seedlings inoculated with Frankia sp. and Pinus contorta Doug, ex Loud seedlings connected by a common ectomycorrhizal mycelium. New Phytol. 124, 231–242 (1993).Article 

Google Scholar 
Finlay, R. D. Functional aspects of phosphorus uptake and carbon translocation in incompatible ectomycorrhizal associations between Pinus sylvestris and Suillus grevillei and Boletinus cauipes. New Phytol. 112, 185–192 (1989).Article 
CAS 

Google Scholar 
Cahanovitc, R., Livne-Luzon, S., Angel, R. & Klein, T. Ectomycorrhizal fungi mediate belowground carbon transfer between pines and oaks. ISME J. 16, 1420–1429 (2022).Article 
CAS 

Google Scholar 
Teste, F. P., Veneklass, E. J., Dixon, K. W. & Lambers, H. Is nitrogen transfer among plants enhanced by contrasting nutrient-acquisition strategies? Plant Cell Environ. 38, 50–60 (2015).Article 
CAS 

Google Scholar 
Simard, S. W. et al. Reciprocal transfer of carbon isotopes between ectomycorrhizal Betula papyrifera and Pseudotsuga menziesii. New Phytol. 137, 529–542 (1997).Article 
CAS 

Google Scholar 
Egerton-Warburton, L. M., Querejeta, J. I. & Allen, M. F. Common mycorrhizal networks provide a potential pathway for the transfer of hydraulically lifted water between plants. J. Exp. Bot. 58, 1473–1483 (2007).Article 
CAS 

Google Scholar 
He, X., Critchley, C., Ng, H. & Bledsoe, C. Nodulated N2-fixing Casuarina cunninghamiana is the sink for net N transfer from non-N2-fixing Eucalyptus maculata via an ectomycorrhizal fungus Pisolithus sp. using 15NH4+ or 15NO3− supplied as ammonium nitrate. New Phytol. 167, 897–912 (2005).Article 
CAS 

Google Scholar 
He, X., Critchley, C., Ng, H. & Bledsoe, C. Reciprocal N (15NH4+ or 15NO3−) transfer between nonN2-fixing Eucalyptus maculata and N2-fixing Casuarina cunninghamiana linked by the ectomycorrhizal fungus Pisolithus sp. New Phytol. 163, 629–640 (2004).Article 

Google Scholar 
Bingham, M. A. & Simard, S. W. Do mycorrhizal network benefits to survival and growth of interior Douglas-fir seedlings increase with soil moisture stress? Ecol. Evol. 1, 306–316 (2011).Article 

Google Scholar 
Babikova, Z. et al. Underground signals carried through common mycelial networks warn neighbouring plants of aphid attack. Ecol. Lett. 16, 835–843 (2013).Article 

Google Scholar 
Birch, J. D., Simard, S. W., Beiler, K. J. & Karst, J. Beyond seedlings: ectomycorrhizal fungal networks and growth of mature Pseudotsuga menziesii. J. Ecol. 109, 806–818 (2021).Article 
CAS 

Google Scholar 
Färkkilä, S. M. A. et al. Fluorescent nanoparticles as tools in ecology and physiology. Biol. Rev. 96, 2392–2424 (2021).Article 

Google Scholar  More

Brown, A. R. et al. Assessing risks and mitigating impacts of harmful algal blooms on mariculture and marine fisheries. Rev. Aquac. 12, 1663–1688 (2020).
Google Scholar 
Bechard, A. Red tide at morning, tourists take warning? County-level economic effects of HABS on tourism dependent sectors. Harmful Algae 85, 101689–101689 (2019).Article 

Google Scholar 
Landsberg, J. H. The effects of harmful algal blooms on aquatic organisms. Rev. Fish. Sci. 10, 113–390 (2002).Article 

Google Scholar 
Flewelling, L. J. et al. Brevetoxicosis: Red tides and marine mammal mortalities. Nature 435, 755–756 (2005).Article 
CAS 

Google Scholar 
Gannon, D. P. et al. Effects of Karenia brevis harmful algal blooms on nearshore fish communities in southwest Florida. Mar. Ecol. Prog. Ser. 378, 171–186 (2009).Article 
CAS 

Google Scholar 
Driggers, W. B. et al. Environmental conditions and catch rates of predatory fishes associated with a mass mortality on the West Florida Shelf. Estuar. Coast. Shelf Sci. 168, 40–49 (2016).Article 
CAS 

Google Scholar 
Hallett, C. S., Valesini, F. J., Clarke, K. R. & Hoeksema, S. D. Effects of a harmful algal bloom on the community ecology, movements and spatial distributions of fishes in a microtidal estuary. Hydrobiologia 763, 267–284 (2016).Article 

Google Scholar 
Anderson, D. M. et al. Marine harmful algal blooms (HABs) in the United States: History, current status and future trends. Harmful Algae 102, 101975–101975 (2021).Article 
CAS 

Google Scholar 
Steidinger, K. A. & Haddad, K. Biologic and hydrographic aspects of red tides. Bioscience 31, 814–819 (1981).Article 

Google Scholar 
Soto, I. M. et al. Advection of Karenia brevis blooms from the Florida Panhandle towards Mississippi coastal waters. Harmful Algae 72, 46–64 (2018).Article 

Google Scholar 
Steidinger, K. A. & Ingle, R. M. Observations on the 1971 summer red tide in tampa bay, Florida1. Environ. Lett. 3, 271–278 (1972).Article 
CAS 

Google Scholar 
Liu, Y. et al. Offshore forcing on the “pressure point” of the West Florida Shelf: Anomalous upwelling and its influence on harmful algal blooms. J. Geophys. Res. 121, 5501–5515 (2016).Article 

Google Scholar 
Liu, Y., Weisberg, R. H., Zheng, L., Heil, C. A. & Hubbard, K. A. Termination of the 2018 Florida red tide event: A tracer model perspective. Estuar. Coast. Shelf Sci. 272, 107901 (2022).Article 

Google Scholar 
Weisberg, R. H. & Liu, Y. Local and deep-ocean forcing effects on the West Florida continental shelf circulation and ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.863227 (2022).Article 

Google Scholar 
Walsh, J. J. et al. Red tides in the Gulf of Mexico: Where, when, and why? Journal of Geophysical Research: Oceans 111, (2006).Lapointe, B. E., Herren, L. W., Debortoli, D. D. & Vogel, M. A. Evidence of sewage-driven eutrophication and harmful algal blooms in Florida’s Indian River Lagoon. Harmful Algae 43, 82–102 (2015).Article 
CAS 

Google Scholar 
Medina, M. et al. Nitrogen-enriched discharges from a highly managed watershed intensify red tide (Karenia brevis) blooms in southwest Florida. Sci. Total Environ. 827, 154149–154149 (2022).Article 
CAS 

Google Scholar 
Perkins, S. Ramping up the fight against Florida’s red tides. Proc. Natl. Acad. Sci. U.S.A. 116, 6510–6512 (2019).Article 
CAS 

Google Scholar 
Skripnikov, A. et al. Using localized Twitter activity to assess harmful algal bloom impacts of Karenia brevis in Florida, USA. Harmful Algae 110, 102118–102118 (2021).Article 
CAS 

Google Scholar 
SEDAR. SEDAR 33 Update – Gulf of Mexico gag grouper stock assessment report, 123. https://sedarweb.org/docs/suar/GagUpdateAssessReport_Final_0.pdf (2016).SEDAR. SEDAR 61 – Gulf of Mexico red grouper stock assessment report, 285. https://sedarweb.org/docs/sar/S61_Final_SAR.pdf (2019).SEDAR. SEDAR 10 Stock Assessment Report 2: Gulf of Mexico Gag Grouper, 250. www.sedarweb.org (2004).SEDAR. SEDAR 10 Update – Gulf of Mexico gag grouper stock assessment report. http://www.sedarweb.org (2009).SEDAR. SEDAR 72—Gulf of Mexico gag grouper stock assessment report, 318–318. https://sedarweb.org/docs/sar/S72_SAR_FINAL.pdf%0A (2021).Geary, W. L. et al. A guide to ecosystem models and their environmental applications. Nat. Ecol. Evol. 4, 1459–1471 (2020).Article 

Google Scholar 
Steenbeek, J. et al. Making spatial-temporal marine ecosystem modelling better—A perspective. Environ. Model. Softw. 145, 105209–105209 (2021).Article 

Google Scholar 
Gray DiLeone, A. M. & Ainsworth, C. H. Effects of Karenia brevis harmful algal blooms on fish community structure on the West Florida Shelf. Ecol. Model. 392, 250–267 (2019).Article 

Google Scholar 
Perryman, H. A. et al. A revised diet matrix to improve the parameterization of a West Florida Shelf Ecopath model for understanding harmful algal bloom impacts. Ecol. Model. 416, 108890–108890 (2020).Article 

Google Scholar 
Mayer-Pinto, M., Ledet, J., Crowe, T. P. & Johnston, E. L. Sublethal effects of contaminants on marine habitat-forming species: A review and meta-analysis. Biol. Rev. 95, 1554–1573 (2020).Article 

Google Scholar 
Reis Costa, P. Impact and effects of paralytic shellfish poisoning toxins derived from harmful algal blooms to marine fish. Fish Fish. 17, 226–248 (2016).Article 

Google Scholar 
Dahood, A., de Mutsert, K. & Watters, G. M. Evaluating Antarctic marine protected area scenarios using a dynamic food web model. Biol. Cons. 251, 108766–108766 (2020).Article 

Google Scholar 
de Mutsert, K. et al. Exploring effects of hypoxia on fish and fisheries in the northern Gulf of Mexico using a dynamic spatially explicit ecosystem model. Ecol. Model. 331, 142–150 (2016).Article 

Google Scholar 
de Mutsert, K., Lewis, K. A., White, E. D. & Buszowski, J. End-to-end modeling reveals species-specific effects of large-scale coastal restoration on living resources facing climate change. Front. Mar. Sci. 8, 104–104 (2021).Article 

Google Scholar 
Bauer, B. et al. Erratum: Reducing eutrophication increases spatial extent of communities supporting commercial fisheries: A model case study (ICES Journal of Marine Science (2018) DOI: https://doi.org/10.1093/icesjms/fsy003). ICES Journal of Marine Science, 75, 1155–1155 (2018).Sadchatheeswaran, S., Branch, G. M., Shannon, L. J., Coll, M. & Steenbeek, J. A novel approach to explicitly model the spatiotemporal impacts of structural complexity created by alien ecosystem engineers in a marine benthic environment. Ecol. Model. 459, 109731–109731 (2021).Article 

Google Scholar 
Coll, M. et al. Advancing global ecological modeling capabilities to simulate future trajectories of change in marine ecosystems. Front. Mar. Sci. 7, 741–741 (2020).Article 

Google Scholar 
Hernvann, P. Y. et al. The celtic sea through time and space: Ecosystem modeling to unravel fishing and climate change impacts on food-web structure and dynamics. Front. Mar. Sci. 7, 1018–1018 (2020).Article 

Google Scholar 
Walters, C. Ecospace: Prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areas. Ecosystems 2, 539–554 (1999).Article 

Google Scholar 
Christensen, V., Walters, C. J., Pauly, D. & Forrest, R. Ecopath with Ecosim version 6 user guide. Fish. Cent. Univ. Br. Columbia Vanc. Can. 281, 1–235 (2008).
Google Scholar 
Okey, T. A., Mahmoudi, B., Mackinson, S., Vasconcellos, M. & Vidal-Hernandez, L. An ecosystem model of the West Florida Shelf for use in fisheries management and ecological research: Volume II. Model construction. Fish. Manag. II, 163–163 (2002).
Google Scholar 
Liu, Y. & Weisberg, R. H. Seasonal variability on the West Florida Shelf. Prog. Oceanogr. 104, 80–98 (2012).Article 

Google Scholar 
Moretzsohn, F., Chávez-Sánchez, J. A. & J.W. Tunnell, Jr. GulfBase: Resource Database for Gulf of Mexico Research. World Wide Web electronic publication (2016).Murawski, S. A., Peebles, E. B., Gracia, A., Tunnell, J. W. & Armenteros, M. Comparative abundance, species composition, and demographics of continental shelf fish assemblages throughout the Gulf of Mexico. Mar. Coast. Fish. 10, 325–346 (2018).Article 

Google Scholar 
Darnell, R. M. The American sea: A natural history of the gulf of Mexico. The American Sea: A Natural History of the Gulf of Mexico, 557, https://doi.org/10.5860/choice.193769 (2015).Brochure, I. Marine recreational information program: Implementation plan (2008).Florida Fish and Wildlife Conservation Commission. Commercial fisheries landings summaries (2021).Murawski, S. A. et al. How did the deepwater horizon oil spill affect coastal and continental shelf ecosystems of the Gulf of Mexico?. Oceanography 29, 160–173 (2016).Article 

Google Scholar 
Chagaris, D. D., Patterson, W. F. & Allen, M. S. Relative effects of multiple stressors on reef food webs in the Northern Gulf of Mexico revealed via ecosystem modeling. Front. Mar. Sci. 7, 513–513 (2020).Article 

Google Scholar 
South, A. rnaturalearth: world map data from Natural Earth. R package version 0.1.0. The R Foundation. https://CRAN.R-project.org/package=rnaturalearth (2017).Colleter, M. et al. Global overview of the applications of the Ecopath with Ecosim modeling approach using the EcoBase models repository. Ecol. Model. 302, 42–53 (2015).Article 

Google Scholar 
Ahrens, R. N., Walters, C. J. & Christensen, V. Foraging arena theory. Fish fish. 13, 41–59 (2012).Article 

Google Scholar 
Christensen, V. et al. Representing variable habitat quality in a spatial food web model. Ecosystems 17, 1397–1412 (2014).Article 
CAS 

Google Scholar 
Steenbeek, J. et al. Bridging the gap between ecosystem modeling tools and geographic information systems: Driving a food web model with external spatial–temporal data. Ecol. Model. 263, 139–151 (2013).Article 

Google Scholar 
Walters, C., Christensen, V., Walters, W. & Rose, K. Representation of multistanza life histories in Ecospace models for spatial organization of ecosystem trophic interaction patterns. Bull. Mar. Sci. 86, 439–459 (2010).
Google Scholar 
Heymans, J. J. et al. Best practice in Ecopath with Ecosim food-web models for ecosystem-based management. Ecol. Model. 331, 173–184 (2016).Article 

Google Scholar 
Okey, T. Simulating community effects of sea floor shading by plankton blooms over the West Florida Shelf. Ecol. Model. 172, 339–359 (2004).Article 

Google Scholar 
Chagaris, D. D. Ecosystem-based Evaluation of Fishery Policies and Tradeoffs on the West Florida Shelf Vol. 53, 1699 (University of Florida, 2013).
Google Scholar 
Chagaris, D. D., Mahmoudi, B., Walters, C. J. & Allen, M. S. Simulating the trophic impacts of fishery policy options on the west florida shelf using ecopath with ecosim. Mar. Coast. Fish. 7, 44–58 (2015).Article 

Google Scholar 
Chagaris, D. et al. An ecosystem-based approach to evaluating impacts and management of invasive lionfish. Fisheries 42, 421–431 (2017).Article 

Google Scholar 
Chagaris, D. West Florida Shelf Ecosystem Model. University of Florida. https://ufdc.ufl.edu/IR00011604/00001%0A West Florida Shelf Ecosystem Model (2021).Vilas, D. Spatiotemporal Ecosystem Dynamics on the West Florida Shelf: Prediction, Validation, and Application to Red Tides and Stock Assessment (University of Florida, 2022).
Google Scholar 
Chassignet, E. P. et al. The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system. J. Mar. Syst. 65, 60–83 (2007).Article 

Google Scholar 
NOAA National Geophysical Data Center. U.S. Coastal Relief Model Vol. 3—Florida and East Gulf of Mexico. https://doi.org/10.7289/V5W66HP (2001).NASA. Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Earth Data (2018).Casey, L. Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1950–2020: U.S. Geological Survey, https://doi.org/10.5066/P9P2PF1N (2021).Chagaris, D. & Vilas, D. NOAA RESTORE Science Program: Ecosystem modeling to improve fisheries management in the Gulf of Mexico: model inputs and outputs for the West Florida Shelf, 1985–01–01 to 2018–12–31 (NCEI Accession 0242339), https://doi.org/10.25921/t26e-wj91. (2022).Püts, M. et al. Insights on integrating habitat preferences in process-oriented ecological models—A case study of the southern North Sea. Ecol. Model. 431, 109189–109189 (2020).Article 

Google Scholar 
Vilas, D., Fletcher, R. J. Jr., Siders, Z. A. & Chagaris, D. Understanding the temporal dynamics of estimated environmental niche hypervolumes for marine fishes. Ecol. Evol. 12, e9604 (2022).Article 

Google Scholar 
Grubbs, R. D., Musick, J. A., Conrath, C. L. & Romine, J. G. Long-term movements, migration, and temporal delineation of a summer nursery for Juvenile Sandbar Sharks in the Chesapeake Bay region. In Shark Nursery Grounds of the Gulf of Mexico and the East Coast Waters of the United States. American Fisheries Society Symposium 50 Vol. 50 (eds Grubbs, R. D. et al.) 87–107 (American Fisheries Society, 2007).
Google Scholar 
Addis, D. T., Patterson, W. F., Dance, M. A. & Ingram, G. W. Implications of reef fish movement from unreported artificial reef sites in the northern Gulf of Mexico. Fish. Res. 147, 349–358 (2013).Article 

Google Scholar 
Akins, J. L., Morris, J. A. & Green, S. J. In situ tagging technique for fishes provides insight into growth and movement of invasive lionfish. Ecol. Evol. 4, 3768–3777 (2014).Article 

Google Scholar 
Chen, Z., Xu, S., Qiu, Y., Lin, Z. & Jia, X. Modeling the effects of fishery management and marine protected areas on the Beibu Gulf using spatial ecosystem simulation. Fish. Res. 100, 222–229 (2009).Article 

Google Scholar 
Steenbeek, J. et al. Ecopath with ecosim as a model-building toolbox: Source code capabilities, extensions, and variations. Ecol. Model. 319, 178–189 (2016).Article 

Google Scholar 
Moriasi, D. N. et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50, 885–900 (2007).Article 

Google Scholar 
Hu, C. et al. Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sens. Environ. 97, 311–321 (2005).Article 

Google Scholar 
Chagaris, D., Vilas, D., Siders, Z. A. & Sinnickson, D. Monthly maps of red tide on the West Florida Shelf 2002–2021: A simple approach combining remote sensing and in situ measurements (in prep).Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research Institute. Statewide harmful algal bloom karenia brevis current status map (2022).Wickham, H. et al. ggplot2: Create elegant data visualisations using the grammar of graphics (2016).Landsberg, J. H., Flewelling, L. J. & Naar, J. Karenia brevis red tides, brevetoxins in the food web, and impacts on natural resources: Decadal advancements. Harmful Algae 8, 598–607 (2009).Article 
CAS 

Google Scholar 
Gianelli, I., Ortega, L. & Defeo, O. Modeling short-term fishing dynamics in a small-scale intertidal shellfishery. Fish. Res. 209, 242–250 (2019).Article 

Google Scholar 
Moore, S. K. et al. An index of fisheries closures due to harmful algal blooms and a framework for identifying vulnerable fishing communities on the U.S. West Coast. Mar. Policy 110, 103543–103543 (2019).Article 

Google Scholar 
GSMFC. SEAMAP: Environmental and Biological Atlas of the Gulf of Mexico. www.seamap.org (2020).Bechard, A. Harmful algal blooms and tourism: The economic impact to counties in Southwest Florida. Rev. Reg. Stud. 50, 170–188 (2020).
Google Scholar 
Foley, A. M. et al. Effects of Karenia brevis harmful algal blooms on nearshore fish communities in southwest Florida. Mar. Ecol. Prog. Ser. 378, 171–186 (2009).Article 

Google Scholar 
Karnauskas, M. et al. Timeline of severe red tide events on the West Florida Shelf: insights from oral histories. http://sedarweb.org/docs/wpapers/S61_WP_20_Karnauskasetal_red_tide.pdf (2019).Sagarese, S. R., Gruss, A., Karnauskas, M. & Walter, J. F. Ontogenetic spatial distributions of red grouper (Epinephelus morio) within the northeastern Gulf of Mexico and spatio‐ temporal overlap with red tide events, 35–35. http://sedarweb.org/docs/wpapers/S42_DW_04_Red_tide_distribution.pdf (2014).Sagarese, S. R., Vaughan, N. R., Walter, J. F. & Karnauskas, M. Enhancing single-species stock assessments with diverse ecosystem perspectives: A case study for gulf of mexico red grouper (epinephelus morio) and red tides. Can. J. Fish. Aquat. Sci. 78, 1168–1180 (2021).Article 

Google Scholar 
Sagarese, S. R. & Harford, W. J. Evaluating the risks of red tide mortality misspecification when modeling stock dynamics. Fish. Res. 250, 106271–106271 (2022).Article 

Google Scholar 
Whitehouse, G. A. & Aydin, K. Y. Assessing the sensitivity of three Alaska marine food webs to perturbations: An example of Ecosim simulations using Rpath. Ecol. Model. 429, 109074–109074 (2020).Article 

Google Scholar 
Walter, J. F. et al. Satellite derived indices of red tide severity for input for Gulf of Mexico Gag grouper stock assessment. SEDAR33-DW08 SEDAR. North Charlest. S. C. 43, 40–40 (2013).
Google Scholar 
Jackson, M. C., Pawar, S. & Woodward, G. The temporal dynamics of multiple stressor effects: From individuals to ecosystems. Trends Ecol. Evol. 36, 402–410 (2021).Article 

Google Scholar 
Walters, S., Lowerre-Barbieri, S., Bickford, J., Tustison, J. & Landsberg, J. H. Effects of Karenia brevis red tide on the spatial distribution of spawning aggregations of sand seatrout Cynoscion arenarius in Tampa Bay Florida. Mar. Ecol. Prog. Ser. 479, 191–202 (2013).Article 

Google Scholar 
Reynolds, D. A., Yoo, M. J., Dixson, D. L. & Ross, C. Exposure to the Florida red tide dinoflagellate, Karenia brevis, and its associated brevetoxins induces ecophysiological and proteomic alterations in Porites astreoides. PLoS One 15, e0228414–e0228414 (2020).Article 
CAS 

Google Scholar 
Bornman, E., Cowley, P. D., Adams, J. B. & Strydom, N. A. Daytime intra-estuary movements and harmful algal bloom avoidance by Mugil cephalus (family Mugilidae). Estuar. Coast. Shelf Sci. 260, 107492–107492 (2021).Article 
CAS 

Google Scholar 
Moreira-Santos, M., Ribeiro, R. & Araújo, C. V. M. What if aquatic animals move away from pesticide-contaminated habitats before suffering adverse physiological effects? A critical review. Crit. Rev. Environ. Sci. Technol. 49, 989–1025 (2019).Article 
CAS 

Google Scholar 
Schreck, C. B. & Tort, L. The concept of stress in fish. In Fish Physiology Vol. 35 (eds Schreck, C. B. & Tort, L.) 1–34 (Elsevier, 2016).
Google Scholar 
Madin, E. M. P., Dill, L. M., Ridlon, A. D., Heithaus, M. R. & Warner, R. R. Human activities change marine ecosystems by altering predation risk. Glob. Change Biol. 22, 44–60 (2016).Article 

Google Scholar 
Walsh, J. R., Carpenter, S. R. & Van Der Zanden, M. J. Invasive species triggers a massive loss of ecosystem services through a trophic cascade. Proc. Natl. Acad. Sci. U.S.A. 113, 4081–4085 (2016).Article 
CAS 

Google Scholar 
Short, J. W. et al. Evidence for ecosystem-level trophic cascade effects involving gulf menhaden (Brevoortia patronus) triggered by the Deepwater horizon blowout. J. Mar. Sci. Eng. 9, 1–20 (2021).Article 

Google Scholar 
Zohdi, E. & Abbaspour, M. Harmful algal blooms (red tide): A review of causes, impacts and approaches to monitoring and prediction. Int. J. Environ. Sci. Technol. 16, 1789–1806 (2019).Article 

Google Scholar 
Weisberg, R. H., Barth, A., Alvera-Azcarate, A. & Zheng, L. A coordinated coastal ocean observing and modeling system for the West Florida Continental Shelf. Harmful Algae 8, 585–597 (2009).Article 

Google Scholar 
Turley, B. D., Karnauskas, M., Campbell, M. D., Hanisko, D. S. & Kelble, C. R. Relationships between blooms of Karenia brevis and hypoxia across the West Florida Shelf. Harmful Algae 114, 102223 (2022).Article 

Google Scholar 
Fulton, E. A., Smith, A. D., Smith, D. C. & Johnson, P. An integrated approach is needed for ecosystem based fisheries management: Insights from ecosystem-level management strategy evaluation. PLoS One 9, e84242 (2014).Article 

Google Scholar 
Flynn, K. J. & McGillicuddy, D. J. Modeling marine harmful algal blooms: Current status and future prospects. Harmful Algal Blooms https://doi.org/10.1002/9781118994672.ch3 (2018).Article 

Google Scholar 
Thorson, J. T. Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fish. Res. 210, 143–161 (2019).Article 

Google Scholar 
Fossum, T. O., Travelletti, C., Eidsvik, J., Ginsbourger, D. & Rajan, K. Learning excursion sets of vector-valued gaussian random fields for autonomous ocean sampling. Ann. Appl. Stat. 15, 597–618 (2021).Article 
MathSciNet 
MATH 

Google Scholar 
Fu, F. X., Place, A. R., Garcia, N. S. & Hutchins, D. A. CO2 and phosphate availability control the toxicity of the harmful bloom dinoflagellate Karlodinium veneficum. Aquat. Microb. Ecol. 59, 55–65 (2010).Article 

Google Scholar 
Hardison, D. R., Sunda, W. G., Shea, D. & Litaker, R. W. Increased toxicity of Karenia brevis during phosphate limited growth: Ecological and evolutionary implications. PLoS One 8, e58545–e58545 (2013).Article 
CAS 

Google Scholar 
Errera, R. M., Yvon-Lewis, S., Kessler, J. D. & Campbell, L. Reponses of the dinoflagellate Karenia brevis to climate change: PCO2 and sea surface temperatures. Harmful Algae 37, 110–116 (2014).Article 
CAS 

Google Scholar 
Wells, M. L. et al. Future HAB science: Directions and challenges in a changing climate. Harmful Algae 91, 101632–101632 (2020).Article 

Google Scholar 
Wolny, J. L. et al. Current and future remote sensing of harmful algal blooms in the chesapeake bay to support the shellfish industry. Front. Mar. Sci. 7, 337–337 (2020).Article 

Google Scholar 
Reum, J. C. P. et al. It’s not the destination, It’s the journey: Multispecies model ensembles for ecosystem approaches to fisheries management. Front. Mar. Sci. 8, 75–75 (2021).Article 

Google Scholar 
Howell, D. et al. Combining ecosystem and single-species modeling to provide ecosystem-based fisheries management advice within current management systems. Front. Mar. Sci. 7, 607831 (2021).Article 

Google Scholar 
McPherson, W. C. and J. C. and J. A. and Y. X. and J. shiny: Web application framework for R. R package version 1.4.0, 115–115 (2019). More

Ackerly, D. D., Schwilk, D. W. & Webb, C. O. Niche evolution and adaptive radiation: Testing the order of trait divergence. Ecology 87, 50–61 (2006).Article 

Google Scholar 
Somaweera, R. et al. The ecological importance of crocodylians: Towards evidence-based justification for their conservation. Biol. Rev. Camb. Philos. Soc. 95, 936–959. https://doi.org/10.1111/brv.12594 (2020).Article 

Google Scholar 
Swain, S. et al. Anthropogenic influence on the physico-chemical parameters of Dhamra estuary and adjoining coastal water of the Bay of Bengal. Mar. Pollut. Bull. 162, 111826. https://doi.org/10.1016/j.marpolbul.2020.111826 (2021).Article 
CAS 

Google Scholar 
IUCN. IUCN Red List of Threatened Species. Version 2022.1. www.iucnredlist.org (2022).Markich, S. J. & Jeffree, R. A. (eds) The Finnis River. A Natural Laboratory of Mining Impact—Past, Present and Future (Australian Nuclear Science and Technology Organisation, 2002).
Google Scholar 
Vieira, L. M. et al. Mercury and methyl mercury ratios in caimans (Caiman crocodilus yacare) from the Pantanal area, Brazil. J. Environ. Monitor. 13, 280–287. https://doi.org/10.1039/c0em00561d (2011).Article 
CAS 

Google Scholar 
Quintela, F. M. et al. Arsenic, lead and cadmium concentrations in caudal crests of the yacare caiman (Caiman yacare) from Brazilian Pantanal. Sci. Total Environ. 707, 135479. https://doi.org/10.1016/j.scitotenv.2019.135479 (2020).Article 
CAS 

Google Scholar 
Briggs-Gonzalez, V. S., Basille, M., Cherkiss, M. S. & Mazzotti, F. J. American crocodiles (Crocodylus acutus) as restoration bioindicators in the Florida Everglades. PLoS ONE 16, e0250510. https://doi.org/10.1371/journal.pone.0250510 (2021).Article 
CAS 
PubMed Central 

Google Scholar 
Grigg, G. & Kirshner, D. Biology and Evolution of Crocodylians (CSIRO Publishing, 2015).Book 

Google Scholar 
Subalusky, A. L., Fitzgerald, L. A. & Smith, L. L. Ontogenetic niche shifts in the American alligator establish functional connectivity between aquatic systems. Biol. Conserv. 142, 1507–1514 (2009).Article 

Google Scholar 
Villamarín, F., Escobedo-Galván, A. H., Siroski, P. & Magnusson, W. E. Geographic distribution, habitat, reproduction, and conservation status of crocodilians in the Americas. In Conservation Genetics of New World Crocodilians (eds Zucoloto, R. B. et al.) (Springer, 2021).
Google Scholar 
Albert, C., Luque, G. M. & Courchamp, F. The twenty most charismatic species. PLoS ONE 13, e0199149. https://doi.org/10.1371/journal.pone.0199149 (2018).Article 
CAS 
PubMed Central 

Google Scholar 
Verissimo, D., MacMillan, D. C. & Smith, R. J. Toward a systematic approach for identifying conservation flag ships. Conserv. Lett. 4, 1–8. https://doi.org/10.1111/j.1755-263X.2010.00151.x (2011).Article 

Google Scholar 
Fleishman, E., Murphy, D. D. & Brussard, P. F. A new method for selection of umbrella species for conservation planning. Ecol. Appl. 10, 569–579 (2000).Article 

Google Scholar 
Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M. & Wilson, K. A. Conservation planning in a changing world. Trents Ecol. Evol. 2211, 583–592 (2007).Article 

Google Scholar 
Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758. https://doi.org/10.1111/j.1461-0248.2006.00924.x (2006).Article 

Google Scholar 
Magurran, A. E. Measuring Biological Diversity 2nd edn. (Blackwell Publishing, 2004).
Google Scholar 
Campos, F. S., Lourenço-de-Moraes, R., Llorente, G. A. & Solé, M. Cost-effective conservation of amphibian ecology and evolution. Sci. Adv. 36, e1602929 (2017).Article 

Google Scholar 
Dietz, M. S., Belote, R. T., Aplet, G. H. & Aycrigg, J. L. The world’s largest wilderness protection network after 50 years: An assessment of ecological system representation in the US National Wilderness Preservation System. Biol. Conserv. 184, 431–438 (2015).Article 

Google Scholar 
UNEP-WCMC, IUCN. Protected Planet Report 2016 (UNEP-WCMC and IUCN, 2016).
Google Scholar 
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791. https://doi.org/10.1126/science.aap9565 (2018).Article 
CAS 

Google Scholar 
Rodrigues, A. et al. Effectiveness of the global protected area network in representing species diversity. Nature 428, 640–643. https://doi.org/10.1038/nature02422 (2004).Article 
CAS 

Google Scholar 
Ladle, R. J. & Whittaker, R. J. Conservation Biogeography 301 (Wiley-Blackwell, 2011).Book 

Google Scholar 
Dinerstein, E. et al. A “global safety net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, 2824 (2020).Article 

Google Scholar 
Lourenço-de-Moraes, R. et al. No more trouble: An economic strategy to protect taxonomic, functional and phylogenetic diversity of continental turtles. Biol. Conserv. 261, 109241. https://doi.org/10.1016/j.biocon.2021.109241 (2021).Article 

Google Scholar 
Brochu, C. A. Phylogenetic relationships of Necrosuchus ionensis Simpson, 1937 and the early history of caimanines. Zool. J. Linn. Soc. 163, 228–256. https://doi.org/10.1111/j.1096-3642.2011.00716.x (2011).Article 

Google Scholar 
Buffetaut, E. Systématique, origine et evolution des Gavialidae sud-américains. In Phylógenie et Paléobiogeography: Livre Jubilaire en l´honneur de Robert Hoffstetter (ed. Buffetaut, E.) 127–140 (Géobios, 1982).
Google Scholar 
Griffith, P., Lang, J. W., Turvey, S. T. & Gumbs, R. Data from: Using functional traits to identify conservation priorities for the world’s crocodylians. Zenodo. https://doi.org/10.5281/zenodo.6645415 (2022).Griffith, P., Lang, J. W., Turvey, S. T. & Gumbs, R. Using functional traits to identify conservation priorities for the world’s crocodylians. Funct. Ecol. 37, 112. https://doi.org/10.1111/1365-2435.14140 (2022).Article 
CAS 

Google Scholar 
Milian-Garcia, Y. et al. Evolutionary history of Cuban crocodiles Crocodylus rhombifer and Crocodylus acutus inferred from multilocus markers. J. Exp. Zool. A 315, 358–375. https://doi.org/10.1002/jez.683 (2011).Article 

Google Scholar 
Rodrıguez-Soberon, R., Ross, P. & Seal, U. IUCN/SSC Conservation Breeding Specialist Group (2000).Milián-García, Y., Ramos-Targarona, R., Pérez-Fleitas, E., Espinosa-López, G. & Russello, M. A. Genetic evidence of hybridization between the critically endangered Cuban crocodile and the American crocodile: Implications for population history and in situ/ex situ conservation. Heridity 114, 272–280 (2015).Article 

Google Scholar 
Pacheco-Sierra, G., Gompert, Z., Dominguez-Laso, J. & Vazquez-Dominguez, E. Genetic and morphological evidence of a geographically widespread hybrid zone between two crocodile species, Crocodylus acutus and Crocodylus moreletii. Mol. Ecol. 25, 3484–3498. https://doi.org/10.1111/mec.13694 (2016).Article 

Google Scholar 
Borges, V. S. et al. Evolutionary significant units within populations of Neotropical broad-snouted caimans (Caiman latirostris, Daudin, 1802). J. Herpetol. 52, 282–288 (2018).Article 

Google Scholar 
Palmer, M. L. & Mazzoti, F. J. Structure of everglades alligator holes. Wetlands 24, 115–122 (2004).Article 

Google Scholar 
Marques, T. S. et al. Intraspecific isotopic niche variation in broad-snouted caiman (Caiman latirostris). Isot. Environ. Health Stud. 49, 325–335 (2013).Article 
CAS 

Google Scholar 
Mascarenhas-Junior, P. B. et al. Conflicts between humans and crocodilians in urban areas across Brazil: A new approach to support management and conservation. Ethnobiol. Conserv. 10, 19. https://doi.org/10.15451/ec2021-12-10.37-1-19 (2021).Article 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).Article 
CAS 

Google Scholar 
Ribeiro, M. C., Metzger, J. P., Martensen, A. C., Ponzoni, F. J. & Hirota, M. M. The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation. Biol. Conserv. 142, 1141–1153 (2009).Article 

Google Scholar 
Filogonio, R., Assis, V. B., Passos, L. F. & Coutinho, M. E. Distribution of populations of broad-snouted caiman (Caiman latirostris, Daudin 1802, Alligatoridae) in the São Francisco River basin, Brazil. Braz. J. Biol. https://doi.org/10.1590/S1519-69842010000500007 (2010).Article 

Google Scholar 
Marques, J. F. et al. Fires dynamics in the Pantanal: Impacts of anthropogenic activities and climate change. J. Environ. Manag. 299, 113586. https://doi.org/10.1016/j.jenvman.2021.113586 (2021).Article 

Google Scholar 
Mataveli, G. A. V. et al. 2020 Pantanal’s widespread fire: Short- and long-term implications for biodiversity and conservation. Biodivers. Conserv. https://doi.org/10.1007/s10531-021-02243-2 (2021).Article 
PubMed Central 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 124–148 (2014).Article 

Google Scholar 
Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).Article 
CAS 

Google Scholar 
Canning, A. & Death, R. Trophic cascade direction and flow determine network flow stability. Ecol. Model. 355, 18–23 (2017).Article 

Google Scholar 
Wang, Y. Q., Zhu, W. Q., Huang, L., Zhou, K. Y. & Wang, R. P. Genetic diversity of Chinese alligator (Alligator sinensis) revealed by AFLP analysis: An implication on the management of captive conservation. Biodivers. Conserv. 15, 2945–2955 (2006).Article 

Google Scholar 
Zhai, T. et al. Effects of population bottleneck and balancing selection on the chinese alligator are revealed by locus-specific characterization of MHC genes. Sci. Rep. 7, 5549. https://doi.org/10.1038/s41598-017-05640-2 (2017).Article 
CAS 
PubMed Central 

Google Scholar 
Sharma, S. P. et al. Microsatellite analysis reveals low genetic diversity in managed populations of the critically endangered gharial (Gavialis gangeticus) in India. Sci. Rep. https://doi.org/10.1038/s41598-021-85201-w (2021).Article 
PubMed Central 

Google Scholar 
Nair, T. & Krishna, Y. C. Vertebrate fauna of the Chambal River basin, with emphasis on the National Chambal Sanctuary, India. J. Threat. Taxa 5, 3620–3641 (2013).Article 

Google Scholar 
Sharma, R. & Singh, L. Status of mugger crocodile (Crocodylus palustris) in National Chambal Sanctuary after thirty years and its implications on conservation of Gharial (Gavialis gangeticus). Zoo’s Print 30, 9–16 (2015).
Google Scholar 
Sinhg, H. & Rao, R. Status, threats and conservation challenges to key aquatic fauna (crocodile and dolphin) in National Chambal Sanctuary, India. Aquat. Ecosyst. Health Manag. 20, 59–70 (2017).Article 

Google Scholar 
UNEP-WCMC, IUCN. Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC, IUCN, 2021).
Google Scholar 
Smolensky, N. L., Hurtado, L. A. & Fitzgerald, L. A. DNA barcoding of Cameroon samples enhances our knowledge on the distributional limits of putative species of Osteolaemus (African dwarf crocodiles). Conserv. Genet. 16, 235–240. https://doi.org/10.1007/s10592-014-0639-3 (2014).Article 
CAS 

Google Scholar 
Shirley, M. H., Villanova, V. L., Vliet, K. A. & Austin, J. D. Genetic barcoding facilitates captive and wild management of three cryptic African crocodile species complexes. Anim. Conserv. 18, 322–330 (2015).Article 

Google Scholar 
Shirley, M. H., Carr, A. N., Nestler, J. H., Vliet, K. A. & Brochu, C. A. Systematic revision of the living African Slender-snouted Crocodiles (Mecistops Gray, 1844). Zootaxa 4504, 151–193. https://doi.org/10.11646/zootaxa.4504.2.1 (2018).Article 

Google Scholar 
Murray, C. M., Russo, P., Zorrilla, A. & McMahan, C. D. Divergent morphology among populations of the New Guinea crocodile, Crocodylus novaeguineae (Schmidt, 1928): Diagnosis of an independent lineage and description of a new species. Copeia 107, 517–523. https://doi.org/10.1643/CG-19-240 (2019).Article 

Google Scholar 
Hekkala, E. H. et al. An ancient icon reveals new mysteries: Mummy DNA resurrects a cryptic species within the Nile crocodile. Mol. Ecol. 20, 4199–4215 (2011).Article 
CAS 

Google Scholar 
Mobaraki, A. et al. Conservation status of the mugger crocodile Crocodylus palustris: Establishing a task force for a poster species of climate change. Crocodile Specialist Group Newslett. 40(3), 12–20 (2021).
Google Scholar 
Cunningham, S. W., Shirley, M. H. & Hekkala, E. R. Fine scale patterns of genetic partitioning in the rediscovered African crocodile, Crocodylus suchus (Saint-Hilaire 1807). PeerJ 12, e1901 (2016).Article 

Google Scholar 
Platt, S. G. et al. Siamese Crocodile Crocodylus siamensis. In Crocodiles. Status Survey and Conservation Action Plan 4th edn (eds Manolis, S. C. & Stevenson, C.) (Crocodile Specialist Group, 2019).
Google Scholar 
Arcgis Software v. Version 10.1 (2011).Lourenço-de-Moraes, R. et al. Functional traits explain amphibian distribution in the Brazilian Atlantic Forest. J. Biogeogr. 47, 275–287 (2020).Article 

Google Scholar 
Pavoine, S., Vallet, J., Dufour, A. B., Gachet, S. & Daniel, H. On the challenge of treating various types of variables: Application for improving the measurement of functional diversity. Oikos 118, 391–402. https://doi.org/10.1111/j.1600-0706.2008.16668.x (2009).Article 

Google Scholar 
Colston, T. J., Kulkarni, P., Jetz, W. & Pyron, R. A. Phylogenetic and spatial distribution of evolutionary diversification, isolation, and threat in turtles and crocodilians (non-avian archosauromorphs). BMC Evol. Biol. 20(1), 1–16 (2020).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).Article 

Google Scholar 
Pio, D. V. et al. Spatial predictions of phylogenetic diversity in conservation decision making. Conserv. Biol. 256, 1229–1239 (2011).Article 

Google Scholar 
Rodrigues, A. S. L. & Gaston, K. J. Maximising phylogenetic diversity in the selection of networks of conservation areas. Biol. Conserv. 105, 103–111 (2002).Article 

Google Scholar 
Safi, K. et al. Understanding global patterns of mammalian functional and phylogenetic diversity. Philos. Trans. R. Soc. B 366, 2536–2544 (2011).Article 

Google Scholar 
Trindade-Filho, J., Carvalho, R. A., Brito, D. & Loyola, R. D. How does the inclusion of data deficient species change conservation priorities for amphibians in the Atlantic Forest?. Biodivers. Conserv. 21, 2709–2718 (2012).Article 

Google Scholar 
Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).
Google Scholar 
Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, 2014).Book 
MATH 

Google Scholar 
Mouchet, M., Villéger, S., Mason, N. W. H. & Mouillo, D. Functional diversity measures: An overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24, 867–876 (2010).Article 

Google Scholar 
Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).Article 
CAS 

Google Scholar 
Sharp, R. et al. InVEST 3.10.2.post28+ug.ga4e401c.d20220324 User’s Guide (The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund, 2020).
Google Scholar 
Lourenço-de-Moraes, R. et al. Climate change will decrease the range size of snake species under negligible protection in the Brazilian Atlantic Forest hotspot. Sci. Rep. 9, 8523. https://doi.org/10.1038/s41598-019-44732-z (2019).Article 
CAS 
PubMed Central 

Google Scholar 
Sánchez-Fernandez, D. & Abellán, P. Using null models to identify underrepresented species in protected areas: A case study using European amphibians and reptiles. Biol. Conserv. 184, 290–299 (2015).Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Uribe, M. R. et al. Net loss of biomass predicted for tropical biomes in a changing climate. Nat. Clim. Change https://doi.org/10.1038/s41558-023-01600-z (2023). More

Jerez-Cepa, I., Gorissen, M., Mancera, J. M. & Ruiz-Jarabo, I. What can we learn from glucocorticoid administration in fish? Effects of cortisol and dexamethasone on intermediary metabolism of gilthead seabream (Sparus aurata L.). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 231, 1–10 (2019).Article 
CAS 

Google Scholar 
Brown, C. L., Urbinati, E. C., Zhang, W., Brown, S. B. & McComb-Kobza, M. Maternal thyroid and glucocorticoid hormone interactions in larval fish development, and their applications in aquaculture. Rev. fish. Sci. Aquac. 22, 207–220 (2014).Article 

Google Scholar 
Tort, L. Stress and immune modulation in fish. Dev. Comp. Immunol. 35, 1366–1375 (2011).Article 
CAS 

Google Scholar 
Schreck, C. B. & Tort, L. In Fish Physiology (eds Schreck, C. B. et al.) vol. 35, 1–34 (Elsevier, 2016).Sternberg, E. M., Chrousos, G. P., Wilder, R. L. & Gold, P. W. The stress response and the regulation of inflammatory disease. Ann. Intern. Med. 117, 854–866 (1992).Article 
CAS 

Google Scholar 
Staufenbiel, S. M., Penninx, B. W. J. H., Spijker, A. T., Elzinga, B. M. & van Rossum, E. F. C. Hair cortisol, stress exposure, and mental health in humans: A systematic review. Psychoneuroendocrinology 38, 1220–1235 (2013).Article 
CAS 

Google Scholar 
Pickering, A. D. & Pottinger, T. G. Cortisol can increase the susceptibility of brown trout, Salmo trutta L., to disease without reducing the white blood cell count. J. Fish Biol. 27, 611–619 (1985).Article 
CAS 

Google Scholar 
McCormick, S. D. et al. Repeated acute stress reduces growth rate of Atlantic salmon parr and alters plasma levels of growth hormone, insulin-like growth factor I and cortisol. Aquaculture 168, 221–235 (1998).Article 
CAS 

Google Scholar 
McConnachie, S. H. et al. Consequences of acute stress and cortisol manipulation on the physiology, behavior, and reproductive outcome of female Pacific salmon on spawning grounds. Horm. Behav. 62, 67–76 (2012).Article 
CAS 

Google Scholar 
Moffat, S. D., An, Y., Resnick, S. M., Diamond, M. P. & Ferrucci, L. Longitudinal change in cortisol levels across the adult life span. J. Gerontol. A Biol. Sci. Med. Sci. 75, 394–400 (2020).Article 
CAS 

Google Scholar 
Oh, H.-J. et al. Age-related decrease in stress responsiveness and proactive coping in male mice. Front. Aging Neurosci. 10, 128 (2018).Article 
PubMed Central 

Google Scholar 
Woods, H. A. 2nd. & Hellgren, E. C. Seasonal changes in the physiology of male Virginia opossums (Didelphis virginiana): Signs of the Dasyurid semelparity syndrome?. Physiol. Biochem. Zool. 76, 406–417 (2003).Article 

Google Scholar 
Barry, T. P., Unwin, M. J., Malison, J. A. & Quinn, T. P. Free and total cortisol levels in semelparous and iteroparous Chinook salmon. J. Fish Biol. 59, 1673–1676 (2001).Article 
CAS 

Google Scholar 
Petrosus, E., Silva, E. B., Lay, D. Jr. & Eicher, S. D. Effects of orally administered cortisol and norepinephrine on weanling piglet gut microbial populations and Salmonella passage. J. Anim. Sci. 96, 4543–4551 (2018).PubMed Central 

Google Scholar 
Shi, D. et al. Impact of gut microbiota structure in heat-stressed broilers. Poult. Sci. 98, 2405–2413 (2019).Article 

Google Scholar 
Uren Webster, T. M., Rodriguez-Barreto, D., Consuegra, S. & Garcia de Leaniz, C. Cortisol-related signatures of stress in the fish microbiome. Front. Microbiol. 11, 1621 (2020).Article 
PubMed Central 

Google Scholar 
Ridlon, J. M. et al. Clostridium scindens: A human gut microbe with a high potential to convert glucocorticoids into androgens. J. Lipid Res. 54, 2437–2449 (2013).Article 
CAS 
PubMed Central 

Google Scholar 
UrenWebster, T. M., Consuegra, S. & Garcia de Leaniz, C. Early life stress causes persistent impacts on the microbiome of Atlantic salmon. Comp. Biochem. Physiol. Part D Genomics Proteomics 40, 100888 (2021).Article 
CAS 

Google Scholar 
Bozzi, D. et al. Salmon gut microbiota correlates with disease infection status: Potential for monitoring health in farmed animals. Anim. Microbiome 3, 30 (2021).Article 
CAS 
PubMed Central 

Google Scholar 
Xiong, J.-B., Nie, L. & Chen, J. Current understanding on the roles of gut microbiota in fish disease and immunity. Zool. Res. 40, 70–76 (2019).
Google Scholar 
Williams, C. L., Garcia-Reyero, N., Martyniuk, C. J., Tubbs, C. W. & Bisesi, J. H. Jr. Regulation of endocrine systems by the microbiome: Perspectives from comparative animal models. Gen. Comp. Endocrinol. 292, 113437 (2020).Article 
CAS 

Google Scholar 
Schmidt, K. et al. Prebiotic intake reduces the waking cortisol response and alters emotional bias in healthy volunteers. Psychopharmacology 232, 1793–1801 (2015).Article 
CAS 

Google Scholar 
Crumeyrolle-Arias, M. et al. Absence of the gut microbiota enhances anxiety-like behavior and neuroendocrine response to acute stress in rats. Psychoneuroendocrinology 42, 207–217 (2014).Article 
CAS 

Google Scholar 
Bell, E. A., Ball, A. G., Deprey, K. L. & Uno, J. K. The impact of antibiotics on the intestinal microbiome and the gut-brain axis in zebrafish. FASEB J. 32, 765–771 (2018).Article 

Google Scholar 
Björnsson, B. T., Stefansson, S. O. & McCormick, S. D. Environmental endocrinology of salmon smoltification. Gen. Comp. Endocrinol. 170, 290–298 (2011).Article 

Google Scholar 
Carruth, L. L., Jones, R. E. & Norris, D. O. Cortisol and Pacific Salmon: A new look at the role of stress hormones in olfaction and home-stream migration. Integr. Comp. Biol. 42, 574–581 (2002).Article 
CAS 

Google Scholar 
Donaldson, E. M. & Fagerlund, U. H. M. Effect of sexual maturation and gonadectomy at sexual maturity on cortisol secretion rate in sockeye salmon (Oncorhynchus nerka). J. Fish. Res. Board Can. 27, 2287–2296 (1970).Article 

Google Scholar 
Dickhoff, W. W. Development, Maturation, and Senescence of Neuroendocrine Systems 253–266 (Elsevier, 1989).Book 

Google Scholar 
Maule, A. G., Schreck, C. B. & Kaattari, S. L. Changes in the immune system of coho salmon (Oncorhynchus kisutch) during the parr-to-smolt transformation and after implantation of cortisol. Can. J. Fish. Aquat. Sci. 44, 161–166 (1987).Article 
CAS 

Google Scholar 
Llewellyn, M. S. et al. Parasitism perturbs the mucosal microbiome of Atlantic Salmon. Sci. Rep. 7, 1–10 (2017).Article 

Google Scholar 
Vasemägi, A., Visse, M. & Kisand, V. Effect of environmental factors and an emerging parasitic disease on gut microbiome of wild Salmonid fish. mSphere 2, e00418-17 (2017).Article 
PubMed Central 

Google Scholar 
Kelly, C. & Salinas, I. Under pressure: Interactions between commensal microbiota and the teleost immune system. Front. Immunol. 8, 559 (2017).Article 
PubMed Central 

Google Scholar 
Fast, M. D., Hosoya, S., Johnson, S. C. & Afonso, L. O. B. Cortisol response and immune-related effects of Atlantic salmon (Salmo salar Linnaeus) subjected to short- and long-term stress. Fish Shellfish Immunol. 24, 194–204 (2008).Article 
CAS 

Google Scholar 
Carrizo, V. et al. Effect of cortisol on the immune-like response of rainbow trout (Oncorhynchus mykiss) myotubes challenged with Piscirickettsia salmonis. Vet. Immunol. Immunopathol. 237, 110240 (2021).Article 
CAS 

Google Scholar 
Nervino, S. Intestinal lesions and parasites associated with prespawn mortality in Chinook salmon (Oncorhynchus tshawytscha). (2022).Couch, C. E. et al. Enterocytozoon schreckii n. sp. infects the enterocytes of adult chinook salmon (Oncorhynchus tshawytscha) and may be a sentinel of immunosenescence. mSphere 7, e0090821 (2022).Article 

Google Scholar 
Redding, J. M., Schreck, C. B., Birks, E. K. & Ewing, R. D. Cortisol and its effects on plasma thyroid hormone and electrolyte concentrations in fresh water and during seawater acclimation in yearling coho salmon, Oncorhynchus kisutch. Gen. Comp. Endocrinol. 56, 146–155 (1984).Article 
CAS 

Google Scholar 
Marotz, C. et al. DNA extraction for streamlined metagenomics of diverse environmental samples. Biotechniques 62, 290–293 (2017).Article 
CAS 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA. 108(Suppl 1), 4516–4522 (2011).Article 
CAS 

Google Scholar 
Minich, J. J. et al. High-throughput miniaturized 16S rRNA amplicon library preparation reduces costs while preserving microbiome integrity. mSystems 3, e00166-18 (2018).Article 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).Article 
CAS 
PubMed Central 

Google Scholar 
Escalas, A. et al. Ecological specialization within a carnivorous fish family is supported by a herbivorous microbiome shaped by a combination of gut traits and specific diet. Front. Mar. Sci. 8, 622883 (2021).Article 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).Article 
CAS 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (2020). https://www.R-project.org/.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
CAS 

Google Scholar 
Wright, E. Using DECIPHER v2.0 to analyze big biological sequence data in R. R. J. 8, 352 (2016).Article 

Google Scholar 
Schliep, K., Potts, A. J., Morrison, D. A. & Grimm, G. W. Intertwining phylogenetic trees and networks. Methods Ecol. Evol. 8, 1212–1220 (2017).Article 

Google Scholar 
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 623–656 (1948).Article 
MathSciNet 
MATH 

Google Scholar 
Shepard, R. N. The analysis of proximities: Multidimensional scaling with an unknown distance function. II. Psychometrika 27, 219–246 (1962).Article 
MathSciNet 
MATH 

Google Scholar 
Oksanen, J. et al. The vegan package. Community Ecol. Packag. 10, 631–637 (2007).
Google Scholar 
Martinez Arbizu, P. pairwiseAdonis: Pairwise Multilevel Comparison using Adonis. Preprint at (2017)Zhang, Y. Likelihood-based and Bayesian methods for Tweedie compound Poisson linear mixed models. Stat. Comput. 23, 743–757 (2013).Article 
MathSciNet 
CAS 
MATH 

Google Scholar 
Hassenrück, C., Reinwald, H., Kunzmann, A., Tiedemann, I. & Gärdes, A. Effects of thermal stress on the gut microbiome of juvenile milkfish (Chanos chanos). Microorganisms 9, 5 (2020).Article 
PubMed Central 

Google Scholar 
Liu, Y. et al. Response mechanism of gut microbiome and metabolism of European seabass (Dicentrarchus labrax) to temperature stress. Sci. Total Environ. 813, 151786 (2022).Article 
CAS 

Google Scholar 
Du, F. et al. Response of the gut microbiome of Megalobrama amblycephala to crowding stress. Aquaculture 500, 586–596 (2019).Article 
CAS 

Google Scholar 
Stothart, M. R., Palme, R. & Newman, A. E. M. It’s what’s on the inside that counts: Stress physiology and the bacterial microbiome of a wild urban mammal. Proc. Biol. Sci. 286, 20192111 (2019).PubMed Central 

Google Scholar 
Michels, N. et al. Gut microbiome patterns depending on children’s psychosocial stress: Reports versus biomarkers. Brain Behav. Immun. 80, 751–762 (2019).Article 

Google Scholar 
Zhao, H., Jiang, X. & Chu, W. Shifts in the gut microbiota of mice in response to dexamethasone administration. Int. Microbiol. 23, 565–573 (2020).Article 
CAS 

Google Scholar 
Zanuzzo, F. S., Sabioni, R. E., Marzocchi-Machado, C. M. & Urbinati, E. C. Modulation of stress and innate immune response by corticosteroids in pacu (Piaractus mesopotamicus). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 231, 39–48 (2019).Article 
CAS 

Google Scholar 
Timmermans, S., Souffriau, J. & Libert, C. A general introduction to glucocorticoid biology. Front. Immunol. 10, 1545 (2019).Article 
CAS 
PubMed Central 

Google Scholar 
Kugathas, S. & Sumpter, J. P. Synthetic glucocorticoids in the environment: First results on their potential impacts on fish. Environ. Sci. Technol. 45, 2377–2383 (2011).Article 
CAS 

Google Scholar 
Schaal, P. et al. Links between host genetics, metabolism, gut microbiome and amoebic gill disease (AGD) in Atlantic salmon. Anim. Microbiome 4, 53 (2022).Article 
CAS 
PubMed Central 

Google Scholar 
Birlanga, V. B. et al. Dynamic gill and mucus microbiomes during a gill disease episode in farmed Atlantic salmon. Sci. Rep. 12, 16719 (2022).Article 
CAS 
PubMed Central 

Google Scholar 
Cipriano, R. C., Ford, L. A., Smith, D. R., Schachte, J. H. & Petrie, C. J. Differences in detection of Aeromonas salmonicida in covertly infected Salmonid fishes by the stress-inducible furunculosis test and culture-based assays. J. Aquat. Anim. Health 9, 108–113 (1997).Article 

Google Scholar 
Lovy, J., Speare, D. J., Stryhn, H. & Wright, G. M. Effects of dexamethasone on host innate and adaptive immune responses and parasite development in rainbow trout Oncorhynchus mykiss infected with Loma salmonae. Fish Shellfish Immunol. 24, 649–658 (2008).Article 
CAS 

Google Scholar 
Bakhtiyar, Y., Yousuf, T. & Arafat, M. Y. Bacterial Fish Diseases 269–278 (Elsevier, 2022).Book 

Google Scholar 
Benda, S. E., Naughton, G. P., Caudill, C. C., Kent, M. L. & Schreck, C. B. Cool, pathogen-free refuge lowers pathogen-associated prespawn mortality of Willamette River Chinook salmon. Trans. Am. Fish. Soc. 144, 1159–1172 (2015).Article 

Google Scholar 
Barton, B. A. & Iwama, G. K. Physiological changes in fish from stress in aquaculture with emphasis on the response and effects of corticosteroids. Annu. Rev. Fish Dis. 1, 3–26 (1991).Article 

Google Scholar 
Dolan, B. P. et al. Innate and adaptive immune responses in migrating spring-run adult chinook salmon, Oncorhynchus tshawytscha. Fish Shellfish Immunol. 48, 136–144 (2016).Article 
CAS 

Google Scholar 
Wedemeyer, G. A. Physiological response of juvenile coho salmon (Oncorhynchus kisutch) and rainbow trout (Salmo gairdneri) to handling and crowding stress in intensive fish culture. J. Fish. Res. Board Can. 33, 2699–2702 (1976).Article 

Google Scholar 
Suomalainen, L.-R., Tiirola, M. A. & Valtonen, E. T. Influence of rearing conditions on Flavobacterium columnare infection of rainbow trout, Oncorhynchus mykiss (Walbaum). J. Fish Dis. 28, 271–277 (2005).Article 

Google Scholar 
Schmidt-Posthaus, H., Bernet, D., Wahli, T. & Burkhardt-Holm, P. Morphological organ alterations and infectious diseases in brown trout Salmo trutta and rainbow trout Oncorhynchus mykiss exposed to polluted river water. Dis. Aquat. Organ. 44, 161–170 (2001).Article 
CAS 

Google Scholar 
Shi, N., Li, N., Duan, X. & Niu, H. Interaction between the gut microbiome and mucosal immune system. Mil. Med. Res. 4, 1–7 (2017).CAS 

Google Scholar 
Mitchell, S. O. et al. “Candidatus Branchiomonas cysticola” is a common agent of epitheliocysts in seawater-farmed Atlantic salmon Salmo salar in Norway and Ireland. Dis. Aquat. Organ. 103, 35–43 (2013).Article 
CAS 

Google Scholar 
Kormas, K. A., Meziti, A., Mente, E. & Frentzos, A. Dietary differences are reflected on the gut prokaryotic community structure of wild and commercially reared sea bream (Sparus aurata). Microbiologyopen 3, 718–728 (2014).Article 
CAS 
PubMed Central 

Google Scholar 
Engel, M. et al. Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome. PLoS ONE 12, e0180859 (2017).Article 
PubMed Central 

Google Scholar 
Lucasson, A. et al. A core of functionally complementary bacteria colonizes oysters in Pacific Oyster Mortality Syndrome. bioRxiv https://doi.org/10.1101/2020.11.16.384644 (2020).Article 

Google Scholar  More

Sol, D., Lapiedra, O. & González-Lagos, C. Behavioural adjustments for a life in the city. Anim. Behav. 85, 1101–1112 (2013).Article 

Google Scholar 
Ritzel, K. & Gallo, T. Behavior change in urban mammals: A systematic review. Front. Ecol. Evol. 8, 393 (2020).Article 

Google Scholar 
Gil, D. & Brumm, H. Avian Urban Ecology: Behavioural and Physiological Adaptations (Oxford University Press, 2014).
Google Scholar 
Stankowich, T. & Blumstein, D. T. Fear in animals: A meta-analysis and review of risk assessment. Proc. R. Soc. B Biol. Sci. 272, 2627–2634 (2005).Article 

Google Scholar 
Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).Article 

Google Scholar 
Blumstein, D. T. Flight-initiation distance in birds is dependent on intruder starting distance. J. Wildl. Manag. 67, 852–857 (2003).Article 

Google Scholar 
Cooper, W. E. & Frederick, W. G. Optimal flight initiation distance. J. Theor. Biol. 244, 59–67 (2007).Article 
MathSciNet 
MATH 

Google Scholar 
Blumstein, D. T. & Fernández-Juricic, E. A Primer of Conservation Behavior (Sinauer Associates, 2010).
Google Scholar 
Nunes, J. A. C. C. et al. Global trends on reef fishes’ ecology of fear: Flight initiation distance for conservation. Mar. Environ. Res. 136, 153–157 (2018).Article 
CAS 

Google Scholar 
Haidt, A., Kamiński, T., Borowik, T. & Kowalczyk, R. Human and the beast—Flight and aggressive responses of European bison to human disturbance. PLoS ONE 13, e0200635 (2018).Article 

Google Scholar 
Breck, S. W., Poessel, S. A., Mahoney, P. & Young, J. K. The intrepid urban coyote: A comparison of bold and exploratory behavior in coyotes from urban and rural environments. Sci. Rep. 9, 2104 (2019).Article 

Google Scholar 
Andrade, M. & Blumstein, D. T. Anti-predator behavior along elevational and latitudinal gradients in dark-eyed juncos. Curr. Zool. 66, 239–245 (2020).Article 

Google Scholar 
Cooper, W. & Pérez-Mellado, V. Escape by the Balearic Lizard (Podarcis lilfordi) is affected by elevation of an approaching predator, but not by some other potential predation risk factors. Acta Herpetol. 6, 247–259 (2011).
Google Scholar 
Møller, A. P. Interspecific variation in fear responses predicts urbanization in birds. Behav. Ecol. 21, 365–371 (2010).Article 

Google Scholar 
Samia, D. S. M. et al. Rural–urban differences in escape behavior of European birds across a latitudinal gradient. Front. Ecol. Evol. 5, 66 (2017).Article 

Google Scholar 
Morelli, F. et al. Contagious fear: Escape behavior increases with flock size in European gregarious birds. Ecol. Evol. 9, 6096–6104 (2019).Article 

Google Scholar 
Tätte, K., Møller, A. P. & Mänd, R. Towards an integrated view of escape decisions in birds: Relation between flight initiation distance and distance fled. Anim. Behav. 136, 75–86 (2018).Article 

Google Scholar 
Bókony, V., Kulcsár, A., Tóth, Z. & Liker, A. Personality traits and behavioral syndromes in differently urbanized populations of house sparrows (Passer domesticus). PLoS ONE 7, e36639 (2012).Article 

Google Scholar 
Vincze, E. et al. Habituation to human disturbance is faster in urban than rural house sparrows. Behav. Ecol. 27, 1304–1313 (2016).Article 

Google Scholar 
Seress, G., Bókony, V., Heszberger, J. & Liker, A. Response to predation risk in urban and rural house sparrows: Response to predation risk in house sparrows. Ethology 117, 896–907 (2011).Article 

Google Scholar 
Metcalf, B. M., Davies, S. & Ladd, P. G. Adaptation of behaviour by two bird species as a result of habituation to humans. Aust. Field Ornithol. 18, 306–312 (2000).
Google Scholar 
Blumstein, D. T. Attention, habituation, and antipredator behaviour: Implications for urban birds. In Avian Urban Ecology: Behavioural and Physiological Adaptations (eds Gil, D. & Brumm, H.) 41–53 (Oxford University Press, 2014).
Google Scholar 
Cavalli, M., Baladrón, A. V., Isacch, J. P., Biondi, L. M. & Bó, M. S. The role of habituation in the adjustment to urban life: An experimental approach with burrowing owls. Behav. Process. 157, 250–255 (2018).Article 
CAS 

Google Scholar 
Fossett, T. E. & Hyman, J. The effects of habituation on boldness of urban and rural song sparrows (Melospiza melodia). Behaviour 159, 243–257 (2021).Article 

Google Scholar 
Møller, A. P., Grim, T., Ibanez-Alamo, J. D., Marko, G. & Tryjanowski, P. Change in flight initiation distance between urban and rural habitats following a cold winter. Behav. Ecol. 24, 1211–1217 (2013).Article 

Google Scholar 
Møller, A. P. Reproductive behaviour. In Behavioural Responses to a Changing World (eds Candolin, U. & Wong, B. B. M.) 106–118 (Oxford University Press, 2012).Chapter 

Google Scholar 
Seress, G. & Liker, A. Habitat urbanization and its effects on birds. Acta Zool. Acad. Sci. Hung. 61, 373–408 (2015).Article 

Google Scholar 
Eötvös, C. B., Magura, T. & Lövei, G. L. A meta-analysis indicates reduced predation pressure with increasing urbanization. Landsc. Urban Plan. 180, 54–59 (2018).Article 

Google Scholar 
Fischer, J. D., Cleeton, S. H., Lyons, T. P. & Miller, J. R. Urbanization and the predation paradox: The role of trophic dynamics in structuring vertebrate communities. Bioscience 62, 809–818 (2012).Article 

Google Scholar 
Vincze, E. et al. Great tits take greater risk toward humans and sparrowhawks in urban habitats than in forests. Ethology 125, 686–701 (2019).Article 

Google Scholar 
Anderson, T. R. Biology of the Ubiquitous House Sparrow: From Genes to Populations (Oxford University Press, 2006).Book 

Google Scholar 
Santiago-Alarcon, D., Carbó-Ramírez, P., Macgregor-Fors, I., Chávez-Zichinelli, C. A. & Yeh, P. J. The prevalence of avian haemosporidian parasites in an invasive bird is lower in urban than in non-urban environments. Ibis 162, 201–214 (2020).Article 

Google Scholar 
García-Arroyo, M. & MacGregor-Fors, I. Tolerant to humans? Assessment of alert and flight initiation distances of two bird species in relation to sex, flock size, and environmental characteristics. Ethol. Ecol. Evol. 32, 445–456 (2020).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).Article 

Google Scholar 
Cohen, S. B. & Dor, R. Phenotypic divergence despite low genetic differentiation in house sparrow populations. Sci. Rep. 8, 394 (2018).Article 

Google Scholar 
Martin, L. B. & Fitzgerald, L. A taste for novelty in invading house sparrows, Passer domesticus. Behav. Ecol. 16, 702–707 (2005).Article 

Google Scholar 
Quesada, J. et al. Bold or shy? Examining the risk-taking behavior and neophobia of invasive and non-invasive house sparrows. Anim. Biodivers. Conserv. 45, 97–106 (2022).Article 

Google Scholar 
Díaz, M. et al. The geography of fear: A latitudinal gradient in anti-predator escape distances of birds across Europe. PLoS ONE 8, e64634 (2013).Article 

Google Scholar 
Quesada, J. & Calderon, J. Pardal comú. In Atles Dels Ocells Nidificants De Catalunya: Distribució i Abundancia 2015–2018 i Canvi des de 1980 (eds Franch, M. et al.) (Institut Català d’Ornitologia/Cossetània Edicions, 2021).
Google Scholar 
Shochat, E. Credit or debit? Resource input changes population dynamics of city-slicker birds. Oikos 106, 622–626 (2004).Article 

Google Scholar 
Statistical Institute of Catalonia. The municipality in figures. Bages. gencat https://www.idescat.cat/emex/?id=07 (2020).Bellet Sanfeliu, C. The evolution of urban planning in medium-sized Catalan cities (1979–2019). Urban Sci. 5, 36 (2021).Article 

Google Scholar 
Borras, A. & Junyent, F. Vertebrats de la Catalunya Central (Edicions Intercomarcals, 1993).
Google Scholar 
Vangestel, C., Braeckman, B. P., Matheve, H. & Lens, L. Constraints on home range behaviour affect nutritional condition in urban house sparrows (Passer domesticus). Biol. J. Linn. Soc. 101, 41–50 (2010).Article 

Google Scholar 
Herrando, S., Brotons, L., Estrada, J., Guallar, S. & Anton, M. Atles dels Ocells de Catalunya a I’hivern 2006–2009: Catalan Winter Bird Atlas 2006–2009 (Lynx Ed, 2011).
Google Scholar 
MacGregor-Fors, I. How to measure the urban-wildland ecotone: Redefining ‘peri-urban’ areas. Ecol. Res. 25, 883–887 (2010).Article 

Google Scholar 
Lemoine-Rodríguez, R., MacGregor-Fors, I. & Muñoz-Robles, C. Six decades of urban green change in a neotropical city: A case study of Xalapa, Veracruz, Mexico. Urban Ecosyst. 22, 609–618 (2019).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
Shochat, E. et al. Invasion, competition, and biodiversity loss in urban ecosystems. Bioscience 60, 199–208 (2010).Article 

Google Scholar 
Sol, D. et al. Risk-taking behavior, urbanization and the pace of life in birds. Behav. Ecol. Sociobiol. 72, 59 (2018).Article 

Google Scholar 
Geue, D. & Partecke, J. Reduced parasite infestation in urban Eurasian blackbirds (Turdus merula): A factor favoring urbanization?. Can. J. Zool. 86, 1419–1425 (2008).Article 

Google Scholar 
MacGregor-Fors, I., Quesada, J., Lee, J.G.-H. & Yeh, P. J. On the lookout for danger: House sparrow alert distance in three cities. Urban Ecosyst. 22, 955–960 (2019).Article 

Google Scholar  More