Ecology

1.
Kuwae, T. et al. Biofilm grazing in a higher vertebrate: The Western Sandpiper, Calidris mauri. Ecology 89, 599–606 (2008).
PubMed  Article  PubMed Central  Google Scholar 
2.
Góngora, E., Braune, B. M. & Elliott, K. H. Nitrogen and sulfur isotopes predict variation in mercury levels in Arctic seabird prey. Mar. Pollut. Bull. 135, 907–914 (2018).
PubMed  Article  CAS  Google Scholar 

3.
Ben-Yosef, M., Aharon, Y., Jurkevitch, E. & Yuval, B. Give us the tools and we will do the job: Symbiotic bacteria affect olive fly fitness in a diet-dependent fashion. Proc. R. Soc. B Biol. Sci. 277, 1545–1552 (2010).
CAS  Article  Google Scholar 

4.
Alberdi, A., Aizpurua, O., Bohmann, K., Zepeda-Mendoza, M. L. & Gilbert, M. T. P. Do vertebrate gut metagenomes confer rapid ecological adaptation?. Trends Ecol. Evol. 31, 689–699 (2016).
PubMed  Article  PubMed Central  Google Scholar 

5.
Lapanje, A., Zrimec, A., Drobne, D. & Rupnik, M. Long-term Hg pollution-induced structural shifts of bacterial community in the terrestrial isopod (Porcellio scaber) gut. Environ. Pollut. 158, 3186–3193 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Lewis, W. B., Moore, F. R. & Wang, S. Characterization of the gut microbiota of migratory passerines during stopover along the northern coast of the Gulf of Mexico. J. Avian Biol. 47, 659–668 (2016).
Article  Google Scholar 

7.
Bolnick, D. I. et al. Individuals’ diet diversity influences gut microbial diversity in two freshwater fish (threespine stickleback and Eurasian perch). Ecol. Lett. 17, 979–987 (2014).
PubMed  PubMed Central  Article  Google Scholar 

8.
Bolnick, D. I. et al. Individual diet has sex-dependent effects on vertebrate gut microbiota. Nat. Commun. 5, 4500 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Bolnick, D. I., Yang, L. H., Fordyce, J. A., Davis, J. M. & Svanbäck, R. Measuring individual-level resource specialization. Ecology 83, 2936–2941 (2002).
Article  Google Scholar 

10.
Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).
MathSciNet  PubMed  Article  PubMed Central  Google Scholar 

11.
Apajalahti, J. H. A., Kettunen, A., Bedford, M. R. & Holben, W. E. Percent G + C profiling accurately reveals diet-related differences in the gastrointestinal microbial community of broiler chickens. Appl. Environ. Microbiol. 67, 5656–5667 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Apajalahti, J. & Kettunen, A. Microbes of the chicken gastrointestinal tract. In Avian Gut Function in Health and Disease (ed. Perry, G. C.) 124–137 (CAB International, Wallingford, 2006).
Google Scholar 

13.
Oakley, B. B. et al. The chicken gastrointestinal microbiome. FEMS Microbiol. Lett. 360, 100–112 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Bangert, R. L., Ward, A. C. S., Stauber, E. H., Cho, B. R. & Widders, P. R. A survey of the aerobic bacteria in the feces of captive raptors. Avian Dis. 32, 53–62 (1988).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Soucek, Z. & Mushin, R. Gastrointestinal bacteria of certain Antarctic birds and mammals. Appl. Microbiol. 20, 561–566 (1970).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Mead, G. C., Griffiths, N. M., Impey, C. S. & Coplestone, J. C. Influence of diet on the intestinal microflora and meat flavour of intensively-reared broiler chickens. Br. Poult. Sci. 24, 261–272 (1983).
Article  Google Scholar 

17.
Waldenström, J. et al. Prevalence of Campylobacter jejuni, Campylobacter lari, and Campylobacter coli in different ecological guilds and taxa of migrating birds. Appl. Environ. Microbiol. 68, 5911–5917 (2002).
PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Waite, D. W. & Taylor, M. W. Exploring the avian gut microbiota: Current trends and future directions. Front. Microbiol. 6, 1–12 (2015).
Article  Google Scholar 

19.
Maul, J. D., Gandhi, J. P. & Farris, J. L. Community-level physiological profiles of cloacal microbes in songbirds (order: Passeriformes): Variation due to host species, host diet, and habitat. Microb. Ecol. 50, 19–28 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Risely, A., Waite, D. W., Ujvari, B., Hoye, B. J. & Klaassen, M. Active migration is associated with specific and consistent changes to gut microbiota in Calidris shorebirds. J. Anim. Ecol. 87, 428–437 (2018).
PubMed  Article  PubMed Central  Google Scholar 

21.
Dewar, M. L. et al. Interspecific variations in the gastrointestinal microbiota in penguins. Microbiologyopen 2, 195–204 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Waite, D. W. & Taylor, M. W. Characterizing the avian gut microbiota: Membership, driving influences, and potential function. Front. Microbiol. 5, 1–12 (2014).
Article  Google Scholar 

23.
Teyssier, A. et al. Inside the guts of the city: Urban-induced alterations of the gut microbiota in a wild passerine. Sci. Total Environ. 612, 1276–1286 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Hird, S. M., Sánchez, C., Carstens, B. C. & Brumfield, R. T. Comparative gut microbiota of 59 neotropical bird species. Front. Microbiol. 6, 1403 (2015).
PubMed  Article  PubMed Central  Google Scholar 

25.
Capunitan, D. C., Johnson, O., Terrill, R. S. & Hird, S. M. Evolutionary signal in the gut microbiomes of 74 bird species from Equatorial Guinea. Mol. Ecol. 29, 829–847 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Michel, A. J. et al. The gut of the finch: Uniqueness of the gut microbiome of the Galápagos vampire finch. Microbiome 6, 167 (2018).
PubMed  PubMed Central  Article  Google Scholar 

27.
Elliott, K. H., Woo, K. J. & Gaston, A. J. Specialization in murres: The story of eight specialists. Waterbirds 32, 491–506 (2009).
Article  Google Scholar 

28.
Woo, K. J., Elliott, K. H., Davidson, M., Gaston, A. J. & Davoren, G. K. Individual specialization in diet by a generalist marine predator reflects specialization in foraging behaviour. J. Anim. Ecol. 77, 1082–1091 (2008).
PubMed  Article  PubMed Central  Google Scholar 

29.
Elliott, K. H., Gaston, A. J. & Crump, D. Sex-specific behavior by a monomorphic seabird represents risk partitioning. Behav. Ecol. 21, 1024–1032 (2010).
Article  Google Scholar 

30.
Paredes, R., Jones, I. & Boness, D. Parental roles of male and female thick-billed murres and razorbills at the Gannet Islands, Labrador. Behaviour 143, 451–481 (2006).
Article  Google Scholar 

31.
Atwell, L., Hobson, K. A. & Welch, H. E. Biomagnification and bioaccumulation of mercury in an arctic marine food web: Insights from stable nitrogen isotope analysis. Can. J. Fish. Aquat. Sci. 55, 1114–1121 (1998).
CAS  Article  Google Scholar 

32.
Carr, M. K. et al. Stable sulfur isotopes identify habitat-specific foraging and mercury exposure in a highly mobile fish community. Sci. Total Environ. 586, 338–346 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

33.
Peterson, B. J. & Fry, B. Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18, 293–320 (1987).
Article  Google Scholar 

34.
Song, S. J. et al. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 1, e00021-e116 (2016).
PubMed  PubMed Central  Article  Google Scholar 

35.
Grond, K., Sandercock, B. K., Jumpponen, A. & Zeglin, L. H. The avian gut microbiota: Community, physiology and function in wild birds. J. Avian Biol. 49, e01788 (2018).
Article  Google Scholar 

36.
Lawson, P. A., Collins, M. D., Falsen, E. & Foster, G. Catellicoccus marimammalium gen. nov., sp. nov., a novel Gram-positive, catalase-negative, coccus-shaped bacterium from porpoise and grey seal. Int. J. Syst. Evol. Microbiol. 56, 429–432 (2006).
CAS  PubMed  Article  Google Scholar 

37.
Sinigalliano, C. D. et al. Multi-laboratory evaluations of the performance of Catellicoccus marimammalium PCR assays developed to target gull fecal sources. Water Res. 47, 6883–6896 (2013).
CAS  PubMed  Article  Google Scholar 

38.
Ryu, H. et al. Comparison of gull feces-specific assays targeting the 16S rRNA genes of Catellicoccus marimammalium and Streptococcus spp. Appl. Environ. Microbiol. 78, 1909–1916 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Koskey, A. M., Fisher, J. C., Traudt, M. F., Newton, R. J. & McLellan, S. L. Analysis of the gull fecal microbial community reveals the dominance of Catellicoccus marimammalium in relation to culturable enterococci. Appl. Environ. Microbiol. 80, 757–765 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Lu, J., Santo Domingo, J. W., Lamendella, R., Edge, T. & Hill, S. Phylogenetic diversity and molecular detection of bacteria in gull feces. Appl. Environ. Microbiol. 74, 3969–3976 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Benskin, C. M. H., Rhodes, G., Pickup, R. W., Wilson, K. & Hartley, I. R. Diversity and temporal stability of bacterial communities in a model passerine bird, the zebra finch. Mol. Ecol. 19, 5531–5544 (2010).
PubMed  Article  PubMed Central  Google Scholar 

42.
Kreisinger, J. et al. Temporal stability and the effect of transgenerational transfer on fecal microbiota structure in a long distance migratory bird. Front. Microbiol. 8, 1–19 (2017).
Article  Google Scholar 

43.
Grond, K., Ryu, H., Baker, A. J., Santo Domingo, J. W. & Buehler, D. M. Gastro-intestinal microbiota of two migratory shorebird species during spring migration staging in Delaware Bay, USA. J. Ornithol. 155, 969–977 (2014).
Article  Google Scholar 

44.
Santos, S. S. et al. Diversity of cloacal microbial community in migratory shorebirds that use the Tagus estuary as stopover habitat and their potential to harbor and disperse pathogenic microorganisms. FEMS Microbiol. Ecol. 82, 63–74 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Laviad-Shitrit, S., Izhaki, I., Lalzar, M. & Halpern, M. Comparative analysis of intestine microbiota of four wild waterbird species. Front. Microbiol. 10, 1–13 (2019).
Article  Google Scholar 

46.
Weigand, M. R., Ryu, H., Bozcek, L., Konstantinidis, K. T. & Santo Domingo, J. W. Draft genome sequence of Catellicoccus marimammalium, a novel species commonly found in gull feces. Genome Announc. 1, 12–13 (2013).
Article  Google Scholar 

47.
Dewar, M. L. et al. Influence of fasting during moult on the faecal microbiota of penguins. PLoS ONE 9, e99996 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Dewar, M. L., Arnould, J. P. Y., Krause, L., Dann, P. & Smith, S. C. Interspecific variations in the faecal microbiota of Procellariiform seabirds. FEMS Microbiol. Ecol. 89, 47–55 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Roggenbuck, M. et al. The microbiome of New World vultures. Nat. Commun. 5, 5498 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Potrykus, J., White, R. L. & Bearne, S. L. Proteomic investigation of amino acid catabolism in the indigenous gut anaerobe Fusobacterium varium. Proteomics 8, 2691–2703 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Tsuchiya, C., Sakata, T. & Sugita, H. Novel ecological niche of Cetobacterium somerae, an anaerobic bacterium in the intestinal tracts of freshwater fish. Lett. Appl. Microbiol. 46, 071018031740002–000 (2007).
Article  CAS  Google Scholar 

52.
Tegtmeier, D., Riese, C., Geissinger, O., Radek, R. & Brune, A. Breznakia blatticola gen. nov. sp. nov. and Breznakia pachnodae sp. nov., two fermenting bacteria isolated from insect guts, and emended description of the family Erysipelotrichaceae. Syst. Appl. Microbiol. 39, 319–329 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Vandamme, P. et al. Ornithobacterium rhinotracheale gen. nov., sp. nov. isolated from the avian respiratory tract. Int. J. Syst. Bacteriol. 44, 24–37 (1994).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Cerdà-Cuéllar, M. et al. Do humans spread zoonotic enteric bacteria in Antarctica?. Sci. Total Environ. 654, 190–196 (2019).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

55.
Anderson, M. J. & Walsh, D. C. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?. Ecol. Monogr. 83, 557–574 (2013).
Article  Google Scholar 

56.
Lott, C. A., Meehan, T. D. & Heath, J. A. Estimating the latitudinal origins of migratory birds using hydrogen and sulfur stable isotopes in feathers: Influence of marine prey base. Oecologia 134, 505–510 (2003).
ADS  PubMed  Article  PubMed Central  Google Scholar 

57.
Góngora, E., Elliott, K. & Whyte, L. Dataset from Gut microbiome is affected by inter-sexual and inter-seasonal variation in diet for thick-billed murres (Uria lomvia). Mendeley Data v4, (2020).

58.
Eriksson, P., Mourkas, E., González-Acuna, D., Olsen, B. & Ellström, P. Evaluation and optimization of microbial DNA extraction from fecal samples of wild Antarctic bird species. Infect. Ecol. Epidemiol. 7, 1386536 (2017).
PubMed  PubMed Central  Google Scholar 

59.
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).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).
PubMed  PubMed Central  Article  CAS  Google Scholar 

61.
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
PubMed  PubMed Central  Article  Google Scholar 

64.
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Braune, B. M., Gaston, A. J., Hobson, K. A., Gilchrist, H. G. & Mallory, M. L. Changes in food web structure alter trends of mercury uptake at two seabird colonies in the Canadian arctic. Environ. Sci. Technol. 48, 13246–13252 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Callahan, B. J., Sankaran, K., Fukuyama, J. A., McMurdie, P. J. & Holmes, S. P. Bioconductor workflow for microbiome data analysis: From raw reads to community analyses. F1000Research 5, 1492 (2016).
PubMed  PubMed Central  Article  Google Scholar 

67.
Bokulich, N. A. et al. q2-longitudinal: Longitudinal and paired-sample analyses of microbiome data. mSystems 3, 1–9 (2018).
Article  Google Scholar 

68.
Wilcoxon, F. Individual comparisons by Ranking methods. Biometrics Bull. 1, 80 (1945).
Article  Google Scholar 

69.
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Google Scholar 

70.
Lozupone, C. & Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

71.
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Article  Google Scholar 

72.
Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, Champaign, 1949).
Google Scholar 

73.
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952).
MATH  Article  Google Scholar 

74.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).
MathSciNet  PubMed  PubMed Central  MATH  Article  Google Scholar 

75.
Legendre, P. & Legendre, L. Ordination in reduced space. In Numerical Ecology Vol. 24 (eds Legendre, P. & Legendre, L.) 425–520 (Elsevier, Amsterdam, 2012).
Google Scholar 

76.
Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: A tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).
PubMed  PubMed Central  Article  Google Scholar 

77.
Vázquez-Baeza, Y. et al. Bringing the dynamic microbiome to life with animations. Cell Host Microbe 21, 7–10 (2017).
PubMed  Article  CAS  Google Scholar 

78.
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

80.
McMurdie, P. J. & Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

81.
Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).
MathSciNet  MATH  Article  Google Scholar 

82.
Bartoń, K. MuMIn: Multi-Model Inference. (2019). More

Samples included in this dataset were taken from olive trees sampled from November 2013 until April 2018 by the Apulian Regional Phytosanitary Service. From April 2016 to April 2018, sampling was done only in the buffer zone and containment zone (Fig. 1) and was structured in quadrats of one hectares (ha) area, with at least one sample collected in each quadrat. Within each quadrat, priority was given to sample symptomatic trees and if within the quadrat several trees showed disease symptoms, these were also sampled and individually tested. Samples consisted of mature olive twigs (at least 8 twigs/tree), collected close to symptomatic branches, or from the 4 cardinal points of the canopy when sampling asymptomatic trees. The samples were first tested for X. fastidiosa by using Enzyme-linked immunosorbent assay (ELISA)21. All ELISA-positive samples, and those yielding doubtful ELISA results, plus 3% of the negative samples, were subsequently tested using quantitative PCR.
The total data set comprises 409,515 records and 7 columns. The columns are the ID number of the measurement, longitude, latitude, result (0 for negative on X. fastidiosa presence, 1 for positive), day, year, and month. The number of rows was reduced to 298,230 rows after removing NA (not available) values for the result column or missing coordinates for the longitude and latitude columns. We initially tried to work with the point data as observed, but found that these data were extremely difficult to analyse, presumably because of large variability in the data leading to very flat likelihood surfaces that did not support convergence of the optimization algorithms tested for fitting spatial expansion models (Simplex, Simulated annealing, etc.). We therefore grouped the observation data in 1-km wide distance classes from the port of Gallipoli, the likely origin of the disease invasion (latitude: 40.055851, longitude: 17.992615)22 and calculated the proportion of infected trees in each class. We thus obtained a reduced data set with approximately 200 distance classes comprising an inner circle of 1 km radius, and concentric rings of 1 km width each, with for each class the number of sampled trees and the number of infected trees. We then analysed the relationship between the proportion of infected trees and the distance from Gallipoli (Fig. 4). This relationship was first identified separately for each year, and subsequently by assuming a constant rate of displacement over time (i.e. the rate of spread) of a disease front with a fixed shape.
Figure 4

Relationship between proportion of positive samples per each km ring (Y-axis) and distance to Gallipoli (X-axis; km). Points with different colour represents different years.

Full size image

We expected a high proportion of positive samples at short distance from Gallipoli, with the proportion declining with increasing distance. Therefore, we chose for the shape of the disease front the following deterministic functions (1) a negative exponential function, (2) a decreasing logistic function, and (3) a constrained negative exponential function (CNE; constrained to have a maximum proportion diseased trees (p = 1.0)) (Table 1). The shape of the tail of the invasion front is in many instances exponential18,23,24,25,26, but the proportion of disease cannot exceed one, hence the CNE was used as a modification of an exponential relationship. The sampled data is binary count data (number of positive samples out of the total number of samples at a given distance) and the distance is transformed to discrete distance circles. Because the data are based on a known number of samples in each distance class with a stochastic number of positive outcomes, we chose the binomial distribution and the beta-binomial distribution as candidate stochastic models for fitting the model to the data (Table 1). The binomial model is a model for count data with a defined maximum (N), assuming a fixed probability of “success” (infection). The beta-binomial takes overdispersion into account by drawing the probability of success from a beta distribution around the mean probability of success. The probability of success, i.e. the proportion of positive samples, depends on the distance from Gallipoli and the time since first detection. In our model for the invasion front, the mean probability of disease presence at a distance (x) from Gallipoli is described by the deterministic part of the model (e.g. logistic), while the beta-binomial variability in the detection result is described by an overdispersion parameter (theta) which increases in value as the variance tends towards the variance of the binomial distribution (Bolker, 2008). Mathematically, the parameter θ equals the sum of the parameters (a + b), where (a) and (b) are the shape parameters of the beta distribution27. Given a same mean, the beta-binomial distribution has a larger variance than the binomial distribution (Table 1). The beta-binomial distribution tends to the binomial distribution as (theta) gets large. For all model fits, we calculated the AIC (Akaike information criterion):

$${text{AIC}} = 2k – 2 log left( L right)$$
(1)

Table 1 Deterministic and stochastic models used for fitting all combinations of deterministic and stochastic models.
Full size table

where (k) is the number of estimated parameters, log is the natural logarithm, and L is the likelihood27. The model with the lowest AIC was selected as the most supported model. Models with a difference in AIC from the minimum AIC model of two or less are considered equivalent. In that case, we selected the simplest model.
Next, we used the two best fitting models (see “Results” section), the logistic function with beta-binomial distribution and the CNE function with beta-binomial distribution, to analyse the speed with which X. fastidiosa spreads through Puglia. To keep the models in a simplified form, it can be assumed that the dispersal front retains its shape over time and space and moves in space at a constant rate28,29. Therefore, for this analysis the deterministic functions from Table 1 are modified to include a yearly spread rate c (km per year) and time variable t (year):

$${text{Logistic}};{text{function:}};p_{l} = frac{1}{{1 + {text{exp}}left( {rleft( {x – (x_{50} + ct} right)} right))}}$$
(2)

$${text{CNE}};{text{function:}};p_{c} = left{ {begin{array}{ll} 1 & { mid; x < x_{100} + ct,} \ {exp left( { - rleft( {x - left( {x_{100} + ct} right)} right)} right) } & {mid; x ge x_{100} + ct.} \ end{array} } right.$$ (3) where (p_{l}) and (p_{c}) are the proportion of positive measurements of the logistic and CNE functions respectively, (r) is the relative growth rate of the disease in the tail in km-1, (x) is the distance in km from the disease origin, Gallipoli, (x_{50}) is the (negative) x-value (distance from Gallipoli) of the half-maximum of the curve at (t = 0) in km, (x_{100}) is the (negative) (x)-value where the CNE function curve reaches a value of 1.0 at (t = 0) in km, (t) is the time since 2013 in years, and the parameter c is the rate of spread in km per year. With these equations, one curve for every (t) (year) is displayed. 95% confidence limits (CLs) were calculated with the likelihood ratio test method27. To test the adequacy of the methodology for estimating the shape of the invasion front and the rate of spread, we did stochastic simulations in which we generated data on an expanding disease, collected samples in the same spatially heterogeneous manner from the simulated data as we did for the actual data sets, and re-estimated the rate of spread from the data. The estimated parameter values were then compared to the known parameter input values. The simulations were done using the logistic function and CNE function for the shape of the disease front and a beta-binomial distribution to describe variability. Data was randomly generated using a beta-binomial distribution for every distance circle according to the expected proportion of disease ((p)) calculated from the deterministically moving front, while the number of samples (N) within each distance circle was the same as in the empirical data. Again, a constant shape and rate of spread of the dispersal front is assumed29. Because of the uncertainty regarding the location of the front when sampling started (2013) and the rate of spread, the parameters that describe these aspects of the model, (x_{50}) (logistic) or (x_{100}) (CNE) and (c) respectively, were also varied in the stochastic simulations. For the logistic function, the parameters (r) (the relative growth rate of the disease in the tail) and (theta) (overdispersion) were fixed at 0.08 km−1 and 1 respectively, while parameter (x_{50}) was varied from − 40 to − 5 km from Gallipoli with steps of 5 km, and the parameter (c) was varied from 5 to 16 km per year with steps of 1 km per year. For the CNE function, the parameters (r) and (theta) were again fixed at 0.08 km−1 and 1 respectively, while parameter (x_{100}) was varied from − 45 to − 10 km with steps of 5 km, and parameter (c) was varied from 5 to 16 km per year with steps of 1 km per year. Data generation and estimation of parameters was done 10 times for each combination of parameters. For every combination of the location parameter, (x_{50}) or (x_{100}), and the rate of range expansion, c, the mean difference between the set rate of spread and the estimated rate of spread was calculated ((X_{i}); where i is the index for a parameter combination). Using the generated set of differences Xi, we calculated the mean bias ((overline{X})): $$overline{X} = frac{{mathop sum nolimits_{i}^{n} X_{i} }}{n}$$ (4) where (n) is the total number of parameter combinations. We also calculated the root-mean-squared error (RMSE): $${text{RMSE}} = sqrt {frac{{mathop sum nolimits_{i}^{n} X_{i}^{2} }}{n}}$$ (5) We estimated the width of the invasion front using a logistic shape of the invasion front. Width was calculated as the distance between the 1st and 99th percentile of the front or between the 5th and 95th percentile. For this, a curve at any point in time can be used since the curves have the same shape, and the width is the same in every year (Fig. 6). For the logistic function and the calculation of the 1st and 99th percentile the following applies: $$frac{1}{{1 + {text{exp}}left( {rleft( {x_{99} - left( {x_{50} + ct} right)} right)} right)}} = 0.99$$ (6) $$frac{1}{{1 + {text{exp}}left( {rleft( {x_{1} - left( {x_{50} + ct} right)} right)} right)}} = 0.01$$ (7) This is solved to find: $$x_{1} - x_{99} = frac{{2{text{log}}left( {99} right)}}{r}$$ (8) where log is the natural logarithm. Using Eq. (7), we also estimate the supposed starting time of the logistic growth of the disease by calculating (t) for (x_{1} = 0). To assess the sensitivity of our analysis to the point of origin, for which we chose Gallipoli in accordance with the best available evidence, we repeated our analyses of the shape of the front and the rate of spread when assuming different points of origin. For this we use three fictitious origin locations (Fig. 1): Santa Maria di Leuca, Otranto, and Maglie. We choose Santa Maria di Leuca and Otranto because these are also cities in Puglia with ports. We choose Maglie because it lies approximately in between the other three locations. These locations are not chosen because we think they are plausible points where Xylella could have been introduced for the first time, but only because they are suitable locations for a sensitivity analysis. To further asses the sensitivity of choosing Gallipoli as the point of origin, we repeat our simulations when generating data with Santa Maria di Leuca, Otranto, or Maglie as the point of origin, but analyse this data assuming Gallipoli as the point of origin. All calculations and model fitting were done in R 3.6.030. The complete dataset and details on the data analysis are available in the R script online at https://github.com/DBKottelenberg/OQDS_Xf_Puglia. More

1.
Kopatz, A. et al. Connectivity and population subdivision at the fringe of a large brown bear (Ursus arctos) population in North Western Europe. Conserv. Genet. 13, 681–692 (2012).
Article  Google Scholar 
2.
Mohammadi, A. & Kaboli, M. Evaluating wildlife–vehicle collision hotspots using kernel-based estimation: a focus on the endangered Asiatic cheetah in central Iran. Hum. Wildl. Interact. 10, 13 (2016).
Google Scholar 

3.
Murphy, S. M. et al. Consequences of severe habitat fragmentation on density, genetics, and spatial capture–recapture analysis of a small bear population. PLoS ONE 12, e0181849 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Hosseini-Zavarei, F., Farhadinia, M. S., Beheshti-Zavareh, M. & Abdoli, A. Predation by grey wolf on wild ungulates and livestock in central Iran. J. Zool. 290, 1–8 (2013).
Article  Google Scholar 

5.
Tumendemberel, O. et al. Phylogeography, genetic diversity, and connectivity of brown bear populations in Central Asia. PLoS ONE 14, e0220746 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Hilty, J. A., Lidicker, W. Z. Jr. & Merenlender, A. M. Corridor Ecology: The Science and Practice of Linking Landscapes for Biodiversity Conservation (Island Press, Washington, 2012).
Google Scholar 

7.
Cushman, S. A. et al. Limiting factors and landscape connectivity: the American marten in the Rocky Mountains. Landsc. Ecol. 26, 1137 (2011).
Article  Google Scholar 

8.
Oriol-Cotterill, A., Valeix, M., Frank, L. G., Riginos, C. & Macdonald, D. W. Landscapes of coexistence for terrestrial carnivores: the ecological consequences of being downgraded from ultimate to penultimate predator by humans. Oikos 124, 1263–1273 (2015).
Article  Google Scholar 

9.
Cushman, S. A. et al. Prioritizing core areas, corridors and conflict hotspots for lion conservation in southern Africa. PLoS ONE 13, e0196213 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

10.
Rio-Maior, H., Nakamura, M., Álvares, F. & Beja, P. Designing the landscape of coexistence: integrating risk avoidance, habitat selection and functional connectivity to inform large carnivore conservation. Biol. Conserv. 235, 178–188 (2019).
Article  Google Scholar 

11.
Macdonald, D. W. et al. Multi-scale habitat modelling identifies spatial conservation priorities for mainland clouded leopards (Neofelis nebulosa). Divers. Distrib. 25, 1639–1654 (2019).
Article  Google Scholar 

12.
Johansson, Ö. et al. Land sharing is essential for snow leopard conservation. Biol. Conserv. 203, 1–7 (2016).
Article  Google Scholar 

13.
López-Bao, J. V., Bruskotter, J. & Chapron, G. Finding space for large carnivores. Nat. Ecol. Evol. 1, 1–2 (2017).
Article  Google Scholar 

14.
Crespin, S. J. & Simonetti, J. A. Reconciling farming and wild nature: Integrating human–wildlife coexistence into the land-sharing and land-sparing framework. Ambio 48, 131–138 (2019).
PubMed  Article  PubMed Central  Google Scholar 

15.
Kaszta, Ż, Cushman, S. A. & Macdonald, D. W. Prioritizing habitat core areas and corridors for a large carnivore across its range. Anim. Conserv. 23, 1–10 (2020).
Article  Google Scholar 

16.
Kaszta, Ż et al. Simulating the impact of Belt and Road initiative and other major developments in Myanmar on an ambassador felid, the clouded leopard, Neofelis nebulosa. Landsc. Ecol. 35, 727–746 (2020).
Article  Google Scholar 

17.
Cushman, S. A., Compton, B. W. & McGarigal, K. Habitat fragmentation effects depend on complex interactions between population size and dispersal ability: modeling influences of roads, agriculture and residential development across a range of life-history characteristics. In Spatial Complexity, Informatics, and Wildlife Conservation (eds Cushman, S. A. & Huettmann, F.) 369–385 (Springer, Berlin, 2010).
Google Scholar 

18.
Kaszta, Ż et al. Integrating Sunda clouded leopard (Neofelis diardi) conservation into development and restoration planning in Sabah (Borneo). Biol. Conserv. 235, 63–76 (2019).
Article  Google Scholar 

19.
Beier, P., Majka, D. R. & Spencer, W. D. Forks in the road: choices in procedures for designing wildland linkages. Conserv. Biol. 22, 836–851 (2008).
PubMed  Article  PubMed Central  Google Scholar 

20.
Romportl, D. et al. Designing migration corridors for large mammals in the Czech Republic. J. Landsc. Ecol. 6, 47–62 (2013).
Article  Google Scholar 

21.
Ruiz-González, A. et al. Landscape genetics for the empirical assessment of resistance surfaces: the European pine marten (Martes martes) as a target-species of a regional ecological network. PLoS ONE 9, e110552 (2014).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

22.
Cushman, S. A., Elliot, N. B., Macdonald, D. W. & Loveridge, A. J. A multi-scale assessment of population connectivity in African lions (Panthera leo) in response to landscape change. Landsc. Ecol. 31, 1337–1353 (2016).
Article  Google Scholar 

23.
Linnell, J., Salvatori, V. & Boitani, L. Guidelines for population level management plans for large carnivores in Europe. A Large Carnivore Initiative for Europe (2008).

24.
Reljic, S. et al. Challenges for transboundary management of a European brown bear population. Glob. Ecol. Conserv. 16, e00488 (2018).
Article  Google Scholar 

25.
Mateo Sanchez, M. C., Cushman, S. A. & Saura, S. Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain). Int. J. Geogr. Inf. Sci. 28, 1531–1546 (2014).
Article  Google Scholar 

26.
Vergara, M., Cushman, S. A., Urra, F. & Ruiz-González, A. Shaken but not stirred: multiscale habitat suitability modeling of sympatric marten species (Martes martes and Martes foina) in the northern Iberian Peninsula. Landsc. Ecol. 31, 1241–1260 (2016).
Article  Google Scholar 

27.
Ziółkowska, E. et al. Assessing differences in connectivity based on habitat versus movement models for brown bears in the Carpathians. Landsc. Ecol. 31, 1863–1882 (2016).
Article  Google Scholar 

28.
Sarkar, M. S. et al. Multiscale statistical approach to assess habitat suitability and connectivity of common leopard (Panthera pardus) in Kailash Sacred Landscape, India. Spat. Stat. 28, 304–318 (2018).
MathSciNet  Article  Google Scholar 

29.
Ashrafzadeh, M. R. et al. A multi-scale, multi-species approach for assessing effectiveness of habitat and connectivity conservation for endangered felids. Biol. Conserv. 245, 108523 (2020).
Article  Google Scholar 

30.
McGarigal, K., Wan, H. Y., Zeller, K. A., Timm, B. C. & Cushman, S. A. Multi-scale habitat selection modeling: a review and outlook. Landsc. Ecol. 31, 1161–1175 (2016).
Article  Google Scholar 

31.
Wasserman, T. N., Cushman, S. A., Shirk, A. S., Landguth, E. L. & Littell, J. S. Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA. Landsc. Ecol. 27, 211–225 (2012).
Article  Google Scholar 

32.
Mateo-Sánchez, M. C. et al. A comparative framework to infer landscape effects on population genetic structure: Are habitat suitability models effective in explaining gene flow?. Landsc. Ecol. 30, 1405–1420 (2015).
Article  Google Scholar 

33.
Zeller, K. A. et al. Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Divers. Distrib. 24, 868–879 (2018).
Article  Google Scholar 

34.
Cushman, S. A., Lewis, J. S. & Landguth, E. L. Why did the bear cross the road? Comparing the performance of multiple resistance surfaces and connectivity modeling methods. Diversity 6, 844–854 (2014).
Article  Google Scholar 

35.
Adriaensen, F. et al. The application of ‘least-cost’modelling as a functional landscape model. Landsc. Urban Plan. 64, 233–247 (2003).
Article  Google Scholar 

36.
McRae, B. H. Isolation by resistance. Evolution (N. Y.) 60, 1551–1561 (2006).
Google Scholar 

37.
Cushman, S. A., McKelvey, K. S. & Schwartz, M. K. Use of empirically derived source–destination models to map regional conservation corridors. Conserv. Biol. 23, 368–376 (2009).
PubMed  Article  PubMed Central  Google Scholar 

38.
Compton, B. W., McGarigal, K., Cushman, S. A. & Gamble, L. R. A resistant-kernel model of connectivity for amphibians that breed in vernal pools. Conserv. Biol. 21, 788–799 (2007).
PubMed  Article  PubMed Central  Google Scholar 

39.
Panzacchi, M. et al. Predicting the continuum between corridors and barriers to animal movements using step selection functions and randomized shortest paths. J. Anim. Ecol. 85, 32–42 (2016).
PubMed  Article  PubMed Central  Google Scholar 

40.
Cushman, S. A., Lewis, J. S. & Landguth, E. L. Evaluating the intersection of a regional wildlife connectivity network with highways. Mov. Ecol. 1, 12 (2013).
PubMed  PubMed Central  Article  Google Scholar 

41.
Moqanaki, E. M. & Cushman, S. A. All roads lead to Iran: predicting landscape connectivity of the last stronghold for the critically endangered Asiatic cheetah. Anim. Conserv. 20, 29–41 (2017).
Article  Google Scholar 

42.
Khosravi, R., Hemami, M. & Cushman, S. A. Multispecies assessment of core areas and connectivity of desert carnivores in central Iran. Divers. Distrib. 24, 193–207 (2018).
Article  Google Scholar 

43.
Shahnaseri, G. et al. Contrasting use of habitat, landscape elements, and corridors by grey wolf and golden jackal in central Iran. Landsc. Ecol. 34, 1263–1277 (2019).
Article  Google Scholar 

44.
Cushman, S. A. & Landguth, E. L. Ecological associations, dispersal ability, and landscape connectivity in the northern Rocky Mountains. In Research Paper RMRS-RP-90. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. vol. 90, 21 p (2012).

45.
McGarigal, K. & Cushman, S. A. Comparative evaluation of experimental approaches to the study of habitat fragmentation effects. Ecol. Appl. 12, 335–345 (2002).
Article  Google Scholar 

46.
Cozzi, G. et al. Anthropogenic food resources foster the coexistence of distinct life history strategies: year-round sedentary and migratory brown bears. J. Zool. 300, 142–150 (2016).
Article  Google Scholar 

47.
McLellan, B. N., Proctor, M. F., Huber, D. & Michel, S. Ursus arctos (amended version of 2017 assessment). The IUCN Red List of Threatened Species 2017: e. T41688A121229971 (2017).

48.
Penteriani, V. & Melletti, M. Bears of the World: Ecology, Conservation and Management (Cambridge University Press, Cambridge, 2020).
Google Scholar 

49.
Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4, 170052 (2017).
PubMed  PubMed Central  Article  ADS  Google Scholar 

50.
Garshelis, D. & McLellan, B. Are bear subspecies a thing of the past?. Int. Bear News 20, 9–10 (2011).
Google Scholar 

51.
Hajjar, I. The Syrian bear still lives in Syria. Int. Bear News 20, 7–11 (2011).
Google Scholar 

52.
Calvignac, S., Hughes, S. & Hänni, C. Genetic diversity of endangered brown bear (Ursus arctos) populations at the crossroads of Europe, Asia and Africa. Divers. Distrib. 15, 742–750 (2009).
Article  Google Scholar 

53.
Ansari, M. & Ghoddousi, A. Water availability limits brown bear distribution at the southern edge of its global range. Ursus 29, 13–24 (2018).
Article  Google Scholar 

54.
Ashrafzadeh, M. R., Kaboli, M. & Naghavi, M. R. Mitochondrial DNA analysis of Iranian brown bears (Ursus arctos) reveals new phylogeographic lineage. Mamm. Biol. 81, 1–9 (2016).
Article  Google Scholar 

55.
Gutleb, B. & Ziaie, H. On the distribution and status of the Brown Bear, Ursus arctos, and the Asiatic Black Bear, U. thibetanus, Iran. Zool. Middle East 18, 5–8 (1999).
Article  Google Scholar 

56.
Moqanaki, E. M., Jiménez, J., Bensch, S. & López-Bao, J. V. Counting bears in the Iranian Caucasus: remarkable mismatch between scientifically-sound population estimates and perceptions. Biol. Conserv. 220, 182–191 (2018).
Article  Google Scholar 

57.
Yusefi, G. H., Faizolahi, K., Darvish, J., Safi, K. & Brito, J. C. The species diversity, distribution, and conservation status of the terrestrial mammals of Iran. J. Mammal. 100, 55–71 (2019).
Article  Google Scholar 

58.
Almasieh, K., Rouhi, H. & Kaboodvandpour, S. Habitat suitability and connectivity for the brown bear (Ursus arctos) along the Iran–Iraq border. Eur. J. Wildl. Res. 65, 57 (2019).
Article  Google Scholar 

59.
Nezami, B. & Farhadinia, M. S. Litter sizes of brown bears in the Central Alborz Protected Area, Iran. Ursus 22, 167–171 (2011).
Article  Google Scholar 

60.
Darvishsefat, A. A. Atlas of Protected Areas of Iran. (Ravi, 2006).

61.
Atzeni, L. et al. Meta-replication, sampling bias, and multi-scale model selection: a case study on snow leopard (Panthera uncia) in western China. Ecol. Evol. 10, 7686–7712 (2020).
PubMed  PubMed Central  Article  Google Scholar 

62.
Ambarli, H., Erturk, A. & Soyumert, A. Current status, distribution, and conservation of brown bear (Ursidae) and wild canids (gray wolf, golden jackal, and red fox; Canidae) in Turkey (2016).

63.
Brown, J. L. SDM toolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700 (2014).
Article  Google Scholar 

64.
Evans, J. S. & Oakleaf, J. Geomorphometry and gradient metrics toolbox (ArcGIS 10.0) (2012).

65.
Ghorbanian, A. et al. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS J. Photogram. Remote Sens. 167, 276–288 (2020).
Article  ADS  Google Scholar 

66.
Sanderson, E. W. et al. The human footprint and the last of the wild: the human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. Bioscience 52, 891–904 (2002).
Article  Google Scholar 

67.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Article  Google Scholar 

68.
Jueterbock, A. ‘MaxentVariableSelection’vignette. (2015).

69.
R Development Core, team. A Language ans Environment for Statistical Computing. R Found Stat. Comput. Vienna Austria 2, (2018).

70.
Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling?. Ecography (Cop.) 37, 191–203 (2014).
Article  Google Scholar 

71.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Article  Google Scholar 

72.
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 

73.
Evans, J. S. & Cushman, S. A. Gradient modeling of conifer species using random forests. Landsc. Ecol. 24, 673–683 (2009).
Article  Google Scholar 

74.
Wasserman, T. N., Cushman, S. A., Schwartz, M. K. & Wallin, D. O. Spatial scaling and multi-model inference in landscape genetics: Martes Americana in Northern Idaho. Landsc. Ecol. 25, 1601–1612 (2010).
Article  Google Scholar 

75.
Cushman, S. A. & Lewis, J. S. Movement behavior explains genetic differentiation in American black bears. Landsc. Ecol. 25, 1613–1625 (2010).
Article  Google Scholar 

76.
Cushman, S. A., Macdonald, E. A., Landguth, E. L., Malhi, Y. & Macdonald, D. W. Multiple-scale prediction of forest loss risk across Borneo. Landsc. Ecol. 32, 1581–1598 (2017).
Article  Google Scholar 

77.
Zeller, K. A., McGarigal, K. & Whiteley, A. R. Estimating landscape resistance to movement: a review. Landsc. Ecol. 27, 777–797 (2012).
Article  Google Scholar 

78.
Wan, H. Y., Cushman, S. A. & Ganey, J. L. Improving habitat and connectivity model predictions with multi-scale resource selection functions from two geographic areas. Landsc. Ecol. 34, 503–519 (2019).
Article  Google Scholar 

79.
Landguth, E. L., Hand, B. K., Glassy, J., Cushman, S. A. & Sawaya, M. A. UNICOR: a species connectivity and corridor network simulator. Ecography (Cop.) 35, 9–14 (2012).
Article  Google Scholar 

80.
Cushman, S. A., Landguth, E. L. & Flather, C. H. Evaluating population connectivity for species of conservation concern in the American Great Plains. Biodivers. Conserv. 22, 2583–2605 (2013).
Article  Google Scholar 

81.
Kaszta, Ż, Cushman, S. A., Sillero-Zubiri, C., Wolff, E. & Marino, J. Where buffalo and cattle meet: modelling interspecific contact risk using cumulative resistant kernels. Ecography (Cop.) 41, 1616–1626 (2018).
Article  Google Scholar 

82.
Støen, O.-G. Natal Dispersal and Social Organization in Brown Bears. (Norwegian University of Life Sciences, Department of Ecology and Natural, 2006).

83.
Saura, S. & Pascual-Hortal, L. A new habitat availability index to integrate connectivity in landscape conservation planning: comparison with existing indices and application to a case study. Landsc. Urban Plan. 83, 91–103 (2007).
Article  Google Scholar 

84.
Saura, S. & Torné, J. Conefor Sensinode 2.2: a software package for quantifying the importance of habitat patches for landscape connectivity. Environ. Model. Softw. 24, 135–139 (2009).
Article  Google Scholar 

85.
Avon, C. & Bergès, L. Prioritization of habitat patches for landscape connectivity conservation differs between least-cost and resistance distances. Landsc. Ecol. 31, 1551–1565 (2016).
Article  Google Scholar 

86.
Ahmadi, M. et al. SPECIES OR SPACE: a combined gap analysis to guide management planning of conservation areas. Landsc. Ecol. 35, 1505–1517 (2020).
Article  Google Scholar 

87.
Saura, S. & Rubio, L. A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape. Ecography (Cop.) 33, 523–537 (2010).
Google Scholar 

88.
Elliot, N. B., Cushman, S. A., Macdonald, D. W. & Loveridge, A. J. The devil is in the dispersers: predictions of landscape connectivity change with demography. J. Appl. Ecol. 51, 1169–1178 (2014).
Article  Google Scholar 

89.
Noroozi, J., Akhani, H. & Breckle, S.-W. Biodiversity and phytogeography of the alpine flora of Iran. Biodivers. Conserv. 17, 493–521 (2008).
Article  Google Scholar 

90.
Habibzadeh, N. & Ashrafzadeh, M. R. Habitat suitability and connectivity for an endangered brown bear population in the Iranian Caucasus. Wildl. Res. 45, 602–610 (2018).
Article  Google Scholar 

91.
Ashrafzadeh, M.-R., Khosravi, R., Ahmadi, M. & Kaboli, M. Landscape heterogeneity and ecological niche isolation shape the distribution of spatial genetic variation in Iranian brown bears, Ursus arctos (Carnivora: Ursidae). Mamm. Biol. 93, 64–75 (2018).
Article  Google Scholar 

92.
Ash, E., Cushman, S. A., Macdonald, D. W., Redford, T. & Kaszta, Ż. How important are resistance, dispersal ability, population density and mortality in temporally dynamic simulations of population connectivity? A case study of tigers in southeast Asia. Land 9, 415 (2020).
Article  Google Scholar 

93.
Cushman, S. A. et al. Biological corridors and connectivity [Chapter 21]. In Key Topics in Conservation Biology 2 (eds Macdonald, D. W. & Willis, K. J.) 384–404 (Wiley, Hoboken, 2013).
Google Scholar 

94.
Ghoddousi, A. Habitat suitability modelling of the Brown bear Ursus arctos in Croatia and Slovenia using telemetry data (2010).

95.
Steyaert, S. M. J. G. et al. Ecological implications from spatial patterns in human-caused brown bear mortality. Wildl. Biol. 22, 144–152 (2016).
Article  Google Scholar 

96.
Güthlin, D. et al. Estimating habitat suitability and potential population size for brown bears in the Eastern Alps. Biol. Conserv. 144, 1733–1741 (2011).
Article  Google Scholar 

97.
Penteriani, V. et al. Evolutionary and ecological traps for brown bears Ursus arctos in human-modified landscapes. Mamm. Rev. 48, 180–193 (2018).
Article  Google Scholar 

98.
Zarzo-Arias, A. et al. Identifying potential areas of expansion for the endangered brown bear (Ursus arctos) population in the Cantabrian Mountains (NW Spain). PLoS ONE 14, e0209972 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

99.
Morales-González, A., Ruiz-Villar, H., Ordiz, A. & Penteriani, V. Large carnivores living alongside humans: brown bears in human-modified landscapes. Glob. Ecol. Conserv. 22, e00937 (2020).
Article  Google Scholar 

100.
Fedorca, A. et al. Inferring fine-scale spatial structure of the brown bear (Ursus arctos) population in the Carpathians prior to infrastructure development. Sci. Rep. 9, 1–12 (2019).
Article  CAS  Google Scholar 

101.
Liu, C., Newell, G., White, M. & Bennett, A. F. Identifying wildlife corridors for the restoration of regional habitat connectivity: a multispecies approach and comparison of resistance surfaces. PLoS ONE 13, e0206071 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

102.
Macdonald, D. W. et al. Predicting biodiversity richness in rapidly changing landscapes: Climate, low human pressure or protection as salvation?. Biodivers. Conserv. 29, 4035–4057 (2020).
Article  Google Scholar 

103.
Herrero, S., Smith, T., DeBruyn, T. D., Gunther, K. & Matt, C. A. From the field: brown bear habituation to people—safety, risks, and benefits. Wildl. Soc. Bull. 33, 362–373 (2005).
Article  Google Scholar 

104.
Skuban, M. et al. Effects of roads on brown bear movements and mortality in Slovakia. Eur. J. Wildl. Res. 63, 82 (2017).
Article  Google Scholar 

105.
Findo, S., Skuban, M., Kajba, M., Chalmers, J. & Kalaš, M. Identifying attributes associated with brown bear (Ursus arctos) road-crossing and roadkill sites. Can. J. Zool. 97, 156–164 (2019).
Article  Google Scholar 

106.
Watson, J. E. M. et al. Persistent disparities between recent rates of habitat conversion and protection and implications for future global conservation targets. Conserv. Lett. 9, 413–421 (2016).
Article  Google Scholar 

107.
Boitani, L., Ciucci, P., Corsi, F. & Dupre, E. Potential range and corridors for brown bears in the Eastern Alps. Italy. Ursus 11, 123–130 (1999).
Google Scholar 

108.
Cushman, S. A., McKelvey, K. S., Hayden, J. & Schwartz, M. K. Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am. Nat. 168, 486–499 (2006).
PubMed  Article  PubMed Central  Google Scholar 

109.
Mohammadi, A. et al. Road expansion: a challenge to conservation of mammals, with particular emphasis on the endangered Asiatic cheetah in Iran. J. Nat. Conserv. 43, 8–18 (2018).
Article  Google Scholar  More

1.
McCauley, D. J. et al. Marine defaunation: Animal loss in the global ocean. Science 347, 1255641 (2015).
PubMed  Article  CAS  Google Scholar 
2.
Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. eLife 3, 590 (2014).
Article  Google Scholar 

3.
Hutchings, J. A., Myers, R. A., García, V. B., Lucifora, L. O. & Kuparinen, A. Life-history correlates of extinction risk and recovery potential. Ecol. Appl. 22, 1061–1067 (2012).
PubMed  Article  Google Scholar 

4.
Frisk, M. & Miller, T. J. Life histories and vulnerability to exploitation of elasmobranchs: Inferences from elasticity, perturbation and phylogenetic analyses. Artic. J. Northwest Atl. Fish. Sci. https://doi.org/10.2960/J.v35.m514 (2005).
Article  Google Scholar 

5.
Carr, L. A. et al. Illegal shark fishing in the Galápagos Marine Reserve. Mar. Policy 39, 317–321 (2013).
Article  Google Scholar 

6.
Dharmadi, F. & Satria, F. African Journal of Marine Science Fisheries management and conservation of sharks in Indonesia. Afr. J. Mar. Sci. 37, 249–258 (2015).
Article  Google Scholar 

7.
Heupel, M., Carlson, J. & Simpfendorfer, C. Shark nursery areas: Concepts, definition, characterization and assumptions. Mar. Ecol. Prog. Ser. 337, 287–297 (2007).
ADS  Article  Google Scholar 

8.
Meylan, P. A., Meylan, A. B. & Gray, J. A. The ecology and migrations of sea turtles 8. Tests of the developmental habitat hypothesis. Bull. Am. Museum Nat. Hist. 357, 1–70 (2011).
Article  Google Scholar 

9.
Jennings, D. E., Gruber, S. H., Franks, B. R., Kessel, S. T. & Robertson, A. L. Effects of large-scale anthropogenic development on juvenile lemon shark (Negaprion brevirostris) populations of Bimini, Bahamas. Environ. Biol. Fishes 83, 369–377 (2008).
Article  Google Scholar 

10.
Kinney, M. J. & Simpfendorfer, C. A. Reassessing the value of nursery areas to shark conservation and management. Conserv. Lett. 2, 53–60 (2009).
Article  Google Scholar 

11.
Healy, T. J., Hill, N. J., Chin, A. & Barnett, A. A global review of elasmobranch tourism activities, management and risk. Mar. Policy 118, 103964 (2020).
Article  Google Scholar 

12.
White, T. D. et al. Assessing the effectiveness of a large marine protected area for reef shark conservation. Biol. Conserv. 207, 64–71 (2017).
Article  Google Scholar 

13.
Claudet, J., Loiseau, C., Sostres, M. & Correspondence, M. Z. Underprotected marine protected areas in a global biodiversity hotspot. One Earth https://doi.org/10.1016/j.oneear.2020.03.008 (2020).
Article  Google Scholar 

14.
Pierce, S. & Norman, B. Rhincodon typus. IUCN Red List Threat. Species e-T19488A2, (2016).

15.
CITES. Convention on international trade in endangered species of wild fauna and flora. Amendments to Appendices I and II of CITES. (2000).

16.
Convention on Migratory Species. Proposal for the inclusion of the whale shark (Rhincodon typus) on Appendix I of the convention CMS convention on migratory species. (2017).

17.
Simpfendorfer, C. A. & Dulvy, N. K. Bright spots of sustainable shark fishing. Curr. Biol. 27, R97–R98 (2017).
CAS  PubMed  Article  Google Scholar 

18.
Reeve-Arnold, K. E., Kinni, J., Newbigging, R., Pierce, S. J. & Roques, K. Sustaining whale shark tourism in a diminishing population. In (Hamad bin Khalifa University Press, HBKU Press, 2016). https://doi.org/10.5339/qproc.2016.iwsc4.49.

19.
Pravin, P. Whale Shark in the Indian Coast—Need for conservation. Curr. Sci. 79, 310–315 (2000).
Google Scholar 

20.
Li, W., Wang, Y. & Norman, B. A preliminary survey of whale shark Rhincodon typus catch and trade in China: An emerging crisis. J. Fish Biol. 80, 1608–1618 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Hearn, A. R. et al. Adult female whale sharks make long-distance movements past Darwin Island (Galapagos, Ecuador) in the Eastern Tropical Pacific. Mar. Biol. 163, 1–12 (2016).
Article  Google Scholar 

22.
Wilson, S. G., Polovina, J. J., Stewart, B. S. & Meekan, M. G. Movements of whale sharks (Rhincodon typus) tagged at Ningaloo Reef, Western Australia. Mar. Biol. 148, 1157–1166 (2006).
Article  Google Scholar 

23.
Hueter, R. E., Tyminski, J. P. & de la Parra, R. Horizontal movements, migration patterns, and population structure of whale sharks in the Gulf of Mexico and northwestern Caribbean Sea. PLoS ONE 8, e71883 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Robinson, D. P. et al. Some like it hot: Repeat migration and residency of whale sharks within an extreme natural environment. PLoS ONE 12, e0185360 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Araujo, G. et al. Photo-ID and telemetry highlight a global whale shark hotspot in Palawan, Philippines. Sci. Rep. 9, 1–12 (2019).
Article  CAS  Google Scholar 

26.
Bradshaw, C. J. A., Fitzpatrick, B. M., Steinberg, C. C., Brook, B. W. & Meekan, M. G. Decline in whale shark size and abundance at Ningaloo Reef over the past decade: The world’s largest fish is getting smaller. Biol. Conserv. 141, 1894–1905 (2008).
Article  Google Scholar 

27.
Speed, C. W. et al. Scarring patterns and relative mortality rates of Indian Ocean whale sharks. J. Fish Biol. 72, 1488–1503 (2008).
Article  Google Scholar 

28.
Lester, E. et al. Multi-year patterns in scarring, survival and residency of whale sharks in Ningaloo Marine Park, Western Australia. Mar. Ecol. Prog. Ser. 634, 115–125 (2020).
ADS  Article  Google Scholar 

29.
Rowat, D. & Brooks, K. S. A review of the biology, fisheries and conservation of the whale shark Rhincodon typus. J. Fish Biol. 80, 1019–1056 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Cochran, J. E. M. et al. Multi-method assessment of whale shark (Rhincodon typus) residency, distribution, and dispersal behavior at an aggregation site in the Red Sea. PLoS ONE 14, e0222285 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Copping, J. P., Stewart, B. D., McClean, C. J., Hancock, J. & Rees, R. Does bathymetry drive coastal whale shark (Rhincodon typus) aggregations?. PeerJ 6, e4904 (2018).
PubMed  PubMed Central  Article  Google Scholar 

32.
Norman, B. M. et al. Undersea constellations: The global biology of an endangered marine megavertebrate further informed through citizen science. Bioscience 67, 1029–1043 (2017).
Article  Google Scholar 

33.
Donati, G. et al. New insights into the South Ari atoll whale shark, Rhincodon typus, aggregation. In (Hamad bin Khalifa University Press, HBKU Press, 2016). https://doi.org/10.5339/qproc.2016.iwsc4.16.

34.
Riley, M. J., Hale, M. S., Harman, A. & Rees, R. G. Analysis of whale shark Rhincodon typus aggregations near South Ari Atoll, Maldives Archipelago. Aquat. Biol. 8, 145–150 (2010).
Article  Google Scholar 

35.
Rowat, D., Meekan, M. G., Engelhardt, U., Pardigon, B. & Vely, M. Aggregations of juvenile whale sharks (Rhincodon typus) in the Gulf of Tadjoura, Djibouti. Environ. Biol. Fishes 80, 465–472 (2007).
Article  Google Scholar 

36.
Cagua, E. F. et al. Acoustic telemetry reveals cryptic residency of whale sharks. Biol. Lett. 11, 20150092 (2015).
PubMed  PubMed Central  Article  Google Scholar 

37.
Thomson, J. A. et al. Feeding the world’s largest fish: Highly variable whale shark residency patterns at a provisioning site in the Philippines. R. Soc. Open Sci. 4, 170394 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

38.
Perry, C. T. et al. Comparing length-measurement methods and estimating growth parameters of free-swimming whale sharks (Rhincodon typus) near the South Ari Atoll, Maldives. Mar. Freshw. Res. 69, 1487 (2018).
Article  Google Scholar 

39.
Riley, M. J., Harman, A. & Rees, R. G. Evidence of continued hunting of whale sharks Rhincodon typus in the Maldives. Environ. Biol. Fishes 86, 371–374 (2009).
Article  Google Scholar 

40.
Cagua, E. F., Collins, N., Hancock, J. & Rees, R. Whale shark economics: A valuation of wildlife tourism in South Ari Atoll. Maldives. PeerJ 2, e515 (2014).
PubMed  Article  Google Scholar 

41.
Arzoumanian, Z., Holmberg, J. & Norman, B. An astronomical pattern-matching algorithm for computer-aided identification of whale sharks Rhincodon typus. J. Appl. Ecol. 42, 999–1011 (2005).
Article  Google Scholar 

42.
Bradshaw, C. J. A., Mollett, H. F. & Meekan, M. G. Inferring population trends for the world’s largest fish from mark recapture estimates of survival. J. Anim. Ecol. 76, 480–489 (2007).
PubMed  Article  Google Scholar 

43.
Holmberg, J., Norman, B. & Arzoumanian, Z. Estimating population size, structure, and residency time for whale sharks Rhincodon typus through collaborative photo-identification. Endanger. Species Res. 7, 39–53 (2009).
Article  Google Scholar 

44.
Rowat, D., Gore, M., Meekan, M. G., Lawler, I. R. & Bradshaw, C. J. A. Aerial survey as a tool to estimate whale shark abundance trends. J. Exp. Mar. Biol. Ecol. 368, 1–8 (2009).
Article  Google Scholar 

45.
Acuña-Marrero, D. et al. Whale shark (Rhincodon typus) seasonal presence, residence time and habitat use at darwin island, galapagos marine reserve. PLoS ONE 9, e115946 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

46.
Schwarz, C. J., Bailey, R. E., Irvine, J. R. & Dalziel, F. C. Estimating salmon spawning escapement using capture-recapture methods. Can. J. Fish. Aquat. Sci. 50, 1181–1197 (1993).
Article  Google Scholar 

47.
Schwarz, C. J. & Arnason, A. N. A general methodology for the analysis of capture-recapture experiments in open populations. Biometrics 52, 860 (1996).
MathSciNet  MATH  Article  Google Scholar 

48.
Whitehead, H. Analysis of animal movement using opportunistic individual identifications: Application to sperm whales. Ecology 82, 1417–1432 (2001).
Article  Google Scholar 

49.
QGIS.org. QGIS Geographic Information System. Open Source Geospatial Foundation Project. (2020).

50.
Van Tienhoven, A. M., Den Hartog, J. E., Reijns, R. A. & Peddemors, V. M. A computer-aided program for pattern-matching of natural marks on the spotted raggedtooth shark Carcharias taurus. J. Appl. Ecol. 44, 273–280 (2007).
Article  Google Scholar 

51.
RStudio Team. RStudio: Integrated Development for R. (2015).

52.
Laake, J. L. RMark: An R interface for analysis of capture-recapture data with MARK. AFSC Processed Rep. 2013-01 Alaska Fish. Sci. Cent., NOAA, Natl. Mar. Fish. Serv., (2013). https://doi.org/10.1017/CBO9781107415324.004.

53.
Schwarz, C., Arnason, A., Cooch, E. & White, G. Jolly-Seber models in MARK. Progr. MARK–a gentle Introd. 18th Ed. (2018).

54.
Cooch, E. & White, G. Program MARK: A gentle introduction (13th ed.). available online with MARK Program. (2006).

55.
Whitehead, H. SOCPROG programs: Analysing animal social structures. Behav. Ecol. Sociobiol. 63, 765–778 (2009).
Article  Google Scholar 

56.
Whitehead, H. Selection of models of lagged identification rates and lagged association rates using AIC and QAIC. Commun. Stat. Simul. Comput. 36, 1233–1246 (2007).
MathSciNet  MATH  Article  Google Scholar 

57.
Buckland, S. T. & Garthwaite, P. H. Quantifying precision of mark-recapture estimates using the bootstrap and related methods. Biometrics 47, 255 (1991).
Article  Google Scholar 

58.
Rohner, C. A. et al. No place like home? High residency and predictable seasonal movement of whale sharks off Tanzania. Front. Mar. Sci. 7, 423 (2020).
Article  Google Scholar 

59.
Norman, B. M., Whitty, J. M., Beatty, S. J., Reynolds, S. D. & Morgan, D. L. Do they stay or do they go? Acoustic monitoring of whale sharks at Ningaloo Marine Park, Western Australia. J. Fish Biol. 91, 1713–1720 (2017).
CAS  PubMed  Article  Google Scholar 

60.
Araujo, G. et al. Population structure and residency patterns of whale sharks, Rhincodon typus, at a provisioning site in Cebu, Philippines. PeerJ 2014, e543 (2014).
Article  Google Scholar 

61.
Prebble, C. et al. Limited latitudinal ranging of juvenile whale sharks in the Western Indian Ocean suggests the existence of regional management units. Mar. Ecol. Prog. Ser. 601, 167–183 (2018).
ADS  Article  Google Scholar 

62.
Araujo, G. et al. Population structure and residency patterns of whale sharks, Rhincodon typus, at a provisioning site in Cebu, Philippines. PeerJ 2, e543 (2014).
PubMed  PubMed Central  Article  Google Scholar 

63.
Akhilesh, K. V. et al. Landings of whale sharks Rhincodon typus Smith, 1828 in Indian waters since protection in 2001 through the Indian Wildlife (Protection) Act, 1972. Environ. Biol. Fishes 96, 713–722 (2013).
Article  Google Scholar 

64.
Heyman, W., Graham, R., Kjerfve, B. & Johannes, R. Whale sharks Rhincodon typus aggregate to feed on fish spawn in Belize. Mar. Ecol. Prog. Ser. 215, 275–282 (2001).
ADS  Article  Google Scholar 

65.
Meekan, M. et al. Population size and structure of whale sharks Rhincodon typus at Ningaloo Reef, Western Australia. Mar. Ecol. Prog. Ser. 319, 275–285 (2006).
ADS  Article  Google Scholar 

66.
Cochran, J. E. M. et al. Population structure of a whale shark Rhincodon typus aggregation in the Red Sea. J. Fish Biol. 89, 1570–1582 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Araujo, G. et al. Population structure, residency patterns and movements of whale sharks in Southern Leyte, Philippines: Results from dedicated photo-ID and citizen science. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 237–252 (2017).
Article  Google Scholar 

68.
Robinson, D. P. et al. Population structure, abundance and movement of whale sharks in the Arabian Gulf and the Gulf of Oman. PLoS ONE 11, e0158593 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

69.
McCoy, E. et al. Long-term photo-identification reveals the population dynamics and strong site fidelity of adult whale sharks to the coastal waters of Donsol, Philippines. Front. Mar. Sci. 5, 271 (2018).
Article  Google Scholar 

70.
Araujo, G. et al. In-water methods reveal population dynamics of a green turtle Chelonia mydas foraging aggregation in the Philippines. Endanger. Species Res. 40, 207–218 (2019).
Article  Google Scholar 

71.
Sleeman, J. C. et al. To go or not to go with the flow: Environmental influences on whale shark movement patterns. J. Exp. Mar. Biol. Ecol. 390, 84–98 (2010).
Article  Google Scholar 

72.
Calambokidis, J., Laake, J. L. & Klimek, A. Abundance and population structure of seasonal gray whales in the Pacific Northwest, 1998–2008. Sc/62/Brg32, Vol. 2008 (2010).

73.
Branstetter, S. Early Life-History Implications of Selected Carcharhinoid and Lamnoid Sharks of the Northwest Atlantic. Elasmobranchs as Living Resour. Adv. Biol. Ecol. Syst. Status Fish. (1990).

74.
Parker, J. H. & Gischler, E. Modern foraminiferal distribution and diversity in two atolls from the Maldives, Indian Ocean. Mar. Micropaleontol. 78, 30–49 (2011).
ADS  Article  Google Scholar 

75.
Halvorsen, M. B., Casper, B. M., Woodley, C. M., Carlson, T. J. & Popper, A. N. Threshold for onset of injury in Chinook salmon from exposure to impulsive pile driving sounds. PLoS ONE 7, e38968 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Haskell, P. J. et al. Monitoring the effects of tourism on whale shark Rhincodon typus behaviour in Mozambique. ORYX 49, 492–499 (2015).
Article  Google Scholar 

77.
Quiros, A. L. Tourist compliance to a Code of Conduct and the resulting effects on whale shark (Rhincodon typus) behavior in Donsol, Philippines. Fish. Res. 84, 102–108 (2007).
Article  Google Scholar 

78.
Araujo, G., Vivier, F., Labaja, J. J., Hartley, D. & Ponzo, A. Assessing the impacts of tourism on the world’s largest fish Rhincodon typus at Panaon Island, Southern Leyte, Philippines. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 986–994 (2017).
Article  Google Scholar 

79.
Finger, J. S. et al. Rate of movement of juvenile lemon sharks in a novel open field, are we measuring activity or reaction to novelty?. Anim. Behav. 116, 75–82 (2016).
Article  Google Scholar 

80.
Cade, D. E. et al. Whale sharks increase swimming effort while filter feeding, but appear to maintain high foraging efficiencies. J. Exp. Biol. 223, jeb.224402 (2020).
Article  Google Scholar 

81.
Archie, E. A. Wound healing in the wild: stress, sociality, and energetic costs affect wound healing in natural populations. Parasite Immunol. 35, n/a-n/a (2013).

82.
Baker, M. R., Swanson, P. & Young, G. Injuries from non-retention in gillnet fisheries suppress reproductive maturation in escaped fish. PLoS ONE 8, e69615 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
Neat, F. C., Taylor, A. C. & Huntingford, F. A. Proximate costs of fighting in male cichlid fish: The role of injuries and energy metabolism. Anim. Behav. 55, 875–882 (1998).
CAS  PubMed  Article  Google Scholar 

84.
Meekan, M. G., Fuiman, L. A., Davis, R., Berger, Y. & Thums, M. Swimming strategy and body plan of the world’s largest fish: Implications for foraging efficiency and thermoregulation. Front. Mar. Sci. 2, 64 (2015).
Article  Google Scholar 

85.
Chin, A., Mourier, J. & Rummer, J. L. Blacktip reef sharks (Carcharhinus melanopterus) show high capacity for wound healing and recovery following injury. Conserv. Physiol. 3(1) (2015).

86.
Tierney, K. B. & Farrell, A. P. The relationships between fish health, metabolic rate, swimming performance and recovery in return-run sockeye salmon, Oncorhynchus nerka (Walbaum). J. Fish Dis. 27, 663–671 (2004).
CAS  PubMed  Article  Google Scholar 

87.
McGregor, F., Richardson, A. J., Armstrong, A. J., Armstrong, A. O. & Dudgeon, C. L. Rapid wound healing in a reef manta ray masks the extent of vessel strike. PLoS ONE 14, e0225681 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

88.
Tort, L. Stress and immune modulation in fish. Dev. Comp. Immunol. 35, 1366–1375 (2011).
CAS  PubMed  Article  Google Scholar 

89.
Mateus, A. P., Anjos, L., Cardoso, J. R. & Power, D. M. Chronic stress impairs the local immune response during cutaneous repair in gilthead sea bream (Sparus aurata L.). Mol. Immunol. 87, 267–283 (2017).
CAS  PubMed  Article  Google Scholar 

90.
Environmental Protection Agency. Maldivian Whale Shark Tourist Encounter Guidelines. (2009).

91.
Leston, F. A. L. Monitoring Tourist Pressure on Whale Shark (Rhincodon typus) Behaviour in South Ari MPA, Maldive) Behaviour in South Ari MPA, Maldive (The University of Edinburgh, Edinburgh, 2016).
Google Scholar 

92.
Kallsen, H. Regulation of Whale Shark Tourism: A Data Driven Approach for the South Ari Marine Protected Area (Syddansk Universitet, Odense, 2018).
Google Scholar 

93.
Montero-Quintana, A. N., Vázquez-Haikin, J. A., Merkling, T., Blanchard, P. & Osorio-Beristain, M. Ecotourism impacts on the behaviour of whale sharks: An experimental approach. ORYX 54, 270–275 (2020).
Article  Google Scholar 

94.
Bouyoucos, I. A., Simpfendorfer, C. A. & Rummer, J. L. Estimating oxygen uptake rates to understand stress in sharks and rays. Rev. Fish Biol. Fish. 29, 297–311 (2019).
Article  Google Scholar 

95.
Semeniuk, C. A. D., Bourgeon, S., Smith, S. L. & Rothley, K. D. Hematological differences between stingrays at tourist and non-visited sites suggest physiological costs of wildlife tourism. Biol. Conserv. 142, 1818–1829 (2009).
Article  Google Scholar 

96.
Van Rijn, J. A. & Reina, R. D. Distribution of leukocytes as indicators of stress in the Australian swellshark, Cephaloscyllium laticeps. Fish Shellfish Immunol. 29, 534–538 (2010).
PubMed  Article  CAS  PubMed Central  Google Scholar 

97.
Barnett, A., Payne, N. L., Semmens, J. M. & Fitzpatrick, R. Ecotourism increases the field metabolic rate of whitetip reef sharks. Biol. Conserv. 199, 132–136 (2016).
Article  Google Scholar 

98.
Mau, R. Managing for conservation and recreation: The Ningaloo whale shark experience. J. Ecotourism 7, 213–225 (2008).
Article  Google Scholar 

99.
Martin, R. A. A review of behavioural ecology of whale sharks (Rhincodon typus). Fish. Res. 84, 10–16 (2007).
ADS  Article  Google Scholar 

100.
Skomal, G. B. & Mandelman, J. W. The physiological response to anthropogenic stressors in marine elasmobranch fishes: A review with a focus on the secondary response. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 126, 146–155 (2012).
Article  CAS  Google Scholar 

101.
Pankhurst, N. W. The endocrinology of stress in fish: An environmental perspective. Gen. Comp. Endocrinol. 170, 265–275 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

102.
Renshaw, G. M. C., Kutek, A. K., Grant, G. D. & Anoopkumar-Dukie, S. Forecasting elasmobranch survival following exposure to severe stressors. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 126, 101–112 (2012).
Article  CAS  Google Scholar 

103.
Lester, E. et al. Using an electronic monitoring system and photo identification to understand effects of tourism encounters on whale sharks in Ningaloo Marine Park. Tour. Mar. Environ. 14, 121–131 (2019).
Article  Google Scholar  More

1.
DiGiacomo, G., Hadrich, J., Hutchison, W. D., Peterson, H. & Rogers, M. Economic impact of spotted wing drosophila (Diptera: Drosophilidae) yield loss on Minnesota Raspberry farms: A grower survey. J. Integr. Pest Manag. 10, https://doi.org/10.1093/jipm/pmz006 (2019).
2.
Farnsworth, D. et al. Economic analysis of revenue losses and control costs associated with the spotted wing drosophila, Drosophila suzukii (Matsumura), in the California raspberry industry. Pest Manag. Sci. 73, 1083–1090. https://doi.org/10.1002/ps.4497 (2017).
CAS  Article  PubMed  Google Scholar 

3.
Cini, A., Ioriatti, C. & Anfora, G. A review of the invasion of Drosophila suzukii in Europe and a draft research agenda for integrated pest management. Bull. Insectol. 65, 149–160 (2012).
Google Scholar 

4.
Okada, T. Systematic Study of Drosophilidae and Allied Families of Japan. 95–106 (Gihodo Co. Ltd., 1956).

5.
Walsh, D. B. et al. Drosophila suzukii (Diptera: Drosophilidae): Invasive pest of ripening soft fruit expanding its geographic range and damage potential. J. Integr. Pest Manag. 2, G1–G7. https://doi.org/10.1603/Ipm10010 (2011).
Article  Google Scholar 

6.
Kanzawa, T. Studies on Drosophila suzukii mats. J. Plant Proteom. 23, 66–70, 127–132, 183–191 (1939).

7.
Bolda, M. P. & Goodhue, R. E. Spotted wing Drosophila: Potential economic impact of a newly established pest. Agric. Resour. Econ. Updates Univ. Calif. Giannini Found. 13, 5–8, https://doi.org/10.1016/j.jff.2015.04.027 (2010).

8.
Schetelig, M. F. et al. Environmentally sustainable pest control options for Drosophila suzukii. J. Appl. Entomol. 142, 3–17. https://doi.org/10.1111/jen.12469 (2017).
Article  Google Scholar 

9.
Lee, J. C. et al. Biological control of spotted-wing Drosophila (Diptera: Drosophilidae)—Current and pending tactics. J. Integr. Pest Manag. 10, https://doi.org/10.1093/jipm/pmz012 (2019).

10.
Fleury, F., Gibert, P., Ris, N. & Allemand, R. Chapter 1 Ecology and life history evolution of frugivorous Drosophila parasitoids. 70, 3–44, https://doi.org/10.1016/s0065-308x(09)70001-6 (2009).

11.
Daane, K. M. et al. First exploration of parasitoids of Drosophila suzukii in South Korea as potential classical biological agents. J. Pest Sci. 89, 823–835. https://doi.org/10.1007/s10340-016-0740-0 (2016).
ADS  Article  Google Scholar 

12.
Girod, P. et al. The parasitoid complex of D. suzukii and other fruit feeding Drosophila species in Asia. Sci. Rep. 8, 11839, https://doi.org/10.1038/s41598-018-29555-8 (2018).

13.
Girod, P. et al. Host specificity of Asian parasitoids for potential classical biological control of Drosophila suzukii. J. Pest. Sci. 2004(91), 1241–1250. https://doi.org/10.1007/s10340-018-1003-z (2018).
Article  Google Scholar 

14.
Matsuura, A., Mitsui, H. & Kimura, M. T. A preliminary study on distributions and oviposition sites of Drosophila suzukii (Diptera: Drosophilidae) and its parasitoids on wild cherry tree in Tokyo, central Japan. Appl. Entomol. Zool. 53, 47–53. https://doi.org/10.1007/s13355-017-0527-7 (2018).
Article  Google Scholar 

15.
Wang, X. G., Nance, A. H., Jones, J. M. L., Hoelmer, K. A. & Daane, K. M. Aspects of the biology and reproductive strategy of two Asian larval parasitoids evaluated for classical biological control of Drosophila suzukii. Biol. Control 121, 58–65. https://doi.org/10.1016/j.biocontrol.2018.02.010 (2018).
Article  Google Scholar 

16.
Abram, P. K. et al. New records of Leptopilina, Ganaspis, and Asobara species associated with Drosophila suzukii in North America, including detections of L. japonica and G. brasiliensis. J. Hymenoptera Res. 78, 1–17, https://doi.org/10.3897/jhr.78.55026 (2020).

17.
Puppato, S., Grassi, A., Pedrazzoli, F., De Cristofaro, A. & Ioriatti, C. First report of Leptopilina japonica in Europe. Insects 11, https://doi.org/10.3390/insects11090611 (2020).

18.
Kacsoh, B. Z. & Schlenke, T. A. High hemocyte load is associated with increased resistance against parasitoids in Drosophila suzukii, a relative of D. melanogaster. PLoS One 7, e34721, https://doi.org/10.1371/journal.pone.0034721 (2012).

19.
Chabert, S., Allemand, R., Poyet, M., Eslin, P. & Gibert, P. Ability of European parasitoids (Hymenoptera) to control a new invasive Asiatic pest, Drosophila suzukii. Biol. Control 63, 40–47. https://doi.org/10.1016/j.biocontrol.2012.05.005 (2012).
Article  Google Scholar 

20.
Nagaraja, H. in Biological Control of Insect Pests Using Egg Parasitoids (eds S. Sithanantham, Chandish R. Ballal, S. K. Jalali, & N. Bakthavatsalam) Chapter 8, 175–189 (Springer, 2013).

21.
Rossi Stacconi, M. V., Grassi, A., Ioriatti, C. & Anfora, G. Augmentative releases of Trichopria drosophilae for the suppression of early season Drosophila suzukii populations. BioControl 64, 9–19, https://doi.org/10.1007/s10526-018-09914-0 (2018).

22.
Rossi-Stacconi, M. V. et al. Multiple lines of evidence for reproductive winter diapause in the invasive pest Drosophila suzukii: Useful clues for control strategies. J. Pest Sci. 89, 689–700. https://doi.org/10.1007/s10340-016-0753-8 (2016).
Article  Google Scholar 

23.
Mazzetto, F. et al. Drosophila parasitoids in northern Italy and their potential to attack the exotic pest Drosophila suzukii. J. Pest Sci. 89, 837–850. https://doi.org/10.1007/s10340-016-0746-7 (2016).
Article  Google Scholar 

24.
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Foraging efficiency and outcomes of interactions of two pupal parasitoids attacking the invasive spotted wing drosophila. Biol. Control 96, 64–71. https://doi.org/10.1016/j.biocontrol.2016.02.004 (2016).
Article  Google Scholar 

25.
Kacar, G., Wang, X. G., Biondi, A. & Daane, K. M. Linear functional response by two pupal Drosophila parasitoids foraging within single or multiple patch environments. PLoS ONE 12, e0183525. https://doi.org/10.1371/journal.pone.0183525 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Zhu, C. J., Li, J., Wang, H., Zhang, M. & Hu, H. Y. Demographic potential of the pupal parasitoid Trichopria drosophilae (Hymenoptera: Diapriidae) reared on Drosophila suzukii (Diptera: Drosophilidae). J. Asia-Pac. Entomol. 20, 747–751. https://doi.org/10.1016/j.aspen.2017.04.008 (2017).
Article  Google Scholar 

27.
Kruitwagen, A., Beukeboom, L. W. & Wertheim, B. Optimization of native biocontrol agents, with parasitoids of the invasive pest Drosophila suzukii as an example. Evol. Appl. 11, 1473–1497. https://doi.org/10.1111/eva.12648 (2018).
Article  PubMed  PubMed Central  Google Scholar 

28.
Rossi Stacconi, M. V. et al. Host location and dispersal ability of the cosmopolitan parasitoid Trichopria drosophilae released to control the invasive spotted wing Drosophila. Biol. Control 117, 188–196, https://doi.org/10.1016/j.biocontrol.2017.11.013 (2018).

29.
Wolf, S., Boycheva-Woltering, S., Romeis, J. & Collatz, J. Trichopria drosophilae parasitizes Drosophila suzukii in seven common non-crop fruits. J. Pest Sci. 93, 627–638. https://doi.org/10.1007/s10340-019-01180-y (2019).
Article  Google Scholar 

30.
Wang, X. G. et al. Thermal performance of two indigenous pupal parasitoids attacking the invasive Drosophila suzukii (Diptera: Drosophilidae). Environ. Entomol. 47, 764–772. https://doi.org/10.1093/ee/nvy053 (2018).
Article  PubMed  Google Scholar 

31.
Rossi Stacconi, M. V. et al. Comparative life history traits of indigenous Italian parasitoids of Drosophila suzukii and their effectiveness at different temperatures. Biol. Control 112, 20–27, https://doi.org/10.1016/j.biocontrol.2017.06.003 (2017).

32.
Colombari, F., Tonina, L., Battisti, A. & Mori, N. Performance of Trichopria drosophilae (Hymenoptera: Diapriidae), a generalist parasitoid of Drosophila suzukii (Diptera: Drosophilidae), at low temperature. J. Insect Sci. 20, https://doi.org/10.1093/jisesa/ieaa039 (2020).

33.
Carton, Y., Bouletreau, M., Alphen, J. J. M. V. & Lenteren, J. C. V. in The Genetics and Biology of Drosophila Vol. 3 (eds M. Ashburner, H.L. Carson, & J.N. Thompson) Chap. 39, 348–394 (Academic Press, 1986).

34.
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Life-history and host preference of Trichopria drosophilae, a pupal parasitoid of spotted wing drosophila. Biocontrol 61, 387–397. https://doi.org/10.1007/s10526-016-9720-9 (2016).
CAS  Article  Google Scholar 

35.
Boycheva Woltering, S., Romeis, J. & Collatz, J. Influence of the rearing host on biological parameters of Trichopria drosophilae, a potential biological control agent of Drosophila suzukii. Insects 10, https://doi.org/10.3390/insects10060183 (2019).

36.
Yi, C. et al. Life history and host preference of Trichopria drosophilae from Southern China, one of the effective pupal parasitoids on the Drosophila species. Insects 11, https://doi.org/10.3390/insects11020103 (2020).

37.
Lynch, Z. R., Schlenke, T. A. & de Roode, J. C. Evolution of behavioural and cellular defences against parasitoid wasps in the Drosophila melanogaster subgroup. J. Evol. Biol. 29, 1016–1029. https://doi.org/10.1111/jeb.12842 (2016).
CAS  Article  PubMed  Google Scholar 

38.
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Otto, M. & Mackauer, M. The developmental strategy of an idiobiont ectoparasitoid, Dendrocerus carpenteri : Influence of variations in host quality on offspring growth and fitness. Oecologia 117, 353–364. https://doi.org/10.1007/s004420050668 (1998).
ADS  Article  PubMed  Google Scholar 

40.
Friard, O., Gamba, M. & Fitzjohn, R. BORIS: A free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330. https://doi.org/10.1111/2041-210x.12584 (2016).
Article  Google Scholar 

41.
Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).
Article  Google Scholar 

42.
R: A Language and Environment for Statistical Computing (R, Vienna, 2008).

43.
Steidle, J. L. M. & van Loon, J. J. A. in Chemoecology of Insect Eggs and Egg Deposition (eds Monika Hilker & Torsten Meiners) 291–317 (Blackwell, 2003).

44.
Romani, R., Isidoro, N., Bin, F. & Vinson, S. B. Host recognition in the pupal parasitoid Trichopria drosophilae: A morpho-functional approach. Entomol. Exp. Appl. 105, 119–128. https://doi.org/10.1046/j.1570-7458.2002.01040.x (2002).
CAS  Article  Google Scholar 

45.
Ballman, E. S., Collins, J. A. & Drummond, F. A. Pupation behavior and predation on Drosophila suzukii (Diptera: Drosophilidae) pupae in maine wild blueberry fields. J. Econ. Entomol. 110, 2308–2317. https://doi.org/10.1093/jee/tox233 (2017).
Article  PubMed  Google Scholar 

46.
Carton, Y. Biologie de pimpla instigator (Ichneumonidae: Pimplinae). Entomol. Exp. Appl. 17, 265–278. https://doi.org/10.1111/j.1570-7458.1974.tb00344.x (1974).
Article  Google Scholar 

47.
Vinson, S. B. Host selection by insect parasitoids. Annu. Rev. Entomol. 21, 109–133. https://doi.org/10.1146/annurev.en.21.010176.000545 (1976).
Article  Google Scholar 

48.
Poyet, M. et al. Resistance of Drosophila suzukii to the larval parasitoids Leptopilina heterotoma and Asobara japonica is related to haemocyte load. Physiol. Entomol. 38, 45–53. https://doi.org/10.1111/phen.12002 (2013).
Article  Google Scholar 

49.
Honti, V., Csordas, G., Kurucz, E., Markus, R. & Ando, I. The cell-mediated immunity of Drosophila melanogaster: Hemocyte lineages, immune compartments, microanatomy and regulation. Dev. Comp. Immunol. 42, 47–56. https://doi.org/10.1016/j.dci.2013.06.005 (2014).
CAS  Article  PubMed  Google Scholar 

50.
Iacovone, A., Ris, N., Poirie, M. & Gatti, J. L. Time-course analysis of Drosophila suzukii interaction with endoparasitoid wasps evidences a delayed encapsulation response compared to D. melanogaster. PLoS One 13, e0201573, https://doi.org/10.1371/journal.pone.0201573 (2018).

51.
Bozler, J., Kacsoh, B. Z. & Bosco, G. Maternal priming of offspring immune system in Drosophila. G3 (Bethesda) 10, 165–175, https://doi.org/10.1534/g3.119.400852 (2020).

52.
Charnov, E. L., Los-den Hartogh, R. L., Jones, W. T. & van den Assem, J. Sex ratio evolution in a variable environment. Nature 289, 27–33, https://doi.org/10.1038/289027a0 (1981).

53.
Sandlan, K. Sex-ratio regulation in Coccygomimus-Turionella Linnaeus (Hymenoptera, Ichneumonidae) and its ecological implications. Ecol. Entomol. 4, 365–378. https://doi.org/10.1111/j.1365-2311.1979.tb00596.x (1979).
Article  Google Scholar 

54.
King, B. H. Offspring sex-ratios in parasitoid wasps. Q. Rev. Biol. 62, 367–396. https://doi.org/10.1086/415618 (1987).
Article  Google Scholar  More

1.
Cobb, N. A. Nematodes and their relationships.Yearbook Dept. Agric. 1914, 457–490 (Dept. Agric, Washington DC, 1914).
2.
Blaxter, M. Nematodes: The worm and its relatives. PLoS Biol. 9, 4 (2011).
Article  Google Scholar 

3.
Kiontke, K. & Fitch, D. H. A. Nematodes. Curr. Biol. 23, 19 (2013).
Article  Google Scholar 

4.
Sommer, R. J. Pristionchus pacificus. In A Nematode Model for Comparative and Evolutionary Biology (ed. Sommer, R.) (Brill, Netherlands, 2015).
Google Scholar 

5.
Beck, C. & Forrester, D. J. Helminths of the Florida manatee, Trichechus manatus latirostris, with a discussion and summary of the parasites of Sirenians. J. Parasitol. 74, 628–637. https://doi.org/10.2307/3282182 (1988).
CAS  Article  PubMed  Google Scholar 

6.
Fürst von Lieven, A., Uni, S., Ueda, K., Barbuto, M. & Bain, O. Cutidiplogaster manati n. gen., n. sp. (Nematoda: Diplogastridae) from skin lesions of a West Indian manatee (Sirenia) from the Okinawa Churaumi Aquarium. Nematology. 13, 51–59. https://doi.org/10.1163/138855410X500082 (2011).
Article  Google Scholar 

7.
Bledsoe, E. L. et al. A comparison of biofouling communities associated with free-ranging and captive Florida manatees (Trichechus manatus latirostris). Mar. Mammal. Sci. 22, 997–1003. https://doi.org/10.1111/j.1748-7692.2006.00053.x (2006).
Article  Google Scholar 

8.
Kanzaki, N. & Giblin-Davis, R. M. Diplogastrid systematics and phylogeny. In Nematology Monographs & Perspectives 11: Pristionchus pacificus—A Nematode Model for Comparative and Evolutionary Biology (ed. Sommer, R.) 43–76 (Brill, Amsterdam, 2015).
Google Scholar 

9.
Abolafia, J. Order Rhabditida: suborder Rhabditina. In Freshwater Nematodes: Ecology and Taxonomy (eds Abebe, E. et al.) 696–721 (CABI Publishing, Wallingford, 2006).
Google Scholar 

10.
Kanzaki, N., Ragsdale, E. J. & Giblin-Davis, R. M. Revision of the paraphyletic genus Koerneria Meyl, 1960 and resurrection of two other genera of Diplogastridae (Nematoda). ZooKeys. 442, 17–30. https://doi.org/10.3897/zookeys.442.7459 (2014).
Article  Google Scholar 

11.
Romeyn, K., Bouwman, L. A. & Admiraal, W. Ecology and cultivation of the herbivorous brackish-water nematode Eudiplogaster pararmatus. Mar. Ecol. Prog. Ser. 12, 145–153 (1983).
ADS  Article  Google Scholar 

12.
Kanzaki, N., Giblin-Davis, R. M., Gonzalez, R. & Manzoor, M. Nematodes associated with palm and sugarcane weevils in South Florida with description of Acrostichus floridensis n. sp. Nematology. 19, 515–531. https://doi.org/10.1163/15685411-00003065 (2017).
Article  Google Scholar 

13.
Troccoli, A., Oreste, M., Tarasco, E., Fanelli, E. & De Luca, F. Mononchoides macrospiculum n. sp. (Nematoda: Neodiplogaster) and Teratorhabditis synpapillata Sudhaus, 1985 (Nematoda: Rhabditidae): Nematode associates of Rhynchophorus ferrugineus (Oliver) (Coleoptera: Curculionidae) in Italy. Nematology 17, 953–966. https://doi.org/10.1163/15685411-00002916 (2015).
Article  Google Scholar 

14.
Steel, H. et al. Mononchoides composticola n. sp. (Nematoda: Diplogastridae) associated with composting processes: Morphological, molecular and autecological characterization. Nematology 13, 347–363. https://doi.org/10.1163/138855410X523023 (2011).
Article  Google Scholar 

15.
Susoy, V. et al. Large-scale diversification without genetic isolation in nematode symbionts of figs. Sci. Adv. 2, e1501031. https://doi.org/10.1126/sciadv.1501031 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

16.
Mayer, W. E., Herrmann, M. & Sommer, R. J. Molecular phylogeny of beetle associated diplogastrid nematodes suggests host switching rather than nematode-beetle coevolution. BMC Evol. Biol. 9, 212. https://doi.org/10.1186/1471-2148-9-212 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

17.
Sudhaus, W. & Fürst von Lieven, A. A phylogenetic classification and catalogue of the Diplogastridae (Secernentea, Nematoda). J. Nematode Morph. Syst. 6, 43–90 (2003).
Google Scholar 

18.
Halvorsen, K. M. & Keith, E. O. Immunosuppression cascade in the Florida manatee (Trichechus manatus latirostris). Aquat. Mamm. 34, 412–419. https://doi.org/10.1578/AM.34.4.2008.412 (2008).
Article  Google Scholar 

19.
Palopoli, M. F. et al. Global divergence of the human follicle mite Demodex folliculorum: Persistent associations between host ancestry and mite lineages. Proc. Natl. Acad. Sci. USA 112, 15958–15963. https://doi.org/10.1073/pnas.1512609112 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

20.
Ingels, J., Valdes, Y. & Pontes, L. P. Meiofauna life on loggerhead sea turtles-diversely structured abundance and biodiversity hotspots that challenge the meiofauna paradox. Diversity. 12(5), 203 (2020).
Article  Google Scholar 

21.
Kanzaki, N., Giblin-Davis, R. M., Gonzalez, R., Wood, L. A. & Kaufman, P. E. Sudhausia floridensis n. sp. (Diplogastridae) isolated from Onthophagus tuberculifrons (Scarabaeidae) from Florida, USA. Nematology. 19, 575–586. https://doi.org/10.1163/15685411-00003071 (2017).
Article  Google Scholar 

22.
Giblin-Davis, R. M. et al. Stomatal ultrastructure, molecular phylogeny, and description of Parasitodiplogaster laevigata n. sp. (Nematoda: Diplogastridae), a parasite of fig wasps. J. Nematol. 38, 137–149 (2006).
CAS  PubMed  PubMed Central  Google Scholar 

23.
Kanzaki, N., Giblin-Davis, R. M., Ye, W., Herre, E. A. & Center, B. J. Parasitodiplogaster species associated with Pharmacosycea figs in Panama. Nematology. 16, 607–619. https://doi.org/10.1163/15685411-00002791 (2014).
Article  Google Scholar 

24.
Shih, P.-Y. et al. Newly identified nematodes from Mono Lake exhibit extreme arsenic resistance. Curr. Biol. 29, 3339–3344. https://doi.org/10.1016/j.cub.2019.08.024 (2019).
CAS  Article  PubMed  Google Scholar 

25.
Bonde, R. K. et al. Biomedical health assessments of the Florida manatee in Crystal River—Providing opportunities for training during the capture, handling, and processing of this endangered aquatic mammal. J. Mar. Anim. Ecol. 5, 17–28 (2012).
Google Scholar 

26.
Yoder, M. et al. DESS: A versatile solution for preserving morphology and extractable DNA of nematodes. Nematology 8, 367–376. https://doi.org/10.1163/156854106778493448 (2006).
CAS  Article  Google Scholar 

27.
Kikuchi, T., Aikawa, T., Oeda, Y., Karim, N. & Kanzaki, N. A rapid and precise diagnostic method for detecting the pinewood nematode Bursaphelenchus xylophilus by loop-mediated isothermal amplification. Phytopathology 99, 1365–1369. https://doi.org/10.1094/PHYTO-99-12-1365 (2009).
CAS  Article  PubMed  Google Scholar 

28.
Tanaka, R., Kikuchi, T., Aikawa, T. & Kanzaki, N. Simple and quick methods for nematode DNA preparation. Appl. Entomol. Zool. 47, 291–294. https://doi.org/10.1007/s13355-012-0115-9 (2012).
CAS  Article  Google Scholar 

29.
Ye, W., Giblin-Davis, R. M., Braasch, H., Morris, K. & Thomas, W. K. Phylogenetic relationships among Bursaphelenchus species (Nematoda: Parasitaphelenchidae) inferred from nuclear ribosomal and mitochondrial DNA sequence data. Mol. Phylogenet. Evol. 43, 1185–1197. https://doi.org/10.1016/j.ympev.2007.02.006 (2007).
CAS  Article  PubMed  Google Scholar 

30.
Holterman, M. et al. Phylum-wide analysis of SSU rDNA reveals deep phylogenetic relationships among nematodes and accelerated evolution toward crown clades. Mol. Biol. Evol. 23, 1792–1800. https://doi.org/10.1093/molbev/msl044 (2006).
CAS  Article  PubMed  Google Scholar 

31.
Slos, D., Couvreur, M. & Bert, W. Hidden diversity in mushrooms explored: A new nematode species, Neodiplogaster unguispiculata sp. n. (Rhabditida, Diplogastridae), with a key to the species of Neodiplogaster. Zool. Anz. 276, 71–85. https://doi.org/10.1016/j.jcz.2018.07.004 (2018).
Article  Google Scholar 

32.
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066. https://doi.org/10.1093/nar/gkf436 (2002).
CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Kuraku, S., Zmasek, C. M., Nishimura, O. & Katoh, K. aLeaves facilitates on-demand exploration of metazoan gene family trees on MAFFT sequence alignment server with enhanced interactivity. Nucleic Acids Res. 41, W22–W28. https://doi.org/10.1093/nar/gkt389 (2013).
Article  PubMed  PubMed Central  Google Scholar 

34.
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

35.
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755. https://doi.org/10.1093/bioinformatics/17.8.754 (2001).
CAS  Article  Google Scholar 

36.
Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large modelspace. Syst. Biol. 61, 539–542. https://doi.org/10.1093/sysbio/sys029 (2012).
Article  PubMed  PubMed Central  Google Scholar 

37.
Kanzaki, N., Ekino, T., Ide, T., Masuya, H. & Degawa, Y. Three new species of parasitaphelenchids, Parasitaphelenchus frontalis n. sp., P. costati n. sp., and Bursaphelenchus hirsutae n. sp. (Nematoda: Aphelenchoididae), isolated from bark beetles from Japan. Nematology 20, 957–1005. https://doi.org/10.1163/15685411-00003189 (2018).
Article  Google Scholar  More

The fines content of the pellets, agglutinated with wheat starch and kraft lignin (both at 4%), was 125 higher and 75% lower than in the control, respectively (Table 1). The fines generation of the pellets in all treatments was lower than 1% (0.03 to 0.27%) and, therefore, they met the marketing standard EN 14961-232.
Table 1 Fine content (%), hardness (%), bulk density (g m−3), apparent density (g m−3) by gravimetric method and apparent density (g m−3) by X-ray densitometry of Pinus wood pellets produced with different percentages of the additives (A) corn and wheat and kraft lignin and in the control.
Full size table

The lower values of the fines content of the pellets produced with kraft lignin are possibly due to the densification process of the pellet matrix with higher contents of this additive, generating pellets with better bonding characteristics between the particles and, consequently, less fines. In addition, lignin has a cementing action between the cells9 during the pressing process, and high temperature causes this compound to reach the glass transition stage, ensuring a strong bond between the particles8,33. Pellets with lower fines production during handling and transport should be preferred commercially34. The fines content increases with the moisture level of the material, causing cracks to exhaust gases, mainly water vapor, and, consequently, reducing their mechanical resistance during handling35. On the other hand, the low moisture content makes biomass compaction difficult, due to the water’s characteristic of helping the heat transfer and promoting lignin plasticization as a natural biomass binder36. The moisture content between 8 and 12% in the dry basis is ideal for reducing fines generation to within the European standard EN 14961-232.
The hardness of the pellets was similar with the different percentages of corn starch, but it was higher with wheat starch (Table 1). The hardness increased by 22% when the percentage of kraft lignin reached 5%, in relation to the control. The hardness of the pellets with 3 and 5% of corn starch and 4% of kraft lignin was similar to the control.
The similar hardness of the pellets with the different percentages of wheat starch confirms studies that binders can reduce the mechanical properties of pellets at a higher moisture content, because water takes the place of hydrogen bonds, affecting cohesion between the particles37. Higher hardness affects pellet length, because the higher the hardness, the greater the breaking strength after contact with the pelletizing press knife15. In addition, pellets with lower hardness have points for water ingress, increasing the moisture content and consequently the breaking point and causing higher fine generation38. The higher hardness of pellets produced with 5% kraft lignin is possibly due to the decrease of their hygroscopic equilibrium moisture, due to the hydrophobic character of this compound. The kraft lignin residue is a compound of C–C and C–O–C phenylpropane units with low water relationship39. In addition, the constant pressing temperature of 120 °C plasticizes kraft lignin as an adhesive, increasing particle contact and reducing expansion due to lower hygroscopicity, consequently increasing hardness40. Kraft lignin, as an additive, facilitates the use of this residue and confers better properties to pellets by increasing their mechanical strength13,14,15.
The bulk density of pellets with 1% corn or wheat starches and 3% kraft lignin was higher than other mixtures (Table 1). The bulk density of kraft lignin pellets was higher than those with corn or wheat starch. The bulk density of pellets with 1% corn starch and 5% kraft lignin was lower than those with 3% lignin, which were denser than those with only wood (control).
The higher bulk density values for 3% kraft lignin pellets may be associated with a higher amount of lignin in the mixture (wood + additive), which plasticizes more efficiently, generating a smooth and uniform texture in the pellets and improving their density. The pelletizing matrix temperature influences the durability and bulk density of pellets36, as lignin is a natural wood binder and requires temperatures above the glass transition (75–100 °C) to produce bonding between the particles. Temperatures above 90 °C improve pellet characteristics, and require lower compaction pressure at increasing compaction matrix temperatures4,41. The lower density values of wheat starch pellets may be due to the high moisture content of the steam generated during the high temperatures in the compaction process (120 °C), causing micro-cracks in the pellet structure and reducing its density35. Starch acts as a lubricating agent in the pelletizing process, facilitating the flow of raw material through the pelletizing matrix36. The bulk density of the pellets was greater than the minimum required by the European Marketing Standard EN 14961-232, equal to or greater than 0.60 g cm−3 in all treatments. This highlights the potential use of additives in pelletizing, which should be at most 2% relative to the dry mass of primary raw material.
The apparent density of pellets varied in a fashion similar to that of bulk density (Table 1), with no effect from the type and amount of additive added to the particles mass, comparing the three different additives and considering the same proportion used, except for pellets produced with 3% wheat starch, with lower apparent density. The apparent density of pellets produced with 1 and 2% corn starch and 1, 3, 4 and 5% kraft lignin was higher, and the other treatments were similar to the control (Table 1). Lignin and corn starch promoted better connection between particles, favoring biomass compaction and increasing pellet density.
The variation in the apparent density of the pellets, similar to that of bulk density between 1.15 g m−3 (3% wheat) and 1.23 g m−3 (3% lignin), is possibly due to the wheat starch gelatinization process starting at lower temperatures (± 70 °C) than that of corn starch (± 85 °C)42. This leads to the starch adhering to the pellet feeder system wall, reducing the proportion of additive that reaches the pelletizing matrix and consequently diminishing the unit density of the pellet. The higher apparent density of pellets produced with 1 and 2% corn starch and 1, 3, 4 and 5% kraft lignin is due to the lower rate of return of the pelletizing process and the higher molecular weight of the additives, influencing the pellet density7,36. Bulk density and apparent density determine pellet storage and transport conditions, and are directly related to energy density in those with 1 and 2% corn starch and 1, 3, 4 and 5% lignin, with higher density and a higher amount of energy per volume unit43.
The apparent density of the pellets produced with additives and evaluated by X-ray densitometry ranged from 1.00 to 1.31 g m−3 in their longitudinal axis (Table 1), with the lowest value for pellets produced with 1% wheat starch, and the highest value with 1% corn starch.
The lower apparent density values of wheat starch pellets can be associated with the presence of cracks (empty spaces), directly related to the susceptibility to rupture2. Low density peaks indicate small cracks that are attributed to a moisture content of the mixture or particle sizes inadequate for pelletizing4, affecting the physical properties of biomass densification44. The average apparent density of pellets is within the range established by the German standard DIN 51731, from 1.00 to 1.40 g m−345.
Pellet density varied in longitudinal density profiles, with one uniform and one irregular pattern (Fig. 1). The apparent density variation of pellets produced without additives along the longitudinal axis (coefficient of variation of 5.29%) was higher. On the other hand, the apparent density variation of the profile (coefficient of variation of 4.19%) with additives was lower, showing greater cohesion between the particles and the additives. X-ray densitometry showed pellet density variations for all additives and in the control.
Figure 1

Longitudinal variation of pellet density with different proportions of the additives kraft lignin and corn and wheat starch.

Full size image

Uniform or irregular density patterns according to longitudinal pellet density profiles are due to variations in pellet internal density, which can be attributed to factors such as additive molecular weight, particle size, and temperature and pressure during pelletization46,47,48. Cracks are common in compacted material during pelletizing4,6, and can be attributed to inadequate pellet moisture content or particle sizes. The density of biomass varies with the moisture content44 and with the temperature strengthening the adhesion between the particles. Density profiles can explain the performance of pellets, whose cracks and high density variability affect their durability and final quality, since reductions in density are associated with cracks and, consequently, pellet breakage or rupture points, which can generate fines5. The apparent density of the pellets by gravimetric and X-ray densitometry, similar between treatments with additives, confirm that this technique, commonly used to evaluate the apparent density of materials and easier to apply than other methodologies, can be used to evaluate the quality of the pellets. Variations in the apparent density and longitudinal density profile obtained with the gravimetric and X-ray densitometry demonstrate that factors such as moisture, binder type, pressure and particle size interfere with the pelletizing process, causing variations in the material’s internal structure46,47. In addition, this technique accesses different parts of the pellet and therefore identifies point variations in the product density as reported for the 2% wheat starch pellet.
In conclusion, the additives reduced the fines content and increased the hardness and density of the pellets. Therefore, they have the potential to produce pellets with greater resistance to the transport, storage and handling processes. Apparent density along the longitudinal axis of the pellets without starch was higher. The apparent density of pellets containing starch increased the cohesion between the particles and reduced the density variation as shown by their densitometric profiles. More

1.
Norris, D. R. Carry-over effects and habitat quality in migratory populations. Oikos 109(1), 178–186 (2005).
Article  Google Scholar 
2.
Ockendon, N., Leech, D. & Pearce-Higgins, J. W. Climatic effects on breeding grounds are more important drivers of breeding phenology in migrant birds than carryover effects from wintering grounds. Biol. Lett. 9(6). https://doi.org/10.1098/rsbl.2013.0669 (2013).

3.
Lehikoinen, A., Kilpi, M. & Öst, M. Winter climate affects subsequent breeding success of common eiders. Glob. Change Biol. 12(7), 1355–1365 (2006).
ADS  Article  Google Scholar 

4.
Bearhop, S., Hilton, G. M., Votier, S. C. & Waldron, S. Stable isotope ratios indicate that body condition in migrating passerines is influenced by winter habitat. Biol. Lett. 271. https://doi.org/10.1098/rsbl.2003.0129 (2004).

5.
Rowe, L., Ludwig, D. & Schluter, D. Time, condition, and the seasonal decline of avian clutch size. Am. Nat. 143(4), 698–722 (1994).
Article  Google Scholar 

6.
Morrison, C. A., Alves, J. A., Gunnarsson, T. G., Þórisson, B. & Gill, J. A. Why do earlier-arriving migratory birds have better breeding success? Ecol. Evol. 9(15), 8856–8864 (2019).
PubMed  PubMed Central  Article  Google Scholar 

7.
Gill, J. A. et al. The buffer effect and large-scale population regulation in migratory birds. Nature 412, 436–438 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Finch, T., Pearce-Higgins, J. W., Leech, D. I. & Evans, K. L. Carry-over effects from passage regions are more important than breeding climate in determining the breeding phenology and performance of three avian migrants of conservation concern. Biodivers. Conserv. 23, 2427–2444 (2014).
Article  Google Scholar 

9.
Legagneux, P., Fast, P. L. F., Gauthier, G. & Bêty, J. Manipulating individual state during migration provides evidence for carry-over effects modulated by environmental conditions. Proc. R. Soc. B. 279, 876–883 (2012).
PubMed  Article  Google Scholar 

10.
Newton, I. The Migration Ecology of Birds (Academic Press, London, 2008).
Google Scholar 

11.
Lok, T., Overdijk, O. & Piersma, T. The cost of migration: spoonbills suffer higher mortality during trans-Saharan spring migrations only. Biol. Lett., 11(1). https://doi.org/10.1098/rsbl.2014.0944 (2015).

12.
Bregnballe, T., Frederiksen, M. & Gregersen, J. Effects of distance to wintering area on arrival date and breeding performance in Great Cormorants Phalacrocorax carbo. Ardea. 94(3), 619–630 (2006).
Google Scholar 

13.
Hötker, H. Arrival of Pied Avocets Recurvirostra avosetta at the breeding site: effects of winter quarters and consequences for reproductive success. Ardea. 90(3), 379–387 (2002).
Google Scholar 

14.
Lundberg, P. The evolution of partial migration in birds. Trends Ecol. Evol. 3(7), 172–175 (1988).
CAS  PubMed  Article  Google Scholar 

15.
Chapman, B. B., Brönmark, C., Nilsson, J. -Å. & Hansson, L.-A. Partial migration: An introduction. Oikos 120(12), 1761–1763 (2011).
Article  Google Scholar 

16.
Buchan, C., Gilroy, J. J., Catry, I. & Franco, A. M. A. Fitness consequences of different migratory strategies in partially migratory populations: A multi-taxa meta-analysis. J. Anim. Ecol. 89, 678–690 (2020).
PubMed  Article  Google Scholar 

17.
Mueller, J. C., Pulido, F. & Kempenaers, B. Identification of a gene associated with avian migratory behaviour. Proc. R. Soc. B. 278, 2848–2856 (2011).
CAS  PubMed  Article  Google Scholar 

18.
Griswold, C. K., Taylor, C. M. & Norris, D. R. The evolution of migration in a seasonal environment. Proc. R. Soc. B. 277, 2711–2720 (2010).
PubMed  Article  Google Scholar 

19.
Chapman, B. B., Brönmark, C., Nilsson, J-Å. & Hansson, L-A. The ecology and evolution of partial migration. Oikos. 120(12), 1764–1775 (2011).
Article  Google Scholar 

20.
Kokko, H. Directions in modelling partial migration: How adaptation can cause a population decline and why the rules of territory acquisition matter. Oikos 120(12), 1826–1837 (2011).
Article  Google Scholar 

21.
Newton, I. Population limitation in migrants. Ibis. 146(2), 197–226 (2004).
Article  Google Scholar 

22.
Robinson, R. A. et al. Travelling through a warming world: Climate change and migratory species. Endanger. Species Res. 7(2), 87–99 (2009).
ADS  Article  Google Scholar 

23.
IPCC. Summary for Policymakers in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (eds. Stocker, T.F. et al.) (Cambridge University Press, 2013).

24.
Berthold, P. Genetic basis and evolutionary aspects of bird migration. Adv. Study Behav. 33, 175–229 (2003).
Article  Google Scholar 

25.
Møller, A. P., Rubolini, D. & Lehikoinen, E. Populations of migratory bird species that did not show a phenological response to climate change are declining. Proc. Natl. Acad. Sci. U.S.A. 105(42), 16195–16200 (2008).
ADS  PubMed  PubMed Central  Article  Google Scholar 

26.
de Zoeten, T. & Pulido, F. How migratory populations become resident. Proc. R. Soc. B, 287, 20193011. https://doi.org/10.1098/rspb.2019.3011 (2020).

27.
Negro, J. J., de la Riva, M. & Bustamante, J. Patterns of winter distribution and abundance of Lesser Kestrel (Falco naumanni) in Spain. J. Raptor Res. 25, 30–35 (1991).
Google Scholar 

28.
Anderson, A. M., Novak, S. J., Smith, J. F., Steenhof, K. & Heath, J. Nesting phenology, mate choice, and genetic divergence within a partially migratory population of American Kestrels. Auk. 133(1), 99–109 (2016).
Article  Google Scholar 

29.
Lok, T., Veldhoen, L., Overdijk, O., Tinbergen, J. M. & Piersma, T. An age-dependent fitness cost of migration? Old trans-Saharan migrating spoonbills breed later than those staying in Europe, and late breeders have lower recruitment. J. Anim. Ecol. 86(5), 998–1009 (2017).
PubMed  Article  Google Scholar 

30.
Catry, I. et al. Individual variation in migratory movements and winter behaviour of Iberian Lesser Kestrels Falco naumanni revealed by geolocators. Ibis. 153(1), 154–164 (2011).
Article  Google Scholar 

31.
Rodríguez, C., Tapia, L., Kieny, F. & Bustamante, J. Temporal changes in lesser kestrel (Falco naumanni) diet during the breeding season in southern Spain. J. Raptor Res. 44(2), 120–128 (2010).
Article  Google Scholar 

32.
Grist, H. et al. Reproductive performance of resident and migrant males, females and pairs in a partially migratory bird. J. Anim. Ecol. 86(5), 1010–1021 (2017).
PubMed  PubMed Central  Article  Google Scholar 

33.
Morganti, M., Ambrosini, R. & Sarà, M. Different trends of neighboring populations of Lesser Kestrel: effects of climate and other environmental conditions. Popul. Ecol. 61(3), 300–314 (2019).
Article  Google Scholar 

34.
Hegemann, A., Marra, P. P. & Tieleman, B. I. Causes and consequences of partial migration in a passerine bird. Am. Nat. 186(4), 531–546 (2015).
PubMed  Article  PubMed Central  Google Scholar 

35.
Palacín, C., Alonso, J. C., Martín, C. A. & Alonso, J. A. Changes in bird-migration patterns associated with human-induced mortality. Conserv. Biol. 31(1), 106–115 (2017).
PubMed  Article  PubMed Central  Google Scholar 

36.
Clausen, K. K., Madsen, J. & Tombre, I. M. Carry-over or compensation? The impact of winter harshness and post-winter body condition on spring-fattening in a migratory goose species. PLoS One 10(7). https://doi.org/10.1371/journal.pone.0132312 (2015).

37.
Wilson, S., LaDeau, S. L., Tøttrup, A. P. & Marra, P. P. Range-wide effects of breeding- and nonbreeding-season climate on the abundance of a Neotropical migrant songbird. Ecology 92(9), 1789–1798 (2011).
PubMed  Article  PubMed Central  Google Scholar 

38.
Pilard, P., Lelong, V., Sonko, A. & Riols, C. Suivi et conservation du dortoir de rapaces insectivores (faucon crécerellette Falco naumanni et elanion naucler Chelictinia riocourii) de l’Ile de Kousmar (Kaolack/Sénégal). Alauda. 79(4), 295–312 (2011).
Google Scholar 

39.
Rodríguez, A., Negro, J. J., Bustamante, J., Fox, J. & Afanasyev, V. Geolocators map the wintering grounds of threatened lesser kestrels in Africa. Divers. Distrib. 15(6), 1010–1016 (2009).
Article  Google Scholar 

40.
Norris, D. R. & Taylor, C. M. Predicting the consequences of carry-over effects for migratory populations. Biol. Lett. 2(1). https://doi.org/10.1098/rsbl.2005.0397 (2006).

41.
Marra, P. P. et al. Non-breeding season habitat quality mediates the strength of density-dependence for a migratory bird. Proc. R. Soc. B. 282, 20150624. https://doi.org/10.1098/rspb.2015.0624 (2015).
Article  Google Scholar 

42.
Negro, J. J. Falco naumanni Lesser Kestrel. BWP Update. 1, 49–56 (1997).
Google Scholar 

43.
Bustamante, J. Predictive models for lesser kestrel Falco naumanni distribution, abundance and extinction in southern Spain. Bio. Conserv. 80(2), 153–160 (1997).
Article  Google Scholar 

44.
Lepley, M., Brun, L., Foucart, A. & Pilard, P. Régime et comportement alimentaires du faucon crécerellette Falco naumanni en crau en période de reproduction et post-reproduction. Alauda. 68(3), 177–184 (2000).
Google Scholar 

45.
Donázar, J. A., Negro, J. J. & Hiraldo, F. Functional analysis of mate-feeding in the Lesser Kestrel Falco naumanni. Ornis Scand. 23, 190–194 (1992).
Article  Google Scholar 

46.
Braziotis, S. et al. Patterns of postnatal growth in a small falcon, the lesser kestrel Falco naumanni (Fleischer, 1818) (Aves: Falconidae). Eur. Zool. J. 84(1), 277–285 (2017).
Article  Google Scholar 

47.
Donázar, J. A., Negro, J. J. & Hiraldo, F. A note on the adoption of alien young by lesser kestrels Falco naumanni. Ardea. 77, 443–444 (1991).
Google Scholar 

48.
Rakhimberdiev, E. et al. Comparing inferences of solar geolocation data against high-precision GPS data: Annual movements of a double-tagged black-tailed godwit. J. Avian Biol. 47(4), 589–596 (2016).
Article  Google Scholar 

49.
Lisovski, S. et al. Light-level geolocator analyses: A user’s guide. J Anim. Ecol. 89, 221–236 (2020).
PubMed  Article  Google Scholar 

50.
Forsman, D. The Raptors of Europe and the Middle East: A Handbook of Field Identification (Christopher Helm, London, 2006).
Google Scholar 

51.
Bounas, A. Premigratory moult in the lesser kestrel Falco naumanni. Avocetta. 43, 49–54 (2019).
Google Scholar 

52.
Gilbert, N. Movement and Foraging Ecology of Partially Migrant Birds in a Changing World (University of East Anglia, Norwich, 2015).
Google Scholar 

53.
Hobson, K. A. et al. A multi-isotope (δ13C, δ15N, δ2H) feather isoscape to assign Afrotropical migrant birds to origins. Ecosphere. 3, 44. https://doi.org/10.1890/ES12-00018.1 (2012).
Article  Google Scholar 

54.
Tella, J. L. & Forero, M. G. Farmland habitat selection of wintering lesser kestrels in a Spanish pseudosteppe: implications for conservation strategies. Biodivers. Conserv. 9, 433–441 (2000).
Article  Google Scholar 

55.
Piersma, T. & Davidson, N. Confusions of mass and size. Auk 108, 441–444 (1991).
Google Scholar 

56.
Wood, A. S. & Scheipl, F. gamm4: Generalized additive mixed models using ‘mgcv’ and ‘lme4’. in R Package Version 0.2-5. https://CRAN.R-project.org/package=gamm4 (2017).

57.
Bates, D., Machler, M., Bolker, B., & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67. https://doi.org/10.18637/jss.v067.i01 (2015).

58.
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometric. J. 50(3), 346–363 (2008).
MathSciNet  MATH  Article  Google Scholar 

59.
Rousset, F. & Ferdy, J. B. Testing environmental and genetic effects in the presence of spatial autocorrelation. Ecography 37(8), 781–790 (2014).
Article  Google Scholar 

60.
Shmueli, G. A useful distribution for fitting discrete data: Revival of the Conway–Maxwell–Poisson distribution. J. R. Stat. Soc. Ser. C App. Stat. 54, 127–142 (2005).
MathSciNet  MATH  Article  Google Scholar 

61.
Lynch, H. J., Thorson, J. T. & Shelton, A. O. Dealing with under- and over-dispersed count data in life history, spatial, and community ecology. Ecology 95(11), 3173–3180 (2014).
Article  Google Scholar 

62.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, New York, 2002).
Google Scholar 

63.
Bartoń, K. MuMIn: Multi-model inference. in R Package Version 1.43.6. https://CRAN.R-project.org/package=MuMIn (2019).

64.
Cade, B. S. Model averaging and muddled multimodel inferences. Ecology 96(9), 2370–2382 (2015).
PubMed  Article  Google Scholar 

65.
R Core Development Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, http://www.r-project.org, 2018).

66.
Didan, K. MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250m SIN grid V006. NASA EOSDIS Land. Process. DAAC. https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).
Article  Google Scholar 

67.
Büttner, G. CORINE land cover and land cover change products. in Land Use and Cover Mapping in Europe. (eds. Manakos, I. & Braun, M.) 55–74 (Springer, 2014).

68.
Franco, A. M. A., Catry, I., Sutherland, W. J. & Palmeirim, J. M. Do different habitat preference survey methods produce the same conservation recommendations for lesser kestrels?. Anim. Conserv. 7(3), 291–300 (2004).
Article  Google Scholar  More