Philippa Kaur

Constant contacts modelWe assume the affected population is composed of two risk-groups, a fraction p of the population is composed of risk-takers (RT) and the remaining fraction (1-p) are risk-evaders (RE). We differentiate the RT and RE subpopulations by assuming the RE population face a reduced likelihood of infection due to adopting precautionary behaviors. On the other hand, we assume RT do not follow public health recommendations, thus facing a higher risk of infection, relative to the RE population. Political or ideological reasons, economic stress, the lack of reasonable alternatives, epidemic politicization or the lack of trust in public health authorities are some of the documented factors that potentially lead the population to risk the dangers of COVID-19 infection44, 45.Previous mathematical models consider complex within-host disease dynamics46 or the impact of exogenous factors on the COVID-19 transmission dynamics47. In this study, we focus on incorporating individual heterogeneous adaptive behavioral responses, based on group-specific infection risk perceptions. Our model of disease progression assumes that individuals in each behavioral group may show the following health status: Susceptible (S), infectious Exposed (E), Infectious symptomatic (I), infectious Asymptomatic (A), and Recovered (R). We consider a pre-symptomatic infectious health status (E), following evidence suggesting that exposed individuals exhibit a period of viral shedding38, 48,49,50,51. RT susceptible individuals ((S_1)) can get infected by making contacts with either: symptomatic ones (I) with a baseline per-contact likelihood of disease transmission (beta), exposed individuals ((E_1) and (E_2)) with reduced per-contact likelihood of infection (rho beta) , or asymptomatic individuals ((A_1) and (A_2)) with reduced per-contact likelihood of infection (alpha beta). Similarly RE susceptible individuals ((S_2)) may get infected by making contacts with symptomatic, exposed or asymptomatic individuals at respective likelihoods, (epsilon beta), (rho epsilon beta), and (alpha epsilon beta), where (0 More

Thamdrup, B. New Pathways and processes in the global nitrogen cycle. Annu. Rev. Ecol. Evol. Syst. 43, 407–428 (2012).
Google Scholar 
Lam, P. et al. Revising the nitrogen cycle in the Peruvian oxygen minimum zone. Proc. Natl. Acad. Sci. USA 106, 4752–4757 (2009).CAS 

Google Scholar 
Behrendt, A., de Beer, D. & Stief, P. Vertical activity distribution of dissimilatory nitrate reduction in coastal marine sediments. Biogeosciences 10, 7509–7523 (2013).
Google Scholar 
Fossing, H. et al. Concentration and transport of nitrate by the mat-forming sulphur bacterium. Thioploca. Nature 374, 713–715 (1995).CAS 

Google Scholar 
McHatton, S. C., Barry, J. P., Jannasch, H. W. & Nelson, D. C. High nitrate concentrations in vacuolate, autotrophic marine Beggiatoa spp. Appl. Environ. Microbiol. 62, 954–958 (1996).CAS 

Google Scholar 
Kamp, A., Høgslund, S., Risgaard-Petersen, N. & Stief, P. Nitrate storage and dissimilatory nitrate reduction by eukaryotic microbes. Front. Microbiol. 6, 1492 (2015).
Google Scholar 
Eppley, R. W. & Rogers, J. N. Inorganic nitrogen assimilation of Ditylum brightwellii, a marine plankton diatom. J. Phycol. 6, 344–351 (1970).CAS 

Google Scholar 
Lomas, M. & Glibert, P. Comparisons of nitrate uptake, storage, and reduction in marine diatoms and flagellates. J. Phycol. 36, 903–913 (2000).CAS 

Google Scholar 
Jørgensen, B. B. & Gallardo, A. Thioploca spp.: filamentous sulfur bacteria with nitrate vacuoles. FEMS Microbiol. Ecol. 28, 301–313 (1999).
Google Scholar 
Schulz, H. N. et al. Dense populations of a giant sulfur bacterium in Namibian shelf sediments. Science 284, 493–495 (1999).CAS 

Google Scholar 
Risgaard-Petersen, N. et al. Evidence for complete denitrification in a benthic foraminifer. Nature 443, 93–96 (2006).CAS 

Google Scholar 
Kamp, A., de Beer, D., Nitsch, J. L., Lavik, G. & Stief, P. Diatoms respire nitrate to survive dark and anoxic conditions. Proc. Natl. Acad. Sci. USA 108, 5649–5654 (2011).CAS 

Google Scholar 
Stief, P. et al. Dissimilatory nitrate reduction by Aspergillus terreus isolated from the seasonal oxygen minimum zone in the Arabian Sea. BMC Microbiol. 14, 35 (2014).
Google Scholar 
Høgslund, S., Cedhagen, T., Bowser, S. S. & Risgaard-Petersen, N. Sinks and sources of intracellular nitrate in gromiids. Front. Microbiol. 8, 617 (2017).
Google Scholar 
Harold, F. M. The Vital Force: A Study of Bioenergetics (WH Freeman & Co., 1986).Katz, M. E., Finkel, Z. V., Grzebyk, D., Knoll, A. H. & Falkowski, P. G. Evolutionary trajectories and biogeochemical impacts of marine eukaryotic phytoplankton. Annu. Rev. Ecol. Evol. Syst. 35, 523–556 (2004).
Google Scholar 
Villareal, T. A., Altabet, M. A. & Culverrymsza, K. Nitrogen transport by vertically migrating diatom mats in the North Pacific Ocean. Nature 363, 709–712 (1993).CAS 

Google Scholar 
Kamp, A., Stief, P. & Schulz, H. N. Anaerobic sulfide oxidation with nitrate by a freshwater Beggiatoa enrichment culture. Appl. Environ. Microbiol. 72, 4755–4760 (2006).CAS 

Google Scholar 
Merz, E. et al. Nitrate respiration and diel migration patterns of diatoms are linked in sediments underneath a microbial mat. Environ. Microbiol. 23, 1422–1435 (2021).CAS 

Google Scholar 
Leblanc, K. et al. A global diatom database–abundance, biovolume and biomass in the world ocean. Earth Syst. Sci. Data 4, 149–165 (2012).
Google Scholar 
Benoiston, A. S. et al. The evolution of diatoms and their biogeochemical functions. Phil. Trans. R. Soc. B 372, 20160397 (2017).
Google Scholar 
Nelson, D. M., Tréguer, P., Brzezinski, M. A., Leynaert, A. & Queguiner, B. Production and dissolution of biogenic silica in the ocean-revised global estimates, comparison with regional data and relationship to biogenic sedimentation. Global Biogeochem. Cycl. 9, 359–372 (1995).CAS 

Google Scholar 
Sarthou, G., Timmermans, K. R., Blain, S. & Tréguer, P. Growth physiology and fate of diatoms in the ocean: a review. J. Sea Res. 53, 25–42 (2005).CAS 

Google Scholar 
Dortch, Q., Clayton, J. R., Thoresen, S. S. & Ahmed, S. I. Species differences in accumulation of nitrogen pools in phytoplankton. Mar. Biol. 81, 237–250 (1984).CAS 

Google Scholar 
Kamp, A., Stief, P., Knappe, J. & de Beer, D. Response of the ubiquitous pelagic diatom Thalassiosira weissflogii to darkness and anoxia. PLoS ONE 8, e82605 (2013).
Google Scholar 
Kamp, A., Stief, P., Bristow, L. A., Thamdrup, B. & Glud, R. N. Intracellular nitrate of marine diatoms as a driver of anaerobic nitrogen cycling in sinking aggregates. Front. Microbiol. 7, 1669 (2016).
Google Scholar 
Needoba, J. A. & Harrison, P. J. Influence of low light and a light:dark cycle on NO3− uptake, intracellular NO3−, and nitrogen isotope fractionation by marine phytoplankton. J. Phycol. 40, 505–516 (2004).CAS 

Google Scholar 
Lomas, M. W. & Glibert, P. M. Temperature regulation of nitrate uptake: A novel hypothesis about nitrate uptake and reduction in cool-water diatoms. Limnol. Oceanogr. 44, 556–572 (1999).CAS 

Google Scholar 
Lomas, M. W., Rumbley, C. J. & Glibert, P. M. Ammonium release by nitrogen sufficient diatoms in response to rapid increases in irradiance. J. Plankton Res. 22, 2351–2366 (2000).CAS 

Google Scholar 
Van Tol, H. M. & Armbrust, E. V. Genome-scale metabolic model of the diatom Thalassiosira pseudonana highlights the importance of nitrogen and sulfur metabolism in redox balance. PLoS ONE 16, e0241960 (2021).
Google Scholar 
Piña-Ochoa, E. et al. Widespread occurrence of nitrate storage and denitrification among Foraminifera and Gromiida. Proc. Natl. Acad. Sci. USA 107, 1148–1153 (2010).
Google Scholar 
García-Robledo, E., Corzo, A., Papaspyrou, S., Jimenez-Arias, J. L. & Villahermosa, D. Freeze-lysable inorganic nutrients in intertidal sediments: dependence on microphytobenthos abundance. Mar. Ecol. Prog. Ser. 403, 155–163 (2010).
Google Scholar 
Marchant, H. K., Lavik, G., Holtappels, M. & Kuypers, M. M. M. The fate of nitrate in intertidal permeable sediments. PLoS ONE 9, e104517 (2014).
Google Scholar 
Villareal, T. A. & Lipschultz, F. Internal nitrate concentrations in single cells of large phytoplankton from the Sargasso Sea. J. Phycol. 31, 689–696 (1995).CAS 

Google Scholar 
Smith, G. J., Zimmerman, R. C. & Alberte, R. S. Molecular and physiological responses of diatoms to variable levels of irradiance and nitrogen availability: Growth of Skeletonema costatum in simulated upwelling conditions. Limnol. Oceanogr. 37, 989–1007 (1992).CAS 

Google Scholar 
Montagnes, D. J. S. & Franklin, D. J. Effect of temperature on diatom volume, growth rate, and carbon and nitrogen content: Reconsidering some paradigms. Limnol. Oceanogr. 46, 2008–2018 (2001).CAS 

Google Scholar 
Smith, S. R. et al. Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nat. Commun. 10, 4552 (2019).
Google Scholar 
Behrenfeld, M. J. et al. Thoughts on the evolution and ecological niche of diatoms. Ecol. Monogr. 91, e01457 (2021).
Google Scholar 
Bourke, M. F. et al. Metabolism in anoxic permeable sediments is dominated by eukaryotic dark fermentation. Nat. Geosci. 10, 30–35 (2017).CAS 

Google Scholar 
Härnström, K., Ellegaard, M., Andersen, T. J. & Godhe, A. Hundred years of genetic structure in a sediment revived diatom population. Proc. Natl. Acad. Sci. USA 108, 4252–4257 (2011).
Google Scholar 
Pelusi, A., Santelia, M. E., Benvenuto, G., Godhe, A. & Montresor, M. The diatom Chaetoceros socialis: spore formation and preservation. Europ. J. Phycol. 55, 1–10 (2020).CAS 

Google Scholar 
Petterson, K. & Sahlsten, E. Diel patterns of combined nitrogen uptake and intracellular storage of nitrate by phytoplankton in the open Skagerrak. J. Exp. Mar. Biol. Ecol. 138, 167–182 (1990).
Google Scholar 
Petterson, K. Seasonal uptake of carbon and nitrogen and intracellular storage of nitrate in planktonic organisms in the Skagerrak. J. Exp. Mar. Biol. Ecol. 151, 121–1137 (1991).
Google Scholar 
Bode, A., Botas, J. A. & Fernandez, E. Nitrate storage by phytoplankton in a coastal upwelling environment. Mar. Biol. 129, 399–406 (1997).CAS 

Google Scholar 
Stief, P., Kamp, A., Thamdrup, B. & Glud, R. N. Anaerobic nitrogen turnover by sinking diatom aggregates at varying ambient oxygen levels. Front. Microbiol. 7, 98 (2016).
Google Scholar 
Jensen, M. M. et al. Intensive nitrogen loss over the Omani Shelf due to anammox coupled with dissimilatory nitrite reduction to ammonium. ISME J. 5, 1660–1670 (2011).CAS 

Google Scholar 
Magalhaes, C. M., Wiebe, W. J., Joye, S. B. & Bordalo, A. A. Inorganic nitrogen dynamics in intertidal rocky biofilms and sediments of the Douro River estuary (Portugal). Estuaries 28, 592–607 (2005).CAS 

Google Scholar 
Burgin, A. J. & Hamilton, S. K. Have we overemphasized the role of denitrification in aquatic ecosystems? A review of nitrate removal pathways. Front. Ecol. Environ. 5, 89–96 (2007).
Google Scholar 
Kühl, M., Glud, R. N., Ploug, H. & Ramsing, N. B. Microenvironmental control of photosynthesis and photosynthesis-coupled respiration in an epilithic cyanobacterial biofilm. J. Phycol. 32, 799–812 (1996).
Google Scholar 
Heisterkamp, I. M. et al. Shell biofilm-associated nitrous oxide production in marine molluscs: processes, precursors and relative importance. Environ. Microbiol. 15, 1943–1955 (2013).CAS 

Google Scholar 
Fernandez-Mendez, M. et al. Composition, buoyancy regulation and fate of ice algal aggregates in the Central Arctic Ocean. PLoS ONE 9, e107452 (2014).
Google Scholar 
Boetius, A. et al. Export of algal biomass from the melting Arctic sea ice. Science 339, 1430–1432 (2013).CAS 

Google Scholar 
Abed, R. M. M. & Garcia-Pichel, F. Long-term compositional changes after transplant in a microbial mat cyanobacterial community revealed using a polyphasic approach. Environ. Microbiol. 3, 53–62 (2001).CAS 

Google Scholar 
Al-Najjar, M. A. A., de Beer, D., Kühl, M. & Polerecky, L. Light utilization efficiency in photosynthetic microbial mats. Environ. Microbiol. 14, 982–992 (2012).CAS 

Google Scholar 
Heisterkamp, I. M., Kamp, A., Schramm, A. T., de Beer, D. & Stief, P. Indirect control of the intracellular nitrate pool of intertidal sediment by the polychaete Hediste diversicolor. Mar. Ecol. Prog. Ser. 445, 181–192 (2012).
Google Scholar 
García-Robledo, E., Corzo, A. & Papaspyrou, S. A fast and direct spectrophotometric method for the sequential determination of nitrate and nitrite at low concentrations in small volumes. Mar. Chem. 162, 30–36 (2014).
Google Scholar 
Grasshoff, K. In Methods of Seawater Analysis (eds Grasshoff, K., Ehrhardt, M., Kremling, K.) 143–150 (Verlag Chemie Weinheim, 1983).Braman, R. S. & Hendrix, S. A. Nanogram nitrite and nitrate determination in environmental and biological materials by vanadium(III) reduction with chemiluminescence detection. Anal. Chem. 61, 2715–2718 (1989).CAS 

Google Scholar 
Meier, D. V. et al. Limitation of microbial processes at saturation-level salinities in a microbial mat covering a coastal salt flat. Appl. Environ. Microbiol. 87, e00698–21 (2021).CAS 

Google Scholar 
Sode, K., Horikoshi, K., Takeyama, H., Nakamura, N. & Matsunaga, T. Online monitoring or marine cyanobacterial cultivation based on phycocyanin fluorescence. J. Biotechnol. 21, 209–217 (1991).CAS 

Google Scholar 
Berns, D. S., Scott, E. & Oreilly, K. T. C-phycocyanin-minimum molecular weight. Science 145, 1054–1055 (1964).CAS 

Google Scholar 
Hillebrand, H., Durselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).
Google Scholar 
Zimmermann, J., Jahn, R. & Gemeinholzer, B. Barcoding diatoms: evaluation of the V4 subregion on the 18S rRNA gene, including new primers and protocols. Org. Divers. Evol. 11, 173–192 (2011).
Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 

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

Google Scholar 
Round, F. E., Crawford, R. M. & Mann, D. G. The Diatoms: Biology and Morphology of the Genera. 747p (Cambridge University Press, 1990).Medlin, L. K. Evolution of the diatoms: major steps in their evolution and a review of the supporting molecular and morphological evidence. Phycologia 55, 79–103 (2016).CAS 

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

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer Verlag, 2016).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).Stief, P. Intracellular Nitrate Storage by Diatoms-Source data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.19790176.v1 (2022). More

Gabor transformThe Gabor transform has rarely been used as a feature in a landscape classification OBIA approach but has been used in other OBIA processes such as fingerprint enhancement and human iris detection and for data dimensionality reduction24,29,30,31,32,33,34,35. Gabor filters are a bandpass filter applied to an image to identify texture. The different Gabor bandpass filters mathematically model the visual cortical cells of mammalian brains and thus is expected to improve segmentation and classification accuracy when compared to a human delineated and classified image26,27.Samiappan et al.36 compared Gabor filters to other texture features (grey-level co-occurrence matrix, segmentation-based fractal texture analysis, and wavelet texture analysis) within the GEOBIA process, of a wetland, using sub-meter resolution multispectral imagery. These Gabor filters performed comparably, in overall classification accuracy and Kappa coefficients, with other texture features. However, they were still outperformed by all other texture features. This study did not use any other data for analysis for determining the performance of Gabor filters when paired with data sources such as spectral, NDVI, or LiDAR36,37. Wang et al.38 paired a Gabor transformation with a fast Fourier transformation for edge detection on an urban landscape image that contained uniform textures with promising results. Su30 used the textural attributes derived from Gabor filters for classification but had similar results to Samiappan et al.36 where they found that Gabor features were one of the least useful/influential that contributed to the classification of a mostly agricultural landscape.Gabor filters are a Fourier influenced wavelet transformation, or bandpass filter, that identifies texture as intervals in a 2-D Gaussian modulated sinusoidal wave. This modulation differentiates the Gabor transform from the Fourier transform23,26. These Gabor transformed wavelets are parameterized by the angle at which they alter the image and the frequency of the wavelet. Rather than smoothing an image at the cost of losing detail through Fourier transforms or median filters, Gabor transformed images identify the repeated pattern of localized pixels and gives them similar values if they are a part of the same repeated sequence. Gabor features can closely emulate the visual cortex of mammalian brains that utilize texture to identify objects26,27. This is based on the evaluation of neurons associated with the cortical vertex that respond to different images or light profiles39. Marcelja27 identified that cortical cells responded to signals that are localized frequencies of light like what is represented by the Gabor transformations. Within the frequency domain, the Gabor transform can be defined by Eq. (1):$$Gleft(u, v;f, theta right)= {e}^{-frac{{pi }^{2}}{{f}^{2}} ({gamma }^{2}({u}^{{prime}}-f{)}^{2}+{n}^{2}{v}^{{{prime}}2})}$$
(1)

where (f) is the user-determined frequency (or wavelength); (theta) is the user-determined orientation at which the wavelet is applied to the image; (gamma) and (n) are the standard deviations of the Gaussian function in either direction23,38. These parameters define the shape of the band pass filter and determines its effect on one-dimensional signals. Daugman26, created a 2-D application of this filter in Eq. (2);$$gleft(u,vright)= {e}^{-{pi }^{2}/{f}^{2}[{gamma }^{2}{left({u}^{{prime}}-fright)}^{2}+{n}^{2}{{v}^{{prime}}}^{2}]}$$
(2)

where u’ = ucos − vsin θ θ and v’ = usin − vcos θ.In order to implement Gabor filters on multi-band spectral images, we used Matlab’s Gabor feature on the University of Iowa’s Neon high performance computer (HPC)40 which has up to 512 GB of RAM, which was necessary for processing these images. The first implementation of Gabor filters was performed on a 1610 × 687 single band pixel array (a small subset of the study area), a filter bank of 4 orientations and 8 wavelengths, on a 32 GB RAM computer, and took approximately 8 h to complete. Filter banks are a set of Gabor filters with different parameters that is applied to the spectral image and are required to identify different textures with different orientations and frequencies. By lowering the number of wavelengths from 8 to 4 on an 8128 × 8128 single band pixel array on the same machine 32 GB RAM, the processing was reduced to an hour. Using the HPC, this was further reduced to approximately 90 s using the same filter bank. Before implementing on the HPC, the original spectral image was divided into manageable subsets with overlap in order to prevent ‘edge-effect.’ These images were converted to greyscale by averaging values across all three bands33. When wavelengths become too long, they no longer attribute the textural information desired from the image and therefore add unnecessary computing time. The wavelengths that were used for the filter bank were selected as increasing powers of two starting from 2.82842712475 ((24/sqrt{2})) up to the pixel length of the hypotenuse of the input image. From this, we used only 2.82842712475, 7.0710678, 17.6776695, and 44.19417382. The directional orientation was selected as 45° intervals, from 0 to 180: 0, 45, 90, 135. These parameters were based on the reasoning outlined within Jain and Farrokhina25. More directional orientations could have been included but four were used for computational efficiency. The radial frequencies were selected so that they could capture the different texture in the landscape represented by consistent changes in pixels values within each landcover class. When frequencies are too wide or fine of a width they no longer represent the textures of the different landcover classes and thus are not included. This selection of filter bank parameters are similar or the same as other studies that look into the use of Gabor features for OBIA25,30,31.From the different combinations of parameters (four directions and four frequencies) in the Gabor Transform filter bank, sixteen magnitude response images were created from the converted greyscale three band average image. To limit high local variance within the output Gabor texture images, a Gaussian filter was applied. The magnitude response values were normalized across the 16 different bands so that a Principal Component Analysis (PCA) could be applied. The first principal component of the PCA, from these Gabor transformed images, was used for this study since it limits the computation time to process 16 separate Gabor features, in addition to the other data sources, while still retaining the most amount of information from the different Gabor response features. The Gabor band that was used for this study can be viewed in Fig. 2.Figure 2Gabor transformation. Gabor transformed image of study area derived from original image using the first principal component of all gabor outputs using the filter bank parameters. Software: ArcMap (10.x).Full size imageSegmentationFor this study, we used the watershed algorithm for the segmentation of GEOBIA, implemented by ENVI version 5.0 Feature Extraction tool, due to its ubiquitous use within GEOBIA, its ability to create a hierarchy of segmented objects, and support within the literature as a reliable algorithm37,41,39,43. The watershed algorithm can either use a gradient image or intensity image for segmentation. Based on the observed results, this study used the intensity method. The intensity method averages the value of pixels across bands. Scale, a user-defined parameter, is selected to identify the threshold that decides if a given intensity value within the gradient image can be a boundary. This allows the user to decide the size of the objects created. A secondary, user-defined, parameter defines how similar, adjacent, objects need to be before they are combined or merged. The user arbitrarily selects the parameter value based on how it reduces both under and over segmentation. The parameters selected for this study were visually chosen based on a compromise between over and under segmentation relative to the hand demarcated objects.The merging of two separate objects was based on the full lambda schedule where the user selects a merging threshold ({t}_{i, j}) which is defined by Eq. (3):$${t}_{i, j}= frac{frac{left|{O}_{i}right|cdot left|{O}_{j}right|}{left|{O}_{i}right|+ left|{O}_{j}right|}cdot {Vert {u}_{i}-{u}_{j}Vert }^{2}}{mathrm{length}(mathrm{vartheta }left({O}_{i},{O}_{j}right))}$$
(3)

where ({O}_{i}) is the object of the image, (left|{O}_{i}right|) is the area of (i), ({u}_{i}) is the average of object (i), ({u}_{j}) is the average of object (j), (Vert {u}_{i}-{u}_{j}Vert) is the Euclidean distance between the average values of the pixel values in regions (i) and (j), and (mathrm{length}left(mathrm{vartheta }left({O}_{i},{O}_{j}right)right)) is the length of the shared boundary of ({O}_{i}) and ({O}_{j}).To compare the segmentation of a riparian landscape, with and without Gabor features, we conducted segmentation on two separate sets of data. One dataset was a normalized stacked layer of NDVI and CHM (see Fig. 3) with the original multispectral image used as ancillary data; the other dataset differed only by the inclusion of the Gabor feature. For both instances, the bands were converted to an intensity image by averaging across bands rather than being converted into a gradient image for segmentation. The dataset that included the Gabor features had a scale parameter set at 30 with merge settings at 95 and 95.7 for the sub and super-objects, respectively. The dataset that did not include the Gabor features had a scale parameter of 10 with merge settings at 95.6 and 98.5 for the sub and super-objects, respectively. This resulted in the creation of 87,198 and 62,905 segments for the sub and super objects, respectively, that were created when the Gabor feature was included. 191,050 and 51,664 segments were created for the sub and super objects when the Gabor features, respectively, were not included within the segmentation process. As you will see in the next section, these segments also represent the number of training data that will be included within the supervised classification.Figure 3CHM and NDVI. LiDAR derived canopy height model (top) and normalized difference vegetation index derived from original spectral image. Software: ArcMap (10.x).Full size imageTo create a hierarchy of land cover classes, two sets of segmentation parameters needed to be selected for each dataset. One set of parameters would be used for the sub-objects within the hierarchy and the other set would be used to create super-objects. All parameters used the intensity and full lambda schedule algorithms for the watershed method. The only setting that changed between the sub and super-objects, for either dataset, was the merge parameter which helped maintain similar boundaries as much as possible. Despite this, boundaries could moderately change due to the Euclidean distance, between the pixel values of (i) and (j), changing from the merging of objects; causing ({t}_{i, j}) to cross the threshold which results in a new boundary being drawn. A representation of these results can be viewed and visually compared to the hand demarcated objects in Fig. 4.Figure 4Automated and manual segmented comparison. Juxtaposition of hand delineated, sub-objects, and super-objects for segments generated using the Gabor features. Software: ArcMap (10.x).Full size imageTraining dataThe training data, used for this study, is the transfer of class attributes from hand demarcated and classified segments to automatically segmented objects based on the majority overlap of the hand demarcated segments. Experts identified them using two different classification schemes referenced from the General Wetland Vegetation Classification System44. The 7-class scheme within this system identified objects of either being forest, marsh, agriculture, developed, open water, grass/forbs, or sand/mud. The 13-class scheme identified objects of either being agriculture, developed, grass/forbs, open water, road/levee, sand/mud, scrub-shrub, shallow marsh, submerged aquatic vegetation, upland forest, wet forest, wet meadow, and wet shrub. Not every class from the 7-class scheme will have a sub-class (i.e. developed, open water) but some do for example wet and upland forest are sub-objects of the forest class and wet meadow and shallow marsh are sub-objects of marsh. Figure 5 visually illustrates both classification schemes across the study area.Figure 5Hand delineated objects of both scales. Software: ArcMap (10.x).Full size imageENVI’s feature extraction tool calculates several landscape, spectral, and textural metrics. These attributes were used for each random forest classifier. The Gabor and Hierarchical features will be included selectively to be able to compare their contributions to the (out-of-bag) OOB classification errors. When Gabor features are included within the classification, they are computed the same way as the other image bands.Random forestThe random forest classifier was implemented in R using the random forest module45. The number of trees, that were randomly generated, was large enough (n = 250) to where the Strong law of large numbers would take effect as indicated by the decrease in the change of accuracy. The default number of variables randomly sampled as candidates at each split variable (mtry parameter) was the total number of variables divided by 3 for each dataset. R also generates two separate variable indices: mean decrease in accuracy and mean decrease Gini. Mean decrease in accuracy refers to the accuracy change in the random forest when a single variable is left out. This is a practical metric to determine the usefulness of a variable. The Gini index measures the purity change within a dataset when it is split based upon a given variable within a decision tree.The random forest classification accuracy will be based on the OOB error. The random forest algorithm trains numerous decision trees on random subsets of the training set leaving out a number of training samples when training each decision tree. The samples that are left out of each decision tree are then classified by the decision tree that they were not included within during the training step. The OOB error is the average error of each predicted bootstrapped sample across the ensemble of decision trees within the random forest algorithm.Figure 6 illustrates how the Gabor and hierarchal features were included within the classification of the super and sub-objects.Figure 6Classification procedure. Schematic flow chart illustrating how the Gabor and hierarchal features were included within the classification of the super and sub-objects. OOB classification error included in parenthesis.Full size imageHierarchical schemeTo attribute the hierarchical structure to the sub-objects, we first classified the larger segments that were created with and without the Gabor features using the broader 7-class scheme. These classified super objects were then converted to raster to calculate the majority overlap with the smaller sub-objects. This gave the sub-objects an attribute, the broader 7-class scheme, that could be used to contribute to the classification of the sub-objects with the finer 13-class scheme. This builds the hierarchical relationship between the two class schemes into the supervised classification of the sub-objects. Figure 6 illustrates how the hierarchal structure was included within two of the four sub-object’s list of features used within classification. This methodological approach aligns with O’Neill et al.21 landscape ecology principle that a super-object’s class could be a useful property in defining or predicting a sub-object. This is also different than the more common rule-based approach of iteratively classifying the landscape into smaller and smaller sub-classes22.Segmentation assessmentMost studies rely upon the accuracy assessment of their classifiers to provide support for their analysis results. However, this does not provide evidence whether a new data fusion technique improves the ability to delineate objects of interest within an image. To assess the performance of our segmented polygons, this study evaluated the segments created with and without the Gabor feature using a method highlighted in Xiao et al.37.Our segmentation results were evaluated using an empirical discrepancy measure, used frequently in image segmentation evaluation37,46,47. Discrepancy measures utilize ground truth images that represent the “correct” delineated/classified image to compare the semi-automated image results. In our study, the objects that were delineated and classified by experts from the U.S. Fish and Wildlife Service, were used as training data for our random forest classifier and as ground truth for the discrepancy measure. The discrepancy measure used the percentage of right segmented pixels (PR) in the whole image. To calculate PR, we converted the classified segmented and ground truth polygons to raster and measured the ratio of incorrect pixels to total amount of pixels which was converted to a percentage.Additionally, landscape metrics were calculated using FRAGSTATS48, an open source program commonly used for calculating landscape metrics. FRAGSTATS computed these metrics from thematic raster maps that represent the land cover types of interest. These thematic classes, used for analysis, were the classified objects at both the super and sub-object level. Since we are not attempting to compare the segmentation results for any specific class or area, we calculated metrics on a landscape level. Landscape metrics will represent the segmentation patterns for the entire study area.FRAGSTATS can calculate various metrics representing different aspects of the landscape. The metrics for analysis attempts to understand object geometry. The metrics calculated, for these analyses, were the average and standard deviation for the area (AREA), the fractal dimension index (FRAC), and the perimeter area ratio (PARA). The number of patches (NP) was also included in each result. To take a more landscape centric approach, the area weighted mean was chosen over a simple average. More

Nagy, K. A. Field metabolic rate and food requirement scaling in mammals and birds. Ecol. Monogr. 57, 111–128 (1987).Article 

Google Scholar 
Anderson, K. J. & Jetz, W. The broad-scale ecology of energy expenditure of endotherms. Ecol. Lett. 8, 310–318 (2005).Article 

Google Scholar 
Terrien, J., Perret, M. & Aujard, F. Behavioral thermoregulation in mammals: A review. Front. Biosci. 16, 1428–1444 (2011).Article 

Google Scholar 
Mery, F. & Burns, J. G. Behavioural plasticity: An interaction between evolution and experience. Evol. Ecol. 24, 571–583 (2010).Article 

Google Scholar 
Brockmann, H. J. The evolution of alternative strategies and tactics. Adv. Study Behav. 30, 1–51 (2001).Article 

Google Scholar 
Milling, C. R., Rachlow, J. L., Johnson, T. R., Forbey, J. S. & Shipley, L. A. Seasonal variation in behavioral thermoregulation and predator avoidance in a small mammal. Behav. Ecol. 28, 1236–1247 (2017).Article 

Google Scholar 
Guiden, P. W. & Orrock, J. L. Seasonal shifts in activity timing reduce heat loss of small mammals during winter. Anim. Behav. 164, 181–192 (2020).Article 

Google Scholar 
Cotton, C. L. & Parker, K. L. Winter activity patterns of northern flying squirrels in sub-boreal forests. Can. J. Zool. 78, 1896–1901 (2000).Article 

Google Scholar 
Long, R. A., Martin, T. J. & Barnes, B. M. Body temperature and activity patterns in free-living arctic ground squirrels. J. Mammal. 86, 314–322 (2005).Article 

Google Scholar 
Zschille, J., Stier, N. & Roth, M. Gender differences in activity patterns of American mink Neovison vison in Germany. Eur. J. Wildl. Res. 56, 187–194 (2010).Article 

Google Scholar 
Geiser, F. Hibernation. Curr. Biol. 23, R188–R193 (2013).CAS 
PubMed 
Article 

Google Scholar 
Gür, M. K. & Gür, H. Age and sex differences in hibernation patterns in free-living Anatolian ground squirrels. Mamm. Biol. 80, 265–272 (2015).Article 

Google Scholar 
Kisser, B. & Goodwin, H. T. Hibernation and overwinter body temperatures in free-ranging thirteen-lined ground squirrels, Ictidomys tridecemlineatus. Am. Midl. Nat. 167, 396–409 (2012).Article 

Google Scholar 
Dmi’el, R. & Schwarz, M. Hibernation patterns and energy expenditure in hedgehogs from semi-arid and temperate habitats. J. Comp. Physiol. B 155, 117–123 (1984).Article 

Google Scholar 
Abu Baker, M. A. et al. Caught basking in the winter sun: Preliminary data on winter thermoregulation in the Ethiopian hedgehog, Paraechinus aethiopicus in Qatar. J. Arid Environ. 125, 52–55 (2016).ADS 
Article 

Google Scholar 
McKechnie, A. E. & Mzilikazi, N. Heterothermy in Afrotropical mammals and birds: A review. Integr. Comp. Biol. 51, 349–363 (2011).PubMed 
Article 

Google Scholar 
Wacker, C. B., McAllan, B. M., Körtner, G. & Geiser, F. The role of basking in the development of endothermy and torpor in a marsupial. J. Comp. Physiol. B 187, 1029–1038 (2017).PubMed 
Article 

Google Scholar 
Brown, K. J. & Downs, C. T. Basking behaviour in the rock hyrax (Procavia capensis) during winter. Afr. Zool. 42, 70–79 (2007).Article 

Google Scholar 
Humphries, M. M., Thomas, D. W. & Kramer, D. L. The role of energy availability in mammalian hibernation: A cost-benefit approach. Physiol. Biochem. Zool. 76, 165–179 (2003).PubMed 
Article 

Google Scholar 
Field, K. A. et al. Effect of torpor on host transcriptomic responses to a fungal pathogen in hibernating bats. Mol. Ecol. 27, 3727–3743 (2018).CAS 
Article 

Google Scholar 
Bieber, C., Cornils, J. S., Hoelzl, F., Giroud, S. & Ruf, T. The costs of locomotor activity? Maximum body temperatures and the use of torpor during the active season in edible dormice. J. Comp. Physiol. B 187, 803–814 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Eto, T. et al. Individual variation of daily torpor and body mass change during winter in the large Japanese field mouse (Apodemus speciosus). J. Comp. Physiol. B 188, 1005–1014 (2018).PubMed 
Article 

Google Scholar 
Zervanos, S. M., Maher, C. R. & Florant, G. L. Effect of body mass on hibernation strategies of woodchucks (Marmota monax). (2014).Ford, R. G. Home range in a patchy environment: Optimal foraging predictions. Am. Zool. 23, 315–326 (1983).Article 

Google Scholar 
Czenze, Z. J. & Willis, C. K. R. Warming up and shipping out: Arousal and emergence timing in hibernating little brown bats (Myotis lucifugus). J. Comp. Physiol. B 185, 575–586 (2015).PubMed 
Article 

Google Scholar 
Batavia, M., Nguyen, G., Harman, K. & Zucker, I. Hibernation patterns of Turkish hamsters: Influence of sex and ambient temperature. J. Comp. Physiol. B 183, 269–277 (2013).PubMed 
Article 

Google Scholar 
Kato, G. A. et al. Individual differences in torpor expression in adult mice are related to relative birth mass. J. Exp. Biol. 221, jeb171983 (2018).PubMed 
Article 

Google Scholar 
Williams, C. T. et al. Sex-dependent phenological plasticity in an arctic hibernator. Am. Nat. 190, 854–859 (2017).PubMed 
Article 

Google Scholar 
Healy, J. E., Burdett, K. A., Buck, C. L. & Florant, G. L. Sex differences in torpor patterns during natural hibernation in golden-mantled ground squirrels (Callospermophilus lateralis). J. Mammal. 93, 751–758 (2012).Article 

Google Scholar 
Wang, Y., Yuan, L.-L., Peng, X., Wang, Y. & Yang, M. Experimental study on hibernation patterns in different ages and sexes of daurian ground squirrel (Spermophilus Dauricus). Shenyang Shifan Daxue Xuebao (Ziran Kexue Ban) 27, 351–355 (2009).
Google Scholar 
Siutz, C., Franceschini, C. & Millesi, E. Sex and age differences in hibernation patterns of common hamsters: Adult females hibernate for shorter periods than males. J. Comp. Physiol. B 186, 801–811 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Michener, G. R. Sexual differences in over-winter torpor patterns of Richardson’s ground squirrels in natural hibernacula. Oecologia 89, 397–406 (1992).ADS 
PubMed 
Article 

Google Scholar 
Boyles, J. G., Bennett, N. C., Mohammed, O. B. & Alagaili, A. N. Torpor patterns in Desert Hedgehogs (Paraechinus aethiopicus) represent another new point along a thermoregulatory continuum. Physiol. Biochem. Zool. 90, 445–452 (2017).PubMed 
Article 

Google Scholar 
Reeve, N. Hedgehogs (Poyser, 1994).
Google Scholar 
He, K. et al. An estimation of erinaceidae phylogeny: A combined analysis approach. PLoS One 7, e39304 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schoenfeld, M. & Yom-Tov, Y. The biology of two species of hedgehogs, Erinaceus europaeus concolor and Hemiechinus auritus aegyptius, Israel. Mammalia 49, 339–356 (1985).Article 

Google Scholar 
Haigh, A., O’Riordan, R. M. & Butler, F. Nesting behaviour and seasonal body mass changes in a rural Irish population of the Western hedgehog (Erinaceus europaeus). Acta Theriol. (Warsz) 57, 321–331 (2012).Article 

Google Scholar 
Rasmussen, S. L., Berg, T. B., Dabelsteen, T. & Jones, O. R. The ecology of suburban juvenile European hedgehogs (Erinaceus europaeus) in Denmark. Ecol. Evol. 9, 13174–13187 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Jensen, A. B. Overwintering of European hedgehogs (Erinaceus europaeus) in a Danish rural area. Acta Theriol. (Warsz) 49, 145–155 (2004).Article 

Google Scholar 
Jackson, D. B. The breeding biology of introduced hedgehogs (Erinaceus europaeus) on a Scottish Island: Lessons for population control and bird conservation. J. Zool. 268, 303–314 (2006).Article 

Google Scholar 
Rautio, A., Valtonen, A., Auttila, M. & Kunnasranta, M. Nesting patterns of European hedgehogs (Erinaceus europaeus) under northern conditions. Acta Theriol. (Warsz) 59, 173–181 (2014).Article 

Google Scholar 
Hallam, S. L. & Mzilikazi, N. Heterothermy in the southern African hedgehog, Atelerix frontalis. J. Comp. Physiol. B 181, 437–445 (2011).PubMed 
Article 

Google Scholar 
South, K. E., Haynes, K. & Jackson, A. C. Hibernation Patterns of the European Hedgehog, Erinaceus europaeus, at a Cornish Rescue Centre. Animals 10, 1418 (2020).PubMed Central 
Article 

Google Scholar 
Gillies, A. C., Ellison, G. T. H. & Skinner, J. D. The effect of seasonal food restriction on activity, metabolism and torpor in the South African hedgehog (Atelerix frontalis). J. Zool. 223, 117–130 (1991).Article 

Google Scholar 
Gazzard, A. & Baker, P. J. Patterns of feeding by householders affect activity of hedgehogs (Erinaceus europaeus) during the hibernation period. Animals 10, 1344 (2020).PubMed Central 
Article 

Google Scholar 
Dmiel, R. & Schwarz, M. Hibernation patterns and energy expenditure in hedgehogs from semi-arid and temperate habitats. J. Comp. Physiol. B 155, 117–123 (1984).Article 

Google Scholar 
Fowler, P. A. & Racey, P. A. Daily and seasonal cycles of body temperature and aspects of heterothermy in the hedgehog Erinaceus europaeus. J. Comp. Physiol. B 160, 299–307 (1990).CAS 
PubMed 
Article 

Google Scholar 
Rutovskaya, M. V. et al. The dynamics of body temperature of the Eastern European hedgehog (Erinaceus roumanicus) during winter hibernation. Biol. Bull. 46, 1136–1145 (2019).Article 

Google Scholar 
Harrison, D. L. & Bates, P. J. J. The Mammals of Arabia Vol 354 (Harrison Zoological Museum Sevenoaks, 1991).
Google Scholar 
Al-Musfir, H. M. & Yamaguchi, N. Timings of hibernation and breeding of Ethiopian Hedgehogs, Paraechinus aethiopicus in Qatar. Zool. Middle East 45, 3–10 (2008).Article 

Google Scholar 
Pettett, C. E., Al-Hajri, A., Al-Jabiry, H., Macdonald, D. W. & Yamaguchi, N. A comparison of the Ranging behaviour and habitat use of the Ethiopian hedgehog (Paraechinus aethiopicus) in Qatar with hedgehog taxa from temperate environments. Sci. Rep. 8, 1–10 (2018).Article 
CAS 

Google Scholar 
Abu Baker, M. A., Reeve, N., Conkey, A. A. T., Macdonald, D. W. & Yamaguchi, N. Hedgehogs on the move: Testing the effects of land use change on home range size and movement patterns of free-ranging Ethiopian hedgehogs. PLoS One 12, e0180826 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Yamaguchi, N., Al-Hajri, A. & Al-Jabiri, H. Timing of breeding in free-ranging Ethiopian hedgehogs, Paraechinus aethiopicus, from Qatar. J. Arid Environ. 99, 1–4 (2013).ADS 
Article 

Google Scholar 
Alagaili, A. N., Bennett, N. C., Mohammed, O. B. & Hart, D. W. The reproductive biology of the Ethiopian hedgehog, Paraechinus aethiopicus, from central Saudi Arabia: The role of rainfall and temperature. J. Arid Environ. 145, 1–9 (2017).ADS 
Article 

Google Scholar 
Pettett, C. E. et al. Daily energy expenditure in the face of predation: Hedgehog energetics in rural landscapes. J. Exp. Biol. 220, 460–468 (2017).PubMed 
Article 

Google Scholar 
Kraus, C., Eberle, M. & Kappeler, P. M. The costs of risky male behaviour: Sex differences in seasonal survival in a small sexually monomorphic primate. Proc. R. Soc. B Biol. Sci. 275, 1635–1644 (2008).Article 

Google Scholar 
Mzilikazi, N. & Lovegrove, B. G. Reproductive activity influences thermoregulation and torpor in pouched mice, Saccostomus campestris. J. Comp. Physiol. B 172, 7–16 (2002).PubMed 
Article 

Google Scholar 
Richter, M. M., Barnes, B. M., O’reilly, K. M., Fenn, A. M. & Buck, C. L. The influence of androgens on hibernation phenology of free-living male arctic ground squirrels. Horm. Behav. 89, 92–97 (2017).CAS 
PubMed 
Article 

Google Scholar 
Haigh, A., Butler, F. & O’Riordan, R. M. Courtship behaviour of western hedgehogs (Erinaceus europaeus) in a rural landscape in Ireland and the first appearance of offspring. Lutra 55, 41–54 (2012).
Google Scholar 
Nicol, S. C., Morrow, G. E. & Harris, R. L. Energetics meets sexual conflict: The phenology of hibernation in Tasmanian echidnas. Funct. Ecol. 33, 2150–2160 (2019).Article 

Google Scholar 
Pettett, C. W., Macdonald, D., Al-Hajiri, A., Al-Jabiry, H. & Yamaguchi, N. Characteristics and demography of a free-ranging Ethiopian Hedgehog, Paraechinus aethiopicus, population in Qatar. Animals 10, 951 (2020).PubMed Central 
Article 

Google Scholar 
Kenward, R. E. A Manual for Wildlife Radio Tagging (Academic Press, 2000).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2021).
Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).
Google Scholar  More

Ge, J., Peng, W., Wei, H. W., Qu, X. & Singh, S. Quantitative assessment of flow regime alteration using a revised range of variability methods. Water 10(5), 597 (2018).Article 

Google Scholar 
Latrubesse, E. M. et al. Damming the rivers of the Amazon basin. Nature 546(7658), 363–369 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Meade, R. H. & Moody, J. A. Causes for the decline of suspended-sediment discharge in the Mississippi River system, 1940–2007. Hydrol. Process 24(1), 35–49 (2010).
Google Scholar 
Fathi, M. M., Awadallah, A. G., Abdelbaki, A. M. & Haggag, M. A new Budyko framework extension using time series SARIMAX model. J. Hydrol. 570(2019), 827–838 (2019).ADS 
Article 

Google Scholar 
Wang, H., Liu, J. & Guo, W. The variation and attribution analysis of the runoff and sediment in the lower reach of the Yellow River during the past 60 years. Water Supply 21(6), 3193–3209 (2021).Article 

Google Scholar 
Guo, S. L., Guo, J., Hou, Y., Xiong, L. & Hong, X. Prediction of future runoff change based on Budyko hypothesis in Yangtze River basin. Adv. Water Sci. 26(02), 151–160 (2015).
Google Scholar 
Zhang, X., Dong, Z., Gupta, H., Wu, G. & Li, D. Impact of the three gorges dam on the hydrology and ecology of the Yangtze River. Water 590(8), 1–18 (2016).ADS 
CAS 

Google Scholar 
Zhang, J., Zhang, M., Song, Y. & Lai, Y. Hydrological simulation of the Jialing River Basin using the MIKE SHE model in changing climate. J. Water Clim. Change 12(6), 1–20 (2021).
Google Scholar 
Richter, B. D., Baumgartner, J. V., Powell, J. & Braun, P. D. A method for assessing hydrologic alteration within ecosystems. Conserv. Biol. 10(4), 1163–1174 (1996).Article 

Google Scholar 
Richter, B. D., Baumgartner, J. V., Wigington, B. & Braun, D. How much water does a river need?. Freshw. Biol. 37(1), 231–249 (1997).Article 

Google Scholar 
Richter, B. D., Baumgartner, J. V., Braun, D. P. & Powell, J. A spatial assessment of hydrologic alteration within a river network. Regul. River Res. Manag. 14(4), 329–340 (1998).Article 

Google Scholar 
Guo, W., Xu, G., Shao, J., Bing, J. & Chen, X. Research on the middle and lower reaches of the Yangtze River and lake’s hydrological alterations based on RVA. In IOP Conference Series: Earth and Environmental Science Vol 153, No 6, 062047.1–062047.8 (2018).Guo, W., Li, Y., Wang, H. & Zha, H. Assessment of eco-hydrological regime of lower reaches of Three Gorges Reservoir based on IHA-RVA. Resour. Environ. Yangtze Basin 27(09), 2014–2021 (2018).
Google Scholar 
Zuo, Q. & Liang, S. Effects of dams on river flow regime based on IHA/RVA. Proc. Int. Assoc. Hydrol. Sci. 368(368), 275–276 (2015).
Google Scholar 
Mwedzi, T., Katiyo, L., Mugabe, F. T., Bere, T. & Kuoika, O. L. A spatial assessment of stream-flow characteristics and hydrologic alterations, post dam construction in the Manyame catchment, Zimbabwe. Water Sa 42(2), 194–202 (2016).CAS 
Article 

Google Scholar 
Liu, J., Chen, J., Xu, J., Lin, Y. & Zhou, M. Attribution of runoff variation in the headwaters of the Yangtze River based on the Budyko hypothesis. Int. J. Environ. Res. Public Health 16(14), 2506.1-2506.15 (2019).
Google Scholar 
Yan, D. Using budyko-type equations for separating the impacts of climate and vegetation change on runoff in the source area of the yellow river. Water 12(12), 3418.1-3418.15 (2020).ADS 

Google Scholar 
Gunkel, A. & Lange, J. Water scarcity, data scarcity and the Budyko curve—An application in the Lower Jordan River Basin. J. Hydrol. Reg. Stud. 12(C), 136–149 (2017).Article 

Google Scholar 
Fathi, M. M., Awadallah, A. G., Abdelbaki, A. M. & Haggag, M. A new Budyko framework extension using time series SARIMAX model. J. Hydrol. 570, 827–838 (2019).ADS 
Article 

Google Scholar 
Li, Y., Fan, J. & Liao, Y. Variation characteristics of streamflow and sediment in the Jialing river basin in the past 60 years. Mt. Res. 38(03), 339–348 (2020).
Google Scholar 
Liu, Y., Li, F. & Xu, X. Impacts of hydropower development on hydrological regime in mainstream of mid-lower Jialing River. Yangtze River 45(05), 10–15 (2014).
Google Scholar 
Zhou, Y. et al. Distinguishing the multiple controls on the decreased sediment flux in the Jialing River basin of the Yangtze River, Southwestern China. CATENA 193(C), 104593.1-104593.11 (2020).
Google Scholar 
Zeng, X. et al. Changes and relationships of climatic and hydrological droughts in the Jialing River Basin, China. PLoS ONE 10(11), e0141648 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Yan, M., Fang, G. H., Dai, L. H., Tan, Q. F. & Huang, X. F. Optimizing reservoir operation considering downstream ecological demands of water quantity and fluctuation based on IHA parameters. J. Hydrol. 4(2021), 126647 (2021).Article 

Google Scholar 
Wei, R., Liu, J., Zhang, T., Zeng, Q. & Dong, X. Attribution analysis of runoff variation in the upper-middle reaches of Yalong river. Resour. Environ. Yangtze Basin 29(07), 1643–1652 (2020).
Google Scholar 
Xie, J. H., Yu, J. H., Chem, H. S. & Hsu, P. C. Sources of subseasonal prediction skill for heatwaves over the Yangtze river basin revealed from three S2S models. Adv. Atmos. Sci. 37(12), 1435–1450 (2020).Article 

Google Scholar 
Guo, W., Li, Y., Wang, H. & Cha, H. Temporal variations and influencing factors of river runoff and sediment regimes in the Yangtze River, China. Desalin. Water Treat. 174(2020), 258–270 (2020).Article 

Google Scholar 
Tian, X. et al. Hydrologic alteration and possible underlying causes in the Wuding River, China. Sci. Total Environ. 693, 133556.1-133556.9 (2019).Article 
CAS 

Google Scholar 
Tang, B., Wang, W. C. & Fan, X. Study on the influence of reservoir dispatch of the upper Yangtze river on the runoff control. E3S Web Conf. 283(18), 01030 (2021).
Google Scholar 
Liu, Y. et al. Characteristics and resource status of main commercial fish in the middle reaches of Jialing River, China. J. Appl. Environ. Biol. 27(04), 837–847 (2021).
Google Scholar 
Sun, Z., Zhang, M. & Chen, Y. Protection of the rare and endemic fish in the conservation area located in the upstream of the Yangtze River. Freshw. Fish. 44(06), 3–8 (2014).
Google Scholar 
Chen, Q. H. et al. Impacts of climate change and LULC change on runoff in the Jinsha River Basin. J. Geogr. Sci. 30(01), 85–102 (2020).Article 

Google Scholar 
Cui, L., Wang, Z. & Deng, L. Vegetation dynamics based on NDVI in Yangtze River Basin (China) during 1982–2015. IOP Conf. Ser. Materials Sci. Eng. 780(2020), 062049 (2020).Article 

Google Scholar 
Wang, Y., Wang, S., Wu, M. & Wang, S. Impacts of the land use and climate changes on the hydrological characteristics of Jialing River Basin. Res. Soil Water Conserv. 26(01), 135–142 (2019).
Google Scholar 
Wu, Y. L. & Pu, H. W. Y. The influence of hydropower station on sand content detection in Jialing River. Technol. Dev. Enterp. 38(9), 55–58 (2019).
Google Scholar 
Zhuo, Z., Qian, Z., Jiang, H., Wang, H. & Guo, W. Evaluation of hydrological regime in Xiangjiang basin on IHA-RVA method. China Rural Water Hydropower 8(2020), 188–192 (2020).
Google Scholar 
Chen, L. et al. Temporal characteristics detection and attribution analysis of hydrological time-series variation in the seagoing river of southern China under environmental change. Acta Geophys. 66(5), 1151–1170 (2018).ADS 
Article 

Google Scholar 
Zhang, R., Liu, J., Mao, G. & Wang, L. Flow regime alterations of upper Heihe River based on improved RVA. Arid Zone Res. 38(01), 29–38 (2021).
Google Scholar 
Sun, Y. & Wang, X. Changes in runoff and driving force analysis in the key section of the Yellow River diversion project. J. Hydroecol. 41(06), 19–26 (2020).
Google Scholar 
Zhang, L., Dawes, W. R. & Walker, G. R. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resour. Res. 37(3), 701–708 (2001).ADS 
Article 

Google Scholar 
Fu, B. Calculation of soil evaporation. Acta Meteor. Sin. 02(1981), 226–236 (1981).
Google Scholar 
Liu, J., Zhang, Q., Singh, V. P. & Shi, P. Contribution of multiple climatic variables and human activities to streamflow changes across China. J. Hydrol. 545(2016), 145–162 (2016).
Google Scholar 
Yang, D., Zhang, S. & Xu, X. Attribution analysis for runoff decline in Yellow River Basin during past fifty years based on Budyko hypothesis. Sci. Sinica 45(10), 1024–1034 (2015).
Google Scholar 
Schreiber, P. Ber die Beziehungen zwischen dem Niederschlag und der Wasserführung der Flüsse in Mitteleuropa. Meteorol. Z. 21, 441–452 (1904).Budyko, M. Evaporation under Natural Conditions (Gidrometeorizdat, Leningrad, Russia, 1948).Pike, J. The estimation of annual run-off from meteorological data in a tropical climate. J. Hydrol. 2, 116–123 (1964).Ol’dekop, E. On evaporation from the surface of river basins. Trans. Meteorol. Obs. 4, 200 (1911). More

Land coverUrban landscapes are characterized by small clusters of patches, forming land mosaics that are distinct from natural landscapes. An accurate estimation of climate forcing requires a land cover dataset at high resolutions that does not omit small urban patches. In this study, the RF estimates are based on 500-m and 1-km land cover datasets. This fine resolution is necessary to preserve spatial details of small urban patches while avoiding the large underestimation of urban land areas at coarse resolution (e.g., ~19% underestimation at 10 km compared to that at 1 km)3. We used 500-m resolution MODIS Land Cover product (MCD12Q1v006) for historical land cover changes. For future urban land cover distributions, we used the global urban land expansion products simulated under the SSPs for 2030–2100 (i.e., Chen-2020)4. The simulation performance was tested using historical urban expansion from 2000 to 2015 based on Global Human Settlement Layer51, where the agreement between simulated and observed urban land was evaluated using the Figure of Merit (FoM) indicator52 that has showed similar or better values than those reported in other existing land simulation applications4. The high-resolution Chen-2020 also shows very high spatial consistency with the prominent coarse resolution global urban land projection LUH2 that is recommended in CMIP64. Considering different scenarios is also necessary to account for the uncertainties of future socioeconomic and environmental conditions, so we included simulated urban lands under three scenarios (Supplementary Table 1): Sustainability -SSP1, Middle of the Road – SSP2, and Fossil-fueled Development – SSP553. Within each SSP scenario, the product provides a likelihood map of each grid becoming urban, based on 100 urbanization simulations. We used the likelihood map to account for spatial uncertainties of urban expansion by deriving 90% confidence intervals of projected urban land demand within a SSP scenario. We used the MODIS IGBP Land Cover classes (Supplementary Table 2) and resampled the original 500-m resolution MODIS products in 2018 to 1-km resolution to match the future simulations when it was used as a baseline year. To isolate the independent effect of urbanization (vs other types of land uses) in future estimates, land covers that are not converted to urban are assumed to have the same cover types as in 2018 (i.e., the baseline year). Though there are other global land cover products for current periods, we choose the MODIS IGBP land cover products because the albedo look-up maps (LUMs) were based on IGBP land cover types (see Albedo Look-Up Maps).To further evaluate the uncertainties caused by different projections of future urbanization, we also included the other two SSPs from Chen-2020, and another two 1-km resolution urban land cover products projected for the future for the purpose of comparison. The other two products include four projections of SRES scenarios (i.e., A1, B1, A1B, and B2) (i.e., Li-2017 mentioned above)3 and one without scenario description but assumed historical development would continue (i.e., Zhou-2019 mentioned above)2. These projections of future urban land expansion were calibrated with different historical urban land products and can be regarded as independent.Albedo look-up maps (LUMs)Albedo Look-Up Maps (LUMs)31 were derived from the intersection of MODIS land cover54 and surface albedo55 products, which are used to determine the albedo values for each IGBP land cover type by month and by location. Monthly means of white-sky (i.e., diffuse surface illumination condition) and black sky (i.e., direct surface illumination condition) during 2001–2011 were processed for snow-free and snow-covered periods for each of the 17 IGBP land cover classes at spatial resolutions of 0.05°−1°31. The LUMs have been verified by comparing the reconstructed albedo using the LUMs with the original MODIS albedo, which shows very similar values31. We used the LUMs at a resolution of 1° due to the significantly fewer missing values, to assure the spatial continuity of albedo changes at a global scale while keeping the matches with the 1° resolution of radiation data and RF kernels. The underlying assumption is that albedo of the same land cover type varies insignificantly within a 1° grid.Snow and radiation productSnow cover can significantly change the albedo of land regardless of cover types (Supplementary Fig. 4). In this study, we tally monthly albedo using snow-free and snow-covered categories in estimating RF. Past and present snow-free and snow-covered conditions were derived from level 3 MODIS/Terra Snow Cover (MOD10CM.006)56 at 0.05° spatial resolution and resampled to a 1° spatial resolution. Monthly means of 2001–2005 vs 2015–2019 were used for 2001 and 2018 respectively. For future periods, ensemble mean snow cover for each year and month, projected under the CMIP5 framework for three Representative Concentration Pathway (RCP) scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) were used (for more details see Supplementary Note 2B). By comparing the model outputs with MODIS observations for a recent decade (2006–2015), we found that the multi-model mean snow cover was systematically biased compared to MODIS observations. Consequently, we calibrated the ensemble mean projections by subtracting the biases for the grids. In each 10th year of the future (e.g., 2030, 2040, etc.), the decadal monthly mean snow cover (e.g., 2026–2035 for 2030, and 2036–2045 for 2040, etc.) was used for the year.We used the long-term monthly averages (1981–2010) of diffuse and direct incoming surface solar radiation reanalysis Gaussian grid product from National Centers for Environmental Prediction (NCEP)57. Visible and near infrared beam downward radiation and diffuse downward radiation from NCEP were used to compute the white-sky and black-sky fractions. As for snow cover, ensemble mean shortwave radiation at surface (SWSF) and at top-of-atmosphere (SWTOA) projected from CMIP5 models (Supplementary Note 3C) for RCP2.6, RCP4.5, and RCP8.5 were collected for empirically computing future albedo kernels (see section 3.4 below).Radiative kernelsRadiative kernels were used to compute top-of-atmosphere RF due to small perturbations of temperature, water vapor, and albedo. We used the latest state-of-the-art albedo kernels calculated with CESM v1.1.258 to compute RF in 2018 relative to 2001. In brief, the albedo kernel is the change in top-of-atmosphere radiative flux for a 0.01 change in surface albedo. The CESM1.1.2 kernels are separated into clear- and all-sky illumination conditions. We used the all-sky kernels because we include both black-sky and white-sky albedos. For future periods, because there are no available radiative kernels produced from general circulation models, we approximated the future kernels using an empirical parameterization following Bright et al.59:$${K}_{m}left(iright)={{SW}}^{{SF}}(i)times {sqrt}left(frac{{{SW}}^{{SF}}(i)}{{{SW}}^{{TOA}}(i)}right)/(-100)$$
(1)
where m is the month, i is the location, and SWSF and SWTOA are the surface and top-of-atmosphere shortwave radiation; dividing by −100 is for matching the CESM1.1.2 kernel definition of a 0.01 change in surface albedo.Estimation of albedo change and RFWe analyzed the RF in 2018 due to albedo changes caused by urbanization since 2001 (2018–2001), and in the future from 2030 to 2100 at decadal intervals (i.e., 2030, 2040, 2050, …, and 2100) since 2018 under three illustrative scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, which combine SSP-based urbanization projections and RCP-based climate projections. The three illustrative scenarios were selected following the scenario designation of the latest IPCC report50 and represent low greenhouse gas (GHG) emissions with CO2 emissions declining to net zero around or after 2050, intermediate GHG emissions with CO2 emissions remaining around current levels until the mid-century, and very high CO2 emissions that roughly double from current levels by 2050, respectively. We selected 2018 as the baseline year to divide the past from the future because 2018 was the latest year with available MODIS land cover products at the time of this study. We used ArcGIS 10.6 to produce spatial maps of all variables, including area of each land cover type within a 1° × 1°-grid, snow cover, albedo, radiation, and kernels, and R 3.6.1 to compute the RF.We focused only on albedo changes induced by urbanization, including the conversions from all other 16 IGBP land cover types to urban land. The changes of albedo for each grid (x, y) of a month (m) were obtained by computing the difference between albedo of that grid in the baseline year (t = t0) and in a later year (t = t1) with urban expansion:$${triangle alpha }_{m,t1-t0}(x,y)={alpha }_{m,t=t1}(x,y)-{alpha }_{m,t=t0}(x,y)$$
(2)
where αm, t = t1 (x, y) and αm, t = t0) (x, y) is the albedo for each grid (x,y) of a month (m) at the base year and later year respectively; the grid-scale albedo is computed as the weighted sum of albedo by land cover types with the weighing factor corresponding to areal percentage of a land cover within the grid. The albedo for each land cover type of a grid was then obtained by applying the albedo LUMs that provide spatially continuous black-sky, white-sky, snow-covered, and snow-free albedo maps for a given month for each land cover. Firstly, monthly mean albedo is computed as:$${alpha }_{m,t}(x,y)=mathop{sum }limits_{l=1}^{17}mathop{sum }limits_{s=0}^{1}mathop{sum }limits_{r=0}^{1}{{f}_{l,t}(x,y){f}_{s,m,t}(x,y)f}_{r,m,t}(x,y)left({alpha }_{l,s,r,m}(x,y)right)$$
(3)
where m is the month, t is the year, l is the land cover type, fl is the proportion of a cover type within the grid, fs,m,t is the fraction for snow-covered (s = 0) and snow-free (s = 1) conditions of the time (m, t), fr,m,t (x, y) is the fraction for white-sky (r = 0) or black-sky (r = 1) conditions of the time, and αl,s,r,m (x, y) is the albedo for land cover type l in month m that is extracted from the albedo LUMs corresponding to snow condition (s) and radiation condition (r). The annual mean albedo change is reported as the mean of monthly albedo change:$${triangle alpha }_{t1-t0}(x,y)=frac{1}{12}mathop{sum }limits_{m=1}^{m=12}({alpha }_{m,t=t1}(x,y)-{alpha }_{m,t=t0}(x,y))$$
(4)
The conversion of other land covers to urban land can contribute differently to the global RF, as the total area that is converted into urban land is different among non-urban land covers and the albedo differences between urban land and non-urban land cover types vary. To estimate the proportional contributions of different land conversions, we first decomposed the total albedo of each grid into the proportion of each land cover type:$${alpha }_{l,m,t}(x,y)={f}_{l,m,t}(x,y)mathop{sum }limits_{s=0}^{1}mathop{sum }limits_{r=0}^{1}{f}_{s,m,t}(x,y){f}_{r,m,t}(x,y)left({alpha }_{l,s,r,m}(x,y)right)$$
(5)
The global RF due to albedo change caused by conversion from each non-urban land cover type (l ≠ 13) to urban land (l = 13) (see Supplementary Table 2 land cover labels) was calculated as:$${{RF}}_{triangle alpha ,l(lne 13),{global}}=frac{1}{{A}_{{Earth}}}mathop{sum }limits_{i=1}^{n}mathop{sum }limits_{m=1}^{12}{({alpha }_{13,m,t=t1}left(iright)-{alpha }_{l,m,t=t0}left(iright))Delta p}_{lto 13}left(iright){Area}left(iright){K}_{m}(i)$$
(6)
where i refers to a grid, n is the total number of pixels on global lands, AEarth is the global surface area (5.1  ×  108 km2), α13,m,t = t1) (i) is the albedo of urban land in month m in the later year with urban expansion, αl,m,t = t0 (i) is the albedo of a targeted non-urban land cover type in the base year t0, Δpl→13 is the percentage of the non-urban land cover type that is converted to urban land in the year t1 compared to year t0, Area(i) is the area of the pixel, and Km (i) is the radiative kernel at the grid.The global RF due to urbanization-induced albedo changes was then calculated as:$${{RF}}_{triangle alpha ,{global}}=mathop{sum }limits_{l=1}^{17}{{RF}}_{triangle alpha ,l,{global}}(l,ne, 13)$$
(7)
GWP: CO2-equivalentWe followed GWP calculations by explicitly accounting for the lifetime and dynamic behavior of CO2 to convert RF to CO2 equivalent60,61:$${GWP}[{kg},{of},{{CO}}_{2}-{eq}]=frac{{int }_{t=0}^{{TH}}{{RF}}_{triangle alpha ,{global}}(t)}{{k}_{{CO}_2}{int }_{t=0}^{{TH}}{y}_{{{CO}}_{2}}(t)}$$
(8)
where kCO2 is radiative efficiency of CO2 in the atmosphere (W/m2/kg) at a constant background concentration of 389 ppmv, which is taken as 1.76  ×  1015 W/m2/kg62, and RF∆α,global is the global RF caused by albedo changes (W/m2). ({y}_{{{CO}}_{2}}) is the impulse-response function (IRF) for CO2 that ranges from 1 at the time of the emission pulse (t = 0) to 0.41 after 100 years, and here it is set to a mean value of 0.52 over 100 years60. The time horizon (TH) of our GWP calculations was fixed at 100 years following IPCC standards and previous studies60,63,64.Global mean surface air temperature changeWe estimated the 100-year global mean surface temperature change for the estimated RF by adopting an equilibrium climate sensitivity (ECS), defined as the global mean surface air temperature increase that follows a doubling of pre-industrial atmospheric carbon dioxide (RF = 3.7 W/m2). Given a value of RF induced by a forcing agent, the temperature change is estimated as RF/3.7 × ECS. To consider the uncertainties of ECS, we adopted a mean value of 3 °C and a very likely (90% confidence interval) range of 2–5 °C following IPCC AR650. Without knowing the exact distribution shape of ECS and future albedo-change-induced RF, we created a log-normal distribution (Supplementary Note 4) to approximate their asymmetric distribution through numerical simulation. We then conducted Monte Carlo simulations that draw 5000 random samples from each distribution to jointly estimate the uncertainties of global mean surface air temperature changes. We report the mean and 90% interval ranges of the change in temperature. More

Krause, M. & Robins, K. Charismatic species and beyond: How cultural schemas and organisational routines shape conservation. Conserv. Soc. 15, 313–321 (2017).
Google Scholar 
Marshall, K. N., Stier, A. C., Samhouri, J. F., Kelly, R. P. & Ward, E. J. Conservation challenges of predator recovery. Conserv. Lett. 9, 70–78 (2016).
Google Scholar 
Bearzi, G., Holcer, D. & Di Sciara, G. N. The role of historical dolphin takes and habitat degradation in shaping the present status of northern Adriatic cetaceans. Aquat. Conserv. Mar. Freshw. Ecosyst. 14, 363–379 (2004).
Google Scholar 
Lavigne, D. M. Marine mammals and fisheries: The role of science in the culling debate. In Marine Mammals: Fisheries Tourism and Management Issues (eds Gales, N. et al.) 31–47 (CSIRO Publishing, 2003).
Google Scholar 
Bowen, W. D. & Lidgard, D. Marine mammal culling programs: Review of effects on predator and prey populations. Mamm. Rev. 43, 207–220 (2013).
Google Scholar 
Svanbäck, R. & Persson, L. Individual diet specialization, niche width and population dynamics: Implications for trophic polymorphisms. J. Anim. Ecol. 73, 973–982 (2004).
Google Scholar 
Butler, J. R. A. et al. The Moray Firth Seal Management Plan: An adaptive framework for balancing the conservation of seals, salmon, fisheries and wildlife tourism in the UK. Aquat. Conserv. Mar. Freshw. Ecosyst. 18, 1025–1038 (2008).
Google Scholar 
Graham, I. M., Harris, R. N., Matejusová, I. & Middlemas, S. J. Do ‘rogue’ seals exist? Implications for seal conservation in the UK. Anim. Conserv. 14, 587–598 (2011).
Google Scholar 
Linnell, J. D. C., Aanes, R., Swenson, J. E., Odden, J. & Smith, M. E. Large carnivores that kill livestock: Do ‘problem individuals’ really exist?. Wildl. Soc. Bull. 27, 698–705 (1999).
Google Scholar 
Tidwell, K. S., van der Leeuw, B. K., Magill, L. N., Carrothers, B. A. & Wertheimer, R. H. Evaluation of pinniped predation on adult salmonids and other fish in the Bonneville Dam tailrace (2017).Guillemette, M. & Brousseau, P. Does culling predatory gulls enhance the productivity of breeding common terns?. J. Appl. Ecol. 38, 1–8 (2001).
Google Scholar 
Rudolf, V. H. W. & Rasmussen, N. L. Population structure determines functional differences among species and ecosystem processes. Nat. Commun. 4, 2318 (2013).ADS 
PubMed 

Google Scholar 
Harmon, L. J. et al. Evolutionary diversification in stickleback affects ecosystem functioning. Nature 458, 1167–1170 (2009).ADS 
CAS 
PubMed 

Google Scholar 
Adams, J. et al. A century of Chinook salmon consumption by marine mammal predators in the Northeast Pacific Ocean. Ecol. Inform. 34, 44–51 (2016).
Google Scholar 
Chasco, B. et al. Competing tradeoffs between increasing marine mammal predation and fisheries harvest of Chinook salmon. Sci. Rep. 7, 1–14 (2017).CAS 

Google Scholar 
Bearhop, S. et al. Stable isotopes indicate sex-specific and long-term individual foraging specialisation in diving seabirds. Mar. Ecol. Prog. Ser. 311, 157–164 (2006).ADS 

Google Scholar 
Thiemann, G. W., Iverson, S. J., Stirling, I. & Obbard, M. E. Individual patterns of prey selection and dietary specialization in an Arctic marine carnivore. Oikos 120, 1469–1478 (2011).
Google Scholar 
Königson, S., Fjälling, A., Berglind, M. & Lunneryd, S. G. Male gray seals specialize in raiding salmon traps. Fish. Res. 148, 117–123 (2013).
Google Scholar 
Sih, A., Sinn, D. L. & Patricelli, G. L. On the importance of individual differences in behavioural skill. Anim. Behav. 155, 307–317 (2019).
Google Scholar 
Bjorkland, R. H. et al. Stable isotope mixing models elucidate sex and size effects on the diet of a generalist marine predator. Mar. Ecol. Prog. Ser. 526, 213–225 (2015).ADS 

Google Scholar 
Schwarz, D. et al. Large-scale molecular diet analysis in a generalist marine mammal reveals male preference for prey of conservation concern. Ecol. Evol. 8, 9889–9905 (2018).PubMed 
PubMed Central 

Google Scholar 
Tinker, M. T., Costa, D. P., Estes, J. A. & Wieringa, N. Individual dietary specialization and dive behaviour in the California sea otter: Using archival time-depth data to detect alternative foraging strategies. Deep. Res. Part II Top. Stud. Oceanogr. 54, 330–342 (2007).ADS 

Google Scholar 
Voelker, M. R., Schwarz, D., Thomas, A., Nelson, B. W. & Acevedo-Gutiérrez, A. Large-scale molecular barcoding of prey DNA reveals predictors of intrapopulation feeding diversity in a marine predator. Ecol. Evol. 10, 9867–9885 (2020).PubMed 
PubMed Central 

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

Google Scholar 
Harcourt, R. Individual variation in predation on fur seals by southern sea lions (Otaria byronia) in Peru. Can. J. Zool. 71, 1908–1911 (1993).
Google Scholar 
Marine Mammal Commission. Marine Mammal Protection Act. Marine Mammal Protection Act Amendment 1–56 (U.S. Fish and Wildlife Service, 2004). https://doi.org/10.1002/tcr.201190008.Book 

Google Scholar 
National Marine Fisheries Service. Willamette Falls Pinniped-Fishery Interaction Task Force Marine Mammal Protection Act, Section 120 (National Marine Fisheries Service, 2018).
Google Scholar 
Jefferson, T. A., Smultea, M. A., Ward, E. J. & Berejikian, B. Estimating the stock size of harbor seals (Phoca vitulina richardii) in the inland waters of Washington State using line-transect methods. PLoS ONE 16, e0241254 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jeffries, S., Huber, H., Calambokidis, J. & Laake, J. Trends and status of harbor seals in Washington State: 1978–1999. J. Wildl. Manag. 67, 208–219 (2003).
Google Scholar 
Thomas, A. C., Lance, M. M., Jeffries, S. J., Miner, B. G. & Acevedo-Gutiérrez, A. Harbor seal foraging response to a seasonal resource pulse, spawning Pacific herring. Mar. Ecol. Prog. Ser. 441, 225–239 (2011).ADS 

Google Scholar 
Chasco, B. et al. Estimates of chinook salmon consumption in Washington State inland waters by four marine mammal predators from 1970 to 2015. Can. J. Fish. Aquat. Sci. 74, 1173–1194 (2017).
Google Scholar 
Farrer, J. & Acevedo-Gutiérrez, A. Use of haul-out sites by harbor seals (Phoca vitulina) in Bellingham: Implications for future development. Northwest. Nat. 91, 74–79 (2010).
Google Scholar 
Steingass, S., Jeffries, S., Hatch, D. & Dupont, J. Field report: 2020 pinniped research and management activities at Bonneville Dam (2020).Tidwell, K. S., Carrothers, B. A., Blumstein, D. T. & Schakner, Z. A. Steller sea lion (Eumetopias jubatus) response to non-lethal hazing at Bonneville Dam. Front. Conserv. Sci. 2, 1–9 (2021).
Google Scholar 
Hiruki, L. M., Schwartz, M. K. & Boveng, P. L. Hunting and social behaviour of leopard seals (Hydrurga leptonyx) at Seal Island, South Shetland Islands, Antarctica. J. Zool. 249, 97–109 (1999).
Google Scholar 
Ainley, D. G., Ballard, G., Karl, B. J. & Dugger, K. M. Leopard seal predation rates at penguin colonies of different size. Antarct. Sci. 17, 335–340 (2005).ADS 

Google Scholar 
Páez-Rosas, D. et al. Hunting and cooperative foraging behavior of Galapagos sea lion: An attack to large pelagics. Mar. Mammal Sci. 36, 386–391 (2020).
Google Scholar 
Macneale, K. H., Kiffney, P. M. & Scholz, N. L. Pesticides, aquatic food webs, and the conservation of Pacific salmon. Front. Ecol. Environ. 8, 475–482 (2010).
Google Scholar 
Roni, P., Anders, P. J., Beechie, T. J. & Kaplowe, D. J. Review of tools for identifying, planning, and implementing habitat restoration for Pacific salmon and steelhead. North Am. J. Fish. Manag. 38, 355–376 (2018).
Google Scholar 
Morissette, L., Christensen, V. & Pauly, D. Marine mammal impacts in exploited ecosystems: Would large scale culling benefit fisheries?. PLoS ONE 7, 1–18 (2012).
Google Scholar 
Thompson, D., Coram, A. J., Harris, R. N. & Sparling, C. E. Review of non-lethal seal control options to limit seal predation on salmonids in rivers and at finfish farms. Scott. Mar. Freshw. Sci. 12, 137 (2021).
Google Scholar 
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Google Scholar 
Fairbanks, C. & Penttila, D. Bellingham Bay Forage Fish Spawning Assessment (2016).Madsen, S. W. & Nightengale, T. Whatcom Creek Ten-Years After: Summary Report (Department of Public Works, 2009). https://doi.org/10.2307/j.ctt20krzd7.7.Book 

Google Scholar 
Martin, P. & Bateson, P. Measuring Behaviour: An Introductory Guide (Cambridge University Press, 2007).
Google Scholar 
Bolger, D. T., Morrison, T. A., Vance, B., Lee, D. & Farid, H. A computer-assisted system for photographic mark-recapture analysis. Methods Ecol. Evol. 3, 813–822 (2012).
Google Scholar 
Harrison, P. J. et al. Incorporating movement into models of grey seal population dynamics. J. Anim. Ecol. 75, 634–645 (2006).PubMed 

Google Scholar 
Thompson, P. M. & Wheeler, H. Photo-ID-based estimates of reproductive patterns in female harbor seals. Mar. Mammal Sci. 24, 138–146 (2008).
Google Scholar 
Washington Department of Fish and Wildlife. Whatcom Creek Hatchery (WDFW, 2019).
Google Scholar 
R Core Team. R: A language and environment for statistical computing (R Core Team, 2020).
Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Lloyd-Smith, J. O. Maximum likelihood estimation of the negative binomial dispersion parameter for highly overdispersed data, with applications to infectious diseases. PLoS ONE 2, 1–8 (2007).
Google Scholar 
Zhang, D. rsq: R-Squared and Related Measures. R package version 2.1 (2020).Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).ADS 

Google Scholar 
Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6.Book 
MATH 

Google Scholar  More

RESEARCH HIGHLIGHT
01 July 2022

Rock doves on some Scottish islands show almost no sign of having interbred with domestic pigeons.

The relatively long, slender bill of this rock dove from the Outer Hebridean islands of Scotland are characteristic of feral pigeons’ ancestors. Credit: W. J. Smith et al./iScience

.readcube-buybox { display: none !important;}
Charles Darwin developed his theory of natural selection in part by studying a form of artificial selection: the nineteenth-century rage for pigeon breeding, which created a wealth of fantastical varieties of pigeon (Columba livia). So widespread was pigeon fancying that it seeded the world with escaped domestic birds and their feral descendants, which then hybridized with their wild ancestors, the rock doves.

Access options

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0 0;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50%0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:””;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
/* style specs end */Subscribe to Nature+Get immediate online access to the entire Nature family of 50+ journals$29.99monthlySubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueAll prices are NET prices.VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00All prices are NET prices.

Additional access options:

doi: https://doi.org/10.1038/d41586-022-01780-2

References

Subjects

Conservation biology

Subjects

Conservation biology More