More stories

  • in

    Improving biodiversity protection through artificial intelligence

    A biodiversity simulation frameworkWe have developed a simulation framework modelling biodiversity loss to optimize and validate conservation policies (in this context, decisions about data gathering and area protection across a landscape) using an RL algorithm. We implemented a spatially explicit individual-based simulation to assess future biodiversity changes based on natural processes of mortality, replacement and dispersal. Our framework also incorporates anthropogenic processes such as habitat modifications, selective removal of a species, rapid climate change and existing conservation efforts. The simulation can include thousands of species and millions of individuals and track population sizes and species distributions and how they are affected by anthropogenic activity and climate change (for a detailed description of the model and its parameters see Supplementary Methods and Supplementary Table 1).In our model, anthropogenic disturbance has the effect of altering the natural mortality rates on a species-specific level, which depends on the sensitivity of the species. It also affects the total number of individuals (the carrying capacity) of any species that can inhabit a spatial unit. Because sensitivity to disturbance differs among species, the relative abundance of species in each cell changes after adding disturbance and upon reaching the new equilibrium. The effect of climate change is modelled as locally affecting the mortality of individuals based on species-specific climatic tolerances. As a result, more tolerant or warmer-adapted species will tend to replace sensitive species in a warming environment, thus inducing range shifts, contraction or expansion across species depending on their climatic tolerance and dispersal ability.We use time-forward simulations of biodiversity in time and space, with increasing anthropogenic disturbance through time, to optimize conservation policies and assess their performance. Along with a representation of the natural and anthropogenic evolution of the system, our framework includes an agent (that is, the policy maker) taking two types of actions: (1) monitoring, which provides information about the current state of biodiversity of the system, and (2) protecting, which uses that information to select areas for protection from anthropogenic disturbance. The monitoring policy defines the level of detail and temporal resolution of biodiversity surveys. At a minimal level, these include species lists for each cell, whereas more detailed surveys provide counts of population size for each species. The protection policy is informed by the results of monitoring and selects protected areas in which further anthropogenic disturbance is maintained at an arbitrarily low value (Fig. 1). Because the total number of areas that can be protected is limited by a finite budget, we use an RL algorithm42 to optimize how to perform the protecting actions based on the information provided by monitoring, such that it minimizes species loss or other criteria depending on the policy.We provide a full description of the simulation system in the Supplementary Methods. In the sections below we present the optimization algorithm, describe the experiments carried out to validate our framework and demonstrate its use with an empirical dataset.Conservation planning within a reinforcement learning frameworkIn our model we use RL to optimize a conservation policy under a predefined policy objective (for example, to minimize the loss of biodiversity or maximize the extent of protected area). The CAPTAIN framework includes a space of actions, namely monitoring and protecting, that are optimized to maximize a reward R. The reward defines the optimality criterion of the simulation and can be quantified as the cumulative value of species that do not go extinct throughout the timeframe evaluated in the simulation. If the value is set equal across all species, the RL algorithm will minimize overall species extinctions. However, different definitions of value can be used to minimize loss based on evolutionary distinctiveness of species (for example, minimizing phylogenetic diversity loss), or their ecosystem or economic value. Alternatively, the reward can be set equal to the amount of protected area, in which case the RL algorithm maximizes the number of cells protected from disturbance, regardless of which species occur there. The amount of area that can be protected through the protecting action is determined by a budget Bt and by the cost of protection ({C}_{t}^{c}), which can vary across cells c and through time t.The granularity of monitoring and protecting actions is based on spatial units that may include one or more cells and which we define as the protection units. In our system, protection units are adjacent, non-overlapping areas of equal size (Fig. 1) that can be protected at a cost that cumulates the costs of all cells included in the unit.The monitoring action collects information within each protection unit about the state of the system St, which includes species abundances and geographic distribution:$${S}_{t}={{{{H}}}_{{{t}}},{{{D}}}_{{{t}}},{{{F}}}_{{{t}}},{{{T}}}_{{{t}}},{{{C}}}_{{{t}}},{{{P}}}_{{{t}}},{B}_{t}}$$
    (1)
    where Ht is the matrix with the number of individuals across species and cells, Dt and Ft are matrices describing anthropogenic disturbance on the system, Tt is a matrix quantifying climate, Ct is the cost matrix, Pt is the current protection matrix and Bt is the available budget (for more details see Supplementary Methods and Supplementary Table 1). We define as feature extraction the result of a function X(St), which returns for each protection unit a set of features summarizing the state of the system in the unit. The number and selection of features (Supplementary Methods and Supplementary Table 2) depends on the monitoring policy πX, which is decided a priori in the simulation. A predefined monitoring policy also determines the temporal frequency of this action throughout the simulation, for example, only at the first time step or repeated at each time step. The features extracted for each unit represent the input upon which a protecting action can take place, if the budget allows for it, following a protection policy πY. These features (listed in Supplementary Table 2) include the number of species that are not already protected in other units, the number of rare species and the cost of the unit relative to the remaining budget. Different subsets of these features are used depending on the monitoring policy and on the optimality criterion of the protection policy πY.We do not assume species-specific sensitivities to disturbance (parameters ds, fs in Supplementary Table 1 and Supplementary Methods) to be known features, because a precise estimation of these parameters in an empirical case would require targeted experiments, which we consider unfeasible across a large number of species. Instead, species-specific sensitivities can be learned from the system through the observation of changes in the relative abundances of species (x3 in Supplementary Table 2). The features tested across different policies are specified in the subsection Experiments below and in the Supplementary Methods.The protecting action selects a protection unit and resets the disturbance in the included cells to an arbitrarily low level. A protected unit is also immune from future anthropogenic disturbance increases, but protection does not prevent climate change in the unit. The model can include a buffer area along the perimeter of a protected unit, in which the level of protection is lower than in the centre, to mimic the generally negative edge effects in protected areas (for example, higher vulnerability to extreme weather). Although protecting a disturbed area theoretically allows it to return to its initial biodiversity levels, population growth and species composition of the protected area will still be controlled by the death–replacement–dispersal processes described above, as well as by the state of neighbouring areas. Thus, protecting an area that has already undergone biodiversity loss may not result in the restoration of its original biodiversity levels.The protecting action has a cost determined by the cumulative cost of all cells in the selected protection unit. The cost of protection can be set equal across all cells and constant through time. Alternatively, it can be defined as a function of the current level of anthropogenic disturbance in the cell. The cost of each protecting action is taken from a predetermined finite budget and a unit can be protected only if the remaining budget allows it.Policy definition and optimization algorithmWe frame the optimization problem as a stochastic control problem where the state of the system St evolves through time as described in the section above (see also Supplementary Methods), but it is also influenced by a set of discrete actions determined by the protection policy πY. The protection policy is a probabilistic policy: for a given set of policy parameters and an input state, the policy outputs an array of probabilities associated with all possible protecting actions. While optimizing the model, we extract actions according to the probabilities produced by the policy to make sure that we explore the space of actions. When we run experiments with a fixed policy instead, we choose the action with highest probability. The input state is transformed by the feature extraction function X(St) defined by the monitoring policy, and the features are mapped to a probability through a neural network with the architecture described below.In our simulations, we fix monitoring policy πX, thus predefining the frequency of monitoring (for example, at each time step or only at the first time step) and the amount of information produced by X(St), and we optimize πY, which determines how to best use the available budget to maximize the reward. Each action A has a cost, defined by the function Cost(A, St), which here we set to zero for the monitoring action (X) across all monitoring policies. The cost of the protecting action (Y) is instead set to the cumulative cost of all cells in the selected protection unit. In the simulations presented here, unless otherwise specified, the protection policy can only add one protected unit at each time step, if the budget allows, that is if Cost(Y, St)  More

  • in

    Divergence in life-history traits among three adjoining populations of the sea snake Emydocephalus annulatus (Hydrophiinae, Elapidae)

    Andersson, M. Sexual selection (Princeton University Press, 2019).
    Google Scholar 
    Davy, A. J. & Smith, H. Life-history variation and environment. In Plant Population Ecology (eds Davy, A. J., Hutchings, M. J. & Watkinson, A. R.) 1–22. 28th Symposium of the British Ecological Society, Sussex, 1987 (Blackwell, 1988).Wilson, K. L., De Gisi, J., Cahill, C. L., Barker, O. E. & Post, J. R. Life-history variation along environmental and harvest clines of a northern freshwater fish: plasticity and adaptation. J. Anim. Ecol. 88, 717–733 (2019).PubMed 

    Google Scholar 
    Laiolo, P. & Obeso, J. R. Life-history responses to the altitudinal gradient. In High Mountain Conservation in a Changing World (eds Catalan, J. et al.) 253–283 (Springer, 2017).
    Google Scholar 
    Schwarz, R. & Meiri, S. The fast-slow life-history continuum in insular lizards: a comparison between species with invariant and variable clutch sizes. J. Biogeogr. 44, 2808–2815 (2017).
    Google Scholar 
    Holm, S. et al. Size-related life-history traits in geometrid moths: a comparison of a temperate and a tropical community. Ecol. Entomol. 44, 711–716 (2019).
    Google Scholar 
    Ferguson, G. W. & Fox, S. F. Annual variation of survival advantage of large juvenile side-blotched lizards, Uta stansburiana: Its causes and evolutionary significance. Evolution 38, 342–349 (1984).PubMed 

    Google Scholar 
    Madsen, T., Ujvari, B., Shine, R. & Olsson, M. Rain, rats and pythons: Climate-driven population dynamics of predators and prey in tropical Australia. Austral Ecol. 31, 30–37 (2006).
    Google Scholar 
    Brown, G. P. & Shine, R. Rain, prey and predators: climatically driven shifts in frog abundance modify reproductive allometry in a tropical snake. Oecologia 154, 361–368 (2007).ADS 
    PubMed 

    Google Scholar 
    James, C. & Shine, R. Life-history strategies of Australian lizards: a comparison between the tropics and the temperate zone. Oecologia 75, 307–316 (1988).ADS 
    PubMed 

    Google Scholar 
    Mesquita, D. O. et al. Life-history patterns of lizards of the world. Am. Nat. 187, 689–705 (2016).PubMed 

    Google Scholar 
    Meiri, S. et al. The global diversity and distribution of lizard clutch sizes. Glob. Ecol. Biogeogr. 29, 1515–1530 (2020).
    Google Scholar 
    Andrews, R. M. Growth rate in island and mainland anoline lizards. Copeia 1976, 477–482 (1976).
    Google Scholar 
    Jessop, T. S. et al. Maximum body size among insular Komodo dragon populations covaries with large prey density. Oikos 112, 422–429 (2006).
    Google Scholar 
    Van Buskirk, J. & Crowder, L. B. Life-history variation in marine turtles. Copeia 1994, 66–81 (1994).
    Google Scholar 
    Broderick, A. C., Godley, B. J. & Hays, G. C. Trophic status drives interannual variability in nesting numbers of marine turtles. Proc. R. Soc. B 268, 1481–1487 (2001).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Broderick, A. C., Glen, F., Godley, B. J. & Hays, G. C. Variation in reproductive output of marine turtles. J. Exp. Mar. Biol. Ecol. 288, 95–109 (2003).
    Google Scholar 
    Pike, D. A. Climate influences the global distribution of sea turtle nesting. Glob. Ecol. Biogeogr. 22, 555–566 (2013).
    Google Scholar 
    Ujvari, B., Shine, R., Luiselli, L. & Madsen, T. Climate-induced reaction norms for life-history traits in pythons. Ecology 92, 1858–1864 (2011).PubMed 

    Google Scholar 
    Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).
    Google Scholar 
    Shine, R., Brown, G. P. & Goiran, C. Population dynamics of the sea snake Emydocephalus annulatus (Elapidae, Hydrophiinae). Sci. Rep. 11, 20701 (2021). ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 

    Google Scholar 
    Ineich, I. & Laboute, P. Les Serpents Marins de Nouvelle-Calédonie (IRD éditions, 2002).Udyawer, V., Goiran, C. & Shine, R. Peaceful coexistence between people and deadly wildlife: Why are recreational users of the ocean so rarely bitten by sea snakes? People Nat. 3, 335–346 (2021).
    Google Scholar 
    Goiran, C., Brown, G. P. & Shine, R. The behaviour of sea snakes (Emydocephalus annulatus) shifts with the tides. Sci. Rep. 10, 1–8 (2020).
    Google Scholar 
    Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Shine, R., Goiran, C., Shine, T., Fauvel, T. & Brischoux, F. Phenotypic divergence between seasnake (Emydocephalus annulatus) populations from adjacent bays of the New Caledonian lagoon. Biol. J. Linn. Soc. 107, 824–832 (2012).
    Google Scholar 
    Avolio, C., Shine, R. & Pile, A. J. The adaptive significance of sexually dimorphic scale rugosity in sea snakes. Am. Nat. 167, 728–738 (2006).PubMed 

    Google Scholar 
    White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
    Google Scholar 
    Russell, B. C., Anderson, G. R. V. & Talbot, F. H. Seasonality and recruitment of coral reef fishes. Mar. Freshw. Res. 28, 521–528 (1977).
    Google Scholar 
    Shine, R., Bonnet, X., Elphick, M. J. & Barrott, E. G. A novel foraging mode in snakes: browsing by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae). Funct. Ecol. 18, 16–24 (2004).
    Google Scholar 
    Calow, P. Adaptive aspects of energy allocation. In Fish Energetics (eds Tytler, P. & Calow, P.) 13–31 (Springer, 1985).
    Google Scholar 
    Lenormand, T. Gene flow and the limits to natural selection. Trends Ecol. Evol. 17, 183–189 (2002).
    Google Scholar 
    Bronikowski, A. M. Experimental evidence for the adaptive evolution of growth rate in the garter snake Thamnophis elegans. Evolution 54, 1760–1767 (2000).CAS 
    PubMed 

    Google Scholar 
    Cook, T. R., Bonnet, X., Fauvel, T., Shine, R. & Brischoux, F. Foraging behaviour and energy budgets of sea snakes from New Caledonia: Insights from implanted data-loggers. J. Zool. 298, 82–93 (2016).
    Google Scholar 
    Bonnet, X., Brischoux, F., Briand, M. & Shine, R. Plasticity matches phenotype to local conditions despite genetic homogeneity across 13 snake populations. Proc. R. Soc. B 288, 20202916 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Lukoschek, V., Waycott, M. & Marsh, H. Phylogeography of the olive sea snake, Aipysurus laevis (Hydrophiinae) indicates Pleistocene range expansion around northern Australia but low contemporary gene flow. Mol. Ecol. 16, 3406–3422 (2007).CAS 
    PubMed 

    Google Scholar 
    Nitschke, C. R., Hourston, M., Udyawer, V. & Sanders, K. L. Rates of population differentiation and speciation are decoupled in sea snakes. Biol. Lett. 14, 20180563 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Heatwole, H., Grech, A., Monahan, J. F., King, S. & Marsh, H. Thermal biology of sea snakes and sea kraits. Integr. Comp. Biol. 52, 257–273 (2012).PubMed 

    Google Scholar 
    Brischoux, F., Rolland, V., Bonnet, X., Caillaud, M. & Shine, R. Effects of oceanic salinity on body condition in sea snakes. Integr. Comp. Biol. 52, 235–244 (2012).PubMed 

    Google Scholar 
    Bonnet, X. et al. Spatial variation in age structure among populations of a colonial marine snake: the influence of ectothermy. J. Anim. Ecol. 84, 925–933 (2015).PubMed 

    Google Scholar 
    Heatwole, H. Sea Snakes 2nd edn. (Krieger Publishing, 1999).
    Google Scholar 
    Blouin-Demers, G. & Weatherhead, P. J. Thermal ecology of black rat snakes (Elaphe obsoleta) in a thermally challenging environment. Ecology 82, 3025–3043 (2001).
    Google Scholar 
    Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).
    Google Scholar 
    Lowe, J. R. et al. Regional versus latitudinal variation in the life-history traits and demographic rates of a reef fish, Centropyge bispinosa, in the Coral Sea and Great Barrier Reef Marine Parks, Australia. J. Fish Biol. 99, 1602–1612. https://doi.org/10.1111/jfb.14865 (2021).PubMed 

    Google Scholar 
    Gust, N., Choat, J. & Ackerman, J. Demographic plasticity in tropical reef fishes. Mar. Biol. 140, 1039–1051 (2002).
    Google Scholar 
    Kingsford, M. J., Welch, D. & O’Callaghan, M. Latitudinal and cross-shelf patterns of size, age, growth, and mortality of a tropical damselfish Acanthochromis polyacanthus on the Great Barrier Reef. Diversity 11, 67 (2019).
    Google Scholar  More

  • in

    Aggressiveness, ADHD-like behaviour, and environment influence repetitive behaviour in dogs

    Mason, G. J. Stereotypies: A critical review. Anim. Behav. 41, 1015–1037 (1991).
    Google Scholar 
    Cussen, V. A. & Mench, J. A. The relationship between personality dimensions and resiliency to environmental stress in orange-winged Amazon parrots (Amazona amazonica), as indicated by the development of abnormal behaviors. PLoS ONE 10, 1–11 (2015).
    Google Scholar 
    Clubb, R. & Mason, G. Captivity effects on wide-ranging carnivores. Nature 425, 473–474 (2003).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Shepherdson, D., Lewis, K. D., Carlstead, K., Bauman, J. & Perrin, N. Individual and environmental factors associated with stereotypic behavior and fecal glucocorticoid metabolite levels in zoo housed polar bears. Appl. Anim. Behav. Sci. 147, 268–277 (2013).
    Google Scholar 
    Miller, L. J., Bettinger, T. & Mellen, J. The reduction of stereotypic pacing in tigers (Panthera tigris) by obstructing the view of neighbouring individuals. Anim. Welf. 17, 255–258 (2008).CAS 

    Google Scholar 
    Bachmann, I., Bernasconi, P., Herrmann, R., Weishaupt, M. A. & Stauffacher, M. Behavioural and physiological responses to an acute stressor in crib-biting and control horses. Appl. Anim. Behav. Sci. 82, 297–311 (2003).
    Google Scholar 
    Ahola, M. K., Vapalahti, K. & Lohi, H. Early weaning increases aggression and stereotypic behaviour in cats. Sci. Rep. 7, 10412 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Salonen, M. et al. Prevalence, comorbidity, and breed differences in canine anxiety in 13,700 Finnish pet dogs. Sci. Rep. 10, 2962 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Garner, J. P. Stereotypies and other abnormal repetitive behaviors: Potential impact on validity, reliability, and replicability of scientific outcomes. ILAR J. 46, 106–117 (2005).CAS 
    PubMed 

    Google Scholar 
    Tynes, V. V. & Sinn, L. Abnormal repetitive behaviors in dogs and cats. A guide for practitioners. Vet. Clin. North Am. Small Anim. Pract. 44, 543–564 (2014).PubMed 

    Google Scholar 
    Luescher, A. U. Diagnosis and management of compulsive disorders in dogs and cats. Vet. Clin. North Am. Small Anim. Pract. 33, 253–267 (2003).PubMed 

    Google Scholar 
    Mason, G., Clubb, R., Latham, N. & Vickery, S. Why and how should we use environmental enrichment to tackle stereotypic behaviour?. Appl. Anim. Behav. Sci. 102, 163–188 (2007).
    Google Scholar 
    Overall, K. L. & Dunham, A. E. Clinical features and outcome in dogs and cats with obsessive-compulsive disorder: 126 Cases (1989–2000). J. Am. Vet. Med. Assoc. 221, 1445–1452 (2002).PubMed 

    Google Scholar 
    Tiira, K. et al. Environmental effects on compulsive tail chasing in dogs. PLoS One 7, e41684 (2012).Mason, G. & Rushen, J. Stereotypic Animal Behaviour: Fundamentals and Applications to Welfare 2nd edn. (CABI Publishing, 2006).
    Google Scholar 
    Moon-Fanelli, A. A., Dodman, N. H., Famula, T. R. & Cottam, N. Characteristics of compulsive tail chasing and associated risk factors in Bull Terriers. J. Am. Vet. Med. Assoc. 238, 883–889 (2011).PubMed 

    Google Scholar 
    Hewson, C. J., Luescher, U. A. & Ball, R. O. Measuring change in the behavioural severity of canine compulsive disorder: The construct validity of categories of change derived from two rating scales. Appl. Anim. Behav. Sci. 60, 55–68 (1998).
    Google Scholar 
    Vandeleest, J. J., McCowan, B. & Capitanio, J. P. Early rearing interacts with temperament and housing to influence the risk for motor stereotypy in rhesus monkeys (Macaca mulatta). Appl. Anim. Behav. Sci. 132, 81–89 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Tang, R. et al. Candidate genes and functional noncoding variants identified in a canine model of obsessive-compulsive disorder. Genome Biol. 15, 25 (2014).
    Google Scholar 
    Dodman, N. H. et al. A canine chromosome 7 locus confers compulsive disorder susceptibility. Mol. Psychiatry 15, 8–10 (2010).CAS 
    PubMed 

    Google Scholar 
    Jeppesen, L. L., Heller, K. E. & Bildsøe, M. Stereotypies in female farm mink (Mustela vison) may be genetically transmitted and associated with higher fertility due to effects on body weight. Appl. Anim. Behav. Sci. 86, 137–143 (2004).
    Google Scholar 
    Noh, H. J. et al. Integrating evolutionary and regulatory information with a multispecies approach implicates genes and pathways in obsessive-compulsive disorder. Nat. Commun. 8, 1–13 (2017).CAS 

    Google Scholar 
    Koran, L. M. Quality of life in obsessive-compulsive disorder. Psychiatr. Clin. North Am. 23, 509–517 (2000).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Murray, C. J. & Lopez, A. D. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020 (Harvard School of Public Health, 1996).
    Google Scholar 
    Calzà, J. et al. Altered cortico-striatal functional connectivity during resting state in obsessive-compulsive disorder. Front. Psychiatry 10, 319 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Brem, S., Grünblatt, E., Drechsler, R., Riederer, P. & Walitza, S. The neurobiological link between OCD and ADHD. ADHD Atten. Deficit Hyperact. Disord. 6, 175–202 (2014).
    Google Scholar 
    Stein, D. J., Dodman, N. H., Borchelt, P. & Hollander, E. Behavioral disorders in veterinary practice: Relevance to psychiatry. Compr. Psychiatry 35, 275–285 (1994).CAS 
    PubMed 

    Google Scholar 
    Overall, K. L. Natural animal models of human psychiatric conditions: Assessment of mechanism and validity. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 24, 727–776 (2000).CAS 

    Google Scholar 
    Flament, M. F. et al. Obsessive compulsive disorder in adolescence: An epidemiological study. J. Am. Acad. Child Adolesc. Psychiatry 27, 764–771 (1988).CAS 
    PubMed 

    Google Scholar 
    Nestadt, G. et al. A family study of obsessive-compulsive disorder. Arch. Gen. Psychiatry 57, 358–363 (2000).CAS 
    PubMed 

    Google Scholar 
    Protopopova, A., Hall, N. J. & Wynne, C. D. L. Association between increased behavioral persistence and stereotypy in the pet dog. Behav. Processes 106, 77–81 (2014).PubMed 

    Google Scholar 
    Valerius, G., Lumpp, A., Kuelz, A. K., Freyer, T. & Voderholzer, U. Reversal learning as a neuropsychological indicator for the neuropathology of obsessive compulsive disorder? A behavioral study. J. Neuropsychiatry Clin. Neurosci. 20, 210–218 (2008).PubMed 

    Google Scholar 
    Snyder, H. R., Kaiser, R. H., Warren, S. L. & Heller, W. Obsessive-compulsive disorder is associated with broad impairments in executive function: A meta-analysis. Clin. Psychol. Sci. 3, 301–330 (2015).PubMed 

    Google Scholar 
    Ogata, N. et al. Brain structural abnormalities in Doberman pinschers with canine compulsive disorder. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 45, 1–6 (2013).
    Google Scholar 
    Norman, L. J. et al. Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis. JAMA Psychiat. 73, 815–825 (2016).
    Google Scholar 
    Yalcin, E., Ilcol, Y. O. & Batmaz, H. Serum lipid concentrations in dogs with tail chasing. J. Small Anim. Pract. 50, 133–135 (2009).CAS 
    PubMed 

    Google Scholar 
    Vermeire, S. et al. Serotonin 2A receptor, serotonin transporter and dopamine transporter alterations in dogs with compulsive behaviour as a promising model for human obsessive-compulsive disorder. Psychiatry Res. 201, 78–87 (2012).CAS 
    PubMed 

    Google Scholar 
    Moon-Fanelli, A. A. & Dodman, N. H. Description and development of compulsive tail chasing in terriers and response to clomipramine treatment. J. Am. Vet. Med. Assoc. 212, 1252–1257 (1998).CAS 
    PubMed 

    Google Scholar 
    Irimajiri, M. et al. Randomized, controlled clinical trial of the efficacy of fluoxetine for treatment of compulsive disorders in dogs. J. Am. Vet. Med. Assoc. 235, 705–709 (2009).CAS 
    PubMed 

    Google Scholar 
    Walsh, B. R. A critical review of the evidence for the equivalence of canine and human compulsions. Appl. Anim. Behav. Sci. 234, 105166 (2021).
    Google Scholar 
    Wright, H. F., Mills, D. S. & Pollux, P. M. J. Development and validation of a psychometric tool for assessing impulsivity in the domestic dog (Canis familiaris). Int. J. Comp. Psychol. 24, 210–225 (2011).
    Google Scholar 
    Dinwoodie, I. R., Dwyer, B., Zottola, V., Gleason, D. & Dodman, N. H. Demographics and comorbidity of behavior problems in dogs. J. Vet. Behav. 32, 62–71 (2019).
    Google Scholar 
    Sulkama, S. et al. Canine hyperactivity, impulsivity, and inattention share similar demographic risk factors and behavioural comorbidities with human ADHD. Transl. Psychiatry 11, 501 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Kooij, J. J. S. et al. Updated European Consensus Statement on diagnosis and treatment of adult ADHD. Eur. Psychiatry 56, 14–34 (2019).CAS 
    PubMed 

    Google Scholar 
    Nakao, T., Okada, K. & Kanba, S. Neurobiological model of obsessive-compulsive disorder: Evidence from recent neuropsychological and neuroimaging findings. Psychiatry Clin. Neurosci. 68, 587–605 (2014).PubMed 

    Google Scholar 
    Milad, M. R. & Rauch, S. L. Obsessive-compulsive disorder: Beyond segregated cortico-striatal pathways. Trends Cogn. Sci. 16, 43–51 (2012).PubMed 

    Google Scholar 
    Hollander, E. Managing aggressive behavior in patients with obsessive-compulsive disorder and borderline personality disorder. J. Clin. Psychiatry 60, 38–44 (1999).PubMed 

    Google Scholar 
    Marsden, M. D. & Wood-Gush, D. G. M. The use of space by group-housed sheep. Appl. Anim. Behav. Sci. 15, 178 (1986).
    Google Scholar 
    Burn, C. C. A vicious cycle: A cross-sectional study of canine tail-chasing and human responses to it, using a free video-sharing website. PLoS ONE 6, e26553 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stubbs, B. et al. An examination of the anxiolytic effects of exercise for people with anxiety and stress-related disorders: A meta-analysis. Psychiatry Res. 249, 102–108 (2017).PubMed 

    Google Scholar 
    Schneider, B. M., Dodman, N. H. & Maranda, L. Use of memantine in treatment of canine compulsive disorders. J. Vet. Behav. Clin. Appl. Res. 4, 118–126 (2009).
    Google Scholar 
    Mihevc, S. P. & Majdic, G. Canine cognitive dysfunction and Alzheimer’s disease-two facets of the same disease?. Front. Neurosci. 13, 604 (2019).
    Google Scholar 
    Delorme, R. et al. Admixture analysis of age at onset in obsessive-compulsive disorder. Psychol. Med. 35, 237–243 (2005).PubMed 

    Google Scholar 
    Flaisher-Grinberg, S. et al. Ovarian hormones modulate ‘compulsive’ lever-pressing in female rats. Horm. Behav. 55, 356–365 (2009).CAS 
    PubMed 

    Google Scholar 
    Fernández-Guasti, A., Agrati, D., Reyes, R. & Ferreira, A. Ovarian steroids counteract serotonergic drugs actions in an animal model of obsessive-compulsive disorder. Psychoneuroendocrinology 31, 924–934 (2006).PubMed 

    Google Scholar 
    Col, R., Day, C. & Phillips, C. J. C. An epidemiological analysis of dog behavior problems presented to an Australian behavior clinic, with associated risk factors. J. Vet. Behav. Clin. Appl. Res. 15, 1–11 (2016).
    Google Scholar 
    Rusbridge, C. Neurological diseases of the Cavalier King Charles spaniel. J. Small Anim. Pract. 46, 265–272 (2005).CAS 
    PubMed 

    Google Scholar 
    Wrzosek, M., Płonek, M., Nicpoń, J., Cizinauskas, S. & Pakozdy, A. Retrospective multicenter evaluation of the ‘fly-catching syndrome’ in 24 dogs: EEG, BAER, MRI, CSF findings and response to antiepileptic and antidepressant treatment. Epilepsy Behav. 53, 184–189 (2015).PubMed 

    Google Scholar 
    Cao, X. et al. Balancing selection on CDH2 may be related to the behavioral features of the Belgian malinois. PLoS ONE 9, e110075 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moon-Fanelli, A. A., Dodman, N. H. & Cottam, N. Blanket and flank sucking in Doberman Pinschers. J. Am. Vet. Med. Assoc. 231, 907–912 (2007).PubMed 

    Google Scholar 
    Tiira, K. & Lohi, H. Reliability and validity of a questionnaire survey in canine anxiety research. Appl. Anim. Behav. Sci. 155, 82–92 (2014).
    Google Scholar 
    Puurunen, J. et al. Inadequate socialisation, inactivity, and urban living environment are associated with social fearfulness in pet dogs. Sci. Rep. 10, 3527 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hakanen, E. et al. Active and social life is associated with lower non-social fearfulness in pet dogs. Sci. Rep. 10, 1–13 (2020).
    Google Scholar 
    Mikkola, S. et al. Aggressive behaviour is affected by demographic, environmental and behavioural factors in purebred dogs. Sci. Rep. 11, 9433 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hejjas, K. et al. Association of polymorphisms in the dopamine D4 receptor gene and the activity-impulsivity endophenotype in dogs. Anim. Genet. 38, 629–633 (2007).CAS 
    PubMed 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. (2019).Hastie, T. gam: Generalized Additive Models. (2018).Robinson, D. & Hayes, A. broom: Convert Statistical Analysis Objects into Tidy Tibbles. https://cran.r-project.org/package=broom (2018).Wickham, H., François, R., Lionel, H. & Müller, K. dplyr: A Grammar of Data Manipulation. https://cran.r-project.org/package=dplyr (2019).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

    Google Scholar 
    Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage Publications, 2011).
    Google Scholar 
    Robin, X. et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011).
    Google Scholar 
    Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. https://cran.r-project.org/package=emmeans (2019).Fox, J. Effect Displays in R for Generalised Linear Models. J. Stat. Softw. 8, 1–27 (2003).
    Google Scholar 
    Goto, A., Arata, S., Kiyokawa, Y., Takeuchi, Y. & Mori, Y. Risk factors for canine tail chasing behaviour in Japan. Vet. J. 192, 445–448 (2012).PubMed 

    Google Scholar  More

  • in

    Southeast Asia must narrow down the yield gap to continue to be a major rice bowl

    OECD–FAO Agricultural Outlook 2017-2026 (OECD, 2017).Frenken, K. Irrigation in Southern and Eastern Asia in Figures—AQUASTAT Survey 2011 (FAO, 2012).FAOSTAT Production Data (FAO, accessed 2 May 2021); www.fao.org/faostat/en/#dataDawe, D., Jaffee, S. & Santos, N. Rice in the Shadow of Skyscrapers: Policy Choices in a Dynamic East and Southeast Asian Setting (FAO, 2014).Baldwin, K., Childs, N., Dyck, J. & Hansen, J. Southeast Asia’s Rice Surplus. Outlook No. RCS-121-01 (USDA, 2012).World Population Prospects (Department of Economic and Social Affairs, Population Division, UN, 2019).Rejesus, R. M., Mohanty, S. & Balagtas, J. V. Forecasting Global Rice Consumption (North Carolina State Univ., 2012).Clarete, R. L., Adriano, L. & Esteban A. Rice Trade and Price Volatility: Implications on ASEAN and Global Food Security (Asian Development Bank, 2013).Pandey, S. et al. Rice in the Global Economy: Strategic Research and Policy Issues for Food Security (International Rice Research Institute, 2010).Robinson, S. et al. The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description for Version 3, IFPRI Discussion Paper 1483 (International Food Policy Research Institute, 2015).d’Amour, C. B. et al. Future urban land expansion and implications for global croplands. Proc. Natl Acad. Sci. USA 114, 8939–8944 (2017).
    Google Scholar 
    de Fraiture, C. et al. Trends and Transitions in Asian Irrigation: What are the Prospects for the Future? IWMI-FAO Workshop on Asian Irrigation (FAO Regional Office for Asia and the Pacific, 2009)Global Rice Science Partnership. Rice Almanac 4th edn (International Rice Research Institute, 2013).Ladha, J. K. et al. Steady agronomic and genetic interventions are essential for sustaining productivity in intensive rice cropping. Proc. Natl Acad. Sci. USA 118, e2110807118 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mutert, E. & Fairhurst, T. H. Developments in rice production in Southeast Asia. Better Crops Int. 15, 12–17 (2002).
    Google Scholar 
    Dawe, D. C., Piedad, M. & Cheryll B. C. Why Does the Philippines Import Rice?: Meeting the Challenge of Trade Liberalization (International Rice Research Institute, 2006).van Ittersum, M. K. et al. Can Sub-Saharan Africa feed itself? Proc. Natl Acad. Sci. USA 113, 14964–14969 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Lobell, D. B., Cassman, K. G. & Field, C. B. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 179 (2009).
    Google Scholar 
    Agus, F. et al. Yield gaps in intensive rice–maize cropping sequences in the humid tropics of Indonesia. Field Crops Res. 237, 12–22 (2019).
    Google Scholar 
    Cosslett, T. L. & Cosslett, P. D. Rice Trade of the Mainland Southeast Asian Countries: Cambodia, Laos, Thailand, and Vietnam. Sustainable Development of Rice and Water Resources in Mainland Southeast Asia and Mekong River Basin (Springer, 2018).Tran, U. T. & Kajisa, K. The impact of Green Revolution on rice production in Vietnam. Dev. Econ. 44, 167–189 (2006).
    Google Scholar 
    Dobermann, A., Witt, C. & Dawe, D. Increasing Productivity of Intensive Rice Systems Through Site-Specific Nutrient Management (Science Publishers Inc. and International Rice Research Institute, 2004).Hoang, H. K. & Meyers, W. H. Price stabilization and impacts of trade liberalization in the Southeast Asian rice market. Food Policy 57, 26–39 (2015).
    Google Scholar 
    Clapp, J. Food self-sufficiency: making sense of it, and when it makes sense. Food Policy 66, 88–96 (2017).
    Google Scholar 
    Buresh, R. J., Correa, T. Q. Jr, Pabuayon, I. L. B., Laureles, E. V. & Choi, I. R. Yield of irrigated rice affected by asymptomatic disease in a long-term intensive monocropping experiment. Field Crops Res. 265, 108121 (2021).
    Google Scholar 
    Dawe, D. & Timmer, C. P. Why stable food prices are a good thing: lessons from stabilizing rice prices in Asia. Glob. Food Secur. 1, 127–133 (2012).
    Google Scholar 
    Deng, N. et al. Closing yield gaps for rice self-sufficiency in China. Nat. Commun. 10, 1725 (2019).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Ray, D. K. et al. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).PubMed 
    ADS 

    Google Scholar 
    Stuart, A. M. et al. Yield gaps in rice-based farming systems: insights from local studies and prospects for future analysis. Field Crops Res. 194, 43–56 (2016).
    Google Scholar 
    Affholder, F., Poeydebat, C., Corbeels, M., Scopel, E. & Tittonell, P. The yield gap of major food crops in family agriculture in the tropics: assessment and analysis through field surveys and modelling. Field Crops Res. 143, 106–118 (2013).
    Google Scholar 
    Boling, A. A., Bouman, B. A., Tuong, T. P., Konboon, Y. & Harnpichitvitaya, D. Yield gap analysis and the effect of nitrogen and water on photoperiod-sensitive Jasmine rice in north-east Thailand. NJAS-Wagen. J. Life Sci. 58, 11–19 (2011).
    Google Scholar 
    van Oort, P. A. et al. Can yield gap analysis be used to inform R&D prioritisation? Glob. Food Sec. 12, 109–118 (2017).
    Google Scholar 
    Rattalino Edreira, J. I. et al. Spatial frameworks for robust estimation of yield gaps. Nat. Food 2, 773–779 (2021).
    Google Scholar 
    Grassini, P. et al. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Res. 177, 49–63 (2015).
    Google Scholar 
    Redfern, S. K., Azzu, N. & Binamira, J. S. Rice in Southeast Asia: Facing Risks and Vulnerabilities to Respond to Climate Change. Building Resilience for Adaptation to Climate Change in the Agriculture Sector (FAO, 2012).Angulo, C., Becker, M. & Wassmann, R. Yield gap analysis and assessment of climate-induced yield trends of irrigated rice in selected provinces of the Philippines. J. Agric. Rural Dev. Trop. Subtrop. 113, 61–68 (2012).
    Google Scholar 
    Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).CAS 
    PubMed 
    ADS 

    Google Scholar 
    IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Gitz, V., Meybeck, A., Lipper, L., Young, C. D. & Braatz, S. Climate Change and Food Security: Risks and Responses (FAO, 2016).Collins, M. et al. in IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).ADS 

    Google Scholar 
    Pastor, A. V. et al. The global nexus of food–trade–water sustaining environmental flows by 2050. Nat. Sustain. 2, 499–507 (2019).
    Google Scholar 
    Kropff, M. J., Cassman, K. G., Peng, S., Matthews, R. B. & Setter, T. L. Quantitative Understanding of Yield Potential. Breaking the Yield Barrier (International Rice Research Institute, 1994).Matthews, R. B., Kropff, M. J., Bachelet, D. & van Laar, H. H. Modeling the Impact of Climate Change on Rice Production in Asia (CAB International and International Rice Research Institute, 1995).Mitchell P. L., Sheehy J. E. & Woodward F. I. Potential Yields and the Efficiency of Radiation Use in Rice. IRRI Discussion Paper Series 32 (International Rice Research Institute, 1998).Devkota, K. P. et al. Economic and environmental indicators of sustainable rice cultivation: a comparison across intensive irrigated rice cropping systems in six Asian countries. Ecol. Indic. 105, 199–214 (2019).CAS 

    Google Scholar 
    Peng, S. et al. The importance of maintenance breeding: a case study of the first miracle rice variety—IR8. Field Crops Res. 119, 342–347 (2010).ADS 

    Google Scholar 
    Peng, S., Cassman, K. G., Virmani, S. S., Sheehy, J. & Khush, G. S. Yield potential trends of tropical rice since the release of IR8 and the challenge of increasing rice yield potential. Crop Sci. 39, 1552–1559 (1999).
    Google Scholar 
    Kupkanchanakul, T. Bridging the Rice Yield Gap in Thailand. Bridging the Rice Yield Gap in the Asia-Pacific Region (FAO Regional Office for Asia and the Pacific, 2000).Monkham, T. et al. On-farm multi-location evaluation of occurrence of drought types and rice genotypes selected from controlled-water on-station experiments in northeast Thailand. Field Crops Res. 220, 27–36 (2018).
    Google Scholar 
    Naklang, K., Shu, F. & Nathabut, K. Growth of rice cultivars by direct seeding and transplanting under upland and lowland conditions. Field Crops Res. 48, 115–123 (1996).
    Google Scholar 
    Espe, M. B. et al. Rice yield improvements through plant breeding are offset by inherent yield declines over time. Field Crops Res. 222, 59–65 (2018).
    Google Scholar 
    Ermakova, M., Danila, F. R., Furbank, R. T. & von Caemmerer, S. On the road to C4 rice: advances and perspectives. Plant J. 101, 940–950 (2020).CAS 
    PubMed 

    Google Scholar 
    Hari Prasad, A. S., Viraktamath, B. C. & Mohapatra, T. Hybrid Rice Development in Asia: Assessment of Limitations and Potential (FAO Regional Office for Asia and the Pacific, 2014).Report on the Regional Expert Consultation on Hybrid Rice Development in Asia Under FAO–China South–South Cooperation: Constraints and Opportunities (FAO Regional Office for Asia and the Pacific, 2016).Xie, F. & Peng, S. History and prospects of hybrid rice development outside of China. Sci. Bull. 35, 3858–3868 (2016).
    Google Scholar 
    Gummert, M. et al. Assessment of post-harvest losses and carbon footprint in intensive lowland rice production in Myanmar. Sci. Rep. 10, 1–13 (2020).
    Google Scholar 
    A Regional Rice Strategy for Sustainable Food Security in Asia and the Pacific (FAO Regional Office for Asia and the Pacific, 2014).Laborte, A. G. et al. Rice yields and yield gaps in Southeast Asia: past trends and future outlook. Eur. J. Agron. 36, 9–20 (2012).
    Google Scholar 
    Chivenge, P., Saito, K., Bunquin, M. A., Sharma, S. & Dobermann, A. Co-benefits of nutrient management tailored to smallholder agriculture. Glob. Food Sec. 30, 100570 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Thomas, M. B. Ecological approaches and the development of “truly integrated” pest management. Proc. Natl Acad. Sci. USA 96, 5944–5951 (1999).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Adoption of Technologies for Sustainable Farming Systems. Wageningen Workshop Proceeding (OECD, 2001).OECD–FAO Agricultural Outlook 2021–2030 (OECD, 2021).Cassman, K. G. & Grassini, P. A global perspective on sustainable intensification research. Nat. Sustain. 3, 262–268 (2020).
    Google Scholar 
    Mortensen, D. A. & Smith, R. G. Confronting barriers to cropping system diversification. Front. Sustain. Food Syst. 4, 564197 (2020).
    Google Scholar 
    van Bussel, L. G. et al. From field to atlas: upscaling of location-specific yield gap estimates. Field Crops Res. 177, 98–108 (2015).
    Google Scholar 
    van Wart, J. et al. Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Res. 143, 44–55 (2013).
    Google Scholar 
    Bouman, B. A. M. et al. ORYZA2000: Modeling Lowland Rice (International Rice Research Institute, 2001).POWER Data Methodology (NASA, accessed 25 June 2020); https://power.larc.nasa.gov/docs/van Wart, J. et al. Creating long-term weather data from thin air for crop simulation modeling. Agric. For. Meteorol. 209, 49–58 (2015).ADS 

    Google Scholar 
    van Ittersum, M. K. et al. Yield gap analysis with local to global relevance—a review. Field Crops Res. 143, 4–17 (2013).
    Google Scholar 
    Khunthasuvon, S. et al. Lowland rice improvement in northern and northeast Thailand: 1. effects of fertiliser application and irrigation. Field Crops Res. 59, 99–108 (1998).
    Google Scholar 
    Naklang, K., Harnpichitvitaya, D., Amarante, S. T., Wade, L. J. & Haefele, S. M. Internal efficiency, nutrient uptake, and the relation to field water resources in rainfed lowland rice of northeast Thailand. Plant Soil 286, 193–208 (2006).CAS 

    Google Scholar 
    Roy, R. N., Finck, A., Blair, G. J. & Tandon, H. L. S. Plant Nutrition for Food Security—A Guide for Integrated Nutrient Management (FAO, 2006).White, P. F., Oberthür, T. & Sovuthy, P. The Soils Used for Rice Production in Cambodia: A Manual for Their Identification and Management (International Rice Research Institute, 1997).Agustiani, N. et al. Simulating rice and maize yield potential in the humid tropical environment of Indonesia. Eur. J. Agron. 101, 10–19 (2018).
    Google Scholar 
    Espe, M. B. et al. Yield gap analysis of US rice production systems shows opportunities for improvement. Field Crops Res. 196, 276–283 (2016).
    Google Scholar 
    Yuan, S., Peng, S. & Li, T. Evaluation and application of the ORYZA rice model under different crop managements with high-yielding rice cultivars in central China. Field Crops Res. 212, 115–125 (2017).
    Google Scholar 
    Li, T. et al. From ORYZA2000 to ORYZA (v3): an improved simulation model for rice in drought and nitrogen-deficient environments. Agric. For. Meteorol. 237, 246–256 (2017).PubMed 
    ADS 

    Google Scholar 
    Bouman, B. A. M. Developing a System of Temperate and Tropical Aerobic Rice in Asia (STAR), CPWF Project Report (CGIAR Challenge Program on Water and Food, 2008).Regional: Development and Dissemination of Climate-Resilient Rice Varieties for Water-Short Areas of South Asia and Southeast Asia (Asian Development Bank, 2016).Nguyen, V. N. & Tran, D. V. Rice in Producing Countries, FAO Rice Information (FAO, Rome, Italy, 2002).Li, T. et al. Simulation of genotype performances across a larger number of environments for rice breeding using ORYZA2000. Field Crops Res. 149, 312–321 (2013).
    Google Scholar 
    Samson, B. K., Hasan, M. & Wade, L. J. Penetration of hardpans by rice lines in the rainfed lowlands. Field Crops Res. 76, 175–188 (2002).
    Google Scholar 
    Haefele, S. M. et al. Factors affecting rice yield and fertilizer response in rainfed lowlands of northeast Thailand. Field Crops Res. 98, 39–51 (2006).
    Google Scholar 
    Boling, A. A. et al. The effect of toposequence position on soil properties, hydrology, and yield of rainfed lowland rice in Southeast Asia. Field Crops Res. 106, 22–33 (2008).
    Google Scholar 
    Foreign Agricultural Service (USDA, accessed 2 May 2021); https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQueryBalié, J. & Valera, H. G. Domestic and international impacts of the rice trade policy reform in the Philippines. Food Policy 92, 101876 (2020).
    Google Scholar 
    Koizumi, T., Gay, S. H. & Furuhashi, G. Reviewing Indica and Japonica Rice Market Developments (OECD, 2021).Standard Country or Area Codes for Statistical Use (M49) (United Nations Statistical Division, 1999).Rega, C., Helming, J. & Paracchini, M. L. Environmentalism and localism in agricultural and land-use policies can maintain food production while supporting biodiversity. Findings from simulations of contrasting scenarios in the EU. Land Use Policy 87, 103986 (2019).
    Google Scholar 
    Zhou, Y. & Staatz, J. Projected demand and supply for various foods in West Africa: implications for investments and food policy. Food Policy 61, 198–212 (2016).
    Google Scholar 
    Yuan, S. et al. Sustainable intensification for a larger global rice bowl. Nat. Commun. 12, 7163 (2021).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Andrade, J. F. et al. Impact of Urbanization trends on production of key staple crops. Ambio https://doi.org/10.1007/s13280-021-01674-z (2021). More

  • in

    Integrating remote sensing with ecology and evolution to advance biodiversity conservation

    Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).PubMed 

    Google Scholar 
    Soto-Navarro, C. A. et al. Towards a multidimensional biodiversity index for national application. Nat. Sustain. 4, 933–942 (2021).Skidmore, A. K. et al. Priority list of biodiversity metrics to observe from space. Nat. Ecol. Evol. 5, 896–906 (2021).PubMed 

    Google Scholar 
    Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Girardello, M. et al. Global synergies and trade-offs between multiple dimensions of biodiversity and ecosystem services. Sci. Rep. 9, 5636 (2019).PubMed 
    PubMed Central 

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

    Google Scholar 
    Pettorelli, N. et al. Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sens. Ecol. Conserv. 2, 122–131 (2016).
    Google Scholar 
    Paganini, M., Leidner, A. K., Geller, G., Turner, W. & Wegmann, M. The role of space agencies in remotely sensed essential biodiversity variables. Remote Sens. Ecol. Conserv. 2, 132–140 (2016).
    Google Scholar 
    O’Connor, B. et al. Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets. Remote Sens. Ecol. Conserv. 1, 19–28 (2015).
    Google Scholar 
    Skidmore, A. K. et al. Environmental science: agree on biodiversity metrics to track from space. Nature 523, 403–405 (2015).CAS 
    PubMed 

    Google Scholar 
    Reddy, C. S. et al. Remote sensing enabled essential biodiversity variables for biodiversity assessment and monitoring: technological advancement and potentials. Biodivers. Conserv. 30, 1–14 (2021).
    Google Scholar 
    Vihervaara, P. et al. How essential biodiversity variables and remote sensing can help national biodiversity monitoring. Glob. Ecol. Conserv. 10, 43–59 (2017).
    Google Scholar 
    Luque, S., Pettorelli, N., Vihervaara, P. & Wegmann, M. Improving biodiversity monitoring using satellite remote sensing to provide solutions towards the 2020 conservation targets. Methods Ecol. Evol. 9, 1784–1786 (2018).
    Google Scholar 
    Moritz, C. Applications of mitochondrial DNA analysis in conservation: a critical review. Mol. Ecol. 3, 401–411 (1994).CAS 

    Google Scholar 
    Graham, C. H., Ferrier, S., Huettman, F., Moritz, C. & Peterson, A. T. New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol. Evol. 19, 497–503 (2004).PubMed 

    Google Scholar 
    Czyż, E. A. et al. Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series. Ecol. Evol. 10, 7419–7430 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Guillén-Escribà, C. et al. Remotely sensed between-individual functional trait variation in a temperate forest. Ecol. Evol. 11, 10834–10867 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
    PubMed 

    Google Scholar 
    Shaw, R. G. & Etterson, J. R. Rapid climate change and the rate of adaptation: insight from experimental quantitative genetics. New Phytol. 195, 752–765 (2012).PubMed 

    Google Scholar 
    Wang, Z. et al. Foliar functional traits from imaging spectroscopy across biomes in the eastern North America. New Phytol. 228, 494–511 (2020).PubMed 

    Google Scholar 
    Poorter, L. et al. Are functional traits good predictors of demographic rates? Evidence from five neotropical forests. Ecology 89, 1908–1920 (2008).CAS 
    PubMed 

    Google Scholar 
    Cornwell, W. K. & Ackerly, D. D. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol. Monogr. 79, 109–126 (2009).
    Google Scholar 
    Gao, Q. et al. Stimulation of soil respiration by elevated CO2 is enhanced under nitrogen limitation in a decade-long grassland study. Proc. Natl Acad. Sci. USA 117, 33317–33324 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, aad8466 (2016).PubMed 

    Google Scholar 
    Hoffmann, A. A. & Sgrò, C. M. Comparative studies of critical physiological limits and vulnerability to environmental extremes in small ectotherms: how much environmental control is needed? Integr. Zool. 13, 355–371 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Marshall, C. R. A simple method for bracketing absolute divergence times on molecular phylogenies using multiple fossil calibration points. Am. Nat. 171, 726–742 (2008).PubMed 

    Google Scholar 
    Quental, T. B. & Marshall, C. R. Diversity dynamics: molecular phylogenies need the fossil record. Trends Ecol. Evol. 25, 434–441 (2010).PubMed 

    Google Scholar 
    Graham, C. H., Moritz, C. & Williams, S. E. Habitat history improves prediction of biodiversity in rainforest fauna. Proc. Natl Acad. Sci. USA 103, 632–636 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
    Google Scholar 
    Zipkin, E. F. et al. Addressing data integration challenges to link ecological processes across scales. Front. Ecol. Environ. 19, 30–38 (2021).
    Google Scholar 
    Cavender-Bares, J. et al. BII-Implementation: the causes and consequences of plant biodiversity across scales in a rapidly changing world. Res. Ideas Outcomes 7, e63850 (2021).
    Google Scholar 
    Hwang, D. et al. A data integration methodology for systems biology. Proc. Natl Acad. Sci. USA 102, 17296–17301 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    O’Malley, M. A. & Soyer, O. S. The roles of integration in molecular systems biology. Stud. Hist. Philos. Sci. C 43, 58–68 (2012).
    Google Scholar 
    Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).von Humboldt, A. & Bonpland, A. Essai sur la Géographie des Plantes, Accompagné d’un Tableau Physique des Régions Equinoxiales (Levrault & Schoell, 1807).Darwin, C. On the Origin of Species by Means of Natural Selection 6th edn (with corrections and additions to 1872) (John Murray, 1888).Braun, E. L. Deciduous Forests of Eastern North America (Hafner Publishing Company, 1967).Slik, J. W. F. et al. Phylogenetic classification of the world’s tropical forests. Proc. Natl Acad. Sci. USA 115, 1837 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Tingley, M. W., Monahan, W. B., Beissinger, S. R. & Moritz, C. Birds track their Grinnellian niche through a century of climate change. Proc. Natl Acad. Sci. USA 106, 19637–19643 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett. 13, 1310–1324 (2010).PubMed 

    Google Scholar 
    Cavender-Bares, J., Ackerly, D., Hobbie, S. & Townsend, P. Evolutionary legacy effects on ecosystems: biogeographic origins, plant traits, and implications for management in the era of global change. Annu. Rev. Ecol. Evol. Syst. 47, 433–462 (2016).
    Google Scholar 
    Crisp, M. D., Arroyo, M. T. K., Cook, L. G., Gandolfo, M. A. & Jordan, G. J. Phylogenetic biome conservatism on a global scale. Nature 458, 754–756 (2009).CAS 
    PubMed 

    Google Scholar 
    Forrestel, E. J., Donoghue, M. J. & Smith, M. D. Convergent phylogenetic and functional responses to altered fire regimes in mesic savanna grasslands of North America and South Africa. New Phytol. 203, 1000–1011 (2014).PubMed 

    Google Scholar 
    Auler, A. S. & Smart, P. L. Late quaternary paleoclimate in semiarid northeastern Brazil from U-series dating of travertine and water-table speleothems. Quat. Res. 55, 159–167 (2001).CAS 

    Google Scholar 
    Cheng, H. et al. Climate change patterns in Amazonia and biodiversity. Nat. Commun. 4, 1411 (2013).PubMed 

    Google Scholar 
    Ledru, M.-P. et al. The last 50,000 years in the Neotropics (Southern Brazil): evolution of vegetation and climate. Palaeogeogr. Palaeoclimatol. Palaeoecol. 123, 239–257 (1996).
    Google Scholar 
    Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Delsuc, F., Brinkmann, H. & Philippe, H. Phylogenomics and the reconstruction of the tree of life. Nat. Rev. Genet. 6, 361–375 (2005).CAS 
    PubMed 

    Google Scholar 
    Ciccarelli, F. D. et al. Toward automatic reconstruction of a highly resolved tree of life. Science 311, 1283–1287 (2006).CAS 
    PubMed 

    Google Scholar 
    Beck, P. S. A. & Goetz, S. J. Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences. Environ. Res. Lett. 6, 045501 (2011).
    Google Scholar 
    Kokaly, R. F., Asner, G. P., Ollinger, S. V., Martin, M. E. & Wessman, C. A. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens. Environ. 113, S78–S91 (2009).
    Google Scholar 
    Graham, C. H. et al. The origin and maintenance of montane diversity: integrating evolutionary and ecological processes. Ecography 37, 711–719 (2014).
    Google Scholar 
    Carnaval, A. C., Hickerson, M. J., Haddad, C. F., Rodrigues, M. T. & Moritz, C. Stability predicts genetic diversity in the Brazilian Atlantic forest hotspot. Science 323, 785–789 (2009).CAS 
    PubMed 

    Google Scholar 
    Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Carnaval, A. C. et al. Prediction of phylogeographic endemism in an environmentally complex biome. Proc. R. Soc. B 281, 20141461 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Forest, F., Crandall, K. A., Chase, M. W. & Faith, D. P. Phylogeny, extinction and conservation: embracing uncertainties in a time of urgency. Philos. Trans. R. Soc. Lond. B 370, 20140002 (2015).
    Google Scholar 
    Faith, D. P. Phylogenetic diversity, functional trait diversity and extinction: avoiding tipping points and worst-case losses. Philos. Trans. R. Soc. Lond. B 370, 20140011 (2015).
    Google Scholar 
    Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
    Google Scholar 
    Lavorel, S. et al. Assessing functional diversity in the field—methodology matters! Funct. Ecol. 22, 134–147 (2008).
    Google Scholar 
    Petchey, O. L. & Gaston, K. J. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).PubMed 

    Google Scholar 
    Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).
    Google Scholar 
    Suding, K. N. et al. Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants. Glob. Change Biol. 14, 1125–1140 (2008).
    Google Scholar 
    Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).CAS 
    PubMed 

    Google Scholar 
    Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dahlin, K. M., Asner, G. P. & Field, C. B. Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem. Proc. Natl Acad. Sci. USA 110, 6895–6900 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).PubMed 

    Google Scholar 
    Enquist, B., Condit, R., Peet, R., Schildhauer, M. & Thiers, B. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. PeerJ 4, e2615v2612 (2016).
    Google Scholar 
    Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 

    Google Scholar 
    Asner, G. P., Martin, R. E., Anderson, C. B. & Knapp, D. E. Quantifying forest canopy traits: imaging spectroscopy versus field survey. Remote Sens. Environ. 158, 15–27 (2015).
    Google Scholar 
    Fajardo, A. & Siefert, A. Phenological variation of leaf functional traits within species. Oecologia 180, 951–959 (2016).PubMed 

    Google Scholar 
    Townsend, P. A., Foster, J. R., Chastain, R. A. Jr. & Currie, W. S. Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. Geosci. Remote Sens. IEEE Trans. 41, 1347–1354 (2003).
    Google Scholar 
    Féret, J. B., Gitelson, A. A., Noble, S. D. & Jacquemoud, S. PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sens. Environ. 193, 204–215 (2017).
    Google Scholar 
    Berger, K. et al. Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. Int. J. Appl. Earth Obs. Geoinf. 92, 102174 (2020).
    Google Scholar 
    Jacquemoud, S. & Ustin, S. Leaf Optical Properties (Cambridge Univ. Press, 2019).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hoffman, M., Koenig, K., Bunting, G., Costanza, J. & Williams, K. J. Biodiversity hotspots (version 2016.1). Zenodo https://doi.org/10.5281/zenodo.3261807 (2016).Folke, C. et al. Resilience thinking: integrating resilience, adaptability and transformability. Ecol. Soc. 15, 20 (2010).
    Google Scholar 
    Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).CAS 
    PubMed 

    Google Scholar 
    Peterson, G., Allen, C. & Holling, C. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).
    Google Scholar 
    MacDougall, A. S., McCann, K. S., Gellner, G. & Turkington, R. Diversity loss with persistent human disturbance increases vulnerability to ecosystem collapse. Nature 494, 86–89 (2013).CAS 
    PubMed 

    Google Scholar 
    Duncan, B. N. et al. Space‐based observations for understanding changes in the Arctic‐Boreal Zone. Rev. Geophys. 58, e2019RG000652 (2020).
    Google Scholar 
    Wittenberg, L., Malkinson, D., Beeri, O., Halutzy, A. & Tesler, N. Spatial and temporal patterns of vegetation recovery following sequences of forest fires in a Mediterranean landscape, Mt. Carmel Israel. CATENA 71, 76–83 (2007).
    Google Scholar 
    Meng, Y. et al. Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. Ecol. Inform. 57, 101064 (2020).
    Google Scholar 
    Wilson, A. M., Latimer, A. M. & Silander, J. A. Climatic controls on ecosystem resilience: postfire regeneration in the Cape Floristic Region of South Africa. Proc. Natl Acad. Sci. USA 112, 9058 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xie, Z. et al. Landsat and GRACE observations of arid wetland dynamics in a dryland river system under multi-decadal hydroclimatic extremes. J. Hydrol. 543, 818–831 (2016).Allen, C. R. et al. Quantifying spatial resilience. J. Appl. Ecol. 53, 625–635 (2016).
    Google Scholar 
    Lausch, A. et al. Understanding and assessing vegetation health by in situ species and remote-sensing approaches. Methods Ecol. Evol. 9, 1799–1809 (2018).
    Google Scholar 
    Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 
    PubMed 

    Google Scholar 
    Faruk, A., Belabut, D., Ahmad, N., Knell, R. J. & Garner, T. W. J. Effects of oil-palm plantations on diversity of tropical anurans. Conserv. Biol. 27, 615–624 (2013).PubMed 

    Google Scholar 
    Yue, S., Brodie, J. F., Zipkin, E. F. & Bernard, H. Oil palm plantations fail to support mammal diversity. Ecol. Appl. 25, 2285–2292 (2015).PubMed 

    Google Scholar 
    Dislich, C. et al. A review of the ecosystem functions in oil palm plantations, using forests as a reference system. Biol. Rev. Camb. Philos. Soc. 92, 1539–1569 (2017).PubMed 

    Google Scholar 
    Slingsby, J. A., Moncrieff, G. R. & Wilson, A. M. Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics. ISPRS J. Photogramm. Remote Sens. 166, 15–25 (2020).
    Google Scholar 
    Spasojevic, M. J. et al. Scaling up the diversity–resilience relationship with trait databases and remote sensing data: the recovery of productivity after wildfire. Glob. Change Biol. 22, 1421–1432 (2016).
    Google Scholar 
    van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 

    Google Scholar 
    Williams, L. J. et al. Remote spectral detection of biodiversity effects on forest biomass. Nat. Ecol. Evol. 5, 46–54 (2021).PubMed 

    Google Scholar 
    Schweiger, A. K. et al. Coupling spectral and resource-use complementarity in experimental grassland and forest communities. Proc. R. Soc. B 288, 20211290 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: patterns and processes. Ecol. Lett. 12, 443–451 (2009).PubMed 

    Google Scholar 
    Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).CAS 
    PubMed 

    Google Scholar 
    Isbell, F., Tilman, D., Reich, P. B. & Clark, A. T. Deficits of biodiversity and productivity linger a century after agricultural abandonment. Nat. Ecol. Evol. 3, 1533–1538 (2019).PubMed 

    Google Scholar 
    Walters, M. & Scholes, R. The GEO Handbook on Biodiversity Observation Networks (Springer, 2017).Kühl, H. S. et al. Effective biodiversity monitoring needs a culture of integration. One Earth 3, 462–474 (2020).
    Google Scholar 
    Sasaki, T., Furukawa, T., Iwasaki, Y., Seto, M. & Mori, A. S. Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances. Ecol. Indic. 57, 395–408 (2015).
    Google Scholar 
    Thompson, B. K., Olden, J. D. & Converse, S. J. Mechanistic invasive species management models and their application in conservation. Conserv. Sci. Pract. 3, e533 (2021).
    Google Scholar 
    Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).CAS 
    PubMed 

    Google Scholar 
    Ellis, E. C. et al. Used planet: a global history. Proc. Natl Acad. Sci. USA 110, 7978–7985 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McKey, D. et al. Pre-Columbian agricultural landscapes, ecosystem engineers, and self-organized patchiness in Amazonia. Proc. Natl Acad. Sci. USA 107, 7823–7828 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bush, M. B. et al. A 6900-year history of landscape modification by humans in lowland Amazonia. Quat. Sci. Rev. 141, 52–64 (2016).
    Google Scholar 
    Wright, J. L. et al. Sixteen hundred years of increasing tree cover prior to modern deforestation in Southern Amazon and Central Brazilian savannas. Glob. Change Biol. 27, 136–150 (2021).
    Google Scholar 
    Boivin, N. & Crowther, A. Mobilizing the past to shape a better Anthropocene. Nat. Ecol. Evol. 5, 273–284 (2021).PubMed 

    Google Scholar 
    Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. Tropical forests in the Anthropocene. Ann. Rev. Environ. Res. 39, 125–159 (2014).Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).CAS 

    Google Scholar 
    Verburg, P. H., Erb, K.-H., Mertz, O. & Espindola, G. Land system science: between global challenges and local realities. Curr. Opin. Environ. Sustain. 5, 433–437 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Pendrill, F., Persson, U. M., Godar, J. & Kastner, T. Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition. Environ. Res. Lett. 14, 055003 (2019).
    Google Scholar 
    Burke, M., Driscoll, A., Lobell, D. B. & Ermon, S. Using satellite imagery to understand and promote sustainable development. Science 371, eabe8628 (2021).CAS 
    PubMed 

    Google Scholar 
    Schell, C. J. et al. The ecological and evolutionary consequences of systemic racism in urban environments. Science 369, eaay4497 (2020).Trounstine, J. The geography of inequality: how land use regulation produces segregation. Am. Political Sci. Rev. 114, 443–455 (2020).
    Google Scholar 
    Su, S., Pi, J., Xie, H., Cai, Z. & Weng, M. Community deprivation, walkability, and public health: highlighting the social inequalities in land use planning for health promotion. Land Use Policy 67, 315–326 (2017).
    Google Scholar 
    Coomes, O. T., Takasaki, Y. & Rhemtulla, J. M. Forests as landscapes of social inequality tropical forest cover and land distribution among shifting cultivators. Ecol. Soc. 21, 20 (2016).Watmough, G. R. et al. Socioecologically informed use of remote sensing data to predict rural household poverty. Proc. Natl Acad. Sci. USA 116, 1213 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Verburg, P. H. et al. Land system science and sustainable development of the earth system: a global land project perspective. Anthropocene 12, 29–41 (2015).
    Google Scholar 
    Bickenbach, F., Bode, E., Nunnenkamp, P. & Söder, M. Night lights and regional GDP. Rev. World Econ. 152, 425–447 (2016).
    Google Scholar 
    Mayer, A. et al. Applying the human appropriation of net primary production framework to map provisioning ecosystem services and their relation to ecosystem functioning across the European Union. Ecosyst. Serv. 51, 101344 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Li, Y. Urban Green Space Analysis on UBC Vancouver Campus: Integrating Virtual Gaming Technology to Map Cultural Use and Biodiversity Value of Urban Green Space (Univ. British Columbia, 2021).Ghaffarian, S., Roy, D., Filatova, T. & Kerle, N. Agent-based modelling of post-disaster recovery with remote sensing data. Int. J. Disaster Risk Reduct. 60, 102285 (2021).
    Google Scholar 
    Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).PubMed 

    Google Scholar 
    Zeng, Y. et al. Environmental destruction not avoided with the Sustainable Development Goals. Nat. Sustain. 3, 795–798 (2020).
    Google Scholar 
    Mirza, M. U., Xu, C., Bavel, B. V., van Nes, E. H. & Scheffer, M. Global inequality remotely sensed. Proc. Natl Acad. Sci. USA 118, e1919913118 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kavvada, A. et al. Towards delivering on the Sustainable Development Goals using Earth observations. Remote Sens. Environ. 247, 111930 (2020).
    Google Scholar 
    Hooper, D. U. & Vitousek, P. M. Effects of plant composition and diversity on nutrient cycling. Ecol. Monogr. 68, 121–149 (1998).
    Google Scholar 
    Craine, J. M. et al. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytol. 183, 992 (2009).
    Google Scholar 
    Madritch, M. D. et al. Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales. Philos. Trans. R. Soc. B 369, 20130194 (2014).
    Google Scholar 
    Hobbie, S. E. Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol. Evol. 30, 357–363 (2015).PubMed 

    Google Scholar 
    Cline, L. C. et al. Resource availability underlies the plant–fungal diversity relationship in a grassland ecosystem. Ecology 99, 204–216 (2018).PubMed 

    Google Scholar 
    Wardle, D. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).CAS 
    PubMed 

    Google Scholar 
    Meier, C. L. & Bowman, W. D. Links between plant litter chemistry, species diversity, and below-ground ecosystem function. Proc. Natl Acad. Sci. USA 105, 19780–19785 (2008).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gold, K. M. et al. Hyperspectral measurements enable pre-symptomatic detection and differentiation of contrasting physiological effects of late blight and early blight in potato. Remote Sens. 12, 286 (2020).Serbin, S. P., Singh, A., McNeil, B. E., Kingdon, C. C. & Townsend, P. A. Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecol. Appl. 24, 1651–1669 (2014).
    Google Scholar 
    Fisher, J. B., Perakalapudi, N. V., Turner, B. L., Schimel, D. S. & Cusack, D. F. Sci. Rep. 10, 6725 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    van der Heijden, M. G. A., Martin, F. M., Selosse, M.-A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).PubMed 

    Google Scholar 
    Meireles, J. E., O’Meara, B. & Cavender-Bares, J. in Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J. et al.) 155–172 (Springer, 2020).Kothari, S. et al. Community-wide consequences of variation in photoprotective physiology among prairie plants. Photosynthetica 56, 455–467 (2018).CAS 

    Google Scholar 
    Anderegg, L. D. L. et al. Representing plant diversity in land models: an evolutionary approach to make “functional types” more functional. Glob. Change Biol., https://doi.org/10.1111/gcb.16040 (2022).Cavender-Bares, J. M. et al. Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments. Ecol. Monogr., https://doi.org/10.1002/ecm.1488 (2021).Niemann, K. O., Quinn, G., Stephen, R., Visintini, F. & Parton, D. Hyperspectral remote sensing of mountain pine beetle with an emphasis on previsual assessment. Can. J. Remote Sens. 41, 191–202 (2015).
    Google Scholar 
    Chu, H. et al. Soil microbial biogeography in a changing world: recent advances and future perspectives. mSystems 5, e00803–e00819 (2020).King, G. M. Enhancing soil carbon storage for carbon remediation: potential contributions and constraints by microbes. Trends Microbiol. 19, 75–84 (2011).CAS 
    PubMed 

    Google Scholar 
    Singh, A. K., Sisodia, A., Sisodia, V. & Padhi, M. in New and Future Developments in Microbial Biotechnology and Bioengineering (eds. Singh, J. S. & Singh, D. P.) 57–68 (Elsevier, 2019).Eviner, V. T. Plant traits that influence ecosystem processes vary independently among species. Ecology 85, 2215–2229 (2004).
    Google Scholar 
    Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).PubMed 

    Google Scholar 
    Paneque-Gálvez, J. et al. High overlap between traditional ecological knowledge and forest conservation found in the Bolivian Amazon. Ambio 47, 908–923 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Hilbert, M. The bad news is that the digital access divide is here to stay: domestically installed bandwidths among 172 countries for 1986–2014. Telecommun. Policy 40, 567–581 (2016).
    Google Scholar 
    Prados, A. I. et al. Impact of the ARSET program on use of remote-sensing data. ISPRS Int. J. Geo-Inf. 8, 261 (2019).Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).
    Google Scholar 
    Chase, A. S. Z., Chase, D. & Chase, A. Ethics, new colonialism, and lidar data: a decade of lidar in Maya archaeology. J. Comput. Appl. Archaeol. 3, 51–62 (2020).
    Google Scholar 
    Carrino, T. A., Crósta, A. P., Toledo, C. L. B. & Silva, A. M. Hyperspectral remote sensing applied to mineral exploration in southern Peru: a multiple data integration approach in the Chapi Chiara gold prospect. Int. J. Appl. Earth Obs. Geoinf. 64, 287–300 (2018).
    Google Scholar 
    Scafutto, R. D. P. M., de Souza Filho, C. R. & de Oliveira, W. J. Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: implications for onshore exploration and monitoring. ISPRS J. Photogramm. Remote Sens. 128, 146–157 (2017).
    Google Scholar 
    Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).CAS 
    PubMed 

    Google Scholar 
    Ustin, S. L. & Middleton, E. M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1 (2021).Randin, C. F. et al. Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens. Environ. 239, 111626 (2020).
    Google Scholar 
    Geller, G. N. et al. in Remote Sensing of Plant Biodiversity (eds. Cavender Bares, J. et al.) 519–526 (Springer, 2020).Asner, G. P. & Martin, R. E. Spectranomics: emerging science and conservation opportunities at the interface of biodiversity and remote sensing. Glob. Ecol. Conserv. 8, 212–219 (2016).
    Google Scholar 
    Schneider, F. D. et al. Towards mapping the diversity of canopy structure from space with GEDI. Environ. Res. Lett. 15, 115006 (2020).
    Google Scholar 
    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).
    Google Scholar 
    Green, R. O. et al. Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ. 65, 227–248 (1998).
    Google Scholar 
    Hook, S. & Fisher, J. ECO3ETPTJPL v001 ECOSTRESS Evapotranspiration PT-JPL Daily L3 Global 70 m https://doi.org/10.5067/ECOSTRESS/ECO3ETPTJPL.001 (LP DAAC, accessed 8 December 2021).Turner, A. J. et al. A double peak in the seasonality of California’s photosynthesis as observed from space. Biogeosciences 17, 405–422 (2020).CAS 

    Google Scholar 
    Radeloff, V. C. et al. The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity. Remote Sens. Environ. 222, 204–214 (2019).
    Google Scholar 
    Crameri, F. Scientific colour-maps. Zenodo https://doi.org/10.5281/zenodo.1287763 (2018).Li, X. & Xiao, J. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: a global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sensing 11, 2563 (2019).Keil, P. & Chase, J. M. Global patterns and drivers of tree diversity integrated across a continuum of spatial grains. Nat. Ecol. Evol. 3, 390–399 (2019).PubMed 

    Google Scholar 
    Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).Boonman, C. C. F. et al. Assessing the reliability of predicted plant trait distributions at the global scale. Glob. Ecol. Biogeogr. 29, 1034–1051 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).
    Google Scholar 
    Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Mokany, K. et al. Reconciling global priorities for conserving biodiversity habitat. Proc. Natl Acad. Sci. USA 117, 9906 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lausch, A. et al. Linking Earth Observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives. Ecol. Indic. 70, 317–339 (2016).
    Google Scholar 
    Schneider, F. D. et al. Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat. Commun. 8, 1441 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Rocchini, D. et al. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol. Inform. 5, 318–329 (2010).
    Google Scholar 
    Schneider, F. D., Ferraz, A. & Schimel, D. Watching Earth’s interconnected systems at work. Eos, https://doi.org/10.1029/2019EO136205 (2019).Laliberté, E., Schweiger, A. K. & Legendre, P. Partitioning plant spectral diversity into alpha and beta components. Ecol. Lett. 23, 370–380 (2020).PubMed 

    Google Scholar 
    Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).
    Google Scholar 
    Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).PubMed 

    Google Scholar 
    Dubayah, R. et al. The Global Ecosystem Dynamics Investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).
    Google Scholar 
    Omasa, K., Hosoi, F. & Konishi, A. 3D lidar imaging for detecting and understanding plant responses and canopy structure. J. Exp. Bot. 58, 881–898 (2007).CAS 
    PubMed 

    Google Scholar 
    Bae, S. et al. Radar vision in the mapping of forest biodiversity from space. Nat. Commun. 10, 4757 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Stavros, E. N. et al. ISS observations offer insights into plant function. Nat. Ecol. Evol. 1, 0194 (2017).Turner, W. et al. Free and open-access satellite data are key to biodiversity conservation. Biol. Conserv. 182, 173–176 (2015).
    Google Scholar 
    Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).CAS 
    PubMed 

    Google Scholar 
    Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).PubMed 

    Google Scholar 
    Kissling, W. D. et al. Towards global data products of essential biodiversity variables on species traits. Nat. Ecol. Evol. 2, 1531–1540 (2018).PubMed 

    Google Scholar 
    Kissling, W. D. et al. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).PubMed 

    Google Scholar 
    Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).PubMed 

    Google Scholar 
    Fretwell, P. T. & Trathan, P. N. Penguins from space: faecal stains reveal the location of emperor penguin colonies. Glob. Ecol. Biogeogr. 18, 543–552 (2009).
    Google Scholar 
    Davies, A. B. & Asner, G. P. Advances in animal ecology from 3D-LiDAR ecosystem mapping. Trends Ecol. Evol. 29, 681–691 (2014).PubMed 

    Google Scholar 
    Paz, A. et al. in Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J. et al.) 255–266 (Springer International Publishing, 2020).Pinto-Ledezma, J. N. & Cavender-Bares, J. Predicting species distributions and community composition using satellite remote sensing predictors. Sci. Rep. 11, 16448 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Papeş, M., Tupayachi, R., Martínez, P., Peterson, A. T. & Powell, G. V. N. Using hyperspectral satellite imagery for regional inventories: a test with tropical emergent trees in the Amazon Basin. J. Veg. Sci. 21, 342–354 (2010).
    Google Scholar 
    Wang, Z. et al. Mapping foliar functional traits and their uncertainties across three years in a grassland experiment. Remote Sens. Environ. 221, 405–416 (2019).
    Google Scholar  More

  • in

    Enhancing multiple scales of seafloor biodiversity with mussel restoration

    Lees, A. C., Attwood, S., Barlow, J. & Phalan, B. Biodiversity scientists must fight the creeping rise of extinction denial. Nat. Ecol. Evol. 4, 1440–1443 (2020).PubMed 

    Google Scholar 
    Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
    Google Scholar 
    Driscoll, D. A. et al. A biodiversity-crisis hierarchy to evaluate and refine conservation indicators. Nat. Ecol. Evol. 2, 775–781 (2018).PubMed 

    Google Scholar 
    Jackson, J. B. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
    PubMed 

    Google Scholar 
    McCauley, D. J. et al. Marine defaunation: Animal loss in the global ocean. Science 347, 6219 (2015).
    Google Scholar 
    Sala, E. & Knowlton, N. Global marine biodiversity trends. Annu. Rev. Environ. Resour. 31, 93–122 (2006).
    Google Scholar 
    Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Beaumont, N. et al. Identification, definition and quantification of goods and services provided by marine biodiversity: Implications for the ecosystem approach. Mar. Pollut. Bull. 54, 253–265 (2007).CAS 
    PubMed 

    Google Scholar 
    Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
    Google Scholar 
    Turpie, J. K. The existence value of biodiversity in South Africa: How interest, experience, knowledge, income and perceived level of threat influence local willingness to pay. Ecol. Econ. 46, 199–216 (2003).
    Google Scholar 
    Ruiz-Frau, A., Hinz, H., Edwards-Jones, G. & Kaiser, M. Spatially explicit economic assessment of cultural ecosystem services: Non-extractive recreational uses of the coastal environment related to marine biodiversity. Mar. Policy 38, 90–98 (2013).
    Google Scholar 
    Thrush, S. F., Gray, J. S., Hewitt, J. E. & Ugland, K. I. Predicting the effects of habitat homogenization on marine biodiversity. Ecol. Appl. 16, 1636–1642 (2006).PubMed 

    Google Scholar 
    Gillies, C. L. et al. Australian shellfish ecosystems: Past distribution, current status and future direction. PLoS ONE 13, e0190914 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Commito, J. A., Como, S., Grupe, B. M. & Dow, W. E. Species diversity in the soft-bottom intertidal zone: Biogenic structure, sediment, and macrofauna across mussel bed spatial scales. J. Exp. Mar. Biol. Ecol. 366, 70–81 (2008).
    Google Scholar 
    Tokeshi, M. Species Coexistence: Ecological and Evolutionary Perspectives (Wiley, Hoboken, 2009).
    Google Scholar 
    Paul, L. J. A history of the Firth of Thames dredge fishrey for mussels: Use and abuse of a coastal resource. Report No. 94, (Wellington, New Zealand, 2012).Enderlein, P. & Wahl, M. Dominance of blue mussels versus consumer-mediated enhancement of benthic diversity. J. Sea Res. 51, 145–155 (2004).ADS 

    Google Scholar 
    Lejart, M. & Hily, C. Differential response of benthic macrofauna to the formation of novel oyster reefs (Crassostrea gigas, Thunberg) on soft and rocky substrate in the intertidal of the Bay of Brest, France. J. Sea Res. 65, 84–93 (2011).ADS 

    Google Scholar 
    Norling, P. & Kautsky, N. Patches of the mussel Mytilus sp. are islands of high biodiversity in subtidal sediment habitats in the Baltic Sea. Aquat. Biol. 4, 75–87 (2008).
    Google Scholar 
    Norling, P., Lindegarth, M., Lindegarth, S. & Strand, Å. Effects of live and post-mortem shell structures of invasive Pacific oysters and native blue mussels on macrofauna and fish. Mar. Ecol. Prog. Ser. 518, 123–138 (2015).ADS 

    Google Scholar 
    McLeod, I., Parsons, D., Morrison, M., Van Dijken, S. & Taylor, R. Mussel reefs on soft sediments: A severely reduced but important habitat for macroinvertebrates and fishes in New Zealand. N. Z. J. Mar. Freshw. Res. 48, 48–59 (2014).CAS 

    Google Scholar 
    Seitz, R. D., Wennhage, H., Bergström, U., Lipcius, R. N. & Ysebaert, T. Ecological value of coastal habitats for commercially and ecologically important species. ICES J. Mar. Sci. 71, 648–665 (2014).
    Google Scholar 
    zu Ermgassen, P. S., Grabowski, J. H., Gair, J. R. & Powers, S. P. Quantifying fish and mobile invertebrate production from a threatened nursery habitat. J. Appl. Ecol. 53, 596–606 (2016).
    Google Scholar 
    Grabowski, J. H. The influence of trophic interactions, habitat complexity, and landscape setting on community dynamics and restoration of oyster reefs. Ph.D., The University of North Carolina at Chapel Hill (2002).Harding, J. M., Allen, D. M., Haffey, E. R. & Hoffman, K. M. Site fidelity of oyster reef blennies and gobies in saltmarsh tidal creeks. Estuaries Coasts 43, 409–423 (2020).CAS 

    Google Scholar 
    Parsons, D. et al. Snapper (Chrysophrys auratus): A review of life history and key vulnerabilities in New Zealand. N. Z. J. Mar. Freshw. Res. 48, 256–283 (2014).
    Google Scholar 
    Callier, M. D., Richard, M., McKindsey, C. W., Archambault, P. & Desrosiers, G. Responses of benthic macrofauna and biogeochemical fluxes to various levels of mussel biodeposition: An in situ “benthocosm” experiment. Mar. Pollut. Bull. 58, 1544–1553. https://doi.org/10.1016/j.marpolbul.2009.05.010 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Ysebaert, T., Hart, M. & Herman, P. M. Impacts of bottom and suspended cultures of mussels Mytilus spp. on the surrounding sedimentary environment and macrobenthic biodiversity. Helgol. Mar. Res. 63, 59–74 (2009).ADS 

    Google Scholar 
    Sea, M. A., Thrush, S. F. & Hillman, J. R. Environmental predictors of sediment denitrification rates within restored green-lipped mussel (Perna canaliculus) beds. Mar. Ecol. Prog. Ser. 667, 1–13 (2021).ADS 
    CAS 

    Google Scholar 
    Hillman, J. R., O’Meara, T. A., Lohrer, A. M., & Thrush, S. F. Influence of restored mussel reefs on denitrification in
    marine sediments. J. Sea Res. 175, 102099 (2021).
    Google Scholar 
    Bacheler, N. M. et al. Comparison of trap and underwater video gears for indexing reef fish presence and abundance in the southeast United States. Fish. Res. 143, 81–88 (2013).
    Google Scholar 
    Wells, R. D., Boswell, K. M., Cowan, J. H. Jr. & Patterson, W. F. III. Size selectivity of sampling gears targeting red snapper in the northern Gulf of Mexico. Fish. Res. 89, 294–299 (2008).
    Google Scholar 
    Emslie, M. J., Cheal, A. J., MacNeil, M. A., Miller, I. R. & Sweatman, H. P. Reef fish communities are spooked by scuba surveys and may take hours to recover. PeerJ 6, e4886 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Piggott, C. V., Depczynski, M., Gagliano, M. & Langlois, T. J. Remote video methods for studying juvenile fish populations in challenging environments. J. Exp. Mar. Biol. Ecol. 532, 151454 (2020).
    Google Scholar 
    Dean, W. E. Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition: Comparison with other methods. J. Sediment. Res. 44, 242–248 (1974).CAS 

    Google Scholar 
    Lorenzen, C. J. Determination of chlorophyll and pheo-pigments: Spectrophotometric equations. Limnol. Oceanogr. 12, 343–346 (1967).ADS 
    CAS 

    Google Scholar 
    Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
    Google Scholar 
    McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).
    Google Scholar 
    Clarke, K. R. & Gorley, R. N. PRIMER v7: User Manual/Tutorial (2015).Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to software and statistical methods (2008).R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2021).Saier, B. Subtidal and intertidal mussel beds (Mytilus edulis L.) in the Wadden Sea: Diversity differences of associated epifauna. Helgol. Mar. Res. 56, 44–50 (2002).ADS 

    Google Scholar 
    Peterson, C. H., Grabowski, J. H. & Powers, S. P. Estimated enhancement of fish production resulting from restoring oyster reef habitat: Quantitative valuation. Mar. Ecol. Prog. Ser. 264, 249–264 (2003).ADS 

    Google Scholar 
    Gutiérrez, J. L., Jones, C. G., Strayer, D. L. & Iribarne, O. O. Mollusks as ecosystem engineers: The role of shell production in aquatic habitats. Oikos 101, 79–90 (2003).
    Google Scholar 
    Norkko, A., Hewitt, J. E., Thrush, S. F. & Funnell, T. Benthic-pelagic coupling and suspension-feeding bivalves: Linking site-specific sediment flux and biodeposition to benthic community structure. Limnol. Oceanogr. 46, 2067–2072 (2001).ADS 

    Google Scholar 
    Russell, B. The food and feeding habits of rocky reef fish of north-eastern New Zealand. N. Z. J. Mar. Freshw. Res. 17, 121–145 (1983).
    Google Scholar 
    Gillies, C., Creighton, C. & McLeod, I. Shellfish reef habitats: A synopsis to underpin the repair and conservation of Australia’s environmentally, socially and economically important bays and estuaries. Report to the National Environmental Science Programme, Marine Biodiversity Hub, Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER) Publication, James Cook University, Townsville, Qld, Australia (2015).Lenihan, H. S. et al. Cascading of habitat degradation: Oyster reefs invaded by refugee fishes escaping stress. Ecol. Appl. 11, 764–782 (2001).
    Google Scholar 
    Connell, S. & Jones, G. The influence of habitat complexity on postrecruitment processes in a temperate reef fish population. J. Exp. Mar. Biol. Ecol. 151, 271–294 (1991).
    Google Scholar 
    Usmar, N. Ontogeny and Ecology of Snapper (Pagrus auratus) in an estuary, the Mahurangi Harbour (University of Auckland, 2009).
    Google Scholar 
    Willis, T. J. & Anderson, M. J. Structure of cryptic reef fish assemblages: Relationships with habitat characteristics and predator density. Mar. Ecol. Prog. Ser. 257, 209–221 (2003).ADS 

    Google Scholar 
    Thompson, S. Homing in a territorial reef fish. Copeia 1983, 832–834 (1983).
    Google Scholar 
    Thrush, S. F., Schultz, D., Hewitt, J. E. & Talley, D. Habitat structure in soft-sediment environments and abundance of juvenile snapper Pagrus auratus. Mar. Ecol. Prog. Ser. 245, 273–280 (2002).ADS 

    Google Scholar 
    Pickering, H. & Whitmarsh, D. Artificial reefs and fisheries exploitation: A review of the ‘attraction versus production’debate, the influence of design and its significance for policy. Fish. Res. 31, 39–59 (1997).
    Google Scholar 
    Karp, M. A., Seitz, R. D. & Fabrizio, M. C. Faunal communities on restored oyster reefs: Effects of habitat complexity and environmental conditions. Mar. Ecol. Prog. Ser. 590, 35–51 (2018).ADS 

    Google Scholar 
    Hanke, M. H., Posey, M. H. & Alphin, T. D. The effects of intertidal oyster reef habitat characteristics on faunal utilization. Mar. Ecol. Prog. Ser. 581, 57–70 (2017).ADS 

    Google Scholar 
    Cranfield, H., Rowden, A., Smith, D., Gordon, D. & Michael, K. Macrofaunal assemblages of benthic habitat of different complexity and the proposition of a model of biogenic reef habitat regeneration in Foveaux Strait, New Zealand. J. Sea Res. 52, 109–125 (2004).ADS 

    Google Scholar 
    Norling, P. & Kautsky, N. Structural and functional effects of Mytilus edulis on diversity of associated species and ecosystem functioning. Mar. Ecol. Prog. Ser. 351, 163–175 (2007).ADS 

    Google Scholar 
    Jaunatre, R. et al. New synthetic indicators to assess community resilience and restoration success. Ecol. Indicators 29, 468–477 (2013).
    Google Scholar 
    O’Meara, T. A., Hewitt, J. E., Thrush, S. F., Douglas, E. J. & Lohrer, A. M. Denitrification and the role of macrofauna across estuarine gradients in nutrient and sediment loading. Estuaries Coasts 43, 1394–1405. https://doi.org/10.1007/s12237-020-00728-x (2020).CAS 
    Article 

    Google Scholar 
    McCann, L. D. Oligochaete influence on settlement, growth and reproduction in a surface-deposit-feeding polychaete. J. Exp. Mar. Biol. Ecol. 131, 233–253 (1989).
    Google Scholar 
    Hope, J. A., Paterson, D. M. & Thrush, S. F. The role of microphytobenthos in soft-sediment ecological networks and their contribution to the delivery of multiple ecosystem services. J. Ecol. 108, 815–830 (2020).
    Google Scholar 
    Christianen, M. J. et al. Benthic primary producers are key to sustain the Wadden Sea food web: Stable carbon isotope analysis at landscape scale. Ecology 98, 1498–1512 (2017).CAS 
    PubMed 

    Google Scholar 
    Commito, J. A. & Dankers, N. M. Dynamics of spatial and temporal complexity in European and North American soft-bottom mussel beds. In Ecological Comparisons of Sedimentary Shores, 39–59 (Springer, Berlin, 2001).Arribas, L. P., Donnarumma, L., Palomo, M. G. & Scrosati, R. A. Intertidal mussels as ecosystem engineers: Their associated invertebrate biodiversity under contrasting wave exposures. Mar. Biodivers. 44, 203–211 (2014).
    Google Scholar 
    Walles, B., Salvador de Paiva, J., van Prooijen, B. C., Ysebaert, T. & Smaal, A. C. The ecosystem engineer Crassostrea gigas affects tidal flat morphology beyond the boundary of their reef structures. Estuaries Coasts 38, 941–950 (2015).
    Google Scholar 
    Tsuchiya, M. & Nishihira, M. Islands of Mytilus edulis as a habitat for small intertidal animals: Effect of Mytilus age structure on the species composition of the associated fauna and community organization. Mar. Ecol. Prog. Ser. 31, 171–178 (1986).ADS 

    Google Scholar 
    Craeymeersch, J. A. & Jansen, H. M. Bivalve assemblages as hotspots for biodiversity. In Goods and Services of Marine Bivalves, 275–294 (Springer, Cham, 2019).Buschbaum, C. et al. Mytilid mussels: Global habitat engineers in coastal sediments. Helgol. Mar. Res. 63, 47–58 (2009).ADS 

    Google Scholar  More

  • in

    Loss of a globally unique kelp forest from Oman

    Wernberg, T., Krumhansl, K. A., Filbee-Dexter, K. & Pedersen, M. Status and trends for the world’s kelp forests. In World Seas: An Environmental Evaluation (ed. Sheppard, C.) 57–78 (Elsevier, 2019).Chapter 

    Google Scholar 
    Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312. https://doi.org/10.1038/s41558-019-0412-1 (2019).ADS 
    Article 

    Google Scholar 
    Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172. https://doi.org/10.1126/science.aad8745 (2016).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Coleman, M. A., Minne, A. J. P., Vranken, S. & Wernberg, T. Genetic tropicalisation following a marine heatwave. Sci. Rep. UK 10, 12726. https://doi.org/10.1038/s41598-020-69665-w (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. Proc. Natl. Acad. Sci. 113, 13785–13790. https://doi.org/10.1073/pnas.1606102113 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Arafeh-Dalmau, N. et al. Extreme marine heatwaves alter kelp forest community near its equatorward distribution limit. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00499 (2019).Article 

    Google Scholar 
    Tanaka, K., Taino, S., Haraguchi, H., Prendergast, G. & Hiraoka, M. Warming off southwestern Japan linked to distributional shifts of subtidal canopy-forming seaweeds. Ecol. Evol. 2, 2854–2865. https://doi.org/10.1002/ece3.391 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wernberg, T. et al. Genetic diversity and kelp forest vulnerability to climatic stress. Sci. Rep. UK 8, 1851. https://doi.org/10.1038/s41598-018-20009-9 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    Graham, M. H., Kinlan, B. P., Druehl, L. D., Garske, L. E. & Banks, S. Deep-water kelp refugia as potential hotspots of tropical marine diversity and productivity. Proc. Natl. Acad. Sci. 104, 16576. https://doi.org/10.1073/pnas.0704778104 (2007).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marzinelli, E. M. et al. Large-scale geographic variation in distribution and abundance of Australian deep-water kelp forests. PLoS ONE 10, e0118390. https://doi.org/10.1371/journal.pone.0118390 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Varela, R., Rodríguez-Díaz, L., de Castro, M. & Gómez-Gesteira, M. Influence of Eastern Upwelling systems on marine heatwaves occurrence. Glob. Planet Change 196, 103379. https://doi.org/10.1016/j.gloplacha.2020.103379 (2021).Article 

    Google Scholar 
    Assis, J. et al. Deep reefs are climatic refugia for genetic diversity of marine forests. J. Biogeogr. 43, 833–844. https://doi.org/10.1111/jbi.12677 (2016).Article 

    Google Scholar 
    Lourenço, C. R. et al. Upwelling areas as climate change refugia for the distribution and genetic diversity of a marine macroalga. J. Biogeogr. 43, 1595–1607. https://doi.org/10.1111/jbi.12744 (2016).Article 

    Google Scholar 
    Vranken, S. et al. Genotype-environment mismatch of kelp forests under climate change. Mol. Ecol. 30, 3730–3746. https://doi.org/10.1111/mec.15993 (2021).Article 
    PubMed 

    Google Scholar 
    Wood, G. et al. Genomic vulnerability of a dominant seaweed points to future-proofing pathways for Australia’s underwater forests. Glob. Change Biol. 27, 2200–2212. https://doi.org/10.1111/gcb.15534 (2021).ADS 
    Article 

    Google Scholar 
    Wernberg, T. et al. Biology and ecology of the globally significant kelp Ecklonia radiata. Oceanogr. Mar. Biol.: An Annu. Rev. 57, 265–324 (2019).Article 

    Google Scholar 
    Durrant, H. M. S., Barrett, N. S., Edgar, G. J., Coleman, M. A. & Burridge, C. P. Shallow phylogeographic histories of key species in a biodiversity hotspot. Phycologia 54, 556–565. https://doi.org/10.2216/15-24.1 (2015).Article 

    Google Scholar 
    Rothman, M. D. et al. A molecular investigation of the genus Ecklonia (Phaeophyceae, Laminariales) with special focus on the southern hemisphere. J. Phycol. 51, 236–246. https://doi.org/10.1111/jpy.12264 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Starko, S. et al. A comprehensive kelp phylogeny sheds light on the evolution of an ecosystem. Mol. Phylogenetics Evol. 136, 138–150. https://doi.org/10.1016/j.ympev.2019.04.012 (2019).Article 

    Google Scholar 
    Shepherd, S. A. & Edgar, G. J. In (eds Shepherd, S. A. & Edgar, G. J.) (CSIRO Publishing, 2013).Guiry, M. D. et al. AlgaeBase: An on-line resource for algae. Cryptogam. Algol. 35, 105–115, 111 (2014).Barratt, L., Ormond, R. F. G. & Wrathall, T. J. Ecological studies of southern Oman kelp communities. Part 1. Ecology and productivity of the sublittoral algae Ecklonia radiata and Sargassopsis zanardinii (Council for the conservation of the environment and water resources, and regional organisation for the protection of the marine environment, Muscat and Kuwait, 1986).Barratt, L. et al. An ecological study of the rocky shores on the south coast of Oman. Report of IUCN to UNEP’s regional seas programme, Vol. 104 (Tropical Marine Research Unit, York, 1984).Klaus, R. & Turner, J. R. The marine biotopes of the Socotra Archipelago. Fauna Arab. 20, 45–116 (2004).
    Google Scholar 
    Claereboudt, M. R. Oman. In World Seas: An Environmental Evaluation, (ed. Sheppard, C.) 25–47 (Academic Press, 2019).Savidge, G., Lennon, H. J. & Matthews, A. D. A shore based survey of oceanographic variables in the Dhofar region of southern Oman, August–October 1985. In Ecological Studies of Southern Oman Kelp Communities. Summary Report, 4–21. ROPME/GC-6/001 (1988).Hatcher, B. G., Kirkman, H. & Wood, W. F. Growth of the kelp Ecklonia-radiata near the northern limit of its range in Western-Australia. Mar. Biol. 95, 63–73. https://doi.org/10.1007/Bf00447486 (1987).Article 

    Google Scholar 
    Veenhof, R. et al. Kelp gametophytes in changing oceans. Oceanogr. Mar. Biol. Annu. Rev. 60 (in press).Goes, J. I., Thoppil, P. G., Gomes, H. D. R. & Fasullo, J. T. Warming of the Eurasian landmass is making the Arabian sea more productive. Science 308, 545. https://doi.org/10.1126/science.1106610 (2005).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Roxy, M. K. et al. A reduction in marine primary productivity driven by rapid warming over the tropical Indian Ocean. Geophys. Res. Lett. 43, 826–833. https://doi.org/10.1002/2015GL066979 (2016).ADS 
    Article 

    Google Scholar 
    Watanabe, T. K., Watanabe, T., Yamazaki, A., Pfeiffer, M. & Claereboudt, M. R. Oman coral δ18O seawater record suggests that Western Indian Ocean upwelling uncouples from the Indian Ocean Dipole during the global-warming hiatus. Sci. Rep. UK 9, 1887. https://doi.org/10.1038/s41598-018-38429-y (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Watanabe, T. K. et al. Past summer upwelling events in the Gulf of Oman derived from a coral geochemical record. Sci. Rep. UK 7, 4568. https://doi.org/10.1038/s41598-017-04865-5 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Edwards, M. & Estes, J. A. Catastrophe, recovery and range limitation in NE Pacific kelp forests: a large-scale perspective. Mar. Ecol. Prog. Ser. 320, 79–87 (2006).ADS 
    Article 

    Google Scholar 
    Glynn, P. W. Monsoonal upwelling and episodic Acanthaster predation as probable controls of coral reef distribution and community structure in Oman, Indian Ocean. Atoll Res. Bull. 379, 1–66 (1993).Article 

    Google Scholar 
    Hiscock, S., Barratt, L. & Ormond, R. The marine algae of Dhofar, Oman-an upwelling system in the Arabian Sea. Br. Phycol. J. 19, 194 (1984).Article 

    Google Scholar 
    Kirkman, H. The 1st year in the life-history and the survival of the juvenile marine macrophyte, Ecklonia-radiata (Turn) J Agardh. J. Exp. Mar. Biol. Ecol. 55, 243–254. https://doi.org/10.1016/0022-0981(81)90115-5 (1981).Article 

    Google Scholar 
    Maeda, T., Kawai, T., Nakaoka, M. & Yotsukura, N. Effective DNA extraction method for fragment analysis using capillary sequencer of the kelp, Saccharina. J. Appl. Phycol. 25, 337–347 (2013).CAS 
    Article 

    Google Scholar 
    Voisin, M., Engel, C. R. & Viard, F. Differential shuffling of native genetic diversity across introduced regions in a brown alga: Aquaculture vs. maritime traffic effects. Proc. Natl. Acad. Sci. USA 102, 5432. https://doi.org/10.1073/pnas.0501754102 (2005).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lane, C. E., Lindstrom, S. C. & Saunders, G. W. A molecular assessment of northeast Pacific Alaria species (Laminariales, Phaeophyceae) with reference to the utility of DNA barcoding. Mol. Phylogenetics Evol. 44, 634–648 (2007).CAS 
    Article 

    Google Scholar 
    Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Johnson, M. et al. NCBI BLAST: A better web interface. Nucl. Acids Res. 36, W5–W9 (2008).CAS 
    Article 

    Google Scholar 
    Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucl. Acids Res. 44, W232–W235 (2016).CAS 
    Article 

    Google Scholar 
    Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K., Von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
    Article 

    Google Scholar 
    Chernomor, O., von Haeseler, A. & Minh, B. Q. Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol. 65, 997–1008. https://doi.org/10.1093/sysbio/syw037 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hoang, D. T., Chernomor, O., Von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).CAS 
    Article 

    Google Scholar 
    Rambaut, A. & Drummond, A. FigTree: Tree Figure Drawing Tool, Version 1.2. 2 (Institute of Evolutionary Biology, University of Edinburgh, 2008).
    Google Scholar 
    Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302. https://doi.org/10.1093/molbev/msx248 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Clement, M., Posada, D. & Crandall, K. A. TCS: A computer program to estimate gene genealogies. Mol. Ecol. 9, 1657–1659. https://doi.org/10.1046/j.1365-294x.2000.01020.x (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    Leigh, J. W. & Bryant, D. popart: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

    Google Scholar 
    Team, R. C. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).Vergés, A. et al. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proc. Natl. Acad. Sci. 113, 13791–13796. https://doi.org/10.1073/pnas.1610725113 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wood, G. et al. Using genetics to test provenance effects and to optimise seaweed restoration. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13707 (2020).Article 

    Google Scholar 
    Wynne, M. J. A checklist of the benthic marine algae of the Northern Arabian Sea coast of the Sultanate of Oman. Bot. Mar. 61, 481–498. https://doi.org/10.1515/bot-2018-0035 (2018).Richards, G. & Wynne, M. J. 57 (2003).Schils, T. Marine Plant Communities of Upwelling Areas Within the Arabian Sea: A Taxonomic, Ecological ABD Biogeographic Case Study on the Marine Flora of the Socotra Archipelago (Yemen) and Masirah Island (Oman). PhD thesis (2002).Schils, T. & Coppejans, E. Phytogeography of upwelling areas in the Arabian Sea. J. Biogeogr. 30, 1339–1356. https://doi.org/10.1046/j.1365-2699.2003.00933.x (2003).Article 

    Google Scholar 
    Schils, T. & Wilson, S. C. temperature threshold as a biogeographic barrier in northern Indian Ocean Macroalgae. J. Phycol. 42, 749–756. https://doi.org/10.1111/j.1529-8817.2006.00242.x (2006).Article 

    Google Scholar 
    Wiggert, J. D., Hood, R. R., Banse, K. & Kindle, J. C. Monsoon-driven biogeochemical processes in the Arabian Sea. Prog. Oceanogr. 65, 176–213. https://doi.org/10.1016/j.pocean.2005.03.008 (2005).ADS 
    Article 

    Google Scholar 
    Serisawa, Y., Imoto, Z., Ishikawa, T. & Ohno, M. Decline of the Ecklonia cava population associated with increased seawater temperatures in Tosa Bay, southern Japan. Fish. Sci. 70, 189–191. https://doi.org/10.1111/j.0919-9268.2004.00788.x (2004).CAS 
    Article 

    Google Scholar 
    Nelson, W., Duffy, C., Trnski, T. & Stewart, R. Mesophotic Ecklonia radiata (Laminariales) at Rangitāhua, Kermadec Islands, New Zealand. Phycologia 57, 534–538. https://doi.org/10.2216/18-9.1 (2018).Article 

    Google Scholar 
    Richmond, S. & Stevens, T. Classifying benthic biotopes on sub-tropical continental shelf reefs: How useful are abiotic surrogates?. Estuar. Coast. Shelf Sci. 138, 79–89. https://doi.org/10.1016/j.ecss.2013.12.012 (2014).ADS 
    Article 

    Google Scholar 
    Davis, T. R., Champion, C. & Coleman, M. A. Climate refugia for kelp within an ocean warming hotspot revealed by stacked species distribution modelling. Mar. Environ. Res. 166, 105267. https://doi.org/10.1016/j.marenvres.2021.105267 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    Jooste, C. M., Oliver, J., Emami-Khoyi, A. & Teske, P. R. Is the Wild Coast in eastern South Africa a distinct marine bioregion?. Helgol. Mar. Res. 72, 6. https://doi.org/10.1186/s10152-018-0509-3 (2018).Article 

    Google Scholar 
    Bolton, J. J. et al. Where is the western limit of the tropical Indian Ocean seaweed flora? An analysis of intertidal seaweed biogeography on the east coast of South Africa. Mar. Biol. 144, 51–59. https://doi.org/10.1007/s00227-003-1182-9 (2004).Article 

    Google Scholar 
    Bolton, J. J. The biogeography of kelps (Laminariales, Phaeophyceae): A global analysis with new insights from recent advances in molecular phylogenetics. Helgol. Mar. Res. 64, 263–279. https://doi.org/10.1007/s10152-010-0211-6 (2010).ADS 
    Article 

    Google Scholar 
    Bolton JJ, De Clerck O, John DM (2003). Seaweed diversity patterns in Sub-Saharan Africa. In Proceedings of the Marine Biodiversity in Sub-Saharan Africa: The Known and the Unknown. (eds. Decker, C. et al. ) Cape Town, South Africa, pp. 229–241 (2003).Wood, M. et al. Zanzibar and Indian Ocean trade in the first millennium CE: The glass bead evidence. Archaeol. Anthropol. Sci. 9, 879–901. https://doi.org/10.1007/s12520-015-0310-z (2017).Article 

    Google Scholar 
    Pollard, E., Bates, R., Ichumbaki, E. B. & Bita, C. Shipwreck evidence from Kilwa, Tanzania. Int. J. Naut. Archaeol. 45, 352–369. https://doi.org/10.1111/1095-9270.12185 (2016).Article 

    Google Scholar 
    Staples, M. In Oman. A Maritime History (eds Al Salimi, A. & Staples, E.) Chap. 4, 81–116 (Georg Olms Verlag, 2017).Assis, J. et al. Past climate changes and strong oceanographic barriers structured low-latitude genetic relics for the golden kelp Laminaria ochroleuca. J. Biogeogr. 45, 2326–2336. https://doi.org/10.1111/jbi.13425 (2018).Article 

    Google Scholar 
    Wade, R. et al. Macroalgal germplasm banking for conservation, food security, and industry. PLoS Biol. 18, e3000641. https://doi.org/10.1371/journal.pbio.3000641 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Coleman, M. A. et al. Restore or redefine: Future trajectories for restoration. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00237 (2020).Article 

    Google Scholar  More

  • in

    Subaqueous foraging among carnivorous dinosaurs

    Kelley, N. P. & Pyenson, N. D. Evolutionary innovation and ecology in marine tetrapods from the Triassic to the Anthropocene. Science 348, aaa3716 (2015).PubMed 

    Google Scholar 
    Gutarra, S. & Rahman, I. A. The locomotion of extinct secondarily aquatic tetrapods. Biol. Rev. 97, 67–98 (2022).PubMed 

    Google Scholar 
    Owen, R. A description of a portion of the skeleton of the Cetiosaurus, a gigantic extinct saurian reptile occurring in the oolitic formations of different portions of England. Proc. Geol. Soc. Lond. 3, 457–462 (1841).
    Google Scholar 
    Cope, E. On the characters of the skull in the Hadrosauridae. Proc. Natl Acad. Nat. Sci. USA 35, 97–107 (1883).
    Google Scholar 
    Bidar, A., Demay, L. & Thomel, G. Compsognathus corallestris, une nouvelle espèce de dinosaurien théropode du Portlandien de Canjuers (Sud-Est de la France). Annales Muséum d’Histoire Naturelle de Nice 1, 9–40 (1972).
    Google Scholar 
    Norell, M. A., Makovicky, P. J. & Currie, P. J. The beaks of ostrich dinosaurs. Nature 412, 873–874 (2001).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Tereschenko, V. S. Adaptive features of protoceratopoids (Ornithischia: Neoceratopsia). Paleontol. J. 42, 273–286 (2008).
    Google Scholar 
    Lee, Y. N. et al. Resolving the long-standing enigmas of a giant ornithomimosaur Deinocheirus mirificus. Nature 515, 257–260 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Ibrahim, N. et al. Semiaquatic adaptations in a giant predatory dinosaur. Science 345, 1613–1616 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Cau, A. et al. Synchrotron scanning reveals amphibious ecomorphology in a new clade of bird-like dinosaurs. Nature 552, 395–399 (2017).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Ibrahim, N. et al. Tail-propelled aquatic locomotion in a theropod dinosaur. Nature 581, 67–70 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Henderson, D. M. A buoyancy, balance and stability challenge to the hypothesis of a semi-aquatic Spinosaurus Stromer, 1915 (Dinosauria: Theropoda). PeerJ 6, e5409 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Hone, D. W. E. & Holtz, T. R. Jr Evaluating the ecology of Spinosaurus: shoreline generalist or aquatic pursuit specialist? Palaeontol. Electronica 24, a03 (2021).
    Google Scholar 
    Thewissen, J. G., Cooper, L. N., Clementz, M. T., Bajpai, S. & Tiwari, B. N. Whales originated from aquatic artiodactyls in the Eocene epoch of India. Nature 450, 1190–1194 (2007).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Houssaye, A. Bone histology of aquatic reptiles: what does it tell us about secondary adaptation to an aquatic life? Biol. J. Linn. Soc. 108, 3–21 (2013).
    Google Scholar 
    Motani, R. et al. A basal ichthyosauriform with a short snout from the Lower Triassic of China. Nature 517, 485–488 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Rauhut, O. W. & Pol, D. Probable basal allosauroid from the early Middle Jurassic Cañadón Asfalto Formation of Argentina highlights phylogenetic uncertainty in tetanuran theropod dinosaurs. Sci. Rep. 9, 1–9 (2019).
    Google Scholar 
    You, H. L. et al. A nearly modern amphibious bird from the Early Cretaceous of northwestern China. Science 312, 1640–1643 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Wilson, L. E. & Chin, K. Comparative osteohistology of Hesperornis with reference to pygoscelid penguins: the effects of climate and behaviour on avian bone microstructure. R. Soc. Open Sci. 1, 140245 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gatesy, S. M. & Dial, K. P. Locomotor modules and the evolution of avian flight. Evolution 50, 331–340 (1996).PubMed 

    Google Scholar 
    Amiot, R. et al. Oxygen isotope evidence for semi-aquatic habits among spinosaurid theropods. Geology 38, 139–142 (2010).ADS 
    CAS 

    Google Scholar 
    Hassler, A. et al. Calcium isotopes offer clues on resource partitioning among Cretaceous predatory dinosaurs. Proc. R. Soc. B 285, 20180197 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Larramendi, A., Paul, G. S. & Hsu, S. Y. A review and reappraisal of the specific gravities of present and past multicellular organisms, with an emphasis on tetrapods. Anat. Rec. 304, 1833–1888 (2021).
    Google Scholar 
    Charig, A. J. & Milner, A. C. Baryonyx, a remarkable new theropod dinosaur. Nature 324, 359–361 (1986).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Houssaye, A. “Pachyostosis” in aquatic amniotes: a review. Integr. Zool. 4, 325–340 (2009).PubMed 

    Google Scholar 
    Houssaye, A., Sander, M. P. & Klein, N. Adaptive patterns in aquatic amniote bone microanatomy—more complex than previously thought. Integr. Comp. Biol. 56, 1349–1369 (2016).PubMed 

    Google Scholar 
    Quemeneur, S., De Buffrenil, V. & Laurin, M. Microanatomy of the amniote femur and inference of lifestyle in limbed vertebrates. Biol. J. Linn. Soc. 109, 644–655 (2013).
    Google Scholar 
    Canoville, A., de Buffrénil, V. & Laurin, M. Microanatomical diversity of amniote ribs: an exploratory quantitative study. Biol. J. Linn. Soc. 118, 706–733 (2016).
    Google Scholar 
    Amson, E., de Muizon, C., Laurin, M., Argot, C. & de Buffrénil, V. Gradual adaptation of bone structure to aquatic lifestyle in extinct sloths from Peru. Proc. R. Soc. B 281, 20140192 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Grafen, A. The phylogenetic regression. Philos. Trans. R. Soc. B 326, 119–157 (1989).ADS 
    CAS 

    Google Scholar 
    Liem, K. F. Adaptive significance of intra-and interspecific differences in the feeding repertoires of cichlid fishes. Am. Zool. 20, 295–314 (1980).
    Google Scholar 
    Turner, A. H., Pol, D., Clarke, J. A., Erickson, G. M. & Norell, M. A. A basal dromaeosaurid and size evolution preceding avian flight. Science 317, 1378–1381 (2007).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Voeten, D. F. et al. Wing bone geometry reveals active flight in Archaeopteryx. Nat. Commun. 9, 1319 (2018).
    Google Scholar 
    Houssaye, A., Martin, F., Boisserie, J. R. & Lihoreau, F. Paleoecological inferences from long bone microanatomical specializations in Hippopotamoidea (Mammalia, Artiodactyla). J. Mamm. Evol. 28, 847–870 (2021).
    Google Scholar 
    Amson, E. & Bibi, F. Differing effects of size and lifestyle on bone structure in mammals. BMC Biol. 19, 87 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Malafaia, E. et al. A new spinosaurid theropod (Dinosauria: Megalosauroidea) from the upper Barremian of Vallibona, Spain: Implications for spinosaurid diversity in the Early Cretaceous of the Iberian Peninsula. Cret. Res. 106, 104221 (2020).
    Google Scholar 
    Sereno, P. C. et al. A long-snouted predatory dinosaur from Africa and the evolution of spinosaurids. Science 282, 1298–1302 (1998).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Aureliano, T. et al. Semi-aquatic adaptations in a spinosaur from the Lower Cretaceous of Brazil. Cret. Res. 90, 283–295 (2018).
    Google Scholar 
    Barker, C. T. et al. New spinosaurids from the Wessex Formation (Early Cretaceous, UK) and the European origins of Spinosauridae. Sci. Rep. 11, 19340 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Taquet, P. Géologie et Paléontologie du Gisement de Gadoufaoua (Aptien du Niger) (Éditions du Centre national de la Recherche Scientifique, 1976).Rayfield, E. J., Milner, A. C., Xuan, V. B. & Young, P. G. Functional morphology of spinosaur ‘crocodile-mimic’ dinosaurs. J. Vertebr. Paleontol. 27, 892–901 (2007).
    Google Scholar 
    Benson, R. B., Butler, R. J., Carrano, M. T. & O’Connor, P. M. Air‐filled postcranial bones in theropod dinosaurs: physiological implications and the ‘reptile’–bird transition. Biol. Rev. 87, 168–193 (2012).PubMed 

    Google Scholar 
    Reid, R. E. H. Zonal “growth rings” in dinosaurs. Mod. Geol. 15, 19–48 (1990).
    Google Scholar 
    Chinsamy, A. & Raath, M. A. Preparation of fossil bone for histological examination. Palaeont. Afr. 29, 39–44 (1992).
    Google Scholar 
    Griffin, C. T. et al. Assessing ontogenetic maturity in extinct saurian reptiles. Biol. Rev. 96, 470–525 (2021).
    Google Scholar 
    Carrano, M. T., Benson, R. B. & Sampson, S. D. The phylogeny of Tetanurae (Dinosauria: Theropoda). J. Syst. Palaeontol. 10, 211–300 (2012).
    Google Scholar 
    Ibrahim, N. et al. Geology and paleontology of the Upper Cretaceous Kem Kem Group of eastern Morocco. ZooKeys 928, 1–216 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Smyth, R. S., Ibrahim, N. & Martill, D. M. Sigilmassasaurus is Spinosaurus: a reappraisal of African spinosaurines. Cret. Res. 114, 104520 (2020).
    Google Scholar 
    Goloboff, P. A., Farris, J. S. & Nixon, K. C. TNT, a free program for phylogenetic analysis. Cladistics 24, 774–786 (2008).
    Google Scholar 
    Erickson, G. M. Assessing dinosaur growth patterns: a microscopic revolution. Trends Ecol. Evol. 20, 677–684 (2005).PubMed 

    Google Scholar 
    Hayashi, S. et al. Bone inner structure suggests increasing aquatic adaptations in Desmostylia (Mammalia, Afrotheria). PLoS ONE 8, e59146 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Straehl, F. R., Scheyer, T. M., Forasiepi, A. M., MacPhee, R. D. E. & Sánchez-Villagra, M. R. Evolutionary patterns of bone histology and bone compactness in xenarthran mammal long bones. PLoS ONE 8, e69275 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Houssaye, A., Tafforeau, P., de Muizon, C. & Gingerich, P. D. Transition of Eocene whales from land to sea: evidence from bone microstructure. PLoS ONE 10, e0118409 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Girondot, M. & Laurin, M. Bone profiler: a tool to quantify, model, and statistically compare bone-section compactness profiles. J. Vertebr. Paleontol. 23, 458–461 (2003).
    Google Scholar 
    De Ricqlès, A. J., Padian, K., Horner, J. R., Lamm, E. T. & Myhrvold, N. Osteohistology of Confuciusornis sanctus (Theropoda: Aves). Journ. Vertebr. Paleontol. 23, 373–386 (2003).
    Google Scholar 
    Maddison, W. P. Mesquite: a modular system for evolutionary analysis. Evolution 62, 1103–1118 (2008).
    Google Scholar 
    Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Simoes, T. R. et al. The origin of squamates revealed by a Middle Triassic lizard from the Italian Alps. Nature 557, 706–709 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Nesbitt, S. J. et al. The earliest bird-line archosaurs and the assembly of the dinosaur body plan. Nature 544, 484–487 (2017).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Langer, M. C. et al. Untangling the dinosaur family tree. Nature 551, E1–E3 (2017).PubMed 

    Google Scholar 
    Brusatte, S. L., Lloyd, G. T., Wang, S. C. & Norell, M. A. Gradual assembly of avian body plan culminated in rapid rates of evolution across the dinosaur-bird transition. Curr. Biol. 24, 2386–2392 (2014).CAS 
    PubMed 

    Google Scholar 
    Prum, R. O. et al. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature 526, 569–573 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bapst, D. W. paleotree: an R package for paleontological and phylogenetic analyses of evolution. Methods Ecol. Evol. 3, 803–807 (2012).
    Google Scholar 
    Schmitz, L. & Motani, R. Nocturnality in dinosaurs inferred from scleral ring and orbit morphology. Science 332, 705–708 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Motani, R. & Schmitz, L. Phylogenetic versus functional signals in the evolution of form–function relationships in terrestrial vision. Evolution 65, 2245–2257 (2011).PubMed 

    Google Scholar  More