The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlements therefore imposes a fundamental constraint on the spatial distribution of the population through which a communicable disease can spread. In this analysis we use the spatial network structure of lighted development as a proxy for the distribution of ambient population to compare the spatiotemporal evolution of COVID-19 confirmed cases in the USA and China. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor on the NASA/NOAA Suomi satellite has been imaging night light at ~ 700 m resolution globally since 2012. Comparisons with sub-kilometer resolution census observations in different countries across different levels of development indicate that night light luminance scales with population density over ~ 3 orders of magnitude. However, VIIRS’ constant ~ 700 m resolution can provide a more detailed representation of population distribution in peri-urban and rural areas where aggregated census blocks lack comparable spatial detail. By varying the low luminance threshold of VIIRS-derived night light, we depict spatial networks of lighted development of varying degrees of connectivity within which populations are distributed. The resulting size distributions of spatial network components (connected clusters of nodes) vary with degree of connectivity, but maintain consistent scaling over a wide range (5 × to 10 × in area & number) of network sizes. At continental scales, spatial network rank-size distributions obtained from VIIRS night light brightness are well-described by power laws with exponents near −2 (slopes near −1) for a wide range of low luminance thresholds. The largest components (104to 105km2) represent spatially contiguous agglomerations of urban, suburban and periurban development, while the smallest components represent isolated rural settlements. Projecting county and city-level numbers of confirmed cases of COVID-19 for the USA and China (respectively) onto the corresponding spatial networks of lighted development allows the spatiotemporal evolution of the epidemic (infection and detection) to be quantified as propagation within networks of varying connectivity. Results for China show rapid nucleation and diffusion in January 2020 followed by rapid decreases in new cases in February. While most of the largest cities in China showed new confirmed cases approaching zero before the end of February, most of these cities also showed distinct second waves of cases in March or April. Whereas new cases in Wuhan did not approach zero until mid-March, as of December 2020 it has not yet experienced a second wave of cases. In contrast, the results for the USA show a wide range of trajectories, with an abrupt transition from slow increases in confirmed cases in a small number of network components in January and February, to rapid geographic dispersion to a larger number of components shortly before mobility reductions occurred in March. Results indicate that while most of the upper tail of the network had been exposed by the end of March, the lower tail of the component size distribution has only shown steep increases since mid-June.
Spatial synchrony may be tail‐dependent, that is, stronger when populations are abundant than scarce, or vice‐versa. Here, ‘tail‐dependent’ follows from distributions having a lower tail consisting of relatively low values and an upper tail of relatively high values. We present a general theory of how the distribution and correlation structure of an environmental driver translates into tail‐dependent spatial synchrony through a non‐linear response, and examine empirical evidence for theoretical predictions in giant kelp along the California coastline. In sheltered areas, kelp declines synchronously (lower‐tail dependence) when waves are relatively intense, because waves below a certain height do little damage to kelp. Conversely, in exposed areas, kelp is synchronised primarily by periods of calmness that cause shared recovery (upper‐tail dependence). We find evidence for geographies of tail dependence in synchrony, which helps structure regional population resilience: areas where population declines are asynchronous may be more resilient to disturbance because remnant populations facilitate reestablishment.
more » « less- NSF-PAR ID:
- 10367757
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Ecology Letters
- Volume:
- 25
- Issue:
- 5
- ISSN:
- 1461-023X
- Page Range / eLocation ID:
- p. 1189-1201
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Extreme climatic events (ECEs) are becoming more frequent and more intense due to climate change. Furthermore, there is reason to believe ECEs may modify tail associations between distinct population vital rates, or between values of an environmental variable measured in different locations. Tail associations between two variables are associations that occur between values in the left or right tails of the distributions of the variables. Two positively associated variables can be principally left‐tail associated (i.e., more correlated when they take low values than when they take high values) or right‐tail associated (more correlated when they take high than low values), even with the same overall correlation coefficient in both cases. We tested, in the context of non‐spatial stage‐structured matrix models, whether tail associations between stage‐specific vital rates may influence extinction risk. We also tested whether the nature of spatial tail associations of environmental variables can influence metapopulation extinction risk. For instance, if low values of an environmental variable reduce the growth rates of local populations, one may expect that left‐tail associations increase metapopulation extinction risks because then environmental catastrophes are spatially synchronized, presumably reducing the potential for rescue effects. For the non‐spatial, stage‐structured models we considered, left‐tail associations between vital rates did accentuate extinction risk compared to right‐tail associations, but the effect was small. In contrast, we showed that density dependence interacts with tail associations to influence metapopulation extinction risk substantially: For population models showing undercompensatory density dependence, left‐tail associations in environmental variables often strongly accentuated and right‐tail associations mitigated extinction risk, whereas the reverse was usually true for models showing overcompensatory density dependence. Tail associations and their asymmetries are taken into account in assessing risks in finance and other fields, but to our knowledge, our study is one of the first to consider how tail associations influence population extinction risk. Our modeling results provide an initial demonstration of a new mechanism influencing extinction risks and, in our view, should help motivate more comprehensive study of the mechanism and its importance for real populations in future work.
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null (Ed.)Abstract The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlements therefore imposes a fundamental constraint on the spatial distribution of the population through which a communicable disease can spread. In this analysis we use the spatial network structure of lighted development as a proxy for the distribution of ambient population to compare the spatiotemporal evolution of COVID-19 confirmed cases in the USA and China. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor on the NASA/NOAA Suomi satellite has been imaging night light at ~ 700 m resolution globally since 2012. Comparisons with sub-kilometer resolution census observations in different countries across different levels of development indicate that night light luminance scales with population density over ~ 3 orders of magnitude. However, VIIRS’ constant ~ 700 m resolution can provide a more detailed representation of population distribution in peri-urban and rural areas where aggregated census blocks lack comparable spatial detail. By varying the low luminance threshold of VIIRS-derived night light, we depict spatial networks of lighted development of varying degrees of connectivity within which populations are distributed. The resulting size distributions of spatial network components (connected clusters of nodes) vary with degree of connectivity, but maintain consistent scaling over a wide range (5 × to 10 × in area & number) of network sizes. At continental scales, spatial network rank-size distributions obtained from VIIRS night light brightness are well-described by power laws with exponents near −2 (slopes near −1) for a wide range of low luminance thresholds. The largest components (10 4 to 10 5 km 2 ) represent spatially contiguous agglomerations of urban, suburban and periurban development, while the smallest components represent isolated rural settlements. Projecting county and city-level numbers of confirmed cases of COVID-19 for the USA and China (respectively) onto the corresponding spatial networks of lighted development allows the spatiotemporal evolution of the epidemic (infection and detection) to be quantified as propagation within networks of varying connectivity. Results for China show rapid nucleation and diffusion in January 2020 followed by rapid decreases in new cases in February. While most of the largest cities in China showed new confirmed cases approaching zero before the end of February, most of these cities also showed distinct second waves of cases in March or April. Whereas new cases in Wuhan did not approach zero until mid-March, as of December 2020 it has not yet experienced a second wave of cases. In contrast, the results for the USA show a wide range of trajectories, with an abrupt transition from slow increases in confirmed cases in a small number of network components in January and February, to rapid geographic dispersion to a larger number of components shortly before mobility reductions occurred in March. Results indicate that while most of the upper tail of the network had been exposed by the end of March, the lower tail of the component size distribution has only shown steep increases since mid-June.more » « less
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Abstract Dispersal is a crucial component of species' responses to climate warming. Warming‐induced changes in species' distributions are the outcome of how temperature affects dispersal at the individual level. Yet, there is little or no theory that considers the temperature dependence of dispersal when investigating the impacts of warming on species' distributions.
Here I take a first step towards filling this key gap in our knowledge. I focus on ectotherms, species whose body temperature depends on the environmental temperature, not least because they constitute the majority of biodiversity on the planet. I develop a mathematical model of spatial population dynamics that explicitly incorporates mechanistic descriptions of ectotherm life history trait responses to temperature. A novel feature of this framework is the explicit temperature dependence of all phases of dispersal: emigration, transfer and settlement.
I report three key findings. First, dispersal, regardless of whether it is random or temperature‐dependent, allows both tropical and temperate ectotherms to track warming‐induced changes in their thermal environments and to expand their distributions beyond the lower and upper thermal limits of their respective climate envelopes. In the absence of dispersal mortality, warming does not alter these new distributional limits.
Second, an analysis based solely on trait response data predicts that tropical ectotherms should be able to expand their distributions polewards to a greater degree than temperate ectotherms. Analysis of the dynamical model confirms this prediction. Tropical ectotherms have an advantage when moving to cooler climates because they experience lower within‐patch and dispersal mortality, and their higher thermal optima and maximal birth rates allow them to take advantage of the warmer parts of the year. Previous theory has shown that tropical ectotherms are more successful in invading and adapting the temperate climates than vice versa. This study provides the key missing piece, by showing how temperature‐dependent dispersal could facilitate both invasion and adaptation.
Third, dispersal mortality does not affect the poleward expansion of ectotherm distributions. But, it prevents both tropical and temperate ectotherms from maintaining sink populations in localities that are too warm to be viable in the absence of dispersal. Dispersal mortality also affects species' abundance patterns, causing a larger decline in abundance throughout the range when species disperse randomly rather than in response to thermal habitat suitability. In this way, dispersal mortality can facilitate the evolution of dispersal modes that maximize fitness in warmer thermal environments.
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Abstract Because foundation species create structure in a community, understanding their ecological and evolutionary responses to global change is critical for predicting the ecological and economic management of species and communities that rely on them. Giant kelp (
Macrocystis pyrifera ) is a globally distributed foundation species with seasonal fluctuations in abundance in response to local nutrient levels, storm intensity, and ocean temperatures. Here we examine genetic variation in individual and population‐level responses of early life history stages (zoospore settlement, survival, and gametogenesis) to increased temperatures to determine the potential for natural selection on temperature‐tolerant individuals that would allow adaptation to a changing climate. We collected fertileM. pyrifera sporophyll blades from three sites along the California coast (Los Angeles, Santa Barbara, Monterey Bay) and induced zoospore release in the lab. Spores settled on microscope slides at three treatment temperatures (16, 20, and 22°C), matured for 21 days, and were imaged weekly to determine settlement, survival, and maturation success. On average, individuals from all sites showed lower rates of settlement and maturation in response to increasing temperature. However, the magnitude of the responses to temperature varied among populations. Survival tended to increase with temperature in Los Angeles and Santa Barbara populations but decreased with increasing temperature for the Monterey Bay population. We observed little genetic variation in temperature responses among individuals within sites, suggesting little scope for evolution within populations to increase the resilience ofM. pyrifera populations to warming ocean temperatures and predicted declines in kelp abundance. Yet sufficient dispersal among populations could allow for adaptation of early life history traits among populations via evolutionary rescue of declining populations.
