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NEWS AND VIEWS
07 July 2021

How to buffer against an urban food shortage

There is widespread concern that the risk of food shocks — sudden disruptions to food supply — is increasing. It emerges that a city’s vulnerability to food shocks can be reduced by diversifying its supply chains.

Zia Mehrabi

0

Zia Mehrabi

Zia Mehrabi is at the Sustainability Innovation Lab at Colorado and at the Environmental Studies Program, University of Colorado Boulder, Boulder, Colorado 80303, USA.

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More than half of the world’s population lives in urban areas, a proportion that is set to increase1 to 68% by 2050. These urban residents depend on supply chains to produce, procure, prepare and deliver food, and they are exposed to potential supply-chain disruptions and food shortages from changes in human activity and natural processes. There is growing recognition that food-system resilience needs to be improved, but how best to buffer against urban food shortages remains an open question for both research and policy. Writing in Nature, Gomez et al.2 assess how the flow of agricultural products to a city depends on the diversity of the city’s trading partners. The authors apply ideas from engineering — such as those used when ensuring infrastructure is protected from flooding — to inform the design of food systems that can buffer cities against food shortfalls.For decades, scientists and industry have been warning governments and consumers about the risks of food shortages. Such shortages have a range of possible causes, including droughts and heatwaves, pest and disease outbreaks, financial downturns and trade policies3. More recently, the COVID-19 pandemic has led academics, and society more generally, to revisit the question of how fragile urban food supplies really are (Fig. 1).

Figure 1 | Empty supermarket shelves during the COVID-19 pandemic. Many city dwellers around the world experienced such scenes, which were largely driven by panic buying and changing consumer behaviour. Gomez et al.2 demonstrate that the resilience of food supply chains can be increased by boosting their diversity.Credit: Oli Scarff/AFP/Getty

There have been many proposed solutions to deal with the dangers of food shortages, from climate-resilient agricultural management practices to promoting local food systems and self-sufficiency4. One solution that is gaining attention is to increase the number and variety of agricultural products, farms and companies procuring and delivering food. Diverse food supply chains might buffer cities against food shortages — in the same way that, in finance, a varied portfolio of stocks limits investment risk and, in ecology, a diverse mixture of species maintains ecosystem functions.
Read the paper: Supply chain diversity buffers cities against food shocks
Gomez et al. used data on the origin and destination of different agricultural commodities for 284 cities and 45 non-city geographical areas in the United States. They identified domestic food systems for each city — that is, all of the geographical areas that supply crops, meat, live animals or animal feed to that city. The authors then determined how many cities faced different thresholds of abrupt food-supply disruptions, known as food shocks, using the percentage difference between the minimum and mean of supply amounts for each food sector over four years for each city. More specifically, they counted the number of cities in which the minimum was more than a particular percentage (ranging from 3% to 15%) smaller than the mean in any one of those four years.Next, Gomez and colleagues combined those data with simple indicators of geographical similarity — such as the physical distance and difference in climate between each city and the geographical areas in that city’s supply network. With this information in hand, the authors tested the idea that groups of cities with more-diverse supply chains are better able to buffer against food shocks than are groups whose supply chains are less diverse. Indeed, they found that cities importing food from suppliers that are more dissimilar from themselves are less likely to face shocks than are cities whose supply-chain partners are less diverse. Such supply-chain benefits would not be reaped from having solely local food systems.Gomez et al. then considered design concepts from engineering, where infrastructure systems should be planned to withstand shocks — such as extreme flooding — of a given frequency and magnitude. The authors undertook some bold extrapolations, in which they estimated the size of food shocks that would be faced by different US cities given their current supply-chain diversity. They found that a rare shock, such as one occurring once in 100 years, would cause a food-supply loss of about 22–32% across different cities.
Transforming the global food system
The other implicit finding from Gomez and colleagues’ model is that even moderate supply-chain diversity is effective at reducing the probability of extremely large shocks. The authors also applied their analysis to shocks happening in multiple food sectors simultaneously. They obtained similar results to those for single-sector shocks — with supply-chain diversity also providing a buffering effect for these even rarer occurrences.Gomez and colleagues’ work has major implications for the way in which resilient food systems should be built, but it also has a few caveats. First, the authors used only four years of data for each city, posing problems for characterizing the distribution of shocks at each city. This limited time series makes it difficult to define the baseline variation in food supply — that is, what is considered normal — for consumers and retailers alike. It also makes it hard to see to what extent diversified supply chains buffer against food shortages under normal conditions compared with years marked by extreme events, and whether the net benefits are large enough to trigger a change in food-procurement policies.Second, the food-flow data used by Gomez et al. do not represent actual flows for each year, but instead are simply annual production quantities proportionally distributed according to observed flows5 in 2012. Therefore, the authors’ analysis does not capture, or allow for, rerouting or other social responses at the onset of extreme events. Such social responses within and after shock years would result in changing food flows across the supply network.
Rural areas drive increases in global obesity
Third, Gomez and colleagues did not validate the predictive worth of their model beyond the four years considered, or outside the United States. This lack of verification is perhaps most limiting for applying the findings in practice — partly because the stability of food supply is itself dynamic, and will change with increasing volumes and types of food consumed, as well as with production technology. Although the observed phenomenon and general patterns might hold in other years and geographical regions, no data or analyses exist to validate whether the authors’ design suggestions will protect against future shocks to the degree claimed.Designing urban food systems to specification is not as easy as engineering a bridge or dam that won’t fail in 100 years. The major global concern with respect to urban food shortages and food security is for populations of middle- to low-income countries, particularly those that are dependent on imports6. Theoretically, supply-chain diversity will also have a buffering effect for these populations when the number of urban dwellers starts to drastically increase in the coming years, especially in Africa. However, such nations are probably not accurately described by the model presented. Moreover, they have different policy options and capacities for producing diverse supply chains compared with those possible in the United States. Nevertheless, Gomez and colleagues’ work provides a timely and refreshing reminder that building diverse supply chains offers a crucial mechanism for protecting urban dwellers from food shortages.

Nature 595, 175-176 (2021)
doi: https://doi.org/10.1038/d41586-021-01758-6

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Dataset of food flow networksWe derive annual, intranational food flow networks for the USA using the Freight Analysis Framework version 4 (FAF4) database30. The derived networks are for different food sectors and include all metropolitan areas in the USA. The FAF4 database consists of annual commodity flows during 2012−2015 for 115 geographic areas in the USA and 43 different sectors. We focus on the following four food sectors in the FAF4 database: crops, live animals, animal feed and meat. The 115 geographic areas in the FAF4 database cover the entire contiguous USA, including 69 metropolitan statistical areas and 46 remainders of states (the remainder is the area of a state that is not part of a FAF4 metropolitan area).To obtain food flows for all metropolitan areas in the USA, we disaggregate the FAF4 database from 115 to 329 areas (Supplementary Fig. 4), out of which 284 are metropolitan or combined statistical areas (120 metropolitan and 164 combined statistical areas). The disaggregation is performed using different socioeconomic and agricultural-related variables as attractors of supply and demand. For each food sector, a flow with origin o and destination d in the FAF4 database is disaggregated to a metropolitan-level flow with origin o′ and destination d′ using a disaggregation variable a as the best attractor of supply or demand.The disaggregation is performed in two stages. In the first stage, the supply U of each FAF4 remainder of state is disaggregated to include all the metropolitan areas in that remainder of state as follows:$${U}_{{o}^{{prime} }d}^{c}=frac{{U}_{od}^{c}}{{a}_{o}}times {a}_{{o}^{{prime} }},$$
(3)
where ({U}_{{o}^{{prime} }d}^{c}) (in tons per year) is the disaggregated supply for food sector c in origin o′ that satisfies demand at the FAF4 destination d, ({U}_{od}^{c}) (in tons per year) is the FAF4 food flow for sector c between areas o and d, and ({a}_{{o}^{{prime} }}) and ao are the attractor variables for the new origin o′ and FAF4 origin o, respectively. In the second stage, ({U}_{{o}^{{prime} }d}^{c}) is further disaggregated into demand E using:$${E}_{{o}^{{prime} }{d}^{{prime} }}^{c}=frac{{U}_{{o}^{{prime} }d}^{c}}{{a}_{d}}times {a}_{{d}^{{prime} }},$$
(4)
where ({E}_{{o}^{{prime} }{d}^{{prime} }}^{c}) (in tons per year) is the demand at destination d′ for food sector c supplied by origin o′, while ({a}_{{d}^{{prime} }}) and ({a}_{d}) are the attractor variables at the disaggregated destination d′ and FAF4 destination d, respectively.The FAF4 database includes food flow data at both the state level (48 states) and metropolitan level (115 areas including 69 metropolitan areas). Prior to performing the disaggregation, we jointly use the FAF4 state data and the FAF4 metropolitan data to select the best performing attractor variables. That is, we first use equations (3) and (4) to disaggregate the FAF4 state-level data to the metropolitan-level for the metropolitan and remainder-of-state areas in FAF4. By comparing the performance of our disaggregated flow data against the empirical FAF4 metropolitan-level data, we select the best attractor variable for each food sector. The following attractor variables are considered: population47, employment48, wages48, number of establishments48 and cropland area49. These variables are selected on the basis of previous analysis and data availability50.To assess the performance of the attractor variables, we use the Pearson correlation coefficient between the empirical FAF4 flows and the disaggregated flows for the metropolitan areas and remainder-of-state areas in FAF4 (Extended Data Fig. 3). The performance is high with correlation values greater than 0.87 and an average of 0.95. Using the best-performing disaggregation variables, we build the food flow networks employed in this study. The nodes in the networks represent metropolitan and remainder-of-state areas, and the weighted links represent annual food flows during 2012−2015 for crops, live animals, feed and meat (Supplementary Fig. 5).The FAF4 metropolitan and remainder-of-state areas we used to select the attractor variables span a wide range of populations, cropland areas, and number of establishments, since these FAF4 areas include the largest cities in the USA and a broad range of medium-size cities. The values of the attractor variables used in the disaggregation are within the ranges implied by the FAF4 metropolitan data (Supplementary Fig. 6), indicating that the variables are reliable. The exception to this is population, which is only used to disaggregate meat demand. Population, however, has a high disaggregation performance with a correlation coefficient of 0.97 (Extended Data Fig. 3). In addition, the use of population to disaggregate meat demand is consistent with previous scaling results for metropolitan areas in the USA51 that have shown that metropolitan-level variables that are related to resource consumption scale approximately linearly with population.Food inflows supply chain diversityTo determine annual supply chain diversity, we extract the annual food buyer–supplier subgraph of each city and food sector from the food flow networks19. We refer to each of these subgraphs as a food system. The food buyer–supplier subgraph of a city i consists of all the supply chain interactions with its trading partners or neighbours j for a specific food sector. Our measure of supply chain diversity is based on the notion of functional distance52. We compute the functional distance d between i and any of its trading partners j by combining five different indicators: physical distance, climate correlation, urban classification, economic specialization and network modularity. The indicators are described below in the ‘Functional distance indicators’ section of the Methods. We also perform statistical analyses to evaluate the influence of the attractor variables on these indicators (Methods). The indicators represent stable characteristics of cities and therefore tend to remain fairly constant during our study period.The functional distance ({d}_{ij}^{r}) for an indicator r between any pair of connected nodes (i,j) is calculated as$${d}_{ij}^{r}={N}^{-1}|{r}_{k}-{r}_{i}|,$$
(5)
where the normalization constant N is determined as the maximum value of (|{r}_{k}-{r}_{i}|) between any node k in the network and i. In equation (5), ({d}_{ij}^{r}=0) for functionally similar nodes and ({d}_{ij}^{r}=1) for dissimilar nodes.For each city’s buyer−supplier subgraph and food sector, any pair of connected nodes has 5 different functional distance indicators associated with it. To combine these distance indicators into a single measure, we calculate the average functional distance indicator (langle {d}_{ij}^{r}rangle ) as the arithmetic average of the 5 functional distance indicators for any pair (i, j) of connected nodes. We use the discrete probability distribution of food inflows binned by (langle {d}_{ij}^{r}rangle ) categories, together with Shannon entropy53, to calculate the supply chain diversity ({D}_{i,c}^{t}) of node i and sector c at year t:$${D}_{i,c}^{t}=frac{-{sum }_{k=1}^{K}{Y}_{i,c}^{t}(k)mathrm{ln},{Y}_{i,c}^{t}(k)}{log ,K}.$$
(6)
For sector c and year t, ({Y}_{i,c}^{t}(k)) is the proportion of food inflows to node i within bin k. The k bin is obtained by binning all the (langle {d}_{ij}^{r}rangle ) values for node i into a total number of K bins.({D}_{i,c}^{t}) is sensitive to the total number of bins K. Thus, for each node in our food flow networks, we tested the sensitivity of ({D}_{i,c}^{t}) to the total number of bins K. For K = 15, D values stabilize (Supplementary Fig. 7); therefore, we used 15 bins when performing all calculations of functional diversity.Functional distance indicatorsThe average functional distance between a city and its trading partners is based on the following five indicators:

(1)

Physical distance indicator (PDI). The PDI is obtained by calculating the Euclidean distance from the centroid of each geographic area to the centroid of all other areas. The geometric centroids of all geographical areas are calculated using the GIS software ArcMap (https://desktop.arcgis.com/en/arcmap/).

(2)

Climate indicator (CI). To account for different climates in cities across the USA, the Palmer Drought Severity Index (PDSI) is used54. The monthly PDSI is obtained from the National Oceanic and Atmospheric Administration for the years 1895−2015 at the climate division geographic level. An area-weighted average is performed to aggregate the PDSI data to the metropolitan level. The CI is obtained by calculating the monthly correlation between an area and all other areas.

(3)

Urban classification indicator (UCI). To identify the urbanization level of a geographical area, the Urban-Rural Classification indicator of the National Center for Health Statistics is employed55. This indicator classifies counties using a scale from 1 to 6, where a value of 1 indicates the county is highly rural and a value of 6 means highly urban. The UCI is obtained at the metropolitan level using an area-weighted average of the county-level values.

(4)

Network modularity indicator (NMI). This indicator identifies geographical areas (network nodes) that belong to the same community. A community is a group of nodes whose strength interactions are stronger than with the rest of the network. To identify the network’s communities, we aggregate the flows from the four food sectors (crops, live animals, feed and meat) into a single-layer network. The communities are identified by maximizing the modularity measure of Newman56,57 using the greedy optimization algorithm of Blondel et al.58,59. Network nodes that lie in the same community are assigned a NMI of 1 and 0 otherwise.

(5)

Economic specialization indicator (ESI). Each geographical area is assigned a score based on its dominant economic supply sector. Supply is quantified using the FAF4 intranational commodity flows30. Areas with a dominant meat sector are assigned an ESI of 1, crops an ESI of 2, fruits and vegetables an ESI of 3, animal feed an ESI of 4, live animals an ESI of 5, milled grains an ESI of 6, and industrial products an ESI of 7.

Probabilities of food supply chain shockThe annual probability of food supply chain shock is calculated as the probability that food inflows to a city fall below a percentage of the average inflows for that city during 2012−20158. To compute this probability, we group all nodes from the 4 food flow networks (1,221 observations) into 6 diversity bins ordered from lowest to highest functional diversity D. The bin size is selected to obtain bins with similar number of observations, approximately 204 observations in each bin. For each city i and food sector c in a bin, we calculate the food supply chain shock ({S}_{i}^{c}) as$${S}_{i}^{c}=left[1-frac{min({I}_{i}^{c})}{langle {I}_{i}^{c}rangle }right]times 100,$$
(7)
where ({I}_{i}^{c}) is the time series of total food inflows to node i for sector c during 2012−2015, and ({rm{min }}({I}_{i}^{c})) and (langle {I}_{i}^{c}rangle ) are the minimum and average values of the time series ({I}_{i}^{c}), respectively.For each diversity bin b, we count the number of observations nb that meet the criteria ({S}_{i}^{c} > s) for (sin {3,4,5,ldots ,15}), with s being the shock intensity threshold. The probability of a food supply shock S being greater than s in bin b is calculated as:$${P}_{b}(S > s)=frac{{n}_{b}}{{N}_{b}},$$
(8)
where Nb is the total number of observations in bin b. Thus, for each shock intensity s, we obtain a set of probabilities of food supply chain shock,$$P(S > s)={P}_{b}(S > s),{rm{for}},b={1,ldots ,6}.$$
(9)
Furthermore, we adapt equations (8) and (9) to calculate the probability of a food supply chain shock S being greater than s, P′(S > s), under co-occurrence conditions. We define co-occurrence as any city that experiences a shock to 2 or more food sectors during 2012−2015. With this definition, P′(S > s) is calculated in a fashion similar to that described above. We bin the network’s nodes into 6 groups from lowest to highest diversity and determine the percentage of food supply chain shock with equation (7). Letting ({n}_{b}^{{prime} }) be the total number of cities for which ({S}_{i}^{c} > s,) holds for 2 or more food sectors, the probability of a food supply chain shock S being greater than the shock intensity s in bin b is now$${P}_{b}^{{prime} }(S > s)=frac{{n}_{b}^{{prime} }}{{N}_{b}^{{prime} }},$$
(10)
where ({N}_{b}^{{prime} }) is the total number of cities in bin b. Thus, under co-occurrence conditions, the new set of probabilities for each shock intensity s is$$P{prime} (S > s)={P}_{b}^{{prime} }(S > s),{rm{for}},b={1,ldots ,6}.$$
(11)
Statistical analysesWe use the disaggregated food flow data to calculate both the probability of food supply chain shock and supply chain diversity. Therefore, we perform two complementary analyses to test whether the attractor variables used in the disaggregation are causing a circularity issue in the empirical relationship between the probability of food supply chain shock and supply chain diversity. For the first analysis, we determine the Pearson correlation between the functional distance indicators (PDI, CI, UCI, NMI and ESI) and the attractor variables (Supplementary Fig. 8). We find that the attractor variables are weakly correlated with the functional distance indicators (Supplementary Table 1). For the second analysis, we determine the Pearson correlation between the food supply chain shock intensities and attractor variables for the 4 food sectors (Supplementary Fig. 9). The attractor variables are also weakly correlated with the food supply chain shock intensities (Supplementary Table 2). Thus, circularity is not unduly influencing the empirical relationship between the probability of shock and supply chain diversity.We also evaluate whether the empirical relationship between the probability of food supply chain shock and supply chain diversity is driven by the disaggregation of the original FAF4 data. For this, we recalculate the probability of shock and supply chain diversity using the FAF4 data. For the FAF4 data, the probability of shock also declines with rising supply chain diversity (Supplementary Fig. 10), similar to the reduction observed using the disaggregated food flow data (Fig. 1a), suggesting that the latter data are not driving the relationship.Furthermore, we test whether the relationship between the probability of food supply chain shock and supply chain diversity holds for different demand levels. To control for demand, we stratify all the data into low, medium and high demand levels using population or food inflows as proxies for demand. For both stratifications, the bounds are chosen so that each level has approximately the same number of data points. Using the stratified data, we recalculate the Pearson correlation between the probability of shock at 3%, 5%, 10% and 15% shock intensities and supply chain diversity for each level of population or food inflows (Supplementary Table 3). We find that the relationship between the probability of food supply chain shock and supply chain diversity holds for these different demand levels (Supplementary Table 3). Using the same exponential model in equation (1) to fit the relationship for the stratified data (Supplementary Fig. 11), we determine the exponential model parameters ks and D0,s for each demand level (Extended Data Table 6). These parameters fall within the 95% confidence interval of the parameters of the exponential model in the main text (Extended Data Table 1), indicating that the model is robust.We perform two different sensitivity analyses to assess the influence of the five distance indicators on the empirical relationship between the probability of food supply chain shock and supply chain diversity. The first analysis compares single-indicator diversity measures against the multi-indicator diversity measure calculated using all 5 indicators. Five different single-indicator diversity measures are compared, one measure for each of the 5 indicators: PDI, CI, UCI, NMI and ESI. For the second sensitivity analysis, we leave out one indicator at a time to calculate diversity using the 4 remaining indicators, which results in another 5 different diversity measures. The diversity measures for the sensitivity analyses are all calculated following the approach in the ‘Food inflows supply chain diversity’ section of the Methods. To perform the sensitivity analyses, we plot the empirical relationship between the probability of food supply chain shock and each diversity measure (Supplementary Figs. 12 and 13), and calculate the Pearson correlation of the data (Extended Data Table 7). The correlation coefficients are used to quantify the influence of the distance indicators on the relationship between the probability of food supply chain shock and supply chain diversity (Extended Data Table 7). The probabilities of food supply chain shock are calculated following the approach in the ‘Probabilities of food supply chain shock’ section of the Methods. We find that the five indicators have a varied influence on the relationship between the probability of food supply chain shock and supply chain diversity (Extended Data Table 7). The inclusion of all 5 indicators, however, in the supply chain diversity measure increases the Pearson correlation between the probability of food supply chain shock and supply chain diversity (Extended Data Table 7). More

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Centro de Investigação em Biodiversidade e Recursos Genéticos/Research Network in Biodiversity and Evolutionary Biology, Campus Agrário de Vairão, Universidade do Porto, Vairão, PortugalNuno Queiroz, Ana Couto, Marisa Vedor, Ivo da Costa, Gonzalo Mucientes & António M. SantosMarine Biological Association of the United Kingdom, Plymouth, UKNuno Queiroz, Nicolas E. Humphries, Lara L. Sousa, Samantha J. Simpson, Emily J. Southall & David W. SimsDepartamento de Biologia, Faculdade de Ciências da Universidade do Porto, Porto, PortugalMarisa Vedor & António M. SantosUWA Oceans Institute, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, Western Australia, AustraliaAna M. M. SequeiraSchool of Biological Sciences, University of Western Australia, Crawley, Western Australia, AustraliaAna M. M. SequeiraSpanish Institute of Oceanography, Santa Cruz de Tenerife, SpainFrancisco J. AbascalAbercrombie and Fish, Port Jefferson Station, NY, USADebra L. AbercrombieMarine Biology and Aquaculture Unit, College of Science and Engineering, James Cook University, Cairns, Queensland, AustraliaKatya Abrantes, Adam Barnett, Richard Fitzpatrick & Marcus SheavesInstitute of Natural and Mathematical Sciences, Massey University, Palmerston North, New ZealandDavid Acuña-MarreroUniversidade Federal Rural de Pernambuco (UFRPE), Departamento de Pesca e Aquicultura, Recife, BrazilAndré S. Afonso, Natalia P. A. Bezerra, Fábio H. V. Hazin, Fernanda O. Lana, Bruno C. L. Macena & Paulo TravassosMARE, Marine and Environmental Sciences Centre, Instituto Politécnico de Leiria, Peniche, PortugalAndré S. AfonsoMARE, Laboratório Marítimo da Guia, Faculdade de Ciências da Universidade de Lisboa, Cascais, PortugalPedro Afonso, Jorge Fontes & Frederic VandeperreInstitute of Marine Research (IMAR), Departamento de Oceanografia e Pescas, Universidade dos Açores, Horta, PortugalPedro Afonso, Jorge Fontes, Bruno C. L. Macena & Frederic VandeperreOkeanos – Departamento de Oceanografia e Pescas, Universidade dos Açores, Horta, PortugalPedro Afonso, Jorge Fontes & Frederic VandeperreDepartment of Environmental Affairs, Oceans and Coasts Research, Cape Town, South AfricaDarrell Anders, Michael A. Meÿer, Sarika Singh & Laurenne B. SnydersLarge Marine Vertebrates Research Institute Philippines, Jagna, PhilippinesGonzalo AraujoFins Attached Marine Research and Conservation, Colorado Springs, CO, USARandall ArauzPrograma Restauración de Tortugas Marinas PRETOMA, San José, Costa RicaRandall ArauzMigraMar, Olema, CA, USARandall Arauz, Sandra Bessudo Lion, Eduardo Espinoza, Alex R. Hearn, Mauricio Hoyos, James T. Ketchum, A. Peter Klimley, Cesar Peñaherrera-Palma, George Shillinger & German SolerInstitut de Recherche pour le Développement, UMR MARBEC (IRD, Ifremer, Univ. Montpellier, CNRS), Sète, FrancePascal Bach, Antonin V. Blaison, Laurent Dagorn, John D. Filmalter, Fabien Forget, Francois Poisson, Marc Soria & Mariana T. TolottiBiology Department, University of Massachusetts Dartmouth, Dartmouth, MA, USADiego Bernal & Heather MarshallRed Sea Research Center, Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaMichael L. Berumen, Jesse E. M. Cochran & Carlos M. DuarteFundación Malpelo y Otros Ecosistemas Marinos, Bogota, ColombiaSandra Bessudo Lion, Felipe Ladino, Lina Maria Quintero & German SolerHopkins Marine Station of Stanford University, Pacific Grove, CA, USABarbara A. Block, Taylor K. Chapple, George Shillinger & Timothy D. WhiteDepartment of Biological Sciences, Florida International University, North Miami, FL, USAMark E. Bond, Demian D. Chapman & Yannis P. PapastamatiouInstituto de Ciências do Mar, Universidade Federal do Ceará, Fortaleza, BrazilRamon BonfilCSIRO Oceans and Atmosphere, Hobart, Tasmania, AustraliaRussell W. Bradford & Barry D. BruceSchool of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USACamrin D. BraunBiology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USACamrin D. Braun & Simon R. ThorroldShark Research and Conservation Program, Cape Eleuthera Institute, Eleuthera, BahamasEdward J. Brooks, Annabelle Brooks & Sean WilliamsUniversity of Exeter, Exeter, UKAnnabelle BrooksSouth Atlantic Environmental Research Institute, Stanley, Falkland IslandsJudith BrownDepartment of Biological Sciences, The Guy Harvey Research Institute, Nova Southeastern University, Dania Beach, FL, USAMichael E. Byrne, Mahmood Shivji, Jeremy J. Vaudo & Bradley M. WetherbeeSchool of Natural Resources, University of Missouri, Columbia, MO, USAMichael E. ByrneLife and Environmental Sciences, University of Iceland, Reykjavik, IcelandSteven E. CampanaSchool of Marine Science and Policy, University of Delaware, Lewes, DE, USAAaron B. Carlisle & Gregory B. SkomalMassachusetts Division of Marine Fisheries, New Bedford, MA, USAJohn ChisholmMarine Research Facility, Jeddah, Saudi ArabiaChristopher R. Clarke & James S. E. LeaPSL, Labex CORAIL, CRIOBE USR3278 EPHE-CNRS-UPVD, Papetoai, French PolynesiaEric G. CluaAgence de Recherche pour la Biodiversité à la Réunion (ARBRE), Réunion, Marseille, FranceEstelle C. CrocheletInstitut de Recherche pour le Développement, UMR 228 ESPACE-DEV, Réunion, Marseille, FranceEstelle C. CrocheletSave Our Seas Foundation–D’Arros Research Centre (SOSF-DRC), Geneva, SwitzerlandRyan Daly & Clare A. Keating DalySouth African Institute for Aquatic Biodiversity (SAIAB), Grahamstown, South AfricaRyan Daly, John D. Filmalter, Enrico Gennari & Alison A. KockDepartment of Fisheries Evaluation, Fisheries Research Division, Instituto de Fomento Pesquero (IFOP), Valparaíso, ChileDaniel Devia CortésSchool of Biological, Earth and Environmental Sciences, University College Cork, Cork, IrelandThomas K. Doyle & Luke HarmanMaREI Centre, Environmental Research Institute, University College Cork, Cork, IrelandThomas K. DoyleCollege of Science and Engineering, Flinders University, Adelaide, South Australia, AustraliaMichael Drew, Matthew Heard & Charlie HuveneersDepartment of Conservation, Auckland, New ZealandClinton A. J. DuffySouth African Institute for Aquatic Biodiversity, Geological Sciences, UKZN, Durban, South AfricaThor EriksonDireccion Parque Nacional Galapagos, Puerto Ayora, Galapagos, EcuadorEduardo EspinozaAustralian Institute of Marine Science, Indian Ocean Marine Research Centre (UWA), Crawley, Western Australia, AustraliaLuciana C. Ferreira, Mark G. Meekan & Michele ThumsDepartment of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USAFrancesco FerrettiOCEARCH, Park City, UT, USAG. Chris FischerBedford Institute of Oceanography, Dartmouth, Nova Scotia, CanadaMark Fowler, Warren Joyce & Anna MacDonnellNational Institute of Water and Atmospheric Research, Wellington, New ZealandMalcolm P. Francis & Warrick S. LyonBeneath the Waves, Herndon, VA, USAAustin J. GallagherRosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USAAustin J. Gallagher, Neil Hammerschlag & Emily R. NelsonOceans Research Institute, Mossel Bay, South AfricaEnrico GennariDepartment of Ichthyology and Fisheries Science, Rhodes University, Grahamstown, South AfricaEnrico Gennari & Alison TownerSARDI Aquatic Sciences, Adelaide, South Australia, AustraliaSimon D. Goldsworthy & Paul J. RogersZoological Society of London, London, UKMatthew J. Gollock & Fiona LlewellynGalapagos Whale Shark Project, Puerto Ayora, Galapagos, EcuadorJonathan R. GreenGriffith Centre for Coastal Management, Griffith University School of Engineering, Griffith University, Gold Coast, Queensland, AustraliaJohan A. GustafsonSaving the Blue, Cooper City, FL, USATristan L. GuttridgeSmithsonian Tropical Research Institute, Panama City, PanamaHector M. GuzmanLeonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, FL, USANeil HammerschlagGalapagos Science Center, San Cristobal, Galapagos, EcuadorAlex R. HearnUniversidad San Francisco de Quito, Quito, EcuadorAlex R. HearnBlue Water Marine Research, Tutukaka, New ZealandJohn C. HoldsworthUniversity of Queensland, Brisbane, Queensland, AustraliaBonnie J. HolmesMicrowave Telemetry, Columbia, MD, USALucy A. Howey & Lance K. B. JordanPelagios-Kakunja, La Paz, MexicoMauricio Hoyos & James T. KetchumMote Marine Laboratory, Center for Shark Research, Sarasota, FL, USARobert E. Hueter, John J. Morris & John P. TyminskiBiological Sciences, University of Windsor, Windsor, Ontario, CanadaNigel E. HusseyCape Research and Diver Development, Simon’s Town, South AfricaDylan T. IrionInstitute of Zoology, Zoological Society of London, London, UKDavid M. P. JacobyCentre for Sustainable Aquatic Ecosystems, Harry Butler Institute, Murdoch University, Perth, Western Australia, AustraliaOliver J. D. JewellDyer Island Conservation Trust, Western Cape, South AfricaOliver J. D. Jewell & Alison TownerBlue Wilderness Research Unit, Scottburgh, South AfricaRyan JohnsonUniversity of California Davis, Davis, CA, USAA. Peter KlimleyCape Research Centre, South African National Parks, Steenberg, South AfricaAlison A. KockShark Spotters, Fish Hoek, South AfricaAlison A. KockInstitute for Communities and Wildlife in Africa, Department of Biological Sciences, University of Cape Town, Rondebosch, South AfricaAlison A. KockWestern Cape Department of Agriculture, Veterinary Services, Elsenburg, South AfricaPieter KoenDepartamento de Biologia Marinha, Universidade Federal Fluminense (UFF), Niterói, BrazilFernanda O. LanaDepartment of Zoology, University of Cambridge, Cambridge, UKJames S. E. LeaAtlantic White Shark Conservancy, Chatham, MA, USAHeather MarshallFisheries and Aquaculture Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, AustraliaJaime D. McAllister, Jayson M. Semmens, German Soler & Kilian M. StehfestPontificia Universidad Católica del Ecuador Sede Manabi, Portoviejo, EcuadorCesar Peñaherrera-PalmaMarine Megafauna Foundation, Truckee, CA, USASimon J. Pierce & Christoph A. RohnerConservation and Fisheries Department, Ascension Island Government, Georgetown, Ascension Island, UKAndrew J. RichardsonMarine Conservation Society Seychelles, Victoria, SeychellesDavid R. L. RowatCORDIO, East Africa, Mombasa, KenyaMelita SamoilysUpwell, Monterey, CA, USAGeorge ShillingerDepartment of Zoology and Institute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South AfricaMalcolm J. SmaleNational Institute of Polar Research, Tachikawa, Tokyo, JapanYuuki Y. WatanabeSOKENDAI (The Graduate University for Advanced Studies), Tachikawa, Tokyo, JapanYuuki Y. WatanabeCentre for Ecology and Conservation, University of Exeter, Penryn, UKSam B. WeberDepartment of Biological Sciences, University of Rhode Island, Kingston, RI, USABradley M. WetherbeeDepartment of Oceanography and Environment, Fisheries Research Division, Instituto de Fomento Pesquero (IFOP), Valparaíso, ChilePatricia M. ZárateDepartment of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaRobert HarcourtSchool of Life and Environmental Sciences, Deakin University, Geelong, Victoria, AustraliaGraeme C. HaysAZTI – BRTA, Pasaia, SpainXabier IrigoienIKERBASQUE, Basque Foundation for Science, Bilbao, SpainXabier IrigoienInstituto de Fisica Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Cientificas, University of the Balearic Islands, Palma de Mallorca, SpainVictor M. EguiluzWildlife Conservation Research Unit, Department of Zoology, University of Oxford, Tubney, UKLara L. SousaOcean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UKSamantha J. Simpson & David W. SimsCentre for Biological Sciences, University of Southampton, Southampton, UKDavid W. SimsN.Q. and D.W.S. planned the data analysis. N.Q. led the data analysis with contributions from M.V., A.M.M.S. and D.W.S. N.E.H. contributed analysis tools. A.M.M.S. undertook linear-regression modelling. D.W.S. led the manuscript writing with contributions from N.Q., N.E.H., A.M.M.S and all authors. Six of the original authors were not included in the Reply authorship; two authors retired from science and the remaining four, although supportive of our Reply, declined to join the authorship due to potential conflicts of interest with the authors of the Comment and/or their institutions. More

Study areaThe Pearl River Delta (112°45′–113°50′ E, 21°31′–23°10′ N) is located in the central and southern parts of Guangdong Province, including the lower reaches of the Pearl River, adjacent to Hong Kong and Macao, and facing Southeast Asia across the sea with convenient land and sea transportation. As shown in Fig. 1, the Pearl River Delta region includes nine prefecture-level cities, namely Guangzhou, Shenzhen, Zhongshan, Zhuhai, Dongguan, Zhaoqing, Foshan, Huizhou, and Jiangmen.Figure 1Geographical location of Pearl River Delta drawn in ArcGIS 10.6.Full size imageData sourceThe research framework of this paper is shown in Fig. 2, and the data sources are as follows. Taking the basin as the research unit, the raster data of 30 m and 1 km were analyzed by zoning statistics:

(1)

China’s land-use raster data for 1990, 2000, 2010, and 2018 were obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn), with a spatial resolution of 30 m. According to land resources and their utilization attributes, the dataset divides land cover types into six first-level categories: cultivated land, woodland, grassland, water area, construction land, unused land, and land reclamation from ocean. The land urbanization rate (LUR) refers to the proportion of construction land in the whole region, which is calculated by dividing the area of construction land by the area of all land use types.

(2)

Raster data of population density (POP) from 1990, 2000, 2010, and 2015 were obtained from the Environment and Resources Data Cloud Platform of the Chinese Academy of Sciences, with a spatial resolution of 1 km. Owing to the stable growth of population density under normal circumstances, the population density data of 2018 were obtained by linear fitting based on POP data from 2010 and 2015.

(3)

Nighttime Light (NTL) raster data from 1992 to 2018 were obtained from the Nature journal data (https://doi.org/10.6084/m9.figshare.9828827.v2) with a spatial resolution of 500 m45 Calibration was performed to eliminate the differences in the DMSP (1992–2013) and VIIRS (2012–2018) data, generating a complete and consistent NTL dataset on a global scale.

Figure 2Research framework.Full size imageLand-use information TUPUThe land-use information graph is a geospatial analysis model combining attributes, processes, and spaces, which can reflect the spatial differences and temporal changes in land-use types46. In its function expression, let the state variables be (pleft( {p_{1} ,p_{2} ,p_{3} , ldots ,p_{n} } right)), and then set p as a function of spatial position r and time t, as follows:$$ begin{array}{*{20}c} {p = fleft( {r,t} right)} \ end{array} $$
(1)
where (p) represents land-use characteristics. (1) To realize the spatial description of land attributes, when t is constant, the function relation of (p) changing with (r) is constructed. (2) The process description of land attributes can be realized, and when (r) is constant, the function relation of (p) changing with (t) can be constructed. The combination of these two functions can form a conceptual model of the land-use information graph and realize a composite study of land space, process, and attributes.Habitat qualityHabitat quality evaluationWe used InVEST-HQ to evaluate the habitat quality in the Pearl River Delta region. Based on land-use types, InVEST-HQ calculated the habitat degradation degree and habitat quality index by using threat factors, the sensitivity of different habitat types to threat factors, and habitat suitability15. The InVEST-HQ model was co-developed by Stanford University, the Nature Conservancy, and the World Wide Fund for Nature15. InVEST-HQ has a low demand for data and a better spatial visualization effect, which is widely used in the field of urban ecology47,48,49. For example, The InVEST-HQ model has been used to assess dynamic changes in habitat quality in Scottish11, China50,51 and Portugal47. Habitat degradation and habitat quality were calculated using the following formulas:$$ begin{array}{*{20}c} {Q_{{xj}} = ~H_{j} left[ {1 – left( {frac{{D_{{xj}}^{2} }}{{D_{{xj}}^{2} + k^{2} )}}} right)} right]} \ end{array} $$
(2)
$$ begin{array}{*{20}c} {D_{{xj}} = ~mathop sum limits_{{r = 1}}^{r} mathop sum limits_{{y = 1}}^{y} left( {frac{{w_{r} }}{{mathop sum nolimits_{{r = 1}}^{r} w_{r} }}} right)r_{y} i_{{rxy}} beta _{x} S_{{jr}} } \ end{array} $$
(3)
where (Q_{{xj}}) is the habitat quality of grid x in land-use type j, (H_{j}) is the habitat suitability of land-use type j, (D_{{xj}}) is the habitat degradation degree of grid x in land-use type j, k is the half-satiety sum constant, r is the number of threat factors, and y is the relative sensitivity of threat sources. (r_{y} ,w_{r}), and (i_{{rxy}}) are, respectively, the interference intensity and weight of the grid where the threat factor r is located, and the interference generated by the habitat. (beta _{x} ,S_{{jr}}) are the anti-disturbance ability of habitat type x and its relative sensitivity to various threat sources, respectively.The value range of habitat degradation degree is [0, 1], and the larger the value, the more serious the habitat degradation. The value of habitat quality is between 0 and 1, and the higher the value, the better the habitat quality.$$ begin{array}{*{20}c} {Linear,attenuation:~i_{{rxy}} = 1 – left( {d_{{xy}} /d_{{r,max}} } right)} \ end{array} $$
(4)
$$ begin{array}{*{20}c} {Exponential,decay:~i_{{rxy}} = expleft[ { – 2.99d_{{xy}} /d_{{r{text{~}}max}} } right]} \ end{array} $$
(5)

where (d_{{xy}}) is the straight-line distance between grids x and y, and (d_{{r,max}}) is the maximum threat distance of threat factor r.Five categories of documentation are prepared before using InVEST-HQ: LULC maps, threat factor data, threat sources, accessibility of degradation sources, habitat types and their sensitivity to each threat. Threat sources were divided into Cropland, City/town, Rural settlements, Other construction land, Unused land, and land applications. The maps of threat sources are generated in ArcGIS. For example, in the map of threat sources of cultivated land, the raster value of cultivated land is set to 1, and the raster value of other land types is set to 0. Distance between habitats and threat sources, weight of threat factors, decay type of threats factors, habitat suitability and the sensitivity of different habitat types to threat factors were derived from previous studies in similar regions2,25,38,39,50 and user guide manual of InVEST model15, as shown in Tables 1 and 2.Table 1 Threat factors and related coefficients.Full size tableTable 2  Sensitivity of habitat types to each threat factor.Full size tableHabitat quality change index and contribution indexThe CI was used to analyze the causes of the changes in habitat quality, and the following formula was used to qu2,25,38,39,50antitatively represent the contribution of land-use conversion to habitat quality change. In this study, the total value of habitat quality loss caused by land transfer in areas related to construction land expansion from 1990 to 2018 can be expressed as follows:$$ begin{array}{*{20}c} {CI~ = ~frac{{mathop sum nolimits_{1}^{n} left( {Q_{{ij2018}} – Q_{{xj1990}} } right)}}{n}} \ end{array} $$
(6)

where n is the grid number of cultivated land transferred to construction land.To analyze the relationship between land-use change and habitat quality, the HQCI was constructed to describe the mean value of habitat quality reduction caused by land transfer in the areas related to construction land expansion during the study period. The formula is as follows:$$ begin{array}{*{20}c} {HQCI~ = CI_{{ij}} /S_{{ij}} } \ end{array} $$
(7)
where (CI_{{ij}}) represents the total value of habitat quality change when land-use type (i) is converted into land-use type (j), and (S_{{ij}}) represents the area converted from land-use type (i) into land-use type (j). The positive and negative values of HQCI, respectively, represent the positive and negative impacts of land-use change on the habitat, and the higher the absolute value of HQCI, the greater the impact.Correlation analysisGeographically weighted regressionBased on traditional OLS, GWR establishes local spatial regression and considers spatial location factors, which can effectively analyze the spatial heterogeneity of various elements at different locations52. The calculation formula is as follows:$$ Y_{i} = ~beta _{0} left( {mu _{i} ,v_{i} } right) + sum kbeta _{k} left( {mu _{i} ,v_{i} } right)X_{{ik}} + varepsilon _{i} $$where (Y_{i}) is the coupling coordination degree of the ith sample point, (left( {mu _{i} ,v_{i} } right)) is the spatial position coordinate of the ith sample point, (beta _{k} left( {mu _{i} ,v_{i} } right)) is the value of the continuous function (beta _{k} left( {mu ,v} right)) at (left( {mu _{i} ,v_{i} } right)), (X_{{ik}}) is the independent variable, (varepsilon _{i}) is the random error term, and k is the number of spatial units.To simplify the complicated urbanization process, it was divided into three aspects: economic urbanization, population urbanization, and land urbanization according to the existing research38. The NTL, POP, and LUR were used to represent the economic development, population scale, and land urbanization level of the city.The research unit is a river basin, which has both natural and social attributes. It is a relatively independent and complete system, which can connect and explain the coupling phenomenon of society, economy, and nature53. The hydrological analysis module in ArcGIS was used to divide the research area into 374 small basins. When calculating the cumulative flow of the grid, 100,000 was used as the threshold value, and basins less than 5 km2 were combined with the adjacent basins.Zone classification using the Self-organizing feature mapping neural networkThe SOFM neural network was proposed by Kohonen, a Finnish scholar, and constructed by simulating a “lateral inhibition” phenomenon in the human cerebral cortex. It has been widely applied in classification research in geographic and land system science42,43. The advantages of the SOFM neural network in classifying the coupling relationship between urbanization and habitat quality are as follows : (1) it simulates human brain neurons through unsupervised learning, which is objective and reliable. (2) It maintains the data topology during self-learning, training, and simulation to obtain reasonable partition results and identify the differences between different basins. (3) For massive data, the SOFM network has a good clustering function while maintaining its characteristics and uses the weight vector of the output node to represent the original input. The SOFM neural network can compress the data while maintaining a high similarity between the compression results and the original input data54. We exported the data from ArcGIS, and conducted cluster analysis on the four factors of NTL, POP, LUR and habitat quality using SOFM. Finally, the analysis results are imported into ArcGIS for display. More

An umbrella-shaped structure of unknown function crowns a recently described species of fairy lantern. Credit: Siti Munirah Mat Yunoh et al./PhytoKeys (CC BY 4.0)

Conservation biology
07 July 2021
Newfound ‘fairy lantern’ could soon be snuffed out forever

Wild boars have destroyed three of the four known specimens of a bizarre plant in the forests of Malaysia.

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Researchers have discovered a new species of ‘fairy lantern’, leafless plants that look like tiny glowing lights. Sadly, however, the organism might already be on the verge of extinction.Plants in the genus Thismia, colloquially called ‘fairy lanterns’, draw nutrients from underground fungi and grow in parts of Asia, Australasia and the Americas. Siti Munirah Mat Yunoh at the Forest Research Institute Malaysia in Kepong and her colleagues described a new species of Thismia that was first found in 2019 in a Malaysian rain forest. The scientists named the plant Thismia sitimeriamiae after the mother of the local explorer who discovered it, in honour of her support for her son’s nature-conservation efforts.Thismia sitimeriamiae is only about two centimetres tall, and sports an orange flower shaped like a funnel with an umbrella-like structure on top. The plant seems to be so rare that it should be considered critically endangered: just four individuals of T. sitimeriamiae have ever been seen, and wild boars have destroyed all but one of these, the authors say.

PhytoKeys (2021)

Conservation biology More