By Tessa Smith, 7th October 11:30 AM

Like all good netizens, I have at least once gotten into an argument with strangers on the internet.  The argument in question was about whether it was ‘ethical to purposely drive a species (in this case, a mosquito) to extinction’. I argued that it was not ethical, on the basis that the species has the same inherent right to survive as all other species, including the charismatic ones. The other parties (for there were many) argued that I was a privileged idiot with no empathy for the millions of people worldwide affected by the diseases. They probably had a point, but ethics aside, it got me thinking about 1) what the problem with the mosquito was, 2) what was being developed to supress it, and 3) how likely it was that the species could become extinct as a result. Thanks to Emily Flies for her helpful suggestions to the piece.

1. The Problem

According to the World Health Organisation, mosquito-borne diseases cause several million deaths and hundreds of millions of infections each year. Mosquitoes of the genus Aedes, Anopheles and Culex are the primary culprits for transmission of infections (e.g. malaria, Ross River virus). In these genera, the female mosquito feeds on blood from a host, using the protein and iron in the blood to grow her eggs. By feeding on several hosts (from the same or different species), they may transmit the blood-borne diseases if present.


Figure 1: Aedes aegypti mosquito, E. A. Goeldi (1905).

Aedes aegypti (Figure 1), which is a vector for multiple diseases and lives in urban areas, thereby threatening over half of the world’s population. They are one of the most important species for transmitting disease for two main reasons: 1) they feed during the day (from sunrise to after sunset) when people are outdoors and active, giving them more opportunities to infect people, and 2) they lay their eggs in small containers of water around urban areas, making it very hard to target control efforts to breeding sites. While not the only insect vector for the multiple diseases it can spread, A. aegpyti is by far the most common and of the most concern. Some of the diseases transmitted by Aedes aegypti are:

  • Yellow fever is endemic in Africa and South America where its primary reservoir is monkeys. The disease can cause death in a small percentage of infected people and there is a vaccine available. During 2017-2018 there were deadly outbreaks of the disease in Brazil and Nigeria.
  • The Zika virus has been recorded in over 86 countries in the Americas, Africa, Asia and the Pacific. While causing few symptoms from infection in most people, it can cause complications of pregnancy and malformation in babies. Those infected also suffer an increase risk of neuropathic conditions. There is no vaccine currently available for Zika.
  • Chikungunya is present in Africa, Asia, the Americas and Europe. It causes fever and joint pain in affected individuals and there is currently no vaccine for the disease.
  • Dengue is most prevalent in Asia, the Americas and Africa, with almost half of the world’s population at risk (Figure 2). The disease may have been underreported in Africa due to a range of factors including presence of similar illnesses (11). The disease disproportionately affects people in developing countries (especially indigenous people, people of colour and immigrants) (10).There is currently no vaccine for Dengue, and the vaccine discovery process proving difficult. Aedes mosquitos are not a vector for malaria.


Figure 2: World Health Organization Estimated Deaths from Dengue per million persons, 2012. Yellow=0, Red=9 (CC BY-SA 4.0).

Here, I focus on the research aimed at suppressing the ability of this species to transmit disease.

The spread of Aedes aegypti away from its native home in Africa and to a large area of the world, especially tropical and subtropical areas is also a major concern. The distribution of the mosquito in some areas of South America has been attributed to ship dispersal during the slave trade in the 1600’s (13) and more recently the trade in used car tyres (WHO Dengue Control). 

Controlling the spread and reproduction of these mosquitoes is critical for reducing disease transmission. A variety of historical control methods have been used against Aedes mosquitos, some of which have caused widespread environmental problems:

a) The mosquito was nearly eradicated in the Americas in the 1960’s using DDT (the chemical Dichlorodiphenyltrichloroethane), but re-established itself there when the project was discontinued (7) due to strong health and environmental concerns.

b) Biological control of the mosquito with fish or predatory copepods has had limited local success in controlling mosquito numbers and has caused ecological problems when the exotic species escape and consume indigenous fauna. The mosquito fish (Gambusia holbrooki) was listed as one of the IUCN’s 100 most harmful invasive exotic species after being released around the world to eat mosquito larvae.

The challenge of combatting the spread of mosquitoes is also influenced by other factors. The cost of effective management programs using insecticides is ongoing, and often a burden for poorer governments. Many mosquito species are evolving insecticide resistance, making insecticides increasingly ineffective. With climate change there is a predicted increase in A. aegypti’s global distribution (Figure 3) as it establishes in areas that were previously too cold to inhabit (3).


Figure 3: Summary of the modelled global distribution of Aedes aegypti under both current (dark blue) and future (dark orange) climatic conditions in 2050 showing stability of predictions at present and into the future with RCP (representative concentration pathway) 4.5 (3).

Traditional methods of controlling Aedes and its disease spread through chemical and biological control are increasingly inneffective and unsustainable, necessitating the use of new methods for control (4). 

2. New options in the control of mosquito-borne disease

Wolbachia bacteria

Wolbachia is a genera of bacteria that occur naturally in 60% of all insect and nematode species, including some mosquitos. The bacterium modifies the sperm of the host insect, only allowing eggs infected with Wolbachia to develop normally (8). The presence of the bacteria within the mosquito has also been found to reduce the ability of the mosquito to transmit Dengue virus to humans.  


Figure 4: The Percentage of mosquitos with Wolbachia, Townsville, Australia, 2015-2016 (6).

The introduction of the Wolbachia bacteria into A.aegypti mosquitos has been successful in local trials in northern Australia since 2011 ( Once introduced, Wolbachia is self sustaining within the local mosquito population (Figure 4). Modelling by Dorigatti, McCormack (4) predicted that infection with Wolbachia reduces a mosquito’s ability to transmit dengue by 40% .

Genetic modification and gene drive

Another method for reducing infection rates is the release of genetically modified Aedes into the wild population. Unlike normal inheritance where an altered gene is not always spread, with gene drive inheritance, the altered gene is always inherited; male mosquitos carry a dominant lethal gene, that is passed on when they mate with females and kills 96% of their progeny (12). Modified Aedes aegypti mosquitos have so far been released into Burkina Faso and Brazil where they have been able to suppress local populations (4). This process requires the release of many modified mosquitos over several months (9).

Non-gene drive genetically modified mosquitos released in Brazil were found to have passed portions of their DNA into local populations, forming viable hybrid individuals that bred to pre-release numbers within months (2). The establishment of a genetic monitoring program (2) was recommended as an important part of the development process for any new genetic tool to ensure success.

3. How likely is it that the mosquito will become extinct as a result?

In a laboratory population, gene drive was successfully used to eliminate a population of Anopheles gambiae mosquitoes (a species that spreads Malaria)(5). The likelihood of gene drive and Wolbachia to create a complete extinction of the Aedes aegypti species over their entire range is low, but possible; models suggests that non-random mating will likely prevent gene-drives from causing extinction (1). However, this may be investigated by future research teams.  

All posts are personal reflections of the blog-post author and do not necessarily reflect the views of all other DEEP members.


  1. Bull J. J., Remien C. H., Krone S. M. Gene-drive-mediated extinction is thwarted by population structure and evolution of sib mating. Evolution, Medicine, and Public Health. 2019; 2019(1): 66-81.
  2. Evans B. R., Kotsakiozi P., Costa-da-Silva A. L., Ioshino R. S., Garziera L., Pedrosa M. C., Malavasi A., Virginio J. F., Capurro M. L., Powell J. R. Transgenic Aedes aegypti Mosquitoes Transfer Genes into a Natural Population. Scientific Reports. 2019; 9(1): 13047.
  3. Kamal M., Kenawy M. A., Rady M. H., Khaled A. S., Samy A. M. Mapping the global potential distributions of two arboviral vectors Aedes aegypti and Ae. albopictus under changing climate. PloS one. 2019; 13(12): e0210122.
  4. Dorigatti I., McCormack C., Nedjati-Gilani G., Ferguson N. M. Using Wolbachia for Dengue Control: Insights from Modelling. Trends in parasitology. 2018; 34(2): 102-113.
  5. Kyrou K., Hammond A. M., Galizi R., Kranjc N., Burt A., Beaghton A. K., Nolan T., Crisanti A. A CRISPR–Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes. Nature Biotechnology. 2018; 36: 1062.
  6. O’Neill S., Ryan P., Turley A., Wilson G., Retzki K., Iturbe-Ormaetxe I., Dong Y., Kenny N., Paton C., Ritchie S., Brown-Kenyon J., Stanford D., Wittmeier N., Anders K., Simmons C. Scaled deployment of Wolbachia to protect the community from Aedes transmitted arboviruses [version 1; peer review: 1 approved, 1 approved with reservations]. Gates Open Research. 2018; 2(36).
  7. Hotez P. J. Zika in the United States of America and a Fateful 1969 Decision. PLOS Neglected Tropical Diseases. 2016; 10(5): e0004765.
  8. Jiggins F. M. Open questions: how does Wolbachia do what it does? BMC Biology. 2016; 14(1): 92-92.
  9. Carvalho D. O., McKemey A. R., Garziera L., Lacroix R., Donnelly C. A., Alphey L., Malavasi A., Capurro M. L. Suppression of a Field Population of Aedes aegypti in Brazil by Sustained Release of Transgenic Male Mosquitoes. PLoS neglected tropical diseases. 2015; 9(7): e0003864-e0003864.
  10. Hunter P. Tropical diseases and the poor: Neglected tropical diseases are a public health problem for developing and developed countries alike. EMBO reports. 2014; 15(4): 347-350.
  11. Bhatt S., Gething P. W., Brady O. J., Messina J. P., Farlow A. W., Moyes C. L., Drake J. M., Brownstein J. S., Hoen A. G., Sankoh O., Myers M. F., George D. B., Jaenisch T., Wint G. R. W., Simmons C. P., Scott T. W., Farrar J. J., Hay S. I. The global distribution and burden of dengue. Nature. 2013; 496: 504.
  12. Phuc H. K., Andreasen M. H., Burton R. S., Vass C., Epton M. J., Pape G., Fu G., Condon K. C., Scaife S., Donnelly C. A., Coleman P. G., White-Cooper H., Alphey L. Late-acting dominant lethal genetic systems and mosquito control. BMC Biology. 2007; 5(1): 11.
  13. Mousson L., Dauga C., Garrigues T., Schaffner F., Vazeille M., Failloux A.-B. Phylogeography of Aedes (Stegomyia) aegypti (L.) and Aedes (Stegomyia) albopictus (Skuse) (Diptera: Culicidae) based on mitochondrial DNA variations. Genetical Research. 2005; 86(1): 1-11.


By Kasirat Turfi Kasfi, 26th August 11:30 AM

When I sat down to brainstorm a topic to write for the DEEP blog, I thought what would be that one thing that interest me and the rest of the DEEP members? Data, right! We all work with data, we want to find meanings and patterns in our data. We want to be able to make inference, make decisions based on or make predictions from our data. We do that by using statistical learning methods.

I believe that most of us are familiar with one or more statistical learning models. And there seems to be quite a few of them out there! It can be difficult to decide which one to use! Or knowing which one is the best! There is no best method that fits all kinds of data perfectly, and no one method “to rule them all” (only if Tolkien invented the models!). I would therefore like to explain intuitively the bias-variance analysis that helps us to understand if a model is going to capture the true pattern in the “seen” data and will also generalize well to “unseen” data, thus help us in selecting the best model for the given data.

Before diving into what “bias and variance trade-off” means, let’s get a little background on “statistical learning”. Any supervised statistical learning model will try to find a hypothesis function ħ (i.e. mathematical representation) that approximates the relationship between the predictors (independent variables) and the response (dependent variable). The measure of how well the hypothesis function ħ is fitting the given data can be found by getting the error E between the output of the hypothesis function ħ and the output of the target function ƒ that describes the data. It is called an error because it represents the gap between the hypothesis function and the target function. The smaller the value of the error E, better the learning, meaning that the hypothesis has approximated the target function ƒ well. Therefore, there are two objectives: a) finding a good approximation of target function f, and b) the approximation holding for out-of-sample data. A more complex (meaning bigger) hypothesis set has a better chance of approximating the target function, because it is more likely to hold the target function in the set, but it becomes increasingly harder to find that needle in the haystack! On the other hand, if the hypothesis set is simpler (smaller) then it may not hold the target function in it, but luckily if it does hold the function then it is easier to find. In order to find the best candidate function in the hypothesis set, the hypothesis set must be navigated through the means of the sample data provided, which is the only resource in finding one hypothesis over the other.

Bear with me, I will soon get to an explanation of what I am talking about with an illustration! Just two more paragraphs to go!

Now, getting back to bias and variance, these two entities are inherent properties of a learning model. Mathematically, when the error term E is decomposed, we get bias and variance [2]. Simply put, the trade-off is between approximation and generalization, between bias and variance. The total error term E measures how far the hypothesis function ħ learned from the given data is from the target function ƒ. Of the decomposed entities, bias is a measure of approximation ability, it measures how far the best approximation is from the target function ƒ and the variance is a measure of how far the hypothesis function ħ learned from a dataset is from the best possible candidate function that could be obtained from the hypothesis set H. The hypothesis set H chosen is dependent on the data that is provided, so a different set of data will give a different hypothesis set to choose from. (This dependency is very important in the bias-variance analysis.)

The trade-off is that if bias goes up, then the variance goes down, or if the bias goes down the variance goes up.  If the hypothesis set H gets bigger, the bias gets smaller, getting it closer to f, but then there is a greater variety to choose from for the function which increases the variance. Below is the graph [1] of error E, and the relationship between bias and variance, this relationship is independent of the data, and holds for any statistical learning model. Model complexity is equivalent to hypothesis set size, meaning it holds functions with greater complexity.


Finally, if readers are still with me, here is an example with illustration!

Let me explain this trade-off using an example. Imagine that we have to find a target function ƒ. In real-life the target function is what we find by learning, but here for the purpose of illustration let’s assume we know the target function. Assume the target function ƒ is a sinusoid (displayed with orange line in the following figures for the rest of the article). Our objective is to find the best approximation of the target function ƒ given some data points. Also assume that we are using two hypothesis sets namely H0 and H1, where H0 is the constant model, and H1 is the linear model. Again, assume for illustration purpose that we only have these two hypothesis sets, to keep things simple! Both hypothesis sets will give an approximate function and we compare which one is better using the bias-variance analysis.

We start off with approximation, before doing any learning. The H0 hypothesis set should only give constants, and the H1 hypothesis set only gives lines. In the two graphs below, the light grey shaded regions represent all the possible functions (constants and lines) the H0 and H1 model can generate from the range of data points that are available. The shapes of the grey shaded region are a result of the model complexity and the available data points. The olive lines are the mean of each of the hypothesis sets representing the best of that hypothesis set.

The linear model will choose the function that will get most of the data points it possibly can. And the constant model will be better off choosing zero, as the error will be squared. As expected, we can see from the figures below, (the shaded area showing the errors), that clearly the linear model is the winner, it is a better approximate as the error is the smaller of the two, in fact for the constant model, all of it is an error!

Now, let’s look at the generalizing ability of the two models. Let’s say we have only two example data points (yes, we are stingy!) that we will use to approximate the sinusoid using the H0 and H1 hypothesis sets. The first figure below shows the points. The second shows the points with the target function. The third shows the points fitted with approximate functions from the hypothesis sets that best fits the data points provided.

image 4

Let’s bring back the target function and see how much error we get for the constant and the line. As per expectation the error is smallest for the linear model.

The constant and the line we have here are dependent on the data set, if we had another two points then the approximation of the constant and the line would be different. From a learning perspective, which model is generalizing well? Until now we have been looking at a subset of the dataset that defined the target function (the sinusoid). Now if we look at more datapoints from the population set, we are in for a surprise!

For all the unseen data points stretching infinitely before and after the datapoints we were working with, the constant model at least makes the right predictions periodically, but for the linear model it is a complete disaster! The variance error in the constant model stays constant, whereas the variance error for the linear model keeps increasing with more datapoints. In conclusion, the bias-variance analysis helped us to figure out, that for this particular target function (a sinusoid), given a set of data points and choices between a constant and a linear model we will be better off choosing the constant model that will not be the best approximation of the target but will be the best generalized model. In this case we traded off approximation (bias) for generalization (variance).

All posts are personal reflections of the blog-post author and do not necessarily reflect the views of all other DEEP members.

Reference & Source:

[1] The figure taken from

[2] James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An introduction to statistical learning (Vol. 112, p. 18). New York: springer.

[3] All graphs plotted using Matplotlib.Pyplot



By Cristian Montalvo Mancheno, 15th March 11:00 AM

Land-use change is one of the major drivers of biodiversity loss (1). Setting aside protected areas (PAs) has been the main strategy to confront this, but despite the significant increase in PAs over the last 30 years, biodiversity continues to decline globally (2, 3). At the same time, our understanding of PA effectiveness—and the ways we assess it—has evolved. To date, most assessments have only considered impacts at the local scale, but in today’s interconnected world, we must also think about how PAs contribute to net gains or losses in global biodiversity.

The first generation of PA impact assessments simply compared the amount of forest cover, or the number and composition of species, within and outside PA boundaries (e.g., 4, 5), typically finding that biodiversity was higher inside. However, these studies’ findings were swiftly contested due to the inherent location bias of PAs towards areas of low value for agriculture and other human uses. As a result, many areas under legal protection today would have remained undisturbed even in the absence of legal protection. This means that the expansion of PAs has not necessarily led to much additional conservation in terms of representing global biodiversity, and halting land-use change (6).

Furthermore, the simple inside-outside comparisons have often failed to account for land-use dynamics. When a PA is established, it might reduce destructive human activities inside, but if these activities simply move outside, the net effect locally might be reduced or even reversed. This phenomenon is generally known as spillover, that is, the local displacement of land uses to other areas.


These concerns motivated the development of a second generation of studies, in which the non-random allocation of PAs and/or the local spillover effect have explicitly been accounted for (e.g., 7, 8). These studies showed that the effectiveness of PAs not only is lower than those found in inside-outside comparisons, but also varies greatly. Nevertheless, even this last generation of PA impact assessments may be insufficient, because they fail to account for long-distance interactions between land systems, referred as teleconnections (9).


Such teleconnections, for example, may arise when PAs restrict access to resources—limiting the quantity of supply—which shifts the market equilibrium and causes price adjustments and/or the entry of new suppliers across an entire region or the world. This is especially the case in today’s interconnected world where flows of resources, people and capital across large distances affect land-use changes at any spatial scale (1, 9). In fact, teleconnections are more common than many of us would expect.

To illustrate how teleconnections might occur, let us look at the case of the collapse of the Soviet Union in 1991. This shock-like event led to the abandonment of large areas of farmland and war, with large impacts on the environment and biodiversity of many ex-Soviet countries (e.g., 10, 11, 12). In 2016, Schierhorn and colleagues (13) went beyond local impact assessments to illustrate how Russia’s transition from a state-owned to a market-oriented economy caused a long-lasting trade with Brazil, with Brazilian farmers being able to absorb the falling output from Russia due not only to large production potential of beef in the Amazon and Cerrado regions, but also to other enabling factors, such as technological advances in beef production and changes in beef trade flows at global scale. As a result, the gains in forest carbon and biodiversity in one part of the world was in part offset by losses elsewhere.

Teleconnections also play an important role in the effectiveness of policies and interventions aimed at halting biodiversity loss. For example, Ingalls and colleagues (14) investigated the displacement of deforestation under the Reduced Emissions from Deforestation and Forest Degradation (REDD+) framework. They found that the shift from net deforestation to net reforestation in Vietnam occurred through trade of forest-risk commodities[1] with Cambodia and Laos, which was facilitated by Vietnamese companies’ large-scale land acquisitions in these two neighbouring countries. Again, teleconnections meant that one country’s gain was, to some degree, another country’s loss.

While the displacement of land uses over large distances in the Mekong region has diminished the net impact of REDD+ at regional and global scales, teleconnections can also result in net gains for conservation. For example, displacement of agricultural and wood-derived products through trade, in particular to the United States, contributed to Costa Rica’s forest transition. But as Jadin and colleagues (15) showed, such teleconnection had an overall positive impact on the global environment due to striking differences in production and management practices between these two countries and the overall higher biological diversity of Costa Rica compared to the agricultural regions of the United States.

Although challenging, net impact assessments of PAs on the environment across countries, ecoregions and biomes are urgently needed to understand the trade-offs of a future expansion of PAs. This is increasingly relevant today due to the growing interest of conservation scientists and practitioners in setting aside half of the Earth for biodiversity (16, 17). Could such vast expansion bring about perverse outcomes? A recent study showed that the trade-offs between giving back half of the Earth to nature and maintaining food security are large and strongly dependent on the strategy selected and the scales of analyses (18). Therefore, describing coupling of land systems beyond local socio-ecological contexts will be an important next step in in our efforts to effectively conserve global biodiversity within national and regional networks of PAs.

[1] Forest-risk commodities are products that commonly impact forests through their conversion to other land uses or by their degradation, such as timber, semi-processed wood products, mining and agriculture (14).


I would like to thank Linus Blomqvist (Director of Conservation and Food & Agriculture at Breakthrough) for helping me frame and structure this post around my interests in PAs and the teleconnection concept, as well as for his comments and suggestions throughout the development of this blog-post. Also, I am grateful to Carley Fuller (Post-graduate student in the D.E.E.P. Research Group, University of Tasmania) for clarifying the different terminology used in the conservation literature about the spillover effect, and for sharing with me interesting articles around these two types of research projects.


All posts are personal reflections of the blog-post author and do not necessarily reflect the views of all other DEEP members.


  1. Lambin EF, Meyfroidt P. Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences. 108(9): p. 3465–3472.
  2. Butchart SHM, Walpole M, Collen B, van Strien A, Scharlemann JPW, Almond REA, et al. Global biodiversity: indicators of recent declines. 2010. 328(5982): p. 1164–1168.
  3. Rodrigues ASL, Brooks TM, Butchart SHM, Chanson J, Cox N, Hoffmann M, et al. Spatially explicit trends in the global conservation status of vertebrates. PLoS ONE. 9(11): p. e113934.
  4. Greve M, Chown SL, van Rensburg BJ, Dallimer M, Gaston KJ. The ecological effectiveness of protected areas: a case study for South African birds. Animal Conservation. 14(3): p. 295–305.
  5. Southworth J, Nagendra H, Carlson LA, Tucker C. Assessing the impact of Celaque National Park on forest fragmentation in western Honduras. Applied Geography. 24(4): p. 303–322.
  6. Blomqvist L, Nordhaus T, Shellenberger M. Nature unbound: Decoupling for conservation. 2015. Available from:
  7. Andam KS, Ferraro PJ, Pfaff A, Sanchez-Azofeifa GA, Robalino JA. Measuring the effectiveness of protected area networks in reducing deforestation. Proceedings of the National Academy of Sciences. 105(42): p. 16089–16094.
  8. Brandt JS, Butsic V, Schwab B, Kuemmerle T, Radeloff VC. The relative effectiveness of protected areas, a logging ban, and sacred areas for old-growth forest protection in southwest China. Biological Conservation. 181: p. 1–8.
  9. Friis C, Nielsen JØ, Otero I, Haberl H, Niewöhner J, Hostert P. From teleconnection to telecoupling: taking stock of an emerging framework in land system science. Journal of Land Use Science. 11(2): p. 131–153.
  10. Baumann M, Radeloff V, Avedian V, Kuemmerle T. Land-use change in the Caucasus during and after the Nagorno-Karabakh conflict. Regional Environmental Change. 15(8): p. 1703–1716.
  11. Hostert P, Kuemmerle T, Prishchepov A, Sieber A, Lambin EF, Radeloff VC. Rapid land use change after socio-economic disturbances: the collapse of the Soviet Union versus Chernobyl. Environmental Research Letters. 6: p. 045201.
  12. Sieber A, Kuemmerle T, Prishchepov AV, Wendland KJ, Baumann M, Radeloff VC, et al. Landsat-based mapping of post-Soviet land-use change to assess the effectiveness of the Oksky and Mordovsky protected areas in European Russia. Remote Sensing of Environment. 133: p. 38–51.
  13. Schierhorn F, Meyfroidt P, Kastner T, Kuemmerle T, Prishchepov AV, Müller D. The dynamics of beef trade between Brazil and Russia and their environmental implications. Global Food Security. 11: p. 84–92.
  14. Ingalls ML, Meyfroidt P, To PX, Kenney-Lazar M, Epprecht M. The transboundary displacement of deforestation under REDD+: problematic intersections between the trade of forest-risk commodities and land grabbing in the Mekong region. Global Environmental Change. 50: p. 255–67.
  15. Jadin I, Meyfroidt P, Lambin EF. International trade, and land use intensification and spatial reorganization explain Costa Rica’s forest transition. Environmental Research Letters. 11(3): p. 035005.
  16. Dinerstein E, Olson D, Joshi A, Vynne C, Burgess ND, Wikramanayake E, et al. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. 2017. 67(6): p. 534–45.
  17. Wilson EO. Half-earth: our planet’s fight for life. Liveright, NY: WW Norton & Company. 2016.
  18. Mehrabi Z, Ellis EC, Ramankutty N. The challenge of feeding the world while conserving half the planet. Nature Sustainability. 1(8): p. 409–12.




By Stefania Ondei, 8th January 1:30 pm

Bushfires have been ravaging Tasmania for more than a month. This is not an isolated event, but rather the latest episode in a series of intense fires that have threated the unique biodiversity of the island over the past years. In the summer of 2012-2013, record hot temperatures and dry conditions led to a six-month-long fire season in the south-east of Tasmania; this peaked in January 2013 with the fires on the Forestier and Tasman Peninsula, where several towns were evacuated. 100 homes were lost in the town of Dunalley.

It was unprecedented, the longest fire season ever recorded in Tasmania.

In January 2016, another streak of bushfires hit the island. While this time properties were spared, fires burned over 72,000 hectares in central-western Tasmania, 20,000 of which located in the World Heritage Area. This caused severe damage to the unique sub-alpine and alpine habitats where species such as Pencil Pines, King Billy Pines, and cushion plants are found. The iconic Pencil Pines and King Billy pines (Athrotaxis cupressoides and A. selaginoides) are slow growing trees that can live over 1,000 years, but are restricted to fire-sheltered, high rainfall locations, as they lack those traits typically associated with fire-resistance, such as resprouting ability, thick bark, and aerial or soil seedbanks [1, 2]. The peat on which these plants grow, which can take millennia to form, is also prone to fire damage when dry, releasing great amounts of carbon in the atmosphere [3].

1 pencil pines

Pencil Pines and cushion plants at the Walls of Jerusalem National Park. (Credit: Stefania Ondei)

The 2016 fires reached several of those fire-sensitive habitats in western Tasmania and on the Central Highlands, where the burnt areas might take decades or centuries to recover, if at all.

The damage caused was, once again, described as unprecedented.



Alpine vegetation at lake McKenzie (Tasmanian Central highlands) before (top) and immediately after (bottom) the 2016 fires (Credit:; Dan Broun).

This summer Tasmania is facing a new fire emergency, with hundreds of professional and volunteer firefighters constantly battling the over 50 fires that have been burning across the state, reddening the Tasmanian skies and covering half of the island in a blanket of smoke.

The unique Tasmanian fire sensitive vegetation is at risk again, particularly in the Central Highlands and in the south-west, where the largest remaining forest of King Billy pines is located. To date, the Tasmanian Fire Service estimated that nearly 200,000 hectares have burnt, and, despite the relief brought by some needed rain, the fire season might be far from being over.

The extent of these fires is unprecedented.


The frequency at which high-intensity fires have been recently occurring in Tasmania suggests that we will continue to use the word ‘unprecedented’ for quite some time. At the very least, bushfires of similar extent and intensity will likely become the norm rather than the exception. But what is driving this change? To start a fire, three key elements need to be present: fuel, oxygen, and an ignition source. The air provides plenty of oxygen. As for fuel, in Tasmania an increase in average temperatures, as well as natural and anthropogenic variations of the westerly winds of the Southern Annular Mode, led to drier summers [4, 5], facilitating the accumulation of high amount of flammable plant material. The ignition source is provided predominantly by lightning; rarely recorded before 2000, fires started by dry storms have become more frequent in the Tasmanian Wilderness World Heritage Area, and the area burnt by those fires has also increased [6].

How will fire-sensitive vegetation respond to these new fire regimes? A study of the King Billy pine population that was damaged by an intense fire of the Central Plateau in 1961 showed little to no regeneration of the trees in the burnt area, nor presence of new seedlings [7]. This was likely exacerbated by the post-fire establishment of alpine shrubs in the area, which provide high amounts of fuel load under dry condition, potentially increasing fire frequency and determining a further decline of King Billy pines [8]. The vegetation burnt by the 2016 fires also showed scarce signs of regeneration one year after the event. There is no reason to assume that this gloomy scenario could not happen elsewhere in Tasmania. It is likely only a matter of time, before the increasingly dry conditions and the more frequent, intense, and extensive fires will reach the many – if not all – subalpine and alpine vegetation communities, shrinking beyond recovery the fire refugia in which these iconic species are forced.


Alpine vegetation at lake McKenzie one year after the 2016 fire (Credit:

Only a combination of short- and long-term actions will help preventing this announced ecological tragedy. For instance, every year in Tasmania a substantial amount of prescribed burning is conducted to reduce fuel load, to limit fire spread and intensity in both inhabited and natural areas. However, it is challenging to establish where to burn and what pattern to follow. Different fire behaviour models agree that the most effective approach would likely be similar to the burning practices traditionally conducted by Aboriginal people, with a large overall burnt area made by a high number of small patches, which would create a fine-scale fuel mosaic [9, 10]. Sadly, such approach would be too expensive for the limited resources available. More financially realistic treatments were also modelled, but displayed limited effectiveness [9]. Local efforts, while useful to prevent and fundamental to stop ongoing fires, are thus unlikely to be able to completely avert the increasingly catastrophic fires driven by a climate that is changing even faster than predicted. In an ideal world, strong immediate measures would be taken at a global scale to limit climate change effects. In this world, however, this is yet to happen. In the meanwhile, we thank the rain.




  1. Prior, L.D., B.J. French, and D.M.J.S. Bowman, Effect of experimental fire on seedlings of Australian and Gondwanan trees species from a Tasmanian montane vegetation mosaic. Australian Journal of Botany, 2018. 66(7): p. 511-517.
  2. Worth, J.R.P., et al., Gondwanan conifer clones imperilled by bushfire. Scientific Reports, 2016. 6: p. 33930.
  3. Yu, Z., et al., Global peatland dynamics since the Last Glacial Maximum. Geophysical Research Letters, 2010. 37(13).
  4. Bureau of Meteorology. Trend in mean annual temperature – 1970-2018. 2019; Available from:
  5. Mariani, M. and M.-S. Fletcher, The Southern Annular Mode determines interannual and centennial-scale fire activity in temperate southwest Tasmania, Australia. Geophysical Research Letters, 2016. 43(4): p. 1702-1709.
  6. Styger, J., J. Marsden-Smedley, and J. Kirkpatrick, Changes in Lightning Fire Incidence in the Tasmanian Wilderness World Heritage Area, 1980–2016. Fire, 2018. 1(3).
  7. Holz, A., et al., Effects of high-severity fire drove the population collapse of the subalpine Tasmanian endemic conifer Athrotaxis cupressoides. Global Change Biology, 2015. 21(1): p. 445-458.
  8. Holz, A., et al. High severity fires, positive fire feedbacks and alternative stable states in Athrotaxis rainforest ecosystems in western Tasmania. in AGU Fall Meeting Abstracts. 2016.
  9. Furlaud, J.M., G.J. Williamson, and D.M.J.S. Bowman, Simulating the effectiveness of prescribed burning at altering wildfire behaviour in Tasmania, Australia. International Journal of Wildland Fire, 2018. 27(1): p. 15-28.
  10. King, K.J., et al., The relative importance of fine-scale fuel mosaics on reducing fire risk in south-west Tasmania, Australia. International Journal of Wildland Fire, 2008. 17(3): p. 421-430.



All posts are personal reflections of the blog-post author and do not necessarily reflect the views of all other DEEP members



By Vishesh Leon Diengdoh 14th December at 11:30AM

When I had camped nearby Port Arthur in the Tasman Peninsula, this November, I was woken up at five in the morning by eight or nine Yellow Wattlebirds who kept calling in a sequence and repeating this for at least half an hour. From within my tent, I counted eight, based on the loudness and direction of the call, the ninth call was too far to make out. The same thing happened like clockwork the next morning as well. Its call has been described as a person vomiting and I once heard someone say it sounded like a deranged chicken. Those are accurate descriptions and if you have not heard what a Yellow Wattlebird sounds like you should give it a listen. I’ve noticed that a lot of birds in Australia have a screaming like calls (they sound amazing but not at 5 in the morning) and this leads me to read a book by Tim Low called Where song began. One of the things I learned from the book was that birds scream for nectar present in the flowers. The amount of nectar available to birds in Australia is enough to make nectar feeding birds defend it with loud aggressive calls to assert possession.


Figure: Yellow Wattlebird, Anthochaera paradoxa is Australia’s largest honeyeater and endemic to Tasmania and King Island (Image source:

Honeyeaters are nectarivorous birds of the Meliphagidae family which are distributed from eastern Indonesia to Palau, Hawaiian Islands, New Zealand and Australia. It is one of the largest family in the region with more than 170 species, with nearly 70 species occurring in Australia. Some honeyeaters are brightly coloured with striking patterns, but most are covered in shades of green, brown or grey. A common feature is a naked area on the head and presence of conspicuous clump of feathers which are often yellow or white. Males and females are nearly identical in appearance with a few exceptions (Longmore, 1991).

Honeyeaters have brush-tip tongues with numerous bristles that are long and fine allowing them to collect nectar across large surfaces and tiny fissures on tree branches. These features of the honeyeater’s tongue are different from other nectarivorous birds such as sunbirds and hummingbirds (Paton and Collins, 1989). In addition to nectar, they also consume fruits, berries, insects, manna and lerp (Barker and Vestjens, 1990).


Figure: Gross morphology and appearance in transverse sections of the brush-tipped tongue of the Spiny-cheeked Honeyeater (Acanthagenys rufogulari) (Image source: Paton and Collins, 1989)

Honeyeaters are conspicuous, highly active and aggressive both among themselves and with other species (Longmore, 1991). Although honeyeaters exhibit aggressive behaviour, a level of coexistence can exist resulting in a community of diverse honeyeaters. According to Ford (1979), larger species can easily defend a nectar source (interference competition) while smaller species are more efficient at feeding (exploitation completion). These two types of competitions create a balance to maintain species diversity in an area where nectar abundance varies spatially and temporally.

In Australia, Honeyeaters are among the major flower feeders. The genera most frequently visited are Eucalyptus, Callistemon, Banksia, Grevillea, Adenanthos, Epacris, Astroloma, Amyema, Correa, Xanthorrhoea, Anigozanthos and Eremophila. The genus Eucalyptus, with 74 species is visited by 83 species of birds making it one of the most important genus. The plant-bird relation in Australia exhibits a generalist relationship with birds visiting a range of flowers. This is different from the specific relationship between hummingbirds and plants in tropical America. There is no definitive indication as to why numerous and dominant plant genera in Australia would be pollinated by birds. Compared to insects, birds are active throughout the year and are more reliable when the flowering seasons and climate are erratic. Birds travel further distances than insects improving chances of outcrossing in plants (Ford et al., 1979).

According to the projections by Sekercioglu et al. (2004), 6-14% of all bird’s species will be extinct by 2100 and this will result in the decline of important ecosystem processes such as seed dispersal and pollination. Nectar and fruit-eating birds are expected to have a higher than average extinction which will, in turn, affect populations and communities. This would have significant importance in Australia and other oceanic regions where pollinating birds are higher than other parts of the world.

The Australian Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) and the International Union for Conservation of Nature (IUCN) red list have designated the Painted Honeyeater, Black-eared Miner and Regent Honeyeater as vulnerable, endangered and critically endangered respectively. The Helmeted Honeyeater has however been listed as critically endangered by the EPBC. The threats to these species include – agriculture & aquaculture, biological resource use, climate change, invasive non-native/alien species, disease, natural system modifications and residential & commercial development.


Figure: Regent Honeyeater, Anthochaera phrygia (Image source:

The state of the four Honeyeaters listed above represent the current situation in Australia. If we assume the projections of Sekercioglu et al. (2004) to take place, it would have serious consequences for the ecosystem. The aim of my PhD is to assess the influence of land-use and land-cover, climate change and other factors on the distribution of different pollinating birds (i.e. honeyeaters: Black-headed Honeyeater, Eastern Spinebill, Crescent Honeyeater, Little Wattlebird, New Holland Honeyeater, Noisy Miner, Strong-billed Honeyeater, Tawny-crowned Honeyeater, Yellow-throated Honeyeater and Yellow Wattlebird) in Tasmania. I’m focusing on land-use and land-cover and climate change since they are considered as the main drivers of global change (Hansen et al., 2001); with land-use change expected to have the largest impact on terrestrial ecosystems and biodiversity in the future, followed by climate change (Sala et al., 2000).


Figure: Crescent Honeyeater (Phylidonyris pyrrhopterus) at Fortescue Bay. It’s a bit blurry as I only had a macro lens with me.



Barker, R. D. & Vestjens, W. J. M. 1990. The food of Australian birds 2. Passerines, CSIRO PUBLISHING.

Ford, H. A. 1979. Interspecific competition in Australian honeyeaters—depletion of common resources. Australian Journal of Ecology, 4, 145-164.

Ford, H. A., Paton, D. C. & Forde, N. 1979. Birds as Pollinators of Australian Plants. New Zealand Journal of Botany, 17, 509-519.

Hansen, A. J., Neilson, R. P., Dale, V. H., Flather, C. H., Iverson, L. R., Currie, D. J., Shafer, S., Cook, R. & Bartlein, P. J. 2001. Global change in forests: responses of species, communities, and biomes: interactions between climate change and land use are projected to cause large shifts in biodiversity. AIBS Bulletin, 51, 765-779.[0765:GCIFRO]2.0.CO;2

Longmore, W. 1991. Honeyeaters & their allies of Australia, Collins/Angus & Robertson.

Paton, D. & Collins, B. 1989. Bills and tongues of nectar‐feeding birds: A review of morphology, function and performance, with intercontinental comparisons. Australian Journal of Ecology, 14, 473-506.

Sala, O. E., Chapin, F. S., Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L. F., Jackson, R. B. & Kinzig, A. 2000. Global biodiversity scenarios for the year 2100. Science, 287, 1770-1774.

Sekercioglu, C. H., Daily, G. C. & Ehrlich, P. R. 2004. Ecosystem consequences of bird declines. Proceedings of the National Academy of Sciences, 101, 18042-7.


All posts are personal reflections of the blog-post author and do not necessarily reflect the views of all other DEEP members



By Shane Morris, 20th November at 4:30 PM

As I watched “Arrival”, a movie about aliens descending to Earth and their interaction with a human linguist, I got thinking about the science behind the movie. This wasn’t the physics of space travel or the anatomy of the visitors but the Sapir-Whorf hypothesis*, the principle that language shapes a speaker’s world view or cognition. It is generally divided into two hypothesises; 1) the strong, language determines how we perceive the world, and 2) the weak, that language influences thought and decisions. “Arrival” deals with the strong hypothesis, and in this article I will deal with the weak, connecting these thoughts to my research on conservation translocations and proposing a new name for a scientific technique.


There are some fascinating examples of the interplay between language and cognition. The Kuuk Thaayore of Cape York, North Australia, have amazing navigational abilities, which is enabled by their language using only absolute directions (North, South, East, West and everything in between) rather than also using relative directions (like right, left in English)1. So, if a Kuuk Thaayore asked you to set the table you could be told to but the fork on the North North-West side and the knife on the South South-East side of the plate! An English speaker may take a few seconds to orientate themselves, while in contrast the Kuuy Thayore have spent their lives expressing themselves with absolute directions. This is quite an extreme example, but language can also influence how we categorise things due to the relationships it creates in our minds1. Many Indo-European languages have arbitrarily assigned gender to common and abstract nouns, for instance in Spanish the word “key” is feminine while in German it is masculine. It has been shown to influence the speakers of these languages perception for when asked to describe a “key”. Spanish speakers will use words like “intricate”, “lovely”, “little” and “shiny”, while German speakers use “hard”, “heavy”, “serrated” and ”useful”.  And before you think that’s because Spanish speakers are passionate and Germans are practical, or some other stereotype, this trend is consistent when a word in each language is chosen with the gender reversed1.

Kuuy Thayore and Lera Boroditsky

These examples are about the fundamentals of the language and we are therefore stuck with them, but the Sapir-Whorf hypothesis is of great importance when we need new terms for new ideas. In his book “The Invention of Science”, the historian of science, Daniel Wooton writes “a revolution in ideas requires a revolution in language”. Wooton argues, that Christopher Columbus never said he discovered America (he used invenio meaning find out) because the word, in fact the very concept of discovery did not exist at that point.  Before the Age of Exploration, Western culture gazed backwards convinced that all worthwhile information had already been discovered in ancient Athens or Rome. Columbus and his crew had experienced something entirely unknown to the Western civilisations of yore. This then shifted the focus of Western culture to one which, to paraphrase Wooton, recognised experience as the path to discovery, and promptly ushered in the Scientific Revolution2.

At this point you may be asking what a movie about aliens; cardinal direction use by Aboriginal Australians and Christopher Columbus have to do with conservation translocations? You may even be wondering what a conservation translocation is.  A conservation translocation is the movement of a species from one place to another to confer a conservation benefit3. In recent decades, these have become an increasing popular tool in conservation biology but are very controversial amongst scientists in this field.  4,5. The most contentious type of conservation translocation is the movement of a species to an area in which there is no evidence that it had previously inhabited. The main concern, an extremely valid concern, is that these have the potential to cause disastrous, unforeseen circumstances. This debate is beyond the scope of this article but one that isn’t is what this technique should be called, this may seem frivolous on the surface, yet it could have larger implications for its perception by the public. I considered this while watching “Arrival”, how the term we use for this last type of translocation may shape its future. Before you scoff at the seemingly superficial importance of a name, consider that a recent study carried out by researchers at Johns Hopkins Bloomberg School of Public Health found that the language used was key to a treatment garnering higher public support. In this study, 29 percent of people were in favour of “safe consumption sites” yet this rose to 45 percent when “overdose prevention sites” was used6.

The names currently being used for the controversial translocation technique are assisted colonisation, assisted migration, benign introduction and managed relocation. Connie Barlow argues that assisted colonisation should not be used due to its “hegemonic overtones” and its association with invasive species8. Societies that have experienced the suffering caused by colonialism are likely to oppose ideas outright due to associations with past traumas. Malcom Hunter rightly argues against assisted migration as migration is already defined within ecology to mean a round trip, which this would not be7. I oppose the term benign introduction as it is inherently misleading, making the act seem non-interventionist when the antithesis is true. Managed relocation may be the best of a bad bunch but seem more applicable to moving office, than an endangered species! It suffers from the same problems as “safe consumption sites”, it is frigid and vague.

On the advice of Maya Angelou “What you’re supposed to do when you don’t like a thing is change it. If you can’t change it, change the way you think about it. Don’t complain.” I won’t complain but try and change the thing I don’t like. My proposed name is assisted transmigration. The definition of transmigration is “to move from one place, state, or stage to another”. This captures the essence of what is trying to be achieved, we are not only moving a species from one place to another but from one state (endangered) to another (non-endangered). But perhaps you don’t agree with this term. What would you call it? For what it is called may turn out to be important.


*An interesting aside is that the Sapir-Whorf hypothesis is itself shaping how the idea is/was perceived. Edward Sapir ad Benjamin Lee Whorf were famous linguists at the turn of the last century who wrote about the effect they perceived language to have on cognition however they never co-authored any works together and never formulated a hypothesis! So, the name of the idea that is about language shaping thought has been named to heighten the prestige of the idea!



  1. Boroditsky, L 2009, How does our language shape the way we think? Edge, accessed 7 November 2018, < >.
  2. Wootton, D., 2015. The invention of science: a new history of the scientific revolution. Penguin UK.
  3. IUCN, S., 2013. Guidelines for reintroductions and other conservation translocations. Gland Switz Camb UK IUCNSSC Re-Introd Spec Group.
  4. Hoegh-Guldberg, O., Hughes, L., McIntyre, S., Lindenmayer, D.B., Parmesan, C., Possingham, H.P. and Thomas, C.D., 2008. Assisted colonization and rapid climate change. Science 321 (5887), pp 345-346.
  5. Ricciardi, A. and Simberloff, D., 2009. Assisted colonization is not a viable conservation strategy. Trends in ecology & evolution, 24(5), pp.248-253.
  6. Barry, C.L., Sherman, S.G. and McGinty, E.E., 2018.  Language Matters in Combatting the Opioid Epidemic: Safe Consumption Sites Versus Overdose Prevention Sites. American Journal of Public Health; 108 (9): pp 1157
  7. Barlow, C 2009. Pro “Assisted Migration” as the term of reference. Torreya Guardians, accessed 7 November 2018, <;
  8. Hunter Jr, M.L., 2007. Climate change and moving species: furthering the debate on assisted colonization. Conservation Biology, 21(5), pp.1356-1358.

All posts are personal reflections of the blog-post author and do not necessarily reflect the views of all other DEEP members



by Elise Ringwaldt, 6th November at 2:44 PM

In 2017, I was invited to visit The Breakthrough Institute and attend their Dialogue: Democracy in the Anthropocene. Discussions at this event incorporated how human population is the driving ecological force on the planet, and decisions made about agriculture, energy and resources will influence the future planet. In reflection, I describe in this article how there are concerns for the growing human population food requirements, especially for developing nations. One way we can improve the food wasted right now, is through growth in the energy sector, which can improve technology throughout the food supply chain – saving food and corresponding resources.

Human population is estimated to reach 9 billion by 2050, and with it, rising demand for food and energy. To feed our expanding population it is estimated we would need to increase the production of food by ~70 percent! However, few people realize that we could feed billions of undernourished people right now just by reducing the amount of food wasted or lost after harvesting. In the United States alone, 31-40% of the food produced for human consumption is wasted, which is enough to feed 1 billion people. So, there is an obvious paradox here – we need more food, yet we waste enough to feed the undernourished? Where does the problem lie, and what can we do about it?

Food is lost and wasted along the food supply chain; starting from where the food is produced (e.g., farms), packaged, transported and then consumed (purchased and eaten by us). Where it is lost along this chain differs for developed and developing countries, mainly due to differences in access to technology and energy. In developed countries, modern technology and infrastructure help to reduce the amount of food wasted post-harvest along the food supply chain (Loss in developed countries can be as low 1-2% for some commodities, compared to developing countries with an average of 40%). Most developed countries depend on easily accessible energy (e.g., grid-supplied power) to run large scale operations with specialized machinery, high-tech post-harvest treatment, and secure storage facilities to reduce spoilage during post-harvest and transport.  On the other hand, developing nations typically have insufficient electricity supply and inadequate technology and/or storage mechanisms, and because of this they have greater food loss and waste post-harvest. In developing nations, one of the most successful ways to achieve food security and reduce food loss is through an increase in reliable energy. But through what mechanisms?

Energy sources, including fossil fuels, nuclear power, hydro-electricity, and renewable energy (solar and wind) are the basis for modern human activities; in the past increases in energy supply have improved standard of living, economy, and growth. Electricity supply in developing nations is scarce, with only about 40% of the population having access to energy, while in industrialized countries it is closer to 100%. Electricity is especially limited in sub-Saharan Africa where only 24% of the population have access to electricity. Furthermore, in 2013 the entire sub-Saharan Africa (excluding South Africa), which is made up of 46 countries and a population of more than 900 million, was only supported by 28 Gigawatts; this is the same electricity supply as the one country Argentina, which supports 43 million people and is a little over one-tenth of the land size of sub-Saharan Africa. In summary, many developing nations are currently relying on primitive technology and energy supplies (such as basic traditional fuels of firewood and dung cakes) to support their growing nations demand for food. Adequate power supplies will be pivotal for developing nations.

Increasing access to energy in developing nations is, however no easy feat. Lately, there have been attempts to help rural farmers reduce waste and increase the lifetime of foods by using advanced power systems, such as solar energy and biogas. However, while these technological developments are improving food security, they are only effective at small scale, and so longer term solutions are required. Grid-supplied energy is the perfect example of a stable, more wide-reaching alternative for developing nations. Grid-supplied power would provide energy for technology, to build infrastructure such as grain facilities and roads for transportation of foods, provide energy to use modern machinery during harvesting and packaging of foods, and air conditioners to power cool stores. Adequate cooling and storing of food post-harvest has the potential to reduce global food loss by at least 25%. For example, better grain storage facilities in Africa could reduce the amount of annual grain loss, potentially to feed the requirements of 48 million people a year. Additionally, 96% of India’s fresh produce is not refrigerated during storage and transport resulting in losses of over US$5 billion a year. In short, increases in energy supply has enormous potential to influence food security in developing nations.

Modern energy facilities are key to reducing food loss and waste in developing nations; and some organisations are already recognizing this need and are making a difference. Currently, there are 126 independent power projects within 18 sub-Saharan Africa countries, specifically to increase people’s access to electricity; totaling a possible 13 percent of the total power generated and 25 percent if South Africa was excluded. Policies to address future food demand are increasingly in the spotlight, with a focus on holistic investment in technology, infrastructure, and electricity. For example, the 2030 Agenda for Sustainable Development (ADS) and of the Addis Ababa Action Agenda emphasizes food security, nutrition, and sustainable agriculture among their goals, with focus on infrastructure, industrialization and innovation, impacting the 65 percent of the world’s poor whose livelihood is farming. Investment into increasing a developing country’s energy supply has clear benefits, not just through reducing food loss and waste, but also by strengthening economic opportunities.

In summary, energy increases in developing nations will feed some of the poorest due to development in infrastructure and technology which saves food along the production line. Even though developed countries may have sufficiently minimalized food loss post-harvest, consumer standards (such as the aesthetics of food produced) and the overwhelming supply of foods in supermarkets creates a paradox of food waste. Consumer, production, and the oversupply of food in developed countries means that just as much (40%) of food is wasted as developing countries. There needs to be a balance, where policies are in place so that developing nations don’t start to waste food pre-harvest at the consumer level and learn from the industrialized world mistakes to feed people adequately but sustainably.

(All posts are personal reflections of the blog-post author and do not necessarily reflect the views of all other DEEP members).