Research Director CNRS
External Professor Santa Fe Institute
michael (dot) hochberg (at) umontpellier (dot) fr
The Darwin Lecture
The Hallmarks of Biological Failure
An Editor’s Guide
RECENT PREPRINTS, ARTICLES AND REVIEWS
Fighting microbial pathogens by integrating host ecosystem interactions and evolution
Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic
When, why and how tumour clonal diversity predicts survival
Phage steering of antibiotic-resistance evolution in the bacterial pathogenPseudomonas aeruginosa
Environmentally mediated social dilemmas
An ecosystem framework for understanding and treating disease
Spatial competition constrains resistance to targeted cancer therapy
The future of academic publishing
Cancer evolution: from cells to species and back
Phage-bacteria community coevolution: from basic science to applications in phage therapy
A biologist’s perspective of process and pattern in innovation
ALUMNI NOW IN ACADEMIA
Olivier Restif (MSc w/ J. Koella)
Patrick Venail (PhD w/ N. Mouquet)
Our main objective is to produce and test predictive theory of the ecology and evolution of disease. Our work focuses on micro-parasites and cancer cells that can attain enormous population sizes within their hosts and therefore are subject to within host evolution and at larger biological scales, host-parasite co-evolution, and have made significant inroads to fundamental research questions and applications to medicine. As summarized below, we have shown notably how space and environment arbitrate disease ecology and evolution at different biological levels, and also explain evolutionary transitions from conflict to cooperation and from parasitism to mutualism, these latter themes producing a more general conceptual framework for how environment interacts with social evolution.
Our group has been at the forefront of applying the idea of evolutionary-rational therapy (therapeutic approaches that prevent, limit or circumvent the evolution of resistance) to bacterial diseases (Hochberg 2018). These studies commenced with fundamental research on bacteria-phage ecology and evolution in laboratory microcosms (Buckling et al. 2006; Brockhurst et al. 2006; Poullain et al. 2008; Kaltz et al. 2012) and based on some of these findings and insights from our theoretical models conducted over the past three decades, we have made several important discoveries that could advance our understanding of “phage therapies” and support their use as stand-alone therapeutics or in combination with antibiotics.
We began this research by investigating how phage choice could affect coevolutionary dynamics and what this might mean for phage therapies (Betts et al 2014). Although coevolution can drive diversity and specificity within species, there is scant research on whether coevolutionary dynamics differ among functionally similar species. Alex Betts examined coevolution ofPseudomonas aeruginosaPAO1 with a panel of 6 different bacteriophages and found that pathogen identity affected coevolutionary dynamics. For five of six phages tested, time-shift assays revealed temporal peaks in bacterial resistance and phage infectivity, consistent with frequency-dependent selection (Red Queen dynamics). Two of the six phages also imposed additional directional selection, resulting in strongly increased resistance ranges over the entire length of the experiment (ca. 60 generations). Cross-resistance to these two phages was very high, independent of the coevolutionary history of the bacteria. We hypothesized that coevolutionary dynamics are associated with the nature of the receptor used by the phage for infection (i.e., different coevolving genes).
The results from this study and a previous one with Alex (Betts et al. 2013) indicated that the ecological and evolutionary impacts of phages sometimes depended on whether they were ahead or behind their bacterial host in terms of adaptation. While predators and parasites such as phages are known for their effects on bacterial population biology, their impact on the dynamics of bacterial social evolution is unclear. We wanted to see if phages influenced the social structure of their bacterial hosts (Vasse et al. 2015). To do this, Marie Vasse employed the production of a public good: siderophores, which are iron-chelating molecules that are key to the survival of certain bacterial species in iron-limited environments. Siderophore production however can be subject to cheating by non-producing genotypes. In a selection experiment conducted over approximately 20 bacterial generations and involving 140 populations ofPseudomonas aeruginosaPAO1, Marie assessed the impact of a lytic phage on competition between siderophore producers (cooperators) and non-producers (cheaters). We found that the presence of lytic phages favors the non-producing genotype in competition, regardless of whether iron use relies on siderophores. Interestingly, phage pressure resulted in higher siderophore production, which constitutes a cost to the producers and may explain why they were outcompeted by non-producers. By the end of the experiment, however, cheating load reduced the fitness of mixed populations relative to producer monocultures, and only monocultures of producers managed to grow in the presence of phage in situations where siderophores were necessary to access iron. Our results suggest that public goods production may be modulated in the presence of natural enemies with consequences for the evolution of social strategies.
The above results show to what extent antagonisms such as phages and antibiotics can influence fundamental biological evolution, specifically the diversity and evolutionary rate in the coevolutionary process (Betts et al 2014) and social evolution in the same host bacterium (Vasse et al. 2015, 2016). Both of these effects are interesting since they could form the basis for how we can understand and improve therapies against bacterial pathogens. We decided to undertake a series of studies focusing on how combinations of phages and antibiotics may or may not be superior in their impacts on host bacteria than either used alone.
Evolutionary theory predicts that the probability of bacterial resistance to both phages and antibiotics will be lower than to either separately, due for example to fitness costs or to trade- offs between phage resistance mechanisms and bacterial growth. We assessed the population impacts of either individual or combined treatments of a bacteriophage and streptomycin on the bacterial pathogenPseudomonas aeruginosaPAO1 (Torres-Barceló et al. 2014). Clara Torres-Barceló led this study and found that combining phage and antibiotics substantially increased bacterial control compared to either separately, and that there is a specific time delay in antibiotic introduction independent of antibiotic dose, that minimizes both bacterial density and resistance to either antibiotics or phage. We hypothesized that this is because the second antimicrobial needs to be added when the bacterial population is at or near its lowest density enforced by the first agent. It is at these low densities that the target pathogen population is the least likely to harbor resistant mutants to the second antimicrobial. We continued to investigate this same line of reasoning by seeing to what extent our results were robust to other antibiotics, and the impact of therapy on other bacterial life-history traits, specifically, virulence to the host (Torres Barceló et al. 2016). Clara experimentally evaluated the impacts of single and combined applications of antibiotics (different doses and different types) and phages onin vitroevolving populations ofPseudomonas aeruginosaPAO1. We also assessed the effects of past treatments on bacterial virulencein vivo, employing larvae ofGalleria mellonellato survey the treatment consequences for the pathogen. We found a strong synergistic effect of combining antibiotics and phages on bacterial population density and in limiting their recovery rate. Our long-term study established that antibiotic dose is important, but that effects are relatively insensitive to antibiotic type. From an applied perspective, our results indicate that phages can contribute to managing antibiotic resistance levels, with limited consequences for the evolution of bacterial virulence.
Despite the importance of these results to a fundamental understanding of applied problems, few scientists are aware and working on these specific issues. Together with Clara we wrote an opinion article where we advocate the use of phages in combination with antibiotics and present the evolutionary basis for our claim (Torres-Barceló & Hochberg 2016). We presented several possible risks with combined therapies, arguing that they can be developed so that the probability of treatment success is high and risks are low.
We have since engaged in a number of prospective studies (reviews, opinion articles) and laboratory experiments to dig deeper into the fundamentals of phage therapy. In a first study (Gurney et al 2017), we capitalized on methods commonly employed in community ecology to quantify how the structure of community interaction matrices – so-called bipartite networks (Weitz et al 2013) – reflected observed co-evolutionary dynamics, and how phages from these communities may or may not have adapted locally to their bacterial hosts. James Gurney led this study and found a consistent nested network structure for two phage types, one previously demonstrated to exhibit ‘arms race’ co-evolutionary dynamics and the other ‘fluctuating’ co-evolutionary dynamics. Both phages increased their host ranges through evolutionary time, but we found no evidence for a trade-off with impact on bacteria. But only bacteria from the arms race phage showed local adaptation, and we provided preliminary evidence that these bacteria underwent (sometimes different) molecular changes in the wzy gene associated with the lipopolysaccharide receptor, while bacteria co-evolving with the fluctuating selection phage did not show local adaptation and had partial deletions of the pilF gene associated with type IV pili (used by bacteria to move on surfaces). We concluded that the structure of phage–bacteria interaction networks is not necessarily specific to co-evolutionary dynamics and this opens a number of interesting questions regarding whether initially generalist phages tend to specialize through time, or the reverse. This could have implications for phage therapies, where phages are typically introduced as ‘cocktails’ of numerous phage types: the burning question is what kind of diversity in host range is most likely to prevent the evolution of resistance to the phage.
We were in contact with Paul Turner and Ben Chan (Yale University) over this period and proposed a collaboration based on the idea that phages could be employed based on a combination of their ability to kill bacterial hosts and the likelihood that theyselectfor phage resistance. At first sight, this is counterintuitive, but it turns out that the phage isolated by Ben is special in that in selecting for resistance it also produces antibioticsensitivehosts. To be clear: the idea is to introduce phages with theintentionthat they select for phage resistance in their host bacteria! In a recent opinion piece led by James Gurney, we have called this ‘phage steering’ (Gurney et al 2019TIMI), because the phages drive the bacteria to either be lysed (sensitive strains) or resist and be sensitive to combinations with antibiotics. We investigated this experimentally based on groundbreaking work from Paul Turner’s group, published in 2016 and 2018, by employing phage OMKO1. The Turner group showed that this phage interacts with bacterial efflux pumps and in so doing selects for both phage resistance and antibiotic sensitivity. James tested the robustness of phage steering using three different antibioticsin vitroand onein vivo.We showed thatin vitroOMKO1 can reduce antibiotic resistance either in the absence or the presence of antibiotics (Gurney et al 2019bioRxiv). Ourin vivoexperiment also showed that phage increased the survival times of wax moth larvae and increased bacterial sensitivity to the antibiotic erythromycin. Our study is one of the first to our knowledge to show how ‘phage steering’ can succeedin vivo.
Cancer ecology and evolution
Our group began working on questions relating to cancer evolution in 2009 after I stepped down as Editor in Chief ofEcology Letters.Entering a neighboring discipline meant consecrating considerable time to integrating the published literature and it was during my sabbatical at theWissenschaftskolleg zu Berlinin 2013-2014 that our cancer projects matured and began producing interesting findings.
In a study lead by Athena Aktipis (Aktipis et al. 2015) we reviewed how cancer is a fundamental process of cheating in multicellular organisms. Multicellularity is characterized by cooperation among cells for the development, maintenance and reproduction of the multicellular organism. Complex multicellularity, and the cooperation underlying it, has evolved independently multiple times. We reviewed the existing literature on cancer and cancer-like phenomena across life, not only focusing on complex multicellularity but also reviewing cancer-like phenomena across the tree of life more broadly. We found that cancer is characterized by a breakdown of the central features of cooperation that characterize multicellularity, including cheating in proliferation inhibition, cell death, division of labor, resource allocation and extracellular environment maintenance (which we termed the ‘five foundations of multicellularity’). Cheating on division of labor, exhibited by a lack of differentiation and disorganized cell masses, has been observed in all forms of multicellularity. This suggests that deregulation of differentiation is a fundamental and universal aspect of carcinogenesis that may be underappreciated in cancer biology. We argued how understanding cancer as a breakdown of multicellular cooperation provides novel insights into cancer hallmarks and suggests a set of assays and biomarkers that can be applied across species and characterize the fundamental requirements for generating a cancer.
Work with Hanna Kokko (Kokko & Hochberg 2015) considered how cancer defenses are expected to coevolve with the typical lifespan and body size of a species. This relates two studies we published in the 1990s, looking at how parasites can influence selection for life history traits (Hochberg et al 1992; Michalakis and Hochberg 1994). Our claim with Hanna is that studies of body size evolution are conducted without considering cancer as a factor that can end an organism’s reproductive lifespan. This reflects a tacit assumption that predation, parasitism and starvation are of overriding importance in the wild. We argued that even if deaths directly attributable to cancer are a rarity in studies of natural populations, it remains incorrect to infer that cancer has not been of importance in shaping observed life histories. We presented first steps towards a cancer-aware life-history theory, by quantifying the decrease in the length of the expected reproductively active lifespan that follows from an attempt to grow larger than conspecific competitors. If all else is equal, a larger organism is more likely to develop cancer, but, importantly, many factors are unlikely to be equal. Variations in extrinsic mortality as well as in the pace of life—larger organisms are often near the slow end of the fast–slow life-history continuum—can make realized cancer incidences more equal across species than what would be observed in the absence of adaptive responses to cancer risk (alleviating the so-called “Peto’s paradox”). We also discussed reasons why patterns across species can differ from within-species predictions. Even if natural selection diminishes cancer susceptibility differences between species, within-species differences can remain. In many sexually dimorphic cases, we predicted males to be more cancer-prone than females, forming an understudied component of sexual conflict.
With Robert Noble (Noble et al. 2015), we examined in some detail the components of multistage carcinogenesis, applied to different tissues in the human body. Rather than modelling this as an explicit multistage process, Rob separated the relative statistical effects of the number of stem cells in different tissues in the human body, the life time number of stem cell divisions per tissue type, and cancer risk. This follows on from a controversial study published inScienceby Tomasetti and Vogelstein (2015), purporting that the majority of the variation explained in cancer incidence is due to “bad luck”. We discovered a relationship between human tissues that is similar to “Peto’s paradox” for between species comparisons. We showed that the number of stem cell divisions and stem cell number each has an independent effect on cancer risk. When considering the lifetime number of stem cell divisions in an extended dataset, and removing cases associated with other diseases or carcinogens, we found that lifetime cancer risk per tissue saturates at approximately 0.3–1.3% for the types considered by Tomasetti and Vogelstein. We further demonstrated that grouping by anatomical site explains most of the remaining variation. Our results indicate that cancer risk depends not only on the number of stem cell divisions but varies enormously (approx. 10000 times), depending on anatomical site. We concluded that variation in risk of human cancer types is analogous to the paradoxical lack of variation in cancer incidence among animal species and may likewise be understood as a result of evolution by natural selection.
Rob and I subsequently teamed-up to write a perspective on cancer across species and in our human ancestors (Hochberg & Noble 2017). Although there are many challenges to quantifying cancer epidemiology and assessing its causes, we claim that most modern-day cancer in animals – and humans in particular – are due to environments deviating from central tendencies of distributions that have prevailed during cancer resistance evolution. Such novel environmental conditions may be natural and/or of anthropogenic origin, and may interface with cancer risk in numerous ways, broadly classifiable as those: increasing organism body size and/or life span, disrupting processes within the organism, and affecting germline. We scoured the literature for estimates of cancer rates and found that although levels exceeding 20%-30% are common in certain populations, there is no unequivocal case of cancer rates above a conservative level of 5% where environment did not play a role. Environments include (1) conditions that disrupt, such as diet, injury, infections, UV exposure, tobacco smoke, and selective breeding or population bottlenecks leading to inbreeding, but also (2) conditions that protect, such as healthcare, zoos and protection from predators and parasites such as in nature reserves. Our framework provides a basis for understanding how natural environmental variation and human activity impact cancer risk, and how cancer rates can be employed as an indicator of disturbance in both nature and captive populations.
In parallel to this work, our team has made some significant inroads to models for understanding tumor growth and cancer treatment. The first, led by Andrei Akhmetzhanov (Akmetzhanov & Hochberg 2015) compared how prevention did or did not give superior outcomes to classic chemotherapeutic interventions. About one person in every two will get cancer during their lives. Surgery and chemotherapy have long been mainstays of cancer treatment. Both, however, have substantial downsides. Surgery may leave behind undetected cancer cells that can grow into new tumors. Furthermore, in response to chemotherapy drugs, some cancer cells may emerge that resist further treatment. There is therefore interest in whether preventive strategies—including lifestyle changes and medications—could reduce the likelihood of confronting a life-threatening cancer.
Andrei developed a mathematical model to help compare the effectiveness of preventive strategies and traditional cancer treatments. The model—which assumes that a person can only develop a single cancer from a single region of pre-cancerous cells—suggests that long-term cancer prevention strategies reduce the risk of a life-threatening cancer by more than traditional treatment that begins after a tumor is discovered. The preventive measures may be less effective in some cases compared to traditional treatments if they initially fail to stop a tumor growing, although on average they still work better than treating the cancer after detection.
We found that surgical removal followed by chemotherapy is less likely to be successful than prevention, and when successful, requires larger impacts on the cancer (and therefore creates more side-effects for the patient) to achieve the same level of control as prevention. The model also suggests that even at very low levels of impact on residual cancer cells, chemotherapies are likely to be counterproductive by boosting the subsequent emergence of treatment-resistant tumors. Our model predicts how effective preventive measures need to be in terms of slowing the growth of cancer cells to result in given reductions in the future risk of a life-threatening cancer. Future work should test this model by measuring the effects on tumor growth of prevention and of traditional therapies.
In 2011, I was invited to give a talk on tumor evolution at the Institute for Molecular Genetics in Montpellier. Several people in the audience found the evolutionist perspective to be interesting enough to engage in discussions about working together. This led to a collaboration with Daniel Fisher’s group that made the remarkable finding that with sufficient information, we can actually predict how a chemotherapy will affect the evolution of drug resistance, and because of this, introduce the possibility of adjusting drug levels so as to manage the tumor (Bacevic and Noble et al. 2017). So-called “adaptive management” is attracting considerable attention, because it holds the possibility of extending a patient’s lifespan and healthspan (i.e., low, more tolerable drug doses are employed in adaptive treatments compared to classical “maximum tolerated dose” therapies), whereas classic chemotherapy often selects for resistant variants, which cause relapse and death. Both the Akmetzhanhov and the Bacevic/Noble studies indicate that significant life-span is gained through such evolutionary-rational therapies, and the latter study is the first to our knowledge to model the process, based on tumor data and realistic competitive effects between resistance and sensitive cancer cell lines.
We recently pursued this logic – the idea that an evidence-based model for tumor ecology and evolution could inform us about the likelihood that a given therapy will succeed – by modeling tumors with a much more sophisticated, spatial approach (Noble et al. 2019). Intratumor heterogeneity holds promise as a prognostic biomarker for many types of cancer. However, the relationship between this marker and its clinical impact is expected to be mediated by an evolutionary process that is not well understood. Rob Noble developed and employed a spatial computational model of tumor evolution to assess when, why and how intratumor heterogeneity can be used to forecast tumor growth rate, an important predictor of clinical progression. We found three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumor development; selective sweeps are rare; and/or tumors vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. Our results further suggested that prognosis can be better predicted on the basis of both clonal diversity and genomic instability than either factor alone. Nevertheless, we found that, for predicting tumor growth, clonal diversity is likely to performworsethan conventional measures of tumor stage and grade. Our study offers explanations – grounded in evolutionary theory – for empirical findings in various cancers. We believe that this is a significant advance as it informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.
Our research on questions of social evolution grew out of work started in the 1990’s on antagonistic coevolution, the evolution of virulence and the evolution of mutualisms (Hochberg & Holt 1995; Hochberg & van Baalen 1998; Hochberg 1998; Hochberg et al. 2000). I was particularly interested in how space generated delays in the transmission of information and complex feedbacks on the selection of social traits. This generated questions and a series of theoretical studies (typically at 3-4 yr. intervals), beginning with the arrival of Sam Brown as a postdoc in our group and a sabbatical visit by Barry Sinervo in 2001, and continuing at present with members of a working group at the Santa Fe Institute on interactions between social behaviors and the environment.
The study with Sam and Barry originated from a newsworthy paper by Riolo, Cohen and Axelrod, published inNaturein 2001. In that article the authors showed that random encounters between anonymous agents can generate high levels of cooperation as long as their behaviors are contingent on their likeness to one another (so-called ‘tags’). This result was unexpected based on prevailing theory and generated critique, the most fundamental (from Roberts & Sherratt 2002) being overly-restrictive assumptions made by Riolo et al on the range of possible behavioral interactions. We were nevertheless struck by the possibility that phenotypic similarity could be reinforced by selection on social behaviors and embarked on a spatially explicit agent-based model investigation of this problem. Our focus was sympatric speciation – which has proved highly challenging to explain – our hypothesis being that basic social behaviors could reinforce a locally abundant phenotype and through prezygotic reproductive isolation, generate new species. We found however, that group formation was only obtained for altruistic and selfish acts, and hybrids at border areas served as barriers to gene flow resulting in reproductive isolation (Hochberg et al 2003). We were excited about this result because it more generally suggested that basic behaviors in social species and in particular in humans could ultimately be responsible for group splintering and intergroup conflict. We were only able to revisit this idea 10 years later in a study led by Deniz Salali and in collaboration with Harvey Whitehouse (Salali et al 2015). Specifically, Salali et al investigated the concrete question of how human groups transitioned from small-scale (10s of people) to large scale (100s or more), whereby groups were in competition for limited resources. Deniz developed and analyzed a spatially explicit agent-based model as a first step towards understanding the ecological dynamics of small and large-scale human groups. We were able to chart small-scale groups from their formation, growth, eventual transition to large scale, and sometimes death. We found that demographic and expansion behaviors of groups were largely influenced by the distribution and availability of resources. Most exciting was the overall similarity between our basic finding of a U-shaped distribution in group sizes (many small groups, few or no medium sized ones, and a moderate number of large groups) and empirical data on nation states. The generality of our results to various types of grouping in humans (e.g., religious, political) merits further study.
Most theoretical investigations at the time of work with Sam and Barry were centered on direct mechanisms such as policing and punishment and apparently indirect mechanisms like strong reciprocity. Together with Daniel Rankin and Michael Taborsky, we wondered whether the more fundamental processes of social evolution and dispersal evolution may in fact co-evolve so as to generate general patterns. We employed a phenomenological trait evolution model and showed how selection could result in a range of outcomes, arguably the most interesting being the linkage between cheating behaviors and dispersal, that, when taken to the extreme, resulted in complete cooperation within groups, with cheaters being sequestered as functional germline (Hochberg et al. 2008). Numerous assumptions are required for transitions from colonial groups to true multicellular organisms, the most important being that new groups are formed by a single cell, and that the behaviors of this cell’s offspring (that will form the new individual) replicate those of the source individual. Although overlooked by some readers of our paper, we conducted an extensive examination of real biological systems and found some support for our theory that evolved cheater dispersal could be a significant driver of cooperation and explain certain transitions to multicellularity.
At about the same time as the evolution of multicellularity study, I was visiting the Pontificia Universidad Católica de Chile annually, for a few weeks each January to present a short course on writing and publishing scientific articles. My main contact there was Pablo Marquet, and we would talk from year to year about collaborating together. In 2009 we discussed the idea to investigate a fascinating and little-known ancient culture – the Chinchorros – that occupied the coastal extreme of the Atacama Desert between approximately 7000 and 1000 BC. What is perhaps the defining characteristic of this culture – artificial mummification – is a prime example of the evolution of social complexity and the division of labor. We teamed up with a group of specialists to analyze data and confront what is known about hunter-gatherers and the time course of the Chinchorro culture to propose a conceptual model for the emergence of cultural and technological innovations (Marquet et al 2012). Under a scenario of increasing population size and extreme aridity (with little or no decomposition of corpses) our simple demographic model shows that dead individuals may have become a significant part of the landscape, creating the conditions for the manipulation of the dead that led to the emergence of complex mortuary practices. This sparked a longer-term interest in the processes by which novelty and innovation emerge, culminating in the co-edition of a theme issue of thePhilosophical Transactionsin 2017, and the publication of a perspective article on innovations in biology, culture and technology (Hochberg et al 2017).
Our interest in social evolution has always revolved around the question of how the environment – space and environmental conditions – influence the evolution of social behaviors. Marie Vasse work on this theme during her PhD, asking the question of whether the antagonistic effects of viruses and of antibiotics did more than kill bacteria; could they influence social evolution? In a study published in 2016, Marie together with Robert Noble, employed theory and experiments to investigate how of antibiotics influence social dynamics and resistance evolution in the bacterial pathogenPseudomonas aeruginosa. Marie exposed two clones of the bacterium to four doses of antibiotics and assessed growth and frequencies of public goods producing and non-producing genotypes. Our results showed that abiotic stress selects against public goods production. Specifically, we found that non-producers of siderophores most rapidly increased in frequency under intermediate antibiotic pressure. Moreover, the dominance of non-producers in mixed cultures was associated with higher survival and resistance to antibiotics than in either producer or non-producer monocultures. Mathematical modelling led by Robert Noble explained this counterintuitive result, and showed how these qualitative patterns are predicted to generalize to many other systems. Our results shed light on the complex interactions between social traits and ecological antagonisms, the generality of the phenomenon with our previous study on phages (Vasse et al. 2015), and in particular the consequences for bacterial social evolution and antibiotic resistance, which cross-pollinated other studies in our group.
Our research on social evolution gave a clear signal that environmental condition and in particular environmental heterogeneity was capital in explaining how the forces of selection resulted in social behaviors. To have a broader purview of expertise, I teamed up with Jeremy van Cleve, Eric Libby and Sam Brown to co-organize two working groups at the Santa Fe Institute in 2017 and 2018. These brought together 8-10 international scholars to develop a conceptual model for how social behavior – environmental interactions and how dispersal evolution could mediate these. In a paper led by Sylvie Estrela (2019), we presented a new, four-way classification of social behaviors where individual behaviors were categorized into producing/consuming an environmental factor, as well as into helping/harming others. We discuss how counter-intuitive relationships can emerge from simple individual-individual-environment networks. Notably, because dispersal shunts social strategies through space, it constitutes an important mechanism by which organisms modify their environment. We argue that space, movement, and environmental dynamics are currently overlooked ingredients, necessary to understand social dilemmas.
PUBLICATIONS, PREPRINTS AND COMMENTARIES
Burmeister A.R., Hansen E., Cunningham J.J., Rego E.H., Turner P.E., Weitz J.S. & Hochberg M.E. 2021. Fighting microbial pathogens by integrating host ecosystem interactions and evolution.BioEssaysPDF
Noble R.J., Walther V., Roumestand C., Hibner U. Hochberg M.E. & Lassus P. Paracrine behaviors arbitrate parasite-like interactions between tumor subclones.bioRxivhttps://doi.org/10.1101/2020.12.14.422649
Li G., Shivam S., Hochberg M.E., Wardi Y. & Weitz J.S. 2020. Disease-Dependent Interaction Policies to Support Health and Economic Outcomes During the COVID-19 Epidemic.Lancet Preprintshttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3709833
Noble R., Burley J.T., Le Sueur C. & Hochberg M.E. 2020. When, why and how tumour clonal diversity predicts survival. Evolutionary Applications https://doi.org/10.1111/eva.13057
Gurney J., Pradier L., Griffin J.S., Gougat-Barbera C., Chan B.K., Turner P.E. & Hochberg M.E. 2020. Phage steering of antibiotic-resistance evolution in the bacterial pathogenPseudomonas aeruginosa. 2020. EvolutionaryMedicine and Public Healthhttps://doi.org/10.1093/emph/eoaa026
Hochberg ME. 2020. Countries should aim to lower the reproduction number R close to 1.0 for the short-term mitigation of COVID-19 outbreaks.medRxivhttps://doi.org/10.1101/2020.04.14.20065268
Hochberg ME. 2020. Importance of suppression and mitigation measures in managing COVID-19 outbreaks.medRxivhttps://doi.org/10.1101/2020.03.31.20048835
Hochberg ME. 2020. Curing disease.InUnsolved Problems in Ecology. Dobson AP, Tilman D, Holt RD (Eds). Princeton University Press.
Estrela S, Libby E, Van Cleve J, Débarre F, Deforet M, Harcombe WR, Peña J, Brown SP & Hochberg ME. 2019. Environmentally mediated social dilemmas.Trends in Ecology & Evolution DOI10.1016/j.tree.2018.10.004
Gurney J., Pradier L., Griffin J.S., Gougat-Barbera C., Chan B.K., Turner P.E. & Hochberg M.E. 2019. Phage steering of antibiotic-resistance evolution in the bacterial pathogen Pseudomonas aeruginosa. bioRxiv, 868661
Noble R., Burley J.T., Le Sueur C. & Hochberg M.E. 2019. When, why and how clonal diversity predicts future tumour growth. bioRxiv, 10.1101/2019.12.17.879270
Tartally A., et al. 2019. Patterns of host use by brood parasiticMaculineabutterflies across Europe.Philosophical Transactions of the Royal SocietyB 374 (1769), 20180202
Gurney J., Brown S.P., Kaltz O. & Hochberg M.E. 2019. Steering Phages to Combat Bacterial Pathogens.Trends in MicrobiologyDOI 10.1016/j.tim.2019.10.007
Torres-Barceló C., Gurney J., Gougat-Barberá C., Vasse M. & Hochberg M.E. 2018. Transient negative effects of antibiotics on phages do not jeopardise the advantages of combination therapies.FEMS Micro. Ecol.DOI 10.1093/femsec/fiy107
Hochberg ME. 2018. An ecosystem framework for understanding and treating disease.Evolution, Medicine and Public HealthDOI 10.1093/emph/eoy032
Hochberg ME. 2018. Will humanity eventually exhaust the unknown?Edgehttps://www.edge.org/the-last-question-6
Bacevic K, Noble R, Soffar A, Ammar OW, Boszonyik B, Prieto S, Vincent C, Hochberg ME, Krasinska L & Fisher D. 2017. Spatial competition constrains resistance to targeted cancer therapy.Nature CommunicationsDOI 10.1038/s41467-017-01516-1
Hochberg M.E., Marquet P.A., Boyd R. & Wagner A.2017.Innovation: an emerging focus from cells to societies. Philosophical Transactions of the Royal Society of London B 372:20160414.
Gurney J, Aldakak L, Betts A, Gougat-Barbera C, Poisot T, Kaltz O & Hochberg ME. 2017. Network structure and local adaptation in coevolving bacteria phage interactions.Molecular Ecology 26:1764–1777
Hochberg ME & Noble RJ. 2017. A framework for how environment contributes to cancer risk. Ecology Letters20:117-134
Vasse M., Noble R.J., Akhmetzhanov A.R., Torres-Barceló C., Gurney J., Benateau S., Gougat-Barbera C, Kaltz O. & Hochberg M.E. 2017. Antibiotic stress selects against cooperation in the pathogenic bacteriumPseudomonas aeruginosa.Proceedings of the National Academy of Sciences USA.DOI 10.1073/pnas.1612522114
Castillo JP, Rebolledo R, Arim M, Hochberg ME & Marquet PA. 2017. The network structure of cancer ecosystems.BioRxivDOI 10.1101/240796
Shanafelt D.W., Clobert J., Fenichel E.P., Hochberg M.E., Kinzig A., Loreau M., Marquet P.A. & Perrings C. 2017. Species dispersal and biodiversity in human-dominated metacommunities.Journal of Theoretical Biology457:199-210
Hochberg M. 2017. Satisficing.Edge https://www.edge.org/response-detail/27166
Torres-Barceló C. & Hochberg M.E. 2016. Evolutionary rationale for phages as complements of antibiotics.Trends in Microbiology24: 249-256.
Torres-Barceló C., Franzon B., Vasse M. & Hochberg M.E. 2016 Long-term effects of single and combined introductions of antibiotics and bacteriophages on populations ofPseudomonas aeruginosa. Evolutionary Applications 9: 583–595.
Vasse M., Noble R., Akhmetzhanov A.R., Torres-Barceló C., Gurney J., Benateau S., Gougat-Barbara C., Kaltz O. & Hochberg M.E. 2016. Antibiotic stress selects against cooperation in a pathogenic bacterium.BioRxiv053066; DOI 10.1101/053066
Noble R.J., Kaltz O., Nunney L. & Hochberg M.E. 2016. Overestimating the role of environment in cancers.Cancer Prevention ResearchDOI 10.1158/1940-6207.CAPR-16-0126
Hochberg ME, Noble RJ & Braude S. 2016. A hypothesis to explain cancers in confined colonies of naked mole rats.BioRxivDOI 10.1101/079012
Hochberg M.E. 2016. Harnessing our natural defenses against cancer.Edge https://www.edge.org/response-detail/26590
Noble R., Kaltz O. & Hochberg M.E. 2015. Statistical interpretations and new findings on Variation in Cancer Risk Among Tissues.arXivq-bio arXiv:1502.01061
Courchamp F., Dunne J., Le Maho Y., May R.M., Thébaud C. & Hochberg M.E. 2015. Fundamental Ecology is Fundamental.Trends in Ecology and Evolution30:9-15 DOI 10.1016/j.tree.2014.11.005
Courchamp F., Dunne J., Le Maho Y., May R.M., Thébaud C. & Hochberg M.E. 2015. Back to the Fundamentals: a Reply to Barot et al. Trends in Ecology and Evolution 30:370-371
Hochberg M.E. and Jansen G. 2015. Bacteria: Assessing resistance to new antibiotics.Nature519: 158
Poisot T., Kéfi S., Morand S., Stanko M., Marquet P.A. & Hochberg M.E. 2015. A Continuum of Specialists and Generalists in Empirical Communities. PLOS ONE10(5): e0114674
Nunney L, Maley CC, Breen M, Hochberg ME & Schiffman JD. 2015. Peto’s paradox and the promise of comparative oncology.Phil. Trans. R. Soc.B 370: 20140177
Aktipis CA, Boddy AM, Jansen G, Hibner U, Hochberg ME, Maley C & Wilkinson GS. 2015. Cancer across the tree of life: cooperation and cheating in multicellularity.Phil. Trans. R. Soc.B 370, 20140219
Kokko H & Hochberg ME. 2015. Towards cancer-aware life-history modelling.Phil. Trans. R. Soc. B 370, 20140234
Noble R, Kaltz O & Hochberg ME. 2015. Peto’s paradox and human cancers.Phil. Trans. R. Soc.B 370, 20150104
Akhmetzhanov AR & Hochberg ME. 2015. Dynamics of preventive versus post-diagnostic cancer control using low-impact measures.eLifeDOI 10.7554/eLife.06266
Salali G.D., Whitehouse H. & Hochberg M.E. 2015. A Life-Cycle Model of Human Social Groups Produces a U-Shaped Distribution in Group Size.PLoS ONE 10(9): e0138496. DOI 10.1371/journal.pone.0138496
Vasse M., Torres-Barceló C. & Hochberg M.E. 2015. Phage selection for bacterial cheats leads to population decline.Proceedings of the Royal Society of LondonB 282, 20152207; DOI 10.1098/rspb.2015.2207
Betts A., Kaltz O. & Hochberg M.E. 2014. Contrasted coevolutionary dynamics between a bacterial pathogen and its bacteriophages. Proceedings of the National Academy of Sciences USA111: 11109-11114
Poisot T., Bever J.D., Thrall P.H. & M.E. Hochberg. 2014. Dispersal and spatial heterogeneity allow coexistence between enemies and protective mutualists.Ecology & Evolution4:3841-3850
Torres-Barceló C., Arias-Sánchez F.I., Vasse M., Ramsayer J., Kaltz O. & Hochberg M.E. 2014. A window of opportunity to control the bacterial pathogenPseudomonas aeruginosacombining antibiotics and phages.PLOS ONE 9:e106628
Hochberg M.E. & Brown J.H. 2014. Tragedy of The Tragedy of the Commons.Ideas in Ecology and Evolutiondoi:10.4033/iee.2014.7.19.e
Hochberg M.E. 2014. Good science depends on good peer review.Ideas in Ecology and Evolution7: 77–82
Hochberg M.E. 2014: Reply: The Impact Factor Syndrome.Ideas in Ecology and Evolution7: 82-83
Thomas, F., et al. 2013. Applying ecological and evolutionary theory to cancer: a long and winding road.Evolutionary Applications6:1-10
Gonzalez A., Ronce O., Ferriere R. and Hochberg M.E. 2013. Evolutionary rescue: an emerging focus at the intersection between ecology and evolution.Philosophical Transactions of the Royal Society of London B368: 20120404
Gandon S., Hochberg M.E., Holt R.D. and Day T. 2013. What limits the evolutionary emergence of pathogens? Philosophical Transactions of the Royal Society of London B368: 20120086
Weitz J.S., Poisot T., Meyer J.R., Flores C.O., Valverde S., Sullivan M.B. & Hochberg M.E. 2013. Phage-bacteria infection networks.Trends in Microbiology21:82-91.
Hochberg M.E., Thomas F., Assenat E. & Hibner U. 2013. Preventive Evolutionary Medicine of Cancers.Evolutionary Applications6 : 134-43
Ramsayer J., Kaltz O. & Hochberg M.E. 2013. Evolutionary rescue in populations ofPseudomonas fluorescensacross an antibiotic gradient.Evolutionary Applications6 :608-16.
Poisot T., Lounnas, M, & Hochberg M.E. 2013. The structure of natural microbial enemy-victim networks. Ecological Processes 2 :13
Betts A., Vasse M., Kaltz O. & Hochberg M.E. 2013. Back to the future: Evolving bacteriophages to increase their effectiveness against the pathogenPseudomonas aeruginosaPAO1.Evolutionary Applications6 :1054-1063
Hochberg M.E. & Whitehouse H. 2013. To Understand Present Day Cultures We Must Study the Past: a Commentary on David Sloan Wilson.Cliodynamics4:126-128.
Luque G., Hochberg M.E., Holyoak M., Hossaert M., Gaill F. & Courchamp F. 2013. Ecological effects of environmental change.Ecology Letters16: 1–3.
Ramsayer J., Fellous S., Cohen J.E. and Hochberg M.E. 2012. Taylor’s Law holds in experimental bacterial populations but competition does not influence the slope.Biology Letters8:316-9
Poisot T., Canard E., Mouquet N. and Hochberg M.E. 2012. A comparative study of ecological specialization estimators.Methods in Ecology and Evolution3: 537-544
Escobar-Páramo P., Gougat-Barbera C. & Hochberg M.E. 2012. Evolutionary dynamics of separate and combined exposure ofPseudomonas fluorescensSBW25 to antibiotics and bacteriophage.Evolutionary Applications5: 583-92
Kaltz O., Escobar-Paramo P., Hochberg M.E. and Cohen J.E. 2012. Bacterial microcosms obey Taylor’s law: effects of abiotic and biotic stress and genetics on mean and variance of population density.Ecological Processes1 :5
Blumstein D.T., Atran S., Field S., Hochberg M.E., Johnson D., Sagarin R., Sosis R. & Thayer B. 2012. The Peacock’s Tale: Lessons from Evolution for Effective Signaling in International Politics.Cliodynamics3 :191 :214
Lorz A., Lorenzi T., Hochberg M.E., Clairambault J. and Perthame B. 2012. Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies.ESAIM: Mathematical Modelling and Numerical AnalysisDOI: 10.1051/m2an/2012031
Marquet P., Santoro C.M., Latorre C., Standen V.G., Abades S.R., Rivadeneira M.E., and Hochberg M.E. 2012. Emergence of social complexity among coastal hunter-gatherers in the Atacama Desert of northern Chile.Proceedings of the National Academy of Sciences USA109: 14754–14760
Roche B., Hochberg M.E., Caulin A.F., Maley C.C., Gatenby R.A., Missé D. & Thomas F. 2012. Natural resistance to cancers: a Darwinian hypothesis to explain Peto’s paradox.BMC Cancer12:387
Whitehouse H., Kahn K., Hochberg M.E. and Bryson J.J. 2012. The role for simulations in theory construction for the social sciences: case studies concerning Divergent Modes of Religiosity.Religion, Brain and Behavior2:182-224
Poisot T., Bell T., Martinez E., Gougat-Barbera C. & Hochberg M.E. 2012. Terminal investment induced by a bacteriophage in a rhizosphere bacterium.F1000 Research1:21 doi: 10.3410/f1000research.1-21.v2
Hochberg M.E. 2012. There Is No (Single) Holy Grail.Edgehttp://www.edge.org/conversation.php?cid=the-false-allure-of-group-selection
Poisot T., Lepennetier G., Martinez E., Ramsayer J. & Hochberg M.E. 2011. Resource availability affects the structure of a natural bacteria–bacteriophage community. Biology Letters 7 :201-204.
Vacher C., Kossler T.M., Hochberg M.E. & Weis A.E. 2011. Impact of interspecific hybridization between crops and weedy relatives on the evolution of flowering Time in weedy phenotypes. PLoS One 6: e14649.
Gravel D., Guichard F. & M.E. Hochberg. 2011. Species coexistence in a variable world. Ecology Letters 14: 828–83994.
Poisot T., Thrall P.H., Bever J., Nemri A. & Hochberg M.E. 2011. A conceptual framework for the evolution of ecological specialisation. Ecology Letters 14: 841-851
Poisot T., Thrall P.H. and Hochberg M.E. 2011. Trophic network structure emerges through antagonistic coevolution in temporally varying environments. Proceedings of the Royal Society of London B 279:299–308
Turchin P. & Hochberg M.E. 2011. Introducing the Social Evolution Forum. Cliodynamics 2(2)
Sagarin R.D., Alcorta C.S, Atran S., Blumstein D.T., Dietl G.P., Hochberg M.E., Johnson D.D.P., Levin S., Madin E.M.P., Madin J.S., Prescott E.M., Sosis R., Taylor T., Tooby J. & Vermeij G.J. 2010. Decentralize, adapt and cooperate. Nature465, 292-293
Hochberg M.E. 2010. Book Review forThe American Journal of Human Biology: The Mermaid’s Tale: Four Billion Years of Cooperation in the Making of Living Things.
Hochberg M.E. 2010. Youth and the tragedy of the reviewer commons.Ideas in Ecology and Evolution3:8-10
Escobar-Páramo P., Faivre N., Buckling A., Gougat-Barbera C. & Hochberg M.E. 2009. Persistence of costly novel genes in the absence of positive selection. Journal of Evolutionary Biology 22: 536-543.
Hochberg M.E., Chase J.M., Gotelli N.J., Hastings A. & Naeem S. 2009. The tragedy of the reviewer commons. Ecology Letters 12: 2-4
Vogwill T., Fenton A., Buckling A., Hochberg M.E. & Brockhurst M.A. 2009. Source populations act as coevolutionary pacemakers in experimental selection mosaics containing hotspots and coldspots. American Naturalist 173: e171-e176.
Poullain V., Gandon S., Brockhurst M.A., Buckling A. & Hochberg M.E. 2008. The evolution of specificity in evolving and coevolving antagonistic interactions between a bacteria and its phage. Evolution 62: 1-11.
Venail P., MacLean R.C., Bouvier T., Brockhurst M.A., Hochberg M.E. & Mouquet N. 2008. Diversity and productivity peak at intermediate dispersal rate in evolving metacommunities. Nature 452:210-214.
Urban M.C., Leibold M.A., Pantel J.H., Loeuille N., Vellend M., Amarasekare P., Klausmeier C.A., Norberg J., de Mazancourt C., Gomulkiewicz R., Hochberg M.E., Strauss S.Y., De Meester L. & Wade M.J. 2008. The evolutionary ecology of metacommunities. Trends in Ecology and Evolution 23:311-317
Hochberg M.E., Rankin D.J. & Taborsky M. 2008. The coevolution of cooperation and dispersal in social groups and its implications for the emergence of multicellularity. BMC Evolutionary Biology 8:238
Thrall P.H., Hochberg M.E., Burdon J.J. & Bever J.D. 2007. Coevolution of symbiotic mutualists and parasites in a community context. Trends in Ecology and Evolution 22: 120-6
Hovestadt T., Mitesser O., Elmes G.W., Thomas J.A. & Hochberg M.E. 2007. An evolutionarily stable strategy model for the evolution of dimorphic development in the butterfly Maculinea rebeli, a social parasite of Myrmica ant colonies. American Naturalist 169: 466-480
Nettle D., Choisy M., Cornell H.V., Grace J.B., Guégan J-F. & Hochberg M.E. 2007. Cultural diversity, economic development and societal instability. PLoS ONE 2: E929
Brockhurst M.A., Buckling A., Poullain V. & Hochberg M.E. 2007. The impact of migration from parasite-free patches on antagonistic host-parasite coevolution. Evolution 61: 1238-1243
Tscharntke T., Hochberg M.E., Rand T.A., Resh V.H. & Krauss J. 2007. Author sequence and credit for contributions in multi-authored publications. PLoS Biology 5: e18
Buckling A., Wei Y., Massey R.C., Brockhurst M.A. & Hochberg M.E. 2006. Antagonistic coevolution with parasites increases the cost of host deleterious mutations. Proceedings of the Royal Society of London B 273: 45-9.
Sinervo B., Chaine A., Clobert J., Calsbeek R., Hazard L., Lancaster L., McAdam A.G., Alonzo S., Corrigan G., Hochberg M.E. 2006. Self-recognition, color signals, and cycles of greenbeard mutualism and altruism. PNAS 103: 7372-7377
Vacher C., Bourguet D., Desquilbet M., Lemarié S., Ambec S. & Hochberg M.E. 2006. Fees or refuges: which is better for the sustainable management of insect resistance to transgenicBtcorn? Biology Letters 2: 198 – 202
Brockhurst M.A., Hochberg M.E., Bell T. & Buckling A. 2006. Character displacement promotes cooperation in bacterial biofilms. Current Biology 16:2030-2034
André J-P. & Hochberg M.E. 2005. Virulence evolution in emerging infectious diseases. Evolution 59: 1406-1412
Mouquet N., Belrose V., Thomas J.A., Elmes G.W., Clarke R.T. & Hochberg M.E. 2005. Conserving community modules: a case study of the endangered lycaenid butterflyMaculinea alcon. Ecology 86: 3160-3173
Clarke R.T., Mouquet N., Thomas J.A., Hochberg M.E., Elmes G.W., Tesar D., Singer A. & Hale J. 2005.Modelling the local population dynamics of Maculinea and their spatial interactions with their larval foodplant and Myrmica ant species. In: Studies on the Ecology and Conservation of Butterflies in Europe. Vol. 2: Species Ecology along a European Gradient: Maculinea Butterflies as a Model, J. Settele, E. Kühn & J.A. Thomas (Eds). Pensoft, Sofia.
Mouquet N., Thomas J.A., Elmes G.W., Clarke R.T. & Hochberg M.E. 2005. Population dynamics and conservation of a highly specialized predator: A case study ofMaculinea arion. Ecological Monographs 75: 525-542
Vacher C., Brown S.P.& Hochberg M.E. 2005. Avoid, attack or do both? Behavioral and physiological adaptations in natural enemies faced with novel hosts. BMC Evolutionary Biology 5: 60
Hochberg M.E. & Gotelli N.J. 2005. An Invasions Special Issue: Introduction. Trends in Ecology and Evolution 20:211
Vacher C., Bourguet D., Rousset F., Chevillon C. and M.E. Hochberg. 2004. High dose refuge strategies and genetically modified crops – Reply to Tabashnik et al. Journal of Evolutionary Biology 17: 913-918.
Vacher C., Weis A.E., Hermann D., Kossler T., Young C. & Hochberg M.E. 2004. Impact of ecological factors on the initial invasion ofBttransgenes into wild populations. Theoretical and Applied Genetics 109: 806-14.
Hochberg M.E. 2004. A theory of modern cultural shifts and meltdowns. Proc R Soc Lond B 271: S313 – S316.
Guernier V., Hochberg M.E. & Guégan J-F. 2004. Ecology drives the worldwide distribution of human diseases. PLoS Biology 2: 740-746
Poitrineau K., Brown S.P. & Hochberg M.E. 2004. The joint evolution of defence and inducibility against natural enemies. Journal of Theoretical Biology 231: 389-396.
Hochberg M.E., Sinervo B. & Brown S. 2003. Socially mediation speciation. Evolution 57:154-8
Hochberg M.E., Berthault G., Poitrineau K. & Janssen A. 2003. Olfactory orientation of the Truffle Beetle,Leiodes cinnamomea. Entomologia Experimentalis et Applicata 109: 147-153.
Poitrineau K., Brown S.P. & Hochberg M.E. 2003. Defence against multiple enemies. Journal of Evolutionary Biology 16: 1319-1327.
Vacher C., Bourguet D., Rousset F., Chevillon C. and M.E. Hochberg. 2003. Modelling the spatial configuration of refuges for a sustainable control of pests: a case study ofBtcotton. Journal of Evolutionary Biology 16: 378-387
Brown S., Hochberg M.E. & Grenfell B. 2002. Does multiple infection select for raised virulence? Trends in Microbiology 10: 401-405
Lynch LD, Ives AR, Waage JK, Hochberg ME, Thomas MB. 2002. The risks of biocontrol: Transient impacts and minimum nontarget densities. Ecological Applications 12: 1872-1882
Holt R.D. & Hochberg M.E. 2002. Infectious disease in sources and sinks. Virulence Management. Dieckmann U., Metz H. & Sabelis M. (Eds). Cambidge University.
Hochberg M.E. & Holt R.D. 2002. A biogeographical perspective on natural enemies. Virulence Management. Dieckmann, U., Metz H. & Sabelis M. (Eds.). Cambridge University Press
Hochberg M.E. & Møller A.P. 2001. Insularity and adaptation in coupled victim-enemy interactions. Journal of Evolutionary Biology 14: 539-551
Restif O., Hochberg M.E. & Koella J.C. 2001. Virulence and age at reproduction: new insights into host-parasite coevolution. Journal of Evolutionary Biology 14:967-979.
Bertault G., Rousset F., Berthomieu G., Hochberg M.E., Callot G. & Raymond M. 2001. Population genetics and dynamics of the black truffle in a man-made truffle field. Heredity 86 :451-458
Guégan J-F., Thomas F., Hochberg M.E., deMeeus T. & Renaud F. 2001. Disease diversity and human fertility. Evolution 55: 1308-1314
Dedeine F., Vavre F., Fleury F., Hochberg M.E. & Boulétreau M. 2001.Wolbachiasymbionts necessary for reproduction in the parasitoid waspAsobara tabida(Hymenoptera: Braconidae). PNAS 98: 6247-6252
Shaw M.R. & Hochberg M.E. 2001. The neglect of parasitic Hymenoptera in insect conservation strategies: The British fauna as a prime example. Journal of Insect Conservation 5: 253-263
Holt R.D. & Hochberg M.E. 2001. Indirect interactions, community modules, and biological control: a theoretical perspective. In: Evaluating Indirect Ecological Effects of Biological Control. Wajnberg E., Scott J.K. & Quimby P.C. (Eds.). CABI Publishers
van Baalen M. & Hochberg M.E. 2001. Dispersal in antagonistic interactions. Pages 299-310 in Dispersal. J. Clobert. E. Danchin. A.A. Dhont. & J. Nichols (Eds.). Oxford University Press.
Hochberg M.E. & Weis A.R. 2001. Bagging the lag. Nature (N&V) 409: 992-993
Hochberg M.E., Gomulkiewicz R., Holt R.D. & Thompson J.N. 2000. Weak sinks could cradle mutualisms—strong sources should harbor pathogens. J. Evolutionary Biology 13:213-222
Gomulkiewicz R., Thompson J.N., Holt R.D., Nuismer S.L. & Hochberg M.E. 2000. Hot spots, cold spots and the geographic mosaic theory of coevolution. American Naturalist 156: 156-174
Weis A.E. & Hochberg M.E. 2000. The diverse effects of intra-specific competition on the selective advantage to resistance: a model and its predictions. American Naturalist 156:276-292
Weis A.E., Simms E.M. & Hochberg M.E. 2000. Will plant vigor and tolerance be genetically correlated? Effects of intrinsic growth rate and self-limitation on regrowth. Evolutionary Ecology 14: 331-352.
Hochberg M.E. & Ives A.R. 2000. Introduction. Parasitoid Population Biology. Hochberg M.E. & Ives A.R (Eds.). Pages 3-14. Princeton University Press
Hochberg M.E. 2000. What, conserve parasitoids? Parasitoid Population Biology. Hochberg M.E. & Ives A.R. (Eds.). Pages 266-277. Princeton University Press
Ives A.R. & Hochberg M.E. 2000. Conclusions: Debating parasitoid population biology over the next twenty years. Parasitoid Population Biology. Hochberg M.E. & Ives A.R (Eds.). Pages 278-303. Princeton University Press
Hochberg M.E. & van Baalen M. 2000. A geographical perspective of virulence. In: Evolutionary Biology of Host-Parasite Relationships: Theory Meets Reality. Poulin R., Morand S. & Skorping A. (Eds.). Elsevier Ltd.
Hochberg M.E. 2000. Displaced characters get some space. Trends in Ecology and Evolution 15: 355-356
Hochberg M.E. 2000. Evidence that specialists are special. TREE 15: 491
Hochberg M.E. & Ives A.R. 1999. Can natural enemies enforce geographical range limits? Ecography 22:268-276
Robert M., Sorci G., Møller A.P., Hochberg M.E., Pomiankowski A. & Pagel M. 1999. Retaliatory cuckoos and the evolution of host resistance to brood parasites. Animal Behavior 58:817-824
Thomas J.A., Clarke R.T., Elmes G.W. & Hochberg M.E. 1999. Population dynamics in the genusMaculinea(Lepidoptera: Lycaenidae). In: Insect Populations. Pages 261-290. Dempster J.P. & McLean I.F.G. (Eds.). Kluwer Academic
Hochberg M.E. & Holt R.D. 1999. The uniformity and density of pest exploitation as guides to success in biological control programs. In: Theoretical Approaches to Biological Control. Pages 71-88. Hawkins B.A. & Cornell H.V. (Eds.). Cambridge University Press
Holt R.D., Hochberg M.E. & Barfield M. 1999. Unstable population dynamics enhances the evolutionary stability of biological control. In: Theoretical Approaches to Biological Control. Pages 219-230. Hawkins B.A. & Cornell H.V. (Eds.). Cambridge University
Holt R.D. & Hochberg M.E. 1998. Coexistence of competing parasites. II. Hyperparasitism. Journal of Theoretical Biology 193:485-495
Plantard O. & Hochberg M.E. 1998. Factors affecting parasitism in the oak gallerNeuroterus quercusbaccarum.Oikos 81:289-298
Hochberg M.E. & van Baalen M. 1998. Antagonistic coevolution along environmental gradients. American Naturalist 152:620-634
Hochberg M.E. 1998. Establishing genetic correlations involving parasite virulence. Evolution 52:1865-1868
Clarke R.T., Thomas J.A., Elmes G.W., Wardlaw J.C., Munguira M.L. & Hochberg M.E. 1998. Population modelling of the spatial interactions betweenMaculinea, their intitial foodplant andMyrmicaants. Journal of Insect Conservation 2:29-38
Thomas J.A., Simcox D.J., Wardlaw J., Elmes G.W., Hochberg M.E. & Clarke R.T. 1998. Effects of latitude, altitude and climate on the habitat and conservation of the endangered butterfly,Maculinea arionand itsMyrmicaant hosts. Journal of Insect Conservation 2:39-46
Hochberg M.E., Elmes G.W., Thomas J.A. & Clarke R.T. 1998. Effects of habitat reduction on the persistence ofIchneumon eumerus, the specialist parasitoid ofMaculinea rebeli. Journal of Insect Conservation 2:59-66
Elmes G.W., Thomas J.A., Wardlaw J., Hochberg M.E., Clarke R.T. & Simcox D. 1998. The ecology ofMyrmicaants in relation to the conservation ofMaculineabutterflies. Journal of Insect Conservation 2:67-78
Cornell H.V., Hawkins B.A. & Hochberg M.E. 1998. Towards an empirically-based theory of herbivore demography. Ecological Entomology 23:340-349
Hochberg M.E. 1998. The population dynamic role of monophagous parasitoids: some ecological and evolutionary inroads to a synthesis.Population and Community Ecology for Insect Management and Conservation. Pages 169-174. Baumgartener J., Brandmayr P. & Manly B.F.J. (Eds.). Balkema.
Hochberg M.E. 1998. IntroducingEcology Letters.Ecology Letters 1:1.
Clarke R.T., Elmes G.W., Thomas J.A. & Hochberg M.E. 1997. The spatial arrangement of habitat determines species persistence and community re-assembly. Proceedings of the Royal Society of London B 264:347-354
Kerdelhué C., Hochberg M.E. & Rasplus J.Y. 1997. Active pollination of Ficus sur by two sympatric fig wasp species in West Africa. Biotropica 29:69-75
Holt R.D. & Hochberg M.E. 1997. When is biological control evolutionarily stable (or is it)? Ecology 78:1673-1683
Hawkins B.A., Cornell H.V. & Hochberg M.E. 1997. Predators, parasitoids and pathogens as mortality agents in phytophagous insect populations. Ecology 78:2145-2152
Lemel J.Y., Belichon S., Clobert J. & Hochberg M.E. 1997. The evolution of dispersal in a two-patch system: consequences of differences between immigrants and emigrants. Evolutionary Ecology 11:613-629
Thomas J.A., Elmes G.W., Clarke R.T., Munguiara M.L. & Hochberg M.E. 1997. Field evidence and model predictions of butterfly-mediated apparent competition between gentian plants and red ants. Acta Oecologica 18:671-84
Hochberg M.E. 1997. Hide of fight? The competitive evolution of concealment and encapsulation in host-parasitoid associations. Oikos 80:342-352
Hochberg M.E. 1996. Consequences of increasing natural enemy species richness in classical biological control. American Naturalist 147:307-18
Plantard, O., Rasplus J.Y. & Hochberg M.E. 1996. Resource partitioning in the parasitoid assemblage ofNeuroterus quercusbaccarum. Acta Oecologia 17:1-15
Elmes G.W., Clarke R.T., Thomas J.A. & Hochberg M.E. 1996. Empirical tests of specific predictions made from a spatial model of the population dynamics ofMaculinea rebeli, a parasitic butterfly of red ant colonies. Acta Oecolgica 17:61-80
Hochberg M.E., Elmes G.W., Thomas J.A. & Clarke R.T. 1996. Mechanisms of local persistence in coupled host-parasitoid associations: The case model ofMaculinea rebeliandIchneumoneumerus. Philosophical Transactions of the Royal Society of London B 351:1713-1724
Hochberg M.E. 1996. An integrative paradigm of monophagous parasitoid dynamics. Oikos 77:556-560
Hochberg M.E. 1996. When three is a crowd. Nature (News and Views) 381:276-277
Combes C., Hudson P. & Hochberg M.E. 1996. Host-parasite co-evolution: Introduction. Biodiversity Conservation 5:951
Clobert J., Gliddon C., Hawkins B.A. & Hochberg M.E. 1996. Overview. Ecology: from populations to communities to ecosystems. Aspects of the Genesis and Maintenance of Biological Diversity. Hochberg M.E., Clobert J. & Barbault R. (eds). OUP
deMeeus T., Hochberg M.E. & Renaud F. 1995. Maintenance of two genetic entities by habitat selection. Evolutionary Ecology 9:131-138
Hochberg M.E. & Holt R.D. 1995. Refuge evolution and the population dynamics of coupled host-parasitoid associations. Evolutionary Ecology 9:633-661
Hochberg M.E., Clarke R.T., Elmes G.W. & Thomas J.A. 1994. Population dynamic consequences of direct and indirect interactions involving a large blue butterfly and its plant and red ant hosts. Journal of Animal Ecology 63:375-391
Hochberg M.E., Menaut J.C. & Gignoux J. 1994. The role of fire in tree population dynamics in the west African savanna. Journal of Ecology 82:217-226
Hochberg M.E. & Hawkins B.A. 1994. The implications of population dynamics theory to parasitoid diversity and biological control. In: Parasitoid Communities. Pages 451-471. Hawkins B.A. & Sheehan W. (Eds.). Oxford University Press
Michalakis Y. & Hochberg M.E. 1994. Parasitic effects on host life-history traits: a review of recent studies. Parasite 1:291-4
Hochberg M.E. & Hawkins B.A. 1993. Predicting parasitoid species richness. American Naturalist 142:671-693
Hawkins B.A., Thomas M. & Hochberg M.E. 1993. Refuge theory and classical biological control. Science 262:1429-1432
Hawkins B.A., Hochberg M.E. & Thomas M. 1993. Biological control and refuge theory. Science (Technical Comment) 265:812-813
Hochberg M.E. & Crompton D. 1993. Franco-Brittanic meeting on host-parasite coevolution. Parasitology Today 9:399-401
Hochberg M.E. & Hawkins B.A. 1992. Refuges as a predictor of parasitoid diversity. Science 255:973-976
Hochberg M.E., Thomas J.A. & Elmes G.W. 1992. A modelling study of the large blue butterfly,Maculinea rebeli. Journal of Animal Ecology 61:397-410
Hochberg M.E., Michalakis Y. & deMeeus T. 1992. Parasitism as a constraint on the rate of life history evolution. Journal of Evolutionary Biology 5:491-504
Barbault R. & Hochberg M.E. 1992. Population and community level approaches to studying biodiversity in international research programs. Acta. Oecol.13:137–146.
Hochberg M.E. 1992. Book review forQuarterly Review of Biology:Parasite-Host Associations: Coexistence or Conflict?
Hochberg M.E. & Waage J.K. 1991. A model for the biological control ofOryctes rhinoceros(L.) (Coleoptera: Scarabaeidae) by means of pathogens. Journal of Applied Ecology 28:514-531
Hochberg M.E. 1991. Population dynamic consequences of the interplay between parasitism and intraspecific competition for parasite systems. Oikos 61:297-306
Hochberg M.E. 1991. Non-linear transmission rates and the dynamics of infectious disease. Journal of Theoretical Biology 153:301-321
Hochberg M.E. 1991. Intra-host interactions between a braconid endoparasitoid,Apanteles glomeratus, and a baculovirus for larvae ofPieris brassicae. Journal of Animal Ecology 60:51-63
Hochberg M.E. 1991. Extra-host interactions between a braconid endoparasitoid,Apanteles glomeratus, and a baculovirus for larvae ofPieris brassicae.Journal of Animal Ecology 60:65-77
Hochberg M.E. 1991. Viruses as costs to gregarious feeding behaviour in the Lepidoptera. Oikos 61:291-296
Carton Y., Haouas S., Marrakchi M. & Hochberg, M. 1991. Two competing parasitoid species coexist in sympatry. Oikos 60: 222-230
Hochberg M.E. & Waage J.K. 1991. Biopesticides: control engineering. Nature (News and Views) 352:16-17
Hochberg M.E. & Lawton J.H. 1990. Competition between kingdoms. Trends in Ecology and Evolution 5:367-371
Hochberg M.E. & Lawton J.H. 1990. Spatial heterogeneities in parasitism and population dynamics. Oikos 59:9-14
Hochberg M.E. & Holt R.D. 1990. Coexistence of competing parasites. I. The role of cross-species infection. American Naturalist 136:517-541
Hochberg M.E. 1989. The potential role of pathogens in biological control. Nature 337:262-265
Hochberg M.E., Hassell M.P. & May R.M. 1989. The dynamics of host-parasitoid-pathogen interactions. American Naturalist 135:74-94
Getz W.M., Lichtenberg, E.R. & Hochberg M.E. 1987. A decision model for scheduling ricefield mosquito control. J. Environ. Manage. 23:361-371.
Hochberg M.E. 1987. The within-plant distribution and feeding behaviour ofHeliothis armigeraHübner (Lep., Noctuidae) on greenhouse tomatoes. Zeitschrift für Angewandte Entomologie 104: 256-261.
Hochberg M.E., Pickering J. & Getz W.M. 1986. Evaluation of phenology models using field data – case-study for the pea aphid,Acyrthosiphon pisum, and the blue alfalfa aphid,Acyrthosiphon kondoi(Homoptera, Aphididae). Environmental Entomology 15:227-321.
Hochberg M.E. & Volney W.J.A. 1984. A sex pheromone in the California oakwormPhryganidia californicaPackard (Dioptidae). J. Lepidopterists’ Soc. 38:176-178.
Hochberg M.E., Marquet P.A., Boyd R. & Wagner A. 2017. Process and pattern in innovations from cells to societies.Philosophical Transactions of the Royal Society of London B
Schiffman J., Maley C.C., Nunney L., Hochberg M. & Breen M. 2015. Cancer across life: Peto’s paradox and the promise of comparative oncology.Philosophical Transactions of the Royal Society of London B
Holyoak M. & Hochberg M.E. 2013. Ecological effects of environmental change.Ecology Letters
Thomas F., Hibner U., Maley CC., Aktipis C.A. & Hochberg M.E. 2013. Evolution and Cancer.Evolutionary Applications
Gonzalez A., Ronce O., Ferrière R. & Hochberg M.E. 2013. Evolutionary Rescue.Philosophical Transactions of the Royal Socieity of London
Hochberg M.E. & Gotelli N.J. 2005. An invasions special issue.Trends in Ecology and Evolution
Hochberg M.E. & Ives A.R. 2000.Parasitoid Population Biology. Princeton University Press
Combes C., Hudson P. & Hochberg M.E. 1996. Host-parasite co-evolution. Biodiversity and Conservation
Hochberg M.E., Clobert J. & Barbault R. 1996.Aspects of the Genesis and Maintenance of Biological Diversity. Oxford University Press