Category Archives: Ecological Bandwagons

On Rewilding and Conservation

Rewilding is the latest rage in conservation biology, and it is useful to have a discussion of how it might work and what might go wrong. I am reminded of a comment made many years ago by Buzz Holling at UBC in which he said, “do not take any action that cannot be undone”. The examples are classic – do not introduce rabbits to Australia if you can not reverse the process, do not introduce weasels and stoats to New Zealand if you cannot remove them later if they become pests, do not introduce cheatgrass to western USA grasslands and allow it to become an extremely invasive species. There are too many examples that you can find for every country on Earth. But now we approach the converse problem of re-introducing animals and plants that have gone extinct back into their original geographic range, the original notion of rewilding (Schulte to Bühne et al. 2022).

The first question could be to determine what ‘rewilding’ means, since it is a concept used in so many ways. As a general concept it can be thought of as repairing the Earth from the ravages imposed by humans over the last thousands of years. It appeals to our general belief that things were better in the ‘good old days’ with respect to conservation, and that all we have seen are losses of iconic species and the introduction of pests to new locations. But we need to approach rewilding with the principle that “the devil is in the details”, and the problems are triply difficult because they must engage support from ecologists over the science and the public over policies that affect different social groups like farmers and hunters. Rewilding may range from initiatives that range from “full rewilding” to ‘minimal rewilding’ (Pedersen et al. 2020). Rewilding has been focused to a large extent on large-bodied animals and particularly those species of herbivores and predators that are high in the food chain, typified by the reintroduction of wood bison back into the Yukon after they went extinct about 800 years ago (Boonstra et al. 2018). So the first problem is that the term “rewilding” can mean many different things.

Two major issues must be considered by conservation ecologists before a rewilding project is initiated. First, there should be a comprehensive understanding of the food web of the ecosystem that is to be changed. This is a non-trivial matter in that our understanding of the food webs of what we describe as our best-known ecosystems are woefully incomplete. At best we can do a boxes and arrows diagram without understanding the strength of the connections and the essential nature of many of the known linkages. The second major issue is how rewilding will deal with climate change (Bakker and Svenning, 2018). There is now an extensive literature on paleoecology, particularly in Europe and North America. The changes in climate and species distributions that flowed from the retreat of the glaciers some 10,000 years ago are documented as a reminder to all ecologists that ecosystems and communities are not permanent in time. Rewilding at the present has a time frame with less than necessary thought to future changes in climate. We make the gigantic assumption that we can recreate an ecosystem that existed sometime in the past, and without being very specific about how we might measure success or failure in restoring ecological integrity. 

Pedersen et al. (2020) recognize 5 levels of rewilding of which the simplest is called “minimal rewilding” and the measure of success at this level is the “Potential of animal species to advance self-regulating biodiverse ecosystems” which I suggest to you is an impossible task to achieve in any feasible time frame less than 50-100 years, which is exactly the time scale the IPCC suggests for maximum climatic emergencies. We do not know what a ‘biodiverse ecosystem’ is since we do not know the boundaries of ecosystems under climate change, and we cannot measure what “natural population dynamics” is because we have so few long-term studies. Finally, at the best level for rewilding we cannot measure “natural species interaction networks” without much arm waving.

Where does this leave the empirical conservation ecologist (Hayward et al. 2019)? Rewilding appears to be more a public relations science than an empirical one. Conservation issues are immediate, and a full effort is needed to protect species and diagnose conservation problems of the day. Goshawks are declining in a large part of the boreal forest of North America, and no one knows exactly why. Caribou are a conservation issue of the first order in Canada, and they continue to decline despite good ecological understanding of the causes. The remedy of some conservation dilemmas like the caribou are clear, but the political and economic forces deny their implementation. As conservation biologists we are ever limited by public and governmental policies that favour exploitation of the land and jobs and money as the only things that matter. Simple rewilding on a small scale may be useful, but the losses we face are a whole Earth issue, and we need to address these more with traditional conservation actions and an increase in research to find out why many elements in our natural communities are declining with little or no understanding of the cause.

Bakker, E.S. and Svenning, J.-C. (2018). Trophic rewilding: impact on ecosystems under global change. Philosophical Transaction of the Royal Society B 373, 20170432. doi: 10.1098/rstb.2017.0432.

Boonstra, R., et al. (2018). Impact of rewilding, species introductions and climate change on the structure and function of the Yukon boreal forest ecosystem. Integrative Zoology 13, 123-138. doi: 10.1111/1749-4877.12288.

Hayward, M.W., et al. (2019). Reintroducing rewilding to restoration – Rejecting the search for novelty. Biological Conservation 233, 255-259. doi: 10.1016/j.biocon.2019.03.011.

Pedersen, P.B.M., Ejrnæs, R., Sandel, B., and Svenning, J.-C. (2020). Trophic rewilding advancement in Anthropogenically Impacted Landscapes (TRAAIL): A framework to link conventional conservation management and rewilding. Ambio 49, 231-244. doi: 10.1007/s13280-019-01192-z.

Schulte to Bühne, H., Pettorelli, N., and Hoffmann, M. (2022). The policy consequences of defining rewilding. Ambio 51, 93-102. doi: 10.1007/s13280-021-01560-8.

On How Genomics will not solve Ecological Problems

I am responding to this statement in an article in the Conversation by Anne Murgai on April 19, 2022 (https://phys.org/news/2022-04-african-scientists-genes-species.html#google_vignette) : The opening sentence of her article on genomics encapsulates one of the problems of conservation biology today:

“DNA is the blueprint of life. All the information that an organism needs to survive, reproduce, adapt to environments or survive a disease is in its DNA. That is why genomics is so important.”

If this is literally correct, almost all of ecological science should disappear, and our efforts to analyse changes in geographic distributions, abundance, survival and reproductive rates, competition with other organisms, wildlife diseases, conservation of rare species and all things that we discuss in our ecology journals are epiphenomena, and thus our slow progress in sorting out these ecological issues is solely because we have not yet sequenced all our species to find the answers to everything in their DNA.

This is of course not correct, and the statement quoted above is a great exaggeration. But, if it is believed to be correct, it has some important consequences for scientific funding. I will confine my remarks to the fields of conservation and ecology. The first and most important is that belief in this view of genetic determinism is having large effects on where conservation funding is going. Genomics has been a rising star in biological science for the past 2 decades because of technological advances in sequencing DNA. As such, given a fixed budget, it is taking money away from the more traditional approaches to conservation such as setting up protected areas and understanding the demography of declining populations. Hausdorf (2021) explores these conflicting problems in an excellent review, and he concludes that often more cost-effective methods of conservation should be prioritized over genomic analyses. Examples abound of conservation problems that are immediate and typically underfunded (e.g., Turner et al. 2021, Silva et al, 2021).   

What is the resolution of these issues? I can recommend only that those in charge of dispensing funding for conservation science examine the hypotheses being tested and avoid endless funding for descriptive genomics that claim to have a potential and immediate outcome that will forward the main objectives of conservation. Certainly, some genomic projects will fit into this desirable science category, but many will not, and the money should be directed elsewhere.  

The Genomics Paradigm listed above is used in the literature on medicine and social science, and a good critique of this view from a human perspective is given in a review by Feldman and Riskin (2022). Scientists dealing with human breast cancer or schizophrenia show the partial but limited importance of DNA in determining the cause or onset of these complex conditions (e.g., Hilker et al 2018, Manobharathi et al. 2021). Conservation problems are equally complex, and in the climate emergency have a short time frame for action. I suspect that genomics for all its strengths will have only a minor part to play in the resolution of ecological problems and conservation crises in the coming years.

Feldman, Marcus W. and Riskin, Jessica (2022). Why Biology is not Destiny. The New York Review of Books 69 (April 21, 2022), 43-46.

Hausdorf, Bernhard (2021). A holistic perspective on species conservation. Biological Conservation 264, 109375. doi: 10.1016/j.biocon.2021.109375.

Hilker, R., Helenius, D., Fagerlund, B., Skytthe, A., Christensen, K., Werge, T.M., Nordentoft, M., and Glenthøj, B. (2018). Heritability of Schizophrenia and Schizophrenia Spectrum based on the Nationwide Danish Twin Register. Biological Psychiatry 83, 492-498. doi: 10.1016/j.biopsych.2017.08.017.

Manobharathi, V., Kalaiyarasi, D., and Mirunalini, S. (2021). A concise critique on breast cancer: A historical and scientific perspective. Research Journal of Biotechnology 16, 220-230.

Samuel, G. N. and Farsides, B. (2018). Public trust and ‘ethics review’ as a commodity: the case of Genomics England Limited and the UK’s 100,000 genomes project. Medicine, Health Care, and Philosophy 21, 159-168. doi: 10.1007/s11019-017-9810-1.

Silva, F., Kalapothakis, E., Silva, L., and Pelicice, F. (2021). The sum of multiple human stressors and weak management as a threat for migratory fish. Biological Conservation 264, 109392. doi: 10.1016/j.biocon.2021.109392.

Turner, A., Wassens, S., and Heard, G. (2021). Chytrid infection dynamics in frog populations from climatically disparate regions. Biological Conservation 264, 109391. doi: 10.1016/j.biocon.2021.109391.

On Global Science and Local Science

I suggest that the field of ecology is fragmenting into two large visions of the science which for the sake of simplicity I will call Global Science and Local Science. This fragmentation is not entirely new, and some history might be in order.

Local Science deals with local problems, and while it aspires to develop conclusions that apply to a broader area than the small study area, it has always been tied to useful answers for practical questions. Are predators the major control of caribou declines in northern Canada? Can rats on islands reduce ground-nesting birds to extinction? Does phosphate limit primary production in temperate lakes? Historically Local Science has arisen from the practical problems of pest control and wildlife and fisheries management with a strong focus on understanding how populations and communities work and how humans might solve the ecological problems they have largely produced (Kingsland 2005). The focus of Local Science was always on a set of few species that were key to the problem being studied. As more and more wisdom accumulated on local problems, ecologists turned to broadening the scope of enquiry, asking for example if solutions discovered in Minnesota might also be useful in England or vice versa. Consequently, Local Science began to be amalgamated into a broader program of Global Science.

Global Science can be defined in several ways. One is purely financial and big dollars; this not what I will discuss here. I want to discuss Global Science in terms of ecological syntheses, and Global Science papers can often be recognized by having dozens to hundreds of authors, all with data to share, and with meta-analysis as the major tool of analysis. Global Science is now in my opinion moving away from the experimental approach that was a triumph of Local Science. The prelude to Global Science was the International Biological Program (IBP) of the 1970s that attempted to produce large-scale systems analyses of communities and ecosystems but had little effect in convincing many ecologists that this was the way to the future. At the time the problem was largely the development of a theory of stability, a property barely visible in most ecological systems.

Global Science depends on describing patterns that occur across large spatial scales. These patterns can be discovered only by having an extensive, reliable set of local studies and this leads to two problems. The first is that there may be too few reliable local studies. This may occur because different ecologists use different methods of measurement, do not use a statistically reliable sampling design, or may be constrained by a lack of funding or time. The second problem is that different areas may show different patterns of the variables under measurement or have confounding causes that are not recognized. The approach through meta-analysis is fraught with the decisions that must be made to include or exclude specific studies. For example, a recent meta-analysis of the global insect decline surveyed 5100 papers and used 166 of them for analysis (van Klink et al. 2020). It is not that the strengths and limitations of meta-analysis have been missed (Gurevitch et al. 2018) but rather the question of whether they are increasing our understanding of the Earth’s ecology. Meta-analyses can be useful in suggesting patterns that require more detailed analyses. In effect they violate many of the rules of conventional science in not having an experimental design, so that they suggest patterns but can be validated only by a repeat of the observations. So, in the best situations meta-analyses lead us back to Local Science. In some situations, meta-analyses lead to no clear understanding at all, as illustrated in the conclusions of Geary et al. (2020) who investigated the response of terrestrial vertebrate predators to fire:

“There were no clear, general responses of predators to fire, nor relationships with geographic area, biome or life-history traits (e.g. body mass, hunting strategy and diet). Responses varied considerably between species.” (page 955)

Note that this study is informative in that it indicates that ecologists have not yet identified the variables that determine the response of predators to fire. In other cases, meta-analysis has been useful in redirecting ecological questions because the current global model does not fit the facts very well (Szuwalski et al. 2015).

The result of this movement within both ecological and conservation science toward Global Science has been a shift in the amount of field work being done. Rios-Saldana et al. (2018) surveyed the conservation literature over the last 35 years and found that fieldwork-based publications decreased by 20% in comparison to a rise of 600% and 800% in modelling and data analysis studies. This conclusion could be interpreted that ecologists now realize that less fieldwork is needed at this time, or perhaps the opposite. 

In an overview of ecological science David Currie (2019) described an approach to understanding how progress in ecology has differed from that in the physical sciences. He suggests that the physical sciences focused on a set of properties of nature whose variation they analyzed. They developed ‘laws’ Like Newton’s laws or motion that could be tested in simple or complex systems. By contrast ecology has developed largely by asking how processes like competition or predation work, and not by asking questions about the properties of natural systems, which is what interests the general public trying to solve problems in conservation or pest or fisheries management. Currie (2019) summarized his approach as follows:

“Successful disciplines identify specific goals and measure progress toward those goals. Predictive accuracy of properties of nature is a measure of that progress in ecology. Predictive accuracy is the objective evidence of understanding. It is the most useful tool that science can offer society.” (page 18)

Many of these same questions underlay the critical appraisal of ecology by Peters (1991).

There is no one approach to ecological science, but we need to continue to ask what progress is being made with every approach. These are key questions for the future of ecological research, and they are worthy of much more discussion because they determine what students will be taught and what kinds of research will be favoured for funding in the future.

Currie, D.J. (2019). Where Newton might have taken ecology. Global Ecology and Biogeography 28, 18-27. doi: 10.1111/geb.12842.

Geary, W.L., Doherty, T.S., Nimmo, D.G., Tulloch, A.I.T., and Ritchie, E.G. (2020). Predator responses to fire: A global systematic review and meta-analysis. Journal of Animal Ecology 89, 955-971. doi: 10.1111/1365-2656.13153.

Gurevitch, J., Koricheva, J., Nakagawa, S., and Stewart, G. (2018). Meta-analysis and the science of research synthesis. Nature 555, 175-182. doi: 10.1038/nature25753.

Kingsland, Sharon .E. (2005) ‘The Evolution of American Ecology, 1890-2000  ‘ (Johns Hopkins University Press: Baltimore.) ISBN: 0801881714

Peters, R.H. (1991) ‘A Critique for Ecology.’ (Cambridge University Press: Cambridge, England.) ISBN: 0521400171

Ríos-Saldaña, C. Antonio, Delibes-Mateos, Miguel, and Ferreira, Catarina C. (2018). Are fieldwork studies being relegated to second place in conservation science? Global Ecology and Conservation 14: e00389. doi: 10.1016/j.gecco.2018.e00389.

Szuwalski, C.S., Vert-Pre, K.A., Punt, A.E., Branch, T.A., and Hilborn, R. (2015). Examining common assumptions about recruitment: a meta-analysis of recruitment dynamics for worldwide marine fisheries. Fish and Fisheries 16, 633-648. doi: 10.1111/faf.12083.

van Klink, R., Bowler, D.E., Gongalsky, K.B., Swengel, A.B., Gentile, A. and Chase, J.M. (2020). Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417-420. doi: 10.1126/science.aax9931.

On Declining Insect Populations

Judy Myers, Charles Krebs, Gergana Daskalova and Isla Myers-Smith

The rising concern about conservation issues is echoed in recent months by newspaper reports of collapses in insect populations world-wide: the “insect Armageddon”. As part of our general concern that the-devil-is-in-the-details, we want to discuss these reports within the general question of how we decide if this simple statement is correct or not, and what methods are needed to establish declining population trends.

We require four procedures to decide if a population or a series of populations are declining:

(1) Reliable census methods and appropriate statistical analyses must be used. This is not a trivial exercise. Results can be biased by the chance occurrence of particularly high numbers at the beginning of the data trend as in Seibold et al. (2019), the failure to correct for temporal pseudoreplication in data sets as pointed out by Daskalova et al. (2021) or by searching the literature only for studies of insect decline and then claiming to show widespread population declines as in Sánchez-Bayo et al. (2019). It is important to avoid biasing data toward a conclusion that declines are occurring. Increasing trends and examples showing no trend must be acknowledged and published to allow a true assessment.

(2) The taxonomic group of concern must be delineated since what applies to butterflies may or may not apply to carabid beetles. It can be difficult and time consuming to sort through samples to identify taxonomic groups. For this reason, the biomass of trap collections has been used as a surrogate for insect numbers in some studies (Hallman et al. 2019). This tells us nothing about population trends or diversity of different types of insects. Population data are required, and the biology of the focus group identified when considering causal mechanisms for population trends. For example, aquatic and terrestrial species are likely to respond to different environmental conditions and these must be separated (Van Klink et al., 2020).

(3) The scale of the study must be carefully outlined, whether it is 1 ha of grassland, a region, a country, or a continent. Lumping together results from studies done at different scales makes interpretation impossible. Accounting for scale in analyses is challenging, but detected trends in metrics such as species richness can differ markedly across scales (Vellend et al. 2017; Chase et al. 2019).

(4) The duration of the study must be related to the generation time of the insect group and population dynamics of those taxa. Many insects have a single generation a year and others multiple generations. Shorter time series are more variable (Daskalova et al. 2021), time trends in many insect populations are often more saw shaped than linear (Macgregor et al. 2019), and some insect species experience outbreaks or population cycles (Myers and Cory 2013).

These four requirements are not new, and many authors have discussed the details of these issues and how they play out in specific insect populations (Didham et al. 2020; Wagner 2020). A fifth requirement needs to be added when multiple studies are included in meta-analyses:

(5) All data inclusion must be scrutinized to determine if the four above requirements have been met before they are included in the meta-analysis.

Census methods for insect populations were presented long ago by Southwood (1966) in a classic book, updated in Southwood and Henderson (2000) and now reviewed recently in Montgomery et al. (2021). Montgomery et al. (2021) noted that even at this late date there is a general lack of standardization in insect monitoring methods, and that this standardization is essential if we are to track insect population or community changes. Statistical methods for time series data must be rigorous as pointed out by Daskalova et al. (2021).  The general message is that there is no one insect monitoring method that can apply to all species, and the scale of the study, along with the sampling effort needed for reliable inferences on population trends, must be decided well in advance of starting a monitoring study.

Newspaper articles dramatize the collapse of insect populations while the reality shown by detailed studies is much more nuanced. Much of the decline in insects could be traced to climate change, agricultural intensification, forestry, human population growth, urbanization and other factors (Wagner 2021). Consequently, it is important to state what the baseline for any evaluation is. The pure ecologist may wish to know how much insect populations have changed in areas where only one factor like climate change has operated. The agricultural insect ecologist may wish to know overall changes in the presence of all human and natural changes in the agricultural landscapes in which insects live (Laussmann et al. 2021). To find out the actual mechanisms behind the observed declines, a clear experimental protocol is necessary. As useful as monitoring is by itself, it can only provide weak evidence of mechanisms responsible for insect declines.

The restoration of individual species that are declining is more difficult than we might like. Warren et al. (2021) provide details of management changes that attempt to restore populations of the endangered British butterfly Hamearis lucina by landscape level habitat improvements. Funds for restoration will not be available at the scale needed for tropical and subtropical habitats losing insect diversity under stress from agricultural intensification (Raven and Wagner 2021).

The bottom line is that there are enough data now to be concerned about insect declines, but we must be careful not to cry that the “sky is falling” (Saunders et al. 2020). As in many issues with changes in populations and communities, census methods and experimental designs must be sharpened and standardized. Our take-home message is that any tests of insect population, abundance or biodiversity trends require rigorous methods of analysis before publication, or phoning the local newspaper.

Daskalova, G.N., A.B. Phillimore, and I.H. Myers‐Smith. 2021. Accounting for year effects and sampling error in temporal analyses of invertebrate population and biodiversity change: a comment on Seibold et al. 2019. Insect Conservation and Diversity 14:149-154. doi: 10.1111/icad.12468.

Didham, R.K., Basset, Y., Collins, C.M., Leather, S.R., et al. (2020). Interpreting insect declines: seven challenges and a way forward. Insect Conservation and Diversity 13, 103-114. doi: 10.1111/icad.12408.

Chase, J.M., McGill, B.J., Thompson, P.L., Antão, L.H., Bates, A.E., et al. 2019. Species richness change across spatial scales. Oikos 128:1079-1091. doi: 10.1111/oik.05968

Hallmann, C.A., et al. 2017. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809. doi: 10.1371/journal.pone.0185809

Laussmann, T., Dahl, A., Radtke, A., 2021. Lost and found: 160 years of Lepidoptera observations in Wuppertal (Germany). Journal of Insect Conservation (in press). doi: 10.1007/s10841-021-00296-w

Macgregor, C.J., J. H. Williams, J.R. Bell, and C.D. Thomas. 2019. Moth biomass increases and decreases over 50 years in Britain. Nature Ecology & Evolution 3:1645-1649. doi: 10.1038/s41559-019-1028-6

Montgomery, G.A., M.W. Belitz, R.P. Guralnick, and M.W. Tingley. 2021. Standards and best practices for monitoring and benchmarking insects. Frontiers in Ecology and Evolution 8: 579193. doi: 10.3389/fevo.2020.579193.

Myers, J.H., Cory, J.S., 2013. Population cycles in forest Lepidoptera revisited. Annual Review of Ecology, Evolution, and Systematics 44, 565–592. https://doi.org/10.1146/annurev-ecolsys-110512-135858

Raven, P. H., and D. L. Wagner. 2021. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proceedings of the National Academy of Sciences 118 (2): e2002548117. doi: 10.1073/pnas.2002548117. 

Sánchez-Bayo, F., and K. A. Wyckhuys. 2019. Worldwide decline of the entomofauna: A review of its drivers. Biological Conservation 232:8-27. doi: 10.1016/j.biocon.2019.01.020

Saunders, M.E., Janes, J.K. and O’Hanlon, J.C., 2020. Moving on from the insect apocalypse narrative: Engaging with evidence-based insect conservation. BioScience, 70(1):80-89. doi: 10.1093/biosci/biz143

Seibold, S., M. M. Gossner, N. K. Simons, N. Blüthgen, et. al. 2019. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574:671-674. doi: 10.1038/s41586-019-1684-3.

Southwood, T.R.E. (1966) ‘Ecological Methods.’ (Methuen: London.)

Southwood, T.R.E. and Henderson, P.A. (2000) ‘Ecological Methods.’ (Blackwell Science: Oxford.) 575 pp.  ISBN: 0632054778

van Klink, R., Bowler, D.E., Gongalsky, K.B., Swengel, A.B., et al. (2020). Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417-420. doi: 10.1126/science.aax9931.

Vellend, M., Baeten, L., Becker-Scarpitta, A., Boucher-Lalonde, V., McCune, J.L., Messier, J., Myers-Smith, I.H. and Sax, D.F., 2017. Plant biodiversity change across scales during the Anthropocene. Annual Review of Plant Biology 68:563-586. doi: 10.1146/annurev-arplant-042916-040949 .

Wagner, D. L. 2020. Insect declines in the Anthropocene. Annual Review of Entomology 65:457-480. doi: 10.1146/annurev-ento-011019-025151.

Wagner, D.L., Grames, E.M., Forister, M.L., Berenbaum, M.R., and Stopak, D. (2021). Insect decline in the Anthropocene: Death by a thousand cuts. Proceedings of the National Academy of Sciences 118, e2023989118. doi: 10.1073/pnas.2023989118.

Warren, M.S., et al. (2021). The decline of butterflies in Europe: Problems, significance, and possible solutions. Proceedings of the National Academy of Sciences 118 (2), e2002551117. doi: 10.1073/pnas.2002551117.

Is Conservation Ecology Destroying Ecology?

Ecology became a serious science some 100 years ago when the problems that it sought to understand were clear and simple: the reasons for the distribution and abundance of organisms on Earth. It subdivided fairly early into three parts, population, community, and ecosystem ecology. It was widely understood that to understand population ecology you needed to know a great deal about physiology and behaviour in relation to the environment, and to understand community ecology you had to know a great deal about population dynamics. Ecosystem ecology then moved into community ecology plus all the physical and chemical interactions with the whole environment. But the sciences are not static, and ecology in the past 60 years has come to include nearly everything from chemistry and geography to meteorological sciences, so if you tell someone you are an ‘ecologist’ now, they have only a vague idea of what you do.

The latest invader into the ecology sphere has been conservation biology so that in the last 20 years it has become a dominant driver of ecological concerns. This has brought ecology into the forefront of publicity and the resulting political areas of controversy, not necessarily bad but with some scientific consequences. ‘Bandwagons’ are for the most part good in science because it attracts good students and professors and brings public support on side. Bandwagons are detrimental when they draw too much of the available scientific funding away from critical basic research and champion scientific fads.

The question I wish to raise is whether conservation ecology has become the latest fad in the broad science of ecology and whether this has derailed important background research. Conservation science begins with the broad and desirable goal of preserving all life on Earth and thus thwarting extinctions. This is an impossible goal and the question then becomes how can we trim it down to an achievable scientific aim? We could argue that the most important goal is to describe all the species on Earth, so that we would then know what “money” we have in the “bank”. But if we look at the insects alone, we see that this is not an achievable goal in the short term. And the key to many of these issues is what we mean by “the short term”. If we are talking10 years, we may have very specific goals, if 100 years we may redesign the goal posts, and if 1000 years again our views might change.

This is a key point. As humans we design our goals in the time frames of months and a few years, not in general in geological time. Because of climate change we are now being forced to view many things in a shorter and shorter time frame. If you live in Miami, you should do something about sea level rise now. If you grow wheat in Australia, you should worry about decreasing annual rainfall. But science in general does not have a time frame. Technology does, and we need a new phone every year, but the understanding of cancer or the ecology of tropical rain forests does not have a deadline.

But conservation biology has a ticking clock called extinction. Now we can compound our concerns about climate change and conservation to capture more of the funding for biological research in order to prevent extinctions of rare and endangered species. 

Ecological science over the past 40 years has been progressing slowly through population ecology into community and ecosystem ecology while learning that the details of populations are critical to the understanding of community function and learning how communities operate is necessary for understanding ecosystem change. None of this has been linear progress but rather a halting progression with many deviations and false leads. In order to push this agenda forward more funding has clearly been needed because teams of researchers are needed to understand a community and even more people to study an ecosystem. At the same time the value of long-term studies has become evident and equipment has become more expensive.

We have now moved into the Anthropocene in which in my opinion the focus has shifted completely from trying to answer the primary problems of ecological science to the conservation of organisms. In practice this has too often resulted in research that could only be called poor population ecology. Poor in the sense of the need for immediate short-term answers for declining species populations with no proper understanding of the underlying problem. We are faced with calls for funding that are ‘crying wolf’ with inadequate data but heartfelt opinions. Recovery plans for single species or closely related groups focus on a set of unstudied opinions that may well be correct, but to test these ideas in a reliable scientific manner would take years. Triage on a large scale is practiced without discussing the issue, and money is thrown at problems based on the publicity generated. Populations of threatened species continue to decline in what can only be described as failed management. Blame is spread in all directions to developers or farmers or foresters or chemical companies. I do not think these are the signs of a good science which above all ought to work from the strength of evidence and prepare recovery plans based on empirical science.

Part of the problem I think lies in the modern need to ‘do something’, ‘do anything’ to show that you care about a particular problem. ‘We have now no time for slow-moving conventional science, we need immediate results now’. Fortunately, many ecologists are critical of these undesirable trends in our science and carry on (e.g. Amos et al. 2013). You will not likely read tweets about these people or read about them in your daily newspapers. Evidence-based science is rarely quick, and complaints like those that I give here are not new (Sutherland et al. 2004, Likens 2010, Nichols 2012).  

Amos, J.N., Balasubramaniam, S., Grootendorst, L. et al. (2013). Little evidence that condition, stress indicators, sex ratio, or homozygosity are related to landscape or habitat attributes in declining woodland birds. Journal of Avian Biology 44, 45-54. doi: 10.1111/j.1600-048X.2012.05746.x

Likens, G.E. (2010). The role of science in decision making: does evidence-based science drive environmental policy? Frontiers in Ecology and the Environment 8, e1-e9. doi: 10.1890/090132

Nichols, J.D. (2012). Evidence, models, conservation programs and limits to management. Animal Conservation 15, 331-333. doi: 10.1111/j.1469-1795.2012.00574.x

Sutherland, W.J., Pullin, A.S., Dolman, P.M., Knight, T.M. (2004). The need for evidence-based conservation. Trends in Ecology and Evolution 19, 305-308. doi: 10.1016/j.tree.2004.03.018

On Detecting Rare Species with Camera Trapping

If you are a conservation biologist and you wish to save all or as many species as possible, your first problem is detectability. Does the species of concern live in this habitat? If it is present how many are there, and is their abundance changing from year to year? These are fundamental questions in conservation science and there is accordingly a very large literature on how to answer these simple questions for animals in different taxonomic groups. I want to deal briefly here with rare species in which the issue of detectability is most critical.

There is a large array of papers on detection methods in the conservation literature (e.g. Brodie et al. 2018; Crates et al. 2017; Steenweg et al. 2016; Clement et al. 2016, Trolliet et al. 2014). Detection methods vary from live trapping marked individuals, visual sighting of unmarked individuals, camera photos of marked or unmarked individuals, sign data such as tracks or scats in snow, mud or sand, DNA fingerprinting, and many clever natural-history- derived methods to measure detection. These methods are well developed for common animals (Williams et al. 2002).

Rare species are the first problem faced by all these detection methods. Rare species range from those virtually impossible to detect with current technology to those that turn up infrequently in the designated detection device. The conservation challenge of rare species is difficult if they are hard to detect and difficult to study so that we have few natural history parameters to guide conservation actions. For these we can only set aside what we think are suitable areas and conserve them.

The technology of monitoring rare species that can be detected at some reasonable level has greatly improved with the advent of passive-infrared-cameras that can be deployed 24-7 to capture images of whomever walks or swims by. But this technology raises a whole set of methodological issues that must be addressed. The first and most obvious one is the skill of the observer both in setting up the cameras and in looking at the photos to identify correctly the species present. The second and more difficult question is what to count as a detection or ‘hit’. If your question is simply ‘occupancy’ seeing one photograph in the time period of the study provides a + for occupancy. But many ecologists wish to connect the dots from occupancy scores to abundance so that some index of population numbers can arise from these camera data. To make this leap of faith relies heavily on the experimental design of the camera placements, the number of cameras, the make of the cameras (Meek et al. 2014), and the exact placement of cameras on trees or stakes to cover a specific area of habitat. Clearly if cameras are placed too close to one another, the photos from the different cameras are not independent, as most of the models of occupancy assume (Brodie et al. 2018). If bait is used with the cameras the situation becomes even more complex because some species may be attracted while others are repelled by the bait. In general camera detections or ‘hits’ for a particular species are a measure of activity rather than a direct measure of abundance, and so often the assumption is made that activity = abundance, which must be justified. In the extreme case in which a density estimate is needed from camera data, the problem of ‘edge effects’ of the sampled area must be considered just as it does with grid trapping (e.g Thornton and Pekins 2015). New approaches for estimating density from camera data appear almost daily and must be evaluated for accuracy (Nakashima et al. 2018).

We are now in the exponential phase of camera trapping with cameras put up in all sorts of spatial designs for different lengths of time with the hope that someone will have time to look at the photos and some clever statistician can factor out all the potential biases and non-independence of the resulting data. So in a nutshell my simple advice is to use cameras to gather wildlife information but think carefully about what exactly you wish to achieve: occupancy?, an index of abundance?, actual numerical abundance? population density? Or simply beautiful photos of interesting animals? And in the end you may be envious of plant ecologists whose plants do not walk away when you census them.

 

Brodie, J.F., et al. (2018). Models for assessing local-scale co-abundance of animal species while accounting for differential detectability and varied responses to the environment. Biotropica 50, 5-15. doi: 10.1111/btp.12500.

Clement, M. J., J. E. Hines, J. D. Nichols, K. L. Pardieck, and D. J. Ziolkowski. 2016. Estimating indices of range shifts in birds using dynamic models when detection is imperfect. Global Change Biology 22:3273-3285. doi: 10.1111/gcb.13283

Crates, R., L. Rayner, D. Stojanovic, M. Webb, and R. Heinsohn. 2017. Undetected Allee effects in Australia’s threatened birds: implications for conservation. Emu 117:207-221. doi: 10.1080/01584197.2017.1333392

Meek, P.D., et al. (2014). Camera traps can be heard and seen by animals. PLoS ONE 9, e110832. doi: 10.1371/journal.pone.0110832.

Nakashima, Y., Fukasawa, K., and Samejima, H. (2018). Estimating animal density without individual recognition using information derivable exclusively from camera traps. Journal of Applied Ecology 55, 735-744. doi: 10.1111/1365-2664.13059.

Smith, D.H.V. and Weston, K.A. (2017). Capturing the cryptic: a comparison of detection methods for stoats (Mustela erminea) in alpine habitats. Wildlife Research 44, 418-426. doi: 10.1071/WR16159.

Steenweg, R., et al. (2016). Camera-based occupancy monitoring at large scales: Power to detect trends in grizzly bears across the Canadian Rockies. Biological Conservation 201:192-200. doi: 10.1016/j.biocon.2016.06.020

Thornton, D.H. and Pekins, C.E. (2015). Spatially explicit capture-recapture analysis of bobcat (Lynx rufus) density: implications for mesocarnivore monitoring Wildlife Research 42, 394-404. doi: 10.1071/WR15092.

Trolliet, F., et al. (2014). Use of camera traps for wildlife studies. A review. Biotechnology, Agronomy, Society and Environment (BASE) 18, 446-454.

Williams, B.K., Nichols, J.D., and Conroy, M.J. (2002) ‘Analysis and Management of Animal Populations.’ (Academic Press: New York.). 817 pp.

 

On Defining a Statistical Population

The more I do “field ecology” the more I wonder about our standard statistical advice to young ecologists to “random sample your statistical population”. Go to the literature and look for papers on “random environmental fluctuations”, or “non-random processes”, or “random mating” and you will be overwhelmed with references and biology’s preoccupation with randomness. Perhaps we should start with the opposite paradigm, that nothing in the biological world is random in space or time, and then the corollary that if your data show a random pattern or random mating or whatever random, it means you have not done enough research and your inferences are weak.

Since virtually all modern statistical inference rests on a foundation of random sampling, every statistician will be outraged by any concerns that random sampling is possible only in situations that are scientifically uninteresting. It is nearly impossible to find an ecological paper about anything in the real world that even mentions what their statistical “population” is, what they are trying to draw inferences about. And there is a very good reason for this – it is quite impossible to define any statistical population except for those of trivial interest. Suppose we wish to measure the heights of the male 12-year-olds that go to school in Minneapolis in 2017. You can certainly do this, and select a random sample, as all statisticians would recommend. And if you continued to do this for 50 years, you would have a lot of data but no understanding of any growth changes in 12-year-old male humans because the children of 2067 in Minneapolis would be different in many ways from those of today. And so, it is like the daily report of the stock market, lots of numbers with no understanding of processes.

Despite all these ‘philosophical’ issues, ecologists carry on and try to get around this by sampling a small area that is considered homogeneous (to the human eye at least) and then arm waving that their conclusions will apply across the world for similar small areas of some ill-defined habitat (Krebs 2010). Climate change may of course disrupt our conclusions, but perhaps this is all we can do.

Alternatively, we can retreat to the minimalist position and argue that we are drawing no general conclusions but only describing the state of this small piece of real estate in 2017. But alas this is not what science is supposed to be about. We are supposed to reach general conclusions and even general laws with some predictive power. Should biologists just give up pretending they are scientists? That would not be good for our image, but on the other hand to say that the laws of ecology have changed because the climate is changing is not comforting to our political masters. Imagine the outcry if the laws of physics changed over time, so that for example in 25years it might be that CO2 is not a greenhouse gas. Impossible.

These considerations should make ecologists and other biologists very humble, but in fact this cannot be because the media would not approve and money for research would never flow into biology. Humility is a lost virtue in many western cultures, and particularly in ecology we leap from bandwagon to bandwagon to avoid the judgement that our research is limited in application to undefined statistical populations.

One solution to the dilemma of the impossibility of random sampling is just to ignore this requirement, and this approach seems to be the most common solution implicit in ecology papers. Rabe et al. (2002) surveyed the methods used by management agencies to survey population of large mammals and found that even when it was possible to use randomized counts on survey areas, most states used non-random sampling which leads to possible bias in estimates even in aerial surveys. They pointed out that ground surveys of big game were even more likely to provide data based on non-random sampling simply because most of the survey area is very difficult to access on foot. The general problem is that inference is limited in all these wildlife surveys and we do not know the ‘population’ to which the numbers derived are applicable.

In an interesting paper that could apply directly to ecology papers, Williamson (2003) analyzed research papers in a nursing journal to ask if random sampling was utilized in contrast to convenience sampling. He found that only 32% of the 89 studies he reviewed used random sampling. I suspect that this kind of result would apply to much of medical research now, and it might be useful to repeat his kind of analysis with a current ecology journal. He did not consider the even more difficult issue of exactly what statistical population is specified in particular medical studies.

I would recommend that you should put a red flag up when you read “random” in an ecology paper and try to determine how exactly the term is used. But carry on with your research because:

Errors using inadequate data are much less than those using no data at all.

Charles Babbage (1792–1871

Krebs CJ (2010). Case studies and ecological understanding. Chapter 13 in: Billick I, Price MV, eds. The Ecology of Place: Contributions of Place-Based Research to Ecological Understanding. University of Chicago Press, Chicago, pp. 283-302. ISBN: 9780226050430

Rabe, M. J., Rosenstock, S. S. & deVos, J. C. (2002) Review of big-game survey methods used by wildlife agencies of the western United States. Wildlife Society Bulletin, 30, 46-52.

Williamson, G. R. (2003) Misrepresenting random sampling? A systematic review of research papers in the Journal of Advanced Nursing. Journal of Advanced Nursing, 44, 278-288. doi: 10.1046/j.1365-2648.2003.02803.x

 

On Biodiversity and Ecosystem Function

I begin with a quote from Seddon et al. (2016):

By 2012, the consensus view based on 20 years of research was that (i) experimental reduction in species richness, at any trophic level, negatively impacts both the magnitude and stability of ecosystem functioning [12,52], and (ii) the impact of biodiversity loss on ecosystem functioning is comparable in magnitude to other major drivers of global change [13,54].”

The references are to Cardinale et al. (2012), Naeem et al. (2012), Hooper et al. (2012), and Tilman et al. (2012).

The basic conclusion of the literature cited here is that with very extensive biodiversity loss, ecosystem function such as primary productivity will be reduced. I first of all wonder which set of ecologists would doubt this. Secondly, I would like to see these papers analysed for problems of data analysis and interpretation. A good project for a graduate class in experimental design and analysis. Many of the studies I suspect are so artificial in design as to be useless for telling us what will really happen as natural biodiversity is lost. At best perhaps we can view them as political ecology to try to convince politicians and the public to do something about the true drivers of the mess, climate change and overpopulation.

Too many of the graphs I see in published papers on biodiversity and ecosystem function look like this (from Maestre et al. (2012): data from 224 global dryland plots)

There is a trend in these data but zero predictability. And even if you feel that showing trends are good enough in ecology, the trend is very weak.

Many of these analyses utilize meta-analysis. I am a critic of the philosophy of meta-analysis and not alone in wondering how useful many of these are in guiding ecological research (Vetter et al. 2013, Koricheva, and Gurevitch 2014). Perhaps the strongest division in deciding the utility of these meta-analyses is whether one is interested in general trends across ecosystems or predictability which depends largely on understanding the mechanisms behind particular trends.

Another interesting aspect of many of these analyses lies in the preoccupation with stability as a critical ecosystem function maintained by species richness. In contrast to this belief, Jacquet et al. (2016) have argued that in empirical food webs there is no simple relationship between species richness and stability, contrary to conventional theory.

Finally, another quotation from Naeem et al. (2012) which raises a critical issue on which ecologists need to focus more:

“In much of experimental ecological research, nature is seen as the complex, species-rich reference against which treatment effects are measured. In contrast, biodiversity and ecosystem functioning experiments often simply compare replicate ecosystems that differ in biodiversity, without any replicate serving as a reference to nature. Consequently, it has often been difficult to evaluate the external validity of biodiversity and ecosystem functioning research, or how its findings map onto the “real” worlds of conservation and decision making. Put another way, what light can be shed on the stewardship of nature by microbial microcosms that have no analogs in nature, or by experimental grassland studies in which some plots have, by design, no grass species? “ (page 1403)

And for those of you who are animal ecologists, the vast bulk of these studies were done on plants with none of the vertebrate browsers and grazers present. Perhaps some problems here.

Whatever one’s view of these research paradigms, no questions will be answered if we lose too much biodiversity.

Cardinale, B.J., Duffy, J.E., Gonzalez, A., Hooper, D.U., Perrings, C., Venail, P., Narwani, A., Mace, G.M., Tilman, D., Wardle, D.A., Kinzig, A.P., Daily, G.C., Loreau, M., Grace, J.B., Larigauderie, A., Srivastava, D.S. & Naeem, S. (2012) Biodiversity loss and its impact on humanity. Nature, 486, 59-67. doi: 10.1038/nature11148

Hooper, D.U., Adair, E.C., Cardinale, B.J., Byrnes, J.E.K., Hungate, B.A., Matulich, K.L., Gonzalez, A., Duffy, J.E., Gamfeldt, L. & O/’Connor, M.I. (2012) A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature, 486, 105-108. doi: 10.1038/nature11118

Jacquet, C., Moritz, C., Morissette, L., Legagneux, P., Massol, F., Archambault, P. & Gravel, D. (2016) No complexity–stability relationship in empirical ecosystems. Nature Communications, 7, 12573. doi: 10.1038/ncomms12573

Koricheva, J. & Gurevitch, J. (2014) Uses and misuses of meta-analysis in plant ecology. Journal of Ecology, 102, 828-844. doi: 10.1111/1365-2745.12224

Maestre, F.T. et al. (2012) Plant species richness and ecosystem multifunctionality in global drylands. Science, 335, 214-218. doi: 10.1126/science.1215442

Naeem, S., Duffy, J.E. & Zavaleta, E. (2012) The functions of biological diversity in an Age of Extinction. Science, 336, 1401.

Seddon, N., Mace, G.M., Naeem, S., Tobias, J.A., Pigot, A.L., Cavanagh, R., Mouillot, D., Vause, J. & Walpole, M. (2016) Biodiversity in the Anthropocene: prospects and policy. Proceedings of the Royal Society B: Biological Sciences, 283, 20162094. doi: 10.1098/rspb.2016.2094

Tilman, D., Reich, P.B. & Isbell, F. (2012) Biodiversity impacts ecosystem productivity as much as resources, disturbance, or herbivory. Proceedings of the National Academy of Sciences 109, 10394-10397. doi: 10.1073/pnas.1208240109

Vetter, D., Rücker, G. & Storch, I. (2013) Meta-analysis: A need for well-defined usage in ecology and conservation biology. Ecosphere, 4, art74. doi: 10.1890/ES13-00062.1

Technology Can Lead Us Astray

Our iPhones teach us very subtly to have great faith in technology. This leads the public at large to think that technology will solve large issues like greenhouse gases and climate change. But for scientists we should remember that technology must be looked at very carefully when it tells us we have a shortcut to ecological measurement and understanding. For the past 35 years satellite data has been available to calculate an index of greening for vegetation from large landscapes. The available index is called NDVI, normalized difference vegetation index, and is calculated as a ratio of near infrared light to red light reflected from the vegetation being surveyed. I am suspicious that NDVI measurements tell ecologists anything that is useful for the understanding of vegetation dynamics and ecosystem stability. Probably this is because I am focused on local scale events and landscapes of hundreds of km2 and in particular what is happening in the forest understory. The key to one’s evaluation of these satellite technologies most certainly lies in the questions under investigation.

A whole array of different satellites have been used to measure NDVI and since the more recent satellites have different precision and slightly different physical characteristics, there is some problem of comparing results from different satellites in different years if one wishes to study long-term trends (Guay et al. 2014). It is assumed that NDVI measurements can be translated into aboveground net primary production and can be used to start to answer ecological questions about seasonal and annual changes in primary production and to address general issues about the impact of rising CO2 levels on ecosystems.

All inferences about changes in primary production on a broad scale hinge on the reliability of NDVI as an accurate measure of net primary production. Much has been written about the use of NDVI measures and the need for ground truthing. Community ecologists may be concerned about specific components of the vegetation rather than an overall green index, and the question arises whether NDVI measures in a forest community are able to capture changes in both the trees and the understory, or for that matter in the ground vegetation. For overall carbon capture estimates, a greenness index may be accurate enough, but if one wishes to determine whether deciduous trees are replacing evergreen trees, NDVI may not be very useful.

How can we best validate satellite based estimates of primary productivity? To do this on a landscape scale we need to have large areas with ground truthing. Field crops are one potential source of such data. Kang et al. (2016) used crops to quantify the relationship between remotely sensed leaf-area index and other satellite measures such as NDVI. The relationships are clear in a broad sense but highly variable in particular, so that the ability to predict crop yields from satellite data at local levels is subject to considerable error. Johnson (2016, Fig. 6, p. 75) found the same problem with crops such as barley and cotton (see sample data set below). So there is good news and bad news from these kinds of analyses. The good news is that we can have extensive global coverage of trends in vegetation parameters and crop production, but the bad news is that at the local level this information may not be helpful for studies that require high precision for example in local values of net primary production. Simply to assume that satellite measures are accurate measures of ecological variables like net aboveground primary production is too optimistic at present, and work continues on possible improvements.

Many of the critical questions about community changes associated with climate change cannot in my opinion be answered by remote sensing unless there is a much higher correlation of ground-based research that is concurrent with satellite imagery. We must look critically at the available data. Blanco et al. (2016) for example compared NDVI estimates from MODIS satellite data with primary production monitored on the ground in harvested plots in western Argentina. The regression between NDVI and estimated primary production had R2 values of 0.35 for the overall annual values and 0.54 for the data restricted to the peak of annual growth. Whether this is a satisfactory statistical association is up to plant ecologists to decide. I think it is not, and the substitution of p values for the utility of such relationships is poor ecology. Many more of these kind of studies need to be carried out.

The advent of using drones for very detailed spectral data on local study areas will open new opportunities to derive estimates of primary production. For the present I think we should be aware that NDVI and its associated measures of ‘greenness’ from satellites may not be a very reliable measure for local or landscape values of net primary production. Perhaps it is time to move back to the field and away from the computer to find out what is happening to global plant growth.

Blanco, L.J., Paruelo, J.M., Oesterheld, M., and Biurrun, F.N. 2016. Spatial and temporal patterns of herbaceous primary production in semi-arid shrublands: a remote sensing approach. Journal of Vegetation Science 27(4): 716-727. doi: 10.1111/jvs.12398.

Guay, K.C., Beck, P.S.A., Berner, L.T., Goetz, S.J., Baccini, A., and Buermann, W. 2014. Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment. Global Change Biology 20(10): 3147-3158. doi: 10.1111/gcb.12647.

Johnson, D.M. 2016. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. International Journal of Applied Earth Observation and Geoinformation 52(1): 65-81. doi: 10.1016/j.jag.2016.05.010.

Kang, Y., Ozdogan, M., Zipper, S.C., Roman, M.O., and Walker, J. 2016. How universal Is the relationship between remotely sensed vegetation Indices and crop leaf area Index? A global assessment. Remote Sensing 2016 8(7): 597 (591-529). doi: 10.3390/rs8070597.

Cotton yield vs NDVI Index

Biodiversity Conundrums

Conservation ecologists face a conundrum, as many have pointed out before. As scientists we do not make policy. Most conservation problems are essentially a moral issue of dealing with conflicts in goals and allowable actions. Both the United States and Canada have endangered species legislation in which action plans are written for species of concern. In the USA species of concern are allotted some funding and more legal protection than in Canada, where much good material is written but funding for action or research is typically absent. What is interesting from an ecological perspective is the list of species that are designated as endangered or threatened. Most of them can be described colloquially as the “charismatic megafauna”, species that are either large or beautiful or both. There are exceptions of course for some amphibians and rare plants, but by and large the list of species of concern is a completely non-random collection of organisms that people see in their environment. Birds and butterflies and large mammals are at the head of the list.

All of this is fine and useful because it is largely political ecology, but it raises the question of what will happen should these rescue plans for threatened or endangered species fail. This question lands ecologists in a rather murky area of ecosystem function, which leads to the key question: how is ecosystem function affected by the loss of species X? The answer to this question depends very much on how you define ecosystem function. If species X is a plant and the ecosystem function measured is the uptake of CO2 by the plant community, the answer could be a loss of function, no change, or indeed an increase in CO2 uptake if species X for example is replaced by a weed that is more productive that species X. The answer to this simple question is thus very complicated and requires much research. For a hypothetical example, plant X may be replaced by a weed that fixes more CO2, and thus ecosystem function is improved as measured by carbon uptake from the atmosphere. But the weed may deplete soil nitrogen which could adversely affect other plants and soil quality. Again more data are needed to decide this. If the effect size is small, much research could provide an ambiguous answer to the original question, since all measurement involves errors.

So now we are in a box, a biodiversity conundrum. The simplest escape is to say that all species loss is undesirable in any ecosystem, a pontification that is more political than scientific. And, for a contrary view, if the species lost is a disease organism, or an insect that spreads human diseases, we will not mourn its passing. In practice we seem to agree with the public that the species under concern are not all of equal value for conservation. The most serious outcome of this consideration is that where the money goes for conservation is highly idiosyncratic. There are two major calls for funding that perhaps should not be questioned: first, for land (and water) acquisition and protection, and second, for providing compensation for the people whose livelihoods are affected by protected areas with jobs and skills that improve their lives. The remaining funds need to be used for scientific research that will further the cause of conservation in the broad sense. The most useful principle at this stage is that all research has a clear objective and a clear list of what outcomes can be used to judge its success. For conservation outcomes this judgement should be clear cut. Currently they are not.

When Caughley (1994) described the declining population paradigm and the small population paradigm he clearly felt that the small population paradigm, while theoretically interesting, had little to contribute to most of the real world problems of biodiversity conservation. He could not have imagined at the time how genetics would develop into a powerful set of methods of analysis of genomes. But with a few exceptions the small population paradigm and all the elegant genetic work that has sprung from it has delivered a mountain of descriptive information with only a molehill of useful management options for real world problems. Many will disagree with my conclusion, and it is clear that conservation genetics is a major growth industry. That is all well and good but my question remains as to its influence on the solution of current conservation problems (Caro 2008; Hutchings 2015; Mattsson et al. 2008). Conservation genetic papers predicting extinctions in 100 years or more based on low levels of genetic variation are not scientifically testable and rely on a law of conservation genetics that is riddled with exceptions (Nathan et al. 2015; Robinson et al. 2016). Do we need more untestable hypotheses in conservation biology?

Caro, T. 2008. Decline of large mammals in the Katavi-Rukwa ecosystem of western Tanzania. African Zoology 43(1): 99-116. doi:10.3377/1562-7020(2008)43[99:dolmit]2.0.co;2.

Caughley, G. 1994. Directions in conservation biology. Journal of Animal Ecology 63: 215-244. doi: 10.2307/5542

Hutchings, J.A. 2015. Thresholds for impaired species recovery. Proceedings of the Royal Society. B, Biological sciences 282(1809): 20150654. doi:10.1098/rspb.2015.0654.

Mattsson, B.J., Mordecai, R.S., Conroy, M.J., Peterson, J.T., Cooper, R.J., and Christensen, H. 2008. Evaluating the small population paradigm for rare large-bodied woodpeckers, with Implications for the Ivory-billed Woodpecker. Avian Conservation and Ecology 3(2): 5. http://www.ace-eco.org/vol3/iss2/art5/

Nathan, H.W., Clout, M.N., MacKay, J.W.B., Murphy, E.C., and Russell, J.C. 2015. Experimental island invasion of house mice. Population Ecology 57(2): 363-371. doi:10.1007/s10144-015-0477-2.

Robinson, J.A., Ortega-Del Vecchyo, D., Fan, Z., Kim, B.Y., and vonHoldt, B.M. 2016. Genomic flatlining in the endangered Island Fox. Current Biology 26(9): 1183-1189. doi:10.1016/j.cub.2016.02.062.