Simple answers to ecological questions rarely exist - that is largely what I love about ecology. The answers are rarely simple and you should probably be skeptical of simple, one-size-fits-all "solutions" to ecological problems. The answer to most ecological questions is more nuanced and - in my opinion - more interesting. The typical answer is, "It depends" - because it does.
First, a bit about the nature of science. Science does not "prove" things, in fact, the goal of science is to disprove ideas. But as we have more and more evidence to support an idea and have been unable to disprove it despite our best efforts; the support for that idea becomes stronger. Yet we have never proven it to be true. There is always uncertainty in science - an idea that most people have a very difficult time accepting or understanding. And although we have much more support for some ideas, we never prove them to be true but their uncertainty is exceedingly low. That is not to say that there are not facts - observations that we objectively know to be true. We know that the sun is a star, that the Earth revolves around that star, on its axis over the course of what we have defined as a day, and that a fish was 254 mm (10 inches) - those are facts. Evolution - that species change through time - is a fact; we have observed it to be true in the fossil record, in laboratories, and in our daily lives if we are observant enough.
This is where science "gets controversial" - in large part because people generally do not understand how science works. Science relies upon questioning ideas and using data (facts) to support our hypotheses which are often tests of theories. There is probably no more misunderstood word than "scientific theory". I cringe when I hear, "it's just a theory" as theories are major unifying ideas that have been extensively tested and we have not been able to prove them false and replace them with a theory that better explains a set of observations. That the cell is the basis of all life and the cell is the smallest indivisible form of life - cell theory - is "just a theory". Theories are powerful things!
A common misconception is that scientific theories are rudimentary ideas that will eventually graduate into scientific laws when enough data and evidence have been accumulated. A theory does not change into a scientific law with the accumulation of new or better evidence. A theory will always remain a theory; a law will always remain a law. Both theories and laws could potentially be falsified by countervailing evidence.
Scientific laws are not "the Holy Grail" of science, instead laws are developed to explain physical phenomena, typically through a simple mathematical equation - like those of thermodynamics, Newtonian motion, gas laws, and the like. Biology generally lacks laws - though the Theory of Evolution by Natural Selection is arguably a biological law. A good theory describes a pattern based on a set of observations (facts) and proposes a process by which that pattern occurs - an explanation for why that pattern exists. And a good theory has been extensively tested and has not been disproved. As I tell my students each semester, want to be the most famous scientist in the world? Disprove Darwin. That is an important part of science; if a better explanation for a set of observations comes along, a new theory is developed. This is not to say that science always gets it right or is infallible, but it is important to understand that the goal is always to better explain our natural world.
I write all of this so I can talk about how we conduct science, in ecology in particular. Scientists base their experiments on what we already know, and they design experiments to test hypotheses. Experiments can be observational or manipulative. Typically we think of manipulative, controlled experiments where scientists manipulate an explanatory (independent) variable or set of variables and measure the response and compare that response to our controls. Controlled experiments allow us to determine cause and effect (causation), which you certainly remember from somewhere, is different from correlation. Manipulative experiments are powerful; they allow us to determine causation.
However in ecology, it is often difficult, if not impossible, to have true controls. Ecologists are often conducting observational studies to try to better understand and explain what causes a response variable to change. For example, if I am interested in understanding how fish assemblages change in response to climate change in streams of the Driftless Area, there is not a manipulative study that will meaningfully test this question. We can conduct laboratory manipulative experiments to test the effects of temperature on different species - and those results would be meaningful and would help inform us about what we might expect to see in a warming stream. However, that experiment is not truly a test of climate change effects on a fish assemblage. "In the field", there are so many variables and there is almost never a single causal factor to describe something as complex as why fish species are where they are in the proportions that they were found. And pretty much under no circumstances are potential causal factors independent of one another in nature. That is to say when one thing changes, so do others - not always in predictable ways.
Because nature is complex, there are rarely simple answers. Of the biological sciences, ecology is quite easily the most quantitative. We are often conducting observational studies and using mathematical models and statistics to describe and better understand our data. Statistically, there are generally two approaches - inferential statistics (hypothesis testing is the most common) and descriptive statistics. What you probably (hopefully) remember from a stats class are things like t-tests, ANOVA, and simple linear regression. These analyses are univariate analyses which refers to the fact that there is one response (dependent) variable being analyzed at a time. Ecologists often rely on multivariate statistics where more than one response and explanatory variables are analyzed at a time. Multivariate analyses typically use the underlying relationships among variables to better understand the data (more below). As you might expect, multivariate statistics are much more complex to perform and understand.
Multivariate analysis from Petty et al. (2001), a paper we published, that quantifies the differences in habitat channel units in the Shavers Fork of the Cheat River in West Virginia. Essentially this is how we answered the question, do our visual channel units differ in their physical characteristics? They did - except for glides and low gradient riffles which overlapped in their characteristics - both were shallow with slow moving water. If we had a variable that measured exposed rocks, they'd have almost certainly been different. In this figure, the X and Y axes are independent of one another and the points and their standard error bars representing each visual habitat channel unit were the response variables. The axes are from principle component analysis, a descriptive statistical method that uses relationships among variables and defines axes based on these relationships. We could have - and did - estimate the mean and variation of each physical characteristic for each channel unit - a univariate approach; but the relationships among these variables are what are really important. For example, riffle-run complexes were similar in current velocities to intermediate gradient riffles but the riffles were much shallower with a different set of typical substrates.
Of the ecological sciences, fisheries science probably has the strongest quantitative history. This is, in large part, owed to the fact that the economic importance of commercial fisheries has required better understanding of population sizes, their distribution, changes to populations, and other factors that impact commercial angler's livelihoods. Need to convince stakeholders of the need to implement changes to rules and regulations that will impact their livelihood? You better have very good evidence that those changes are necessary. Unsurprisingly, conservation biology - the science with the goal of preventing the extinction of species - is probably the other ecological field with the strongest history of quantitative analysis.
I write all of this - and probably dug deeper than many cared to read - to set the stage for how complex ecological interactions are and how they require sophisticated methods to understand these relationships. Everything in nature is connected is maybe a bit too all encompassing of a statement but the idea is not too far off. Simple answers to complex questions are often rooted in reality but those answers often fail to address other important factors or they scale a truth at one spatial or temporal scale to a scale where that finding has little to no relevance. If in a laboratory X does Y; in the field, X may have no ability to do Y because other factors, under those conditions, are more important. The same is often true for field studies done in different locations - what occurs in one place may not occur in another location because of the differences in those locations. In other words, the answer is, "it depends". Propositional fallacies are common.
A Real World Example
Brook Trout (Salvelinus fontinalis) and Brown Trout (Salmo trutta) interactions seems simple at first glance but as I have dug deeper into the question, the answers seem more muddled than I had ever expected. Here in the Driftless, there is a lot of evidence that Brook Trout are displaced - and replaced - by Brown Trout (Waters 1983) and it has to do with competitive interactions (Fausch and White 1981, Huntsman et al. 2022), habitat and thermal preferences and tolerances (Hitt et al. 2017, Wagner et al. 2013, Huntsman et al. 2022), and the aggressiveness of Brown Trout (Fausch and White 1981, Hitt et al. 2017, and Huntsman et al. 2022). And those answers are pretty satisfying and match my experiences and expectations both in Wisconsin and in West Virginia. However, move out of the the Eastern United States, the Midwest, Wisconsin, and the Driftless Area and the answers tend to vary a lot more. It is not as simple as to say that Brown Trout outcompete Brook Trout, instead, it depends...
This may come as a surprise to you - I know it was to me - but Brown Trout in their native range are being negatively impacted by non-native Brook Trout (Cucheroesset et al. 2007, Korsu et al. 2007, 2009, Lovén Wallerius et al. 2022). All is not as it may first appear.
I have little idea why species in their native ranges may be negatively impacted by the same species that they negatively impact in that species native range - but I have my suspicions. I suspect that part of the answer is that Brown Trout outside of their native range, even where they are outcompeting the native species, they are still being negatively impacted by the native trout species. And part of the answer probably has to do with how and what impact is being measured. Maybe it is that Brook Trout are similarly thermally suited to these European streams whereas in much of America, Brook Trout are less thermally suited our streams. Most of the European papers are from mountainous regions where thermal characteristics of streams may be more like those of Western North American streams - the types of places where Brook Trout are often outcompeting the native species, often Cutthroat Trout (Oncorhynchus clarkii). But the papers from Europe - Cucheroesset et al. 2007, Korsu et al. 2007, 2009, Lovén Wallerius et al. 2022 and there are more, are certainly unexpected given what we have seen and experienced here in the Driftless and I have seen elsewhere.
It is counterintuitive that native species, presumably well adapted to their native habitats, are negatively impacted by non-native species not evolved to these new habitats - A paradox of trout invasions (Fausch 2008). Yet, there are so many examples of invasive species - though many, many more potential invasions never take hold (Whitney and Gabler 2008). We have relatively little understanding of why some species "take hold" and other potential introductions go nowhere. Certainly I would have thought that introducing Brook Trout into the native habitats of Brown Trout would have been a failure or at best, Brook Trout would have been relegated to the same small, cold streams and spring runs that they are largely found in here.
This has been a lot of words to really say one thing - understanding ecology is really difficult. We have tools that help us understand our world better but we will probably never have the tools that give more than a glimpse into why our observations occur. Are Brown Trout replacing native Brook Trout in my part of the Driftless because they rapidly evolved in their new habitat (Whitney and Gabler 2008), that they are more tolerant to the degraded habitats and stream temperatures of the Driftless Area, or any number of other reasons? I do not know - but I know it is happening and statistics allow me a better understanding of what is happening. Figuring out why is has happened and what to do about it is much more difficult. But that is a lot of what makes ecology so interesting.
Beware of those giving simple answers to complex questions.
Cucherousset, J., Aymes, J. C., Santoul, F., & Cereghino, R. (2007). Stable isotope evidence of trophic interactions between introduced brook trout Salvelinus fontinalis and native brown trout Salmo trutta in a mountain stream of south‐west France. Journal of Fish Biology, 71, 210-223.
Fausch, K. D., & White, R. J. (1981). Competition between brook trout (Salvelinus fontinalis) and brown trout (Salmo trutta) for positions in a Michigan stream. Canadian Journal of Fisheries and Aquatic Sciences, 38(10), 1220-1227.
Hitt, N. P., Snook, E. L., & Massie, D. L. (2017). Brook trout use of thermal refugia and foraging habitat influenced by brown trout. Canadian Journal of Fisheries and Aquatic Sciences, 74(3), 406-418.
Korsu, K., Huusko, A., & Muotka, T. (2007). Niche characteristics explain the reciprocal invasion success of stream salmonids in different continents. Proceedings of the National Academy of Sciences, 104(23), 9725-9729.
Lovén Wallerius, M., Moran, V., Závorka, L., & Höjesjö, J. (2022). Asymmetric competition over space use and territory between native brown trout (Salmo trutta) and invasive brook trout (Salvelinus fontinalis). Journal of Fish Biology, 100(4), 1033-1043.
Petty, J. T., Freund, J., Lamothe, P., & Mazik, P. (2001). Quantifying instream habitat in the upper Shavers Fork basin at multiple spatial scales. In Proceedings of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies, 55, 81-94.
Wagner, T., Deweber, J. T., Detar, J., & Sweka, J. A. (2013). Landscape-scale evaluation of asymmetric interactions between brown trout and brook trout using two-species occupancy models. Transactions of the American Fisheries Society, 142(2), 353-361.
Whitney, K. D., & Gabler, C. A. (2008). Rapid evolution in introduced species, ‘invasive traits’ and recipient communities: challenges for predicting invasive potential. Diversity and Distributions, 14(4), 569-580.