Conservation and Quantitative Methods Flashcards
pseudoreplication
The use of inferential statistics to test for treatment effects with data from experiments where either treatment are not replicated (though samples may be) or replicates are not statistically independent.
types of pseudoreplication
simple, sacrificial, and temporal
simple pseudoreplication
samples are grouped together in a way that creates nonrandom differences between groups that don’t include ‘treatment effects. For example, two separate plots where all experimental organisms are in one plot, and all control are in the other
sacrificial pseudoreplication
data is pooled prior to statistical analyses, OR two or more samples taken from each experimental unit are treated as independent replicates; variance between treatments exists, but is inappropriately mixed with variance within treatments when the replicates are pooled
temporal pseudoreplication
samples aren’t taken from experimental units (like in simple pseudoreplication) but sequentially, creating nonrandom differences between grouped samples; samples are taken from same individuals at different time points, and the different time points are treated as independent samples, when there will be correlations between them as they are from the same individual; repeated sampling of experimental units is appropriate, it is only that treating them as independent data points is inappropriate
evidence that climate change is occurring?
global temperature increases - 1 degree celsius surface temps since late 1800s, and 0.3 degrees celsius ocean temps since 1969 (Levitus, 2017) - current warming is occurring roughly 10 times faster than the average rate of warming after an ice age; ice cores showing history of temperatures; melting glaciers and ice sheets; satellites show us that snow cover is decreasing; sea levels rising - 20 cm in the past century; extreme weather events are increasing in frequency and intensity; ocean acidification has increased by 30%
evidence that climate change is human caused?
“Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact.” (IPCC); increasing greenhouse gases, especially carbon dioxide from the burning of fossil fuels (transportation, industrial/factories) as well as deforestation (which reduces CO2 sinks), but also methane (landfills, agriculture, and natural gas leaks), nitrous oxide (agriculture/fossil fuels/burning vegetation), and chlorofluorocarbons (refrigerants, solvents). These greenhouse gases trap the heat within the atmosphere, preventing it from dispersing at the rate it normally would.
Describe some of the effects of climate change on species distributions, community composition, and ecosystem function
extreme weather events/natural disasters (drought, wildfire, hurricane) increases species through ecosystems losses in a stochastic way, as well as changing community composition (shifting alpine bumble bee communities toward species who are better suited for warmer temperatures - Scharnhorst, et al, 2023) (plant communities in most ecoregions in North, Central and South America have experienced thermophilization over the past four decades - Feeley, 2020) and ecosystem function (kelp forests provide food and shelter for animals in the community, but also provide ecosystem services for humans, such as carbon sequestering, reduce the force of storm-driven tides and surges and act like a trash fence to help retain nearshore sand, preventing erosion, but are declining due to increased ocean temperatures and acidification - Smale, 2019); range shifts (a meta-analysis of 764 species (mostly arthropods) found an average rate of poleward migration of 16.9 km/decade - Chen et al 2011); phenology mismatches
How could climate change influence evolutionary processes?
Evolutionary adaptation can be rapid and potentially help species counter stressful conditions or realize ecological opportunities arising from climate change; natural selection - changing or increasing selective pressures; gene flow - increasing or decreasing as species shift ranges; genetic drift - if populations become smaller or isolated due to die-offs or dispersal, would affect them more
paleontological example of climate change influencing evolutionary processes
(Simoes, 2022) time tree for the early evolution of reptiles and their closest relatives to reconstruct how the Permian-Triassic climatic crises shaped their long-term evolutionary trajectory. By combining rates of phenotypic evolution, mode of selection, body size, and global temperature data, we reveal an intimate association between reptile evolutionary dynamics and climate change in the deep past. We show that the origin and phenotypic radiation of reptiles was not solely driven by ecological opportunity following the end-Permian extinction as previously thought but also the result of multiple adaptive responses to climatic shifts spanning 57 million years. a strongly directional evolutionary regime by archelosaurs at the end of the Permian is associated with an adaptive response to those fast climatic shifts. ombined with ecological opportunity arising from the demise of several groups of early synapsids after the EGE and PTE (13, 14, 17, 18), climate change–driven adaptive evolution resulted in the rapid diversification of the vast diversity of reptile morphotypes that came to characterize worldwide ecosystems later on during the Triassic. Smaller body sizes favored (smaller area-volume ratios make them better capable of heat exchange with the surrounding environment). accelerated rates of morphological evolution among large-bodied archosauromorph reptiles, invasion of the marine realm by ichthyosauromorphs and sauropterygians, as well as maintenance of a small-bodied morphotype in lepidosauromorphs.
contemporary example of climate change influencing evolutionary processes
Brassica rapa blooms nearly 2 days earlier than pre-drought plants in response to a multi-year drought caused by climate change (Franks, 2007). One of the best examples of plant evolutionary response to an extreme climatic event comes from a resurrection study of the annual field mustard Brassica rapa [42,60]. The investigators collected a large sample of seeds from two California populations in 1997, after several wet years, and again in 2004 after several years of severe spring drought. They then grew population samples of genotypes collected in 1997 and in 2004 together in a common garden. The 2004 genotypes flowered significantly earlier in the common garden than the 1997 genotypes. Experimental water manipulations showed that early drought onset strongly selected for earlier flowering, evidence that the observed evolutionary change was adaptive. These B. rapa populations also display a genomic signature of temporal drought adaptation [42]. A genome-wide scan for Fst outlier-loci found 855 genes with significant temporal differentiation in allele frequencies between the 1997 and 2004 samples. Many had annotations suggesting involvement in flowering time and drought response. However, only 11 genes exhibited parallel shifts in allele frequencies in both populations. Thus, rapid adaptation to drought in the two populations appears to have occurred along largely independent trajectories.
Pros and cons of conserving ecological and evolutionary processes, rather than preserving of specific phenotypic variants - Moritz (1999)
Can still help individual species, but focusing more on overall eco and evo processes until extinction rates begin to decline; gene flow (via connecting fragmented habitats) helps populations, especially small ones; increase genetic diversity; certain phenotypic variants may be well suited for their current environment, but if they don’t have sufficient underlying genetic diversity, they will not be able to adapt to environmental changes; however, may lose certain species that are needed, like keystone species, if they aren’t given enough individual attention
fixed effects
variables that are constant across individuals; these variables don’t change or change at a constant rate over time; species, feather color, sex
random effects
variables that vary across individuals; colony, site; random effects allow us to control for noise caused by randomly chosen populations; Interested in effect size, not as much in its variation; random intercept or random slope
mixed model
mixed effects model is a type of regression model that combines both fixed and random effects. Mixed effects models are useful when there is variation in the effect of a factor across groups or individuals, but some of the variation is systematic (i.e., can be explained by specific variables) and some is random (i.e., cannot be explained by specific variables).
replication
repetition of an experiment or observation in the same or similar conditions. Replication is important because it adds information about the reliability of the conclusions or estimates to be drawn from the data.
pseudoissue
“those who do not see any problems with reducing spatial and temporal scales in order to obtain replication, and those who understand that experiments must be conducted in spatial and temporal scales relevant for the predictions to be tested, and replicate the experiment as well as possible within this constraint” = sometimes the constraints we work in make true replication impossible, or the more ideal approach is to include pseudoreplication; understand, be aware, avoid in experimental design when possible, and correct for it with models and analysis
pros of observational studies
naturally occurring and not manipulated by the researcher, so the results could be considered more realistic. Observational studies can also allow a researcher to gather a more broad set of information, not limited to the narrow scope of an experiment. Non-scientists can contribute to large observational datasets via “citizen science” efforts. Observational studies can also be done in situations where experiments are not possible, such as across large temporal or spatial scales; more generalizable across contexts
cons of observational studies
results of observational studies could be considered less reliable, as the variables are not directly controlled and manipulated; however, I would argue that a well-planned observational study is just as reliable as an experiment. Observational studies are limited in that a specific question can only be asked if it naturally occurs
pros of experimental studies
control for nearly all possible variables, often leading to more confidence in the results, can also be designed to explore a specific question
cons of experimental studies
can be challenging and expensive, and are not possible in every situation. Experiments, especially in ecology, lead to very specific results that are often not applicable to broader questions; not always natural or realistic; missing ecological context of species interactions, ecosystem effects, etc.
experimental studies
researcher manipulates the conditions or treatments the subjects receive in a controlled and randomized way
observational studies
researcher observes the effects of naturally occurring conditions or treatments, without manipulating them
Are the data treated in the same way for experiments vs observational studies (i.e. will the same statistical analyses be applied)?
A key goal for most research ventures is determining causality, which is done by statistical analysis. Ultimately, determining causality differs in experiments compared to observational studies. In experiments, determining causality is often simpler. Or, as put by Paul Holland (1986), “it is not that I believe an experiment is the only proper setting for discussing causality, but I do feel that an experiment is the simplest such setting.” In observational studies, causality is not as simple. However, using correlations, causation can be inferred - in some cases, correlation DOES mean causation. Causality can be determined by coefficients of correlation between variables (Simon, 1954).; observational studies will have a ton of variables, sources of error, and more complex models