Question Type:
Inference (most strongly supported)
Stimulus Breakdown:
CAUSAL/CONDITIONAL: When development breaks up a continuous forest into separate patches, forest fragmentation (FF) occurs.
CAUSAL: white-footed mice (main carrier of Lyme disease) thrive when FF occurs.
Answer Anticipation:
Inference questions want us to integrate information, to combine multiple ideas to derive another one, typically using CONDITIONAL, CAUSAL, QUANTITATIVE, or CONTRAST language.
Here, the suggested causal chain we could get by combining the ideas says:
development cutting up continuous forest -> forest frag -> thriving white-footed mice --?--> more Lyme disease.
So we might anticipate something like "development that cuts up forests could result in more Lyme disease"
Correct Answer:
E
Answer Choice Analysis:
(A) Too strong/specific: "RARELY found" in unfragmented? (also, that is attempting to just re-state or infer off ONE claim … it's more likely that a correct answer is supported by using multiple claims)
(B) Too strong/specific: "MOST species" benefit from forest defragmentation? We can't assume that what's true for white footed mice is true for at least 51% of species.
(C) Out of scope: "the number and variety of species an area can support"?
(D) Yes! Softly worded: "can help". This is obliquely testing the idea that "development that leads to forest frag is also potentially increasing the risk of Lyme disease. We can't prove this answer, but it's "most strongly supported", where you're allowed to make the supportable (though seemingly illegally negated) inference of "If we DIDN'T have the cause, we WOULDN'T have the effect."
(E) Out of scope: "deer tick population density"
Takeaway/Pattern: Our job isn't to predict the answer with Inference, because we are ultimately at the mercy of the answer choices. But we SHOULD read proactively for conditional/causal/quantitative wording and, when we see it, figure out whether it allows us to combine claims to derive something else.
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