2.4 | Statistical Analysis
We developed occupancy and detection models using Program MARK
accounting for imperfect detection (MacKenzie et al., 2002). There are
four main assumptions for occupancy and detection modeling (MacKenzie et
al., 2018). First, we assume populations are closed; our sampling on a
day and at the three sites associated with a tributary (nine
ichthyoplankton tows) all occurred within an hour to meet the assumption
of no births, deaths, immigration, or emigration during a sampling
event. The next assumption states occupancy and detection probabilities
are constant among sites or that heterogeneity in these parameters is
accounted for using covariates; we used spatial and environmental
covariates to account for spatial and temporal heterogeneity of both
parameters. The third assumption is detection histories are independent
among sites; we accounted for this assumption through temporal
replication where we sampled every 10 d and larvae were < 7 d
old (Camacho et al., 2023; M. Weber, unpublished data), ensuring we did
not repetitively sample the same larval across multiple sampling events.
Finally, the model assumes no false positive detections; we selected
taxa that had low ambiguity in visual identification and genetically
identified a subset of bigheaded carp larvae to verify identification
accuracy (see Camacho et al., 2023 for more details).
We developed encounter histories for each larval taxon and sample date
as presence/absence (0 or 1) at each site upstream, downstream, or at
the confluence of the major tributary. For example, an encounter history
could be comprised of bigheaded carp only collected at the Des Moines
River confluence and not at upstream or downstream locations (encounter
history 010) or upstream and downstream but not within the confluence
(encounter history 101). Occupancy modeling allows the use of covariates
to improve model estimates and evaluate occupancy and detection under a
range of environmental conditions. We assessed the effect of water
volume filtered through the ichthyoplankton net as an effect of sampling
effort on detection probability. We also assessed the effects of Julian
date, mean and CV of weekly water temperature (°C), and discharge
(m3/s) on site occupancy. We evaluated collinearity of
covariates with a Pearson’s product-moment correlation analysis (all r
< 0.6) before including them in occupancy and detection
models. To ensure normality, we centered and scaled all covariates using
z-scores prior to analysis. We assessed quadratic effects of Julian date
and weekly river temperature on occupancy probability, as we sampled
prior to and after hypothesized peak spawning periods and linear models
would be inappropriate. We assessed all other covariates as linear
effects. We assessed detection models by first comparing species and
habitat models before adding additional effects of environmental
covariates. Next, we retained the most supported detection model to
evaluate species and habitat effects on occupancy. Finally, we assessed
potential effects of environmental covariates on occupancy while
retaining the most supported species and habitat effects. We ranked
models based on Akaike’s Information Criterion corrected for sample size
(ΔAICc ) and AICc model
weight (wi ; Burnham et al., 2011). We considered
models ranked ≤ 2 ΔAICc from the top model as
competitive in analyses. Finally, we calculated detection probabilities
under increasing sampling size (cumulative detection probability) using
the most supported detection model (e.g., Kelly et al., 2021).
3 | RESULTS
We collected bigheaded carp larvae in pools 18 to 20 of the Mississippi
River in 53 of 592 sample site-years (naïve Ψ = 0.09). Native larvae
were captured more frequently, with freshwater drum (n = 223 of 592
site-years; naïve Ψ = 0.38) captured most frequently followed by shad (n
= 141 of 592 site-years; naïve Ψ = 0.24) and percids (n = 132 of 592
site-years; naïve Ψ = 0.22). Thalweg and channel border habitats
accounted for the most bigheaded carp (77%) and drum (78%) collections
while shad and percids had similar overall collections among habitats
(29 to 36%). We captured the most bigheaded carp between Julian days
138 to 240 when water temperatures ranged from 17.6 to 26.6°C and during
the highest weekly discharge conditions (mean = 4,124
m3/s, SD = 1,057). We captured percids the earliest
(day 113) during the narrowest time (range = 113 to 228, mean = 160, SD
= 32), coldest temperatures (mean = 19.9°C, SD = 5), and second highest
weekly discharge (mean = 3,629 m3/s, SD = 1,251). We
captured freshwater drum the latest (day 256) across the widest range of
days (range = 122 to 256, mean = 189, SD = 33) associated with highest
weekly temperatures (mean = 23.4 °C, SD = 2.9) and lower discharge (mean
= 3,321 m3/s, SD = 1,061). Finally, we collected shad
on a similar time frame as freshwater drum (range = 120 to 256, mean =
174, SD = 29), with similar water temperature to bigheaded carp (range =
15.1 to 27.7°C, mean = 22.2 °C, SD = 3), under similar discharge
conditions to percids (mean = 3,587 m3/s, SD = 1,100).