Individual variation in dispersal, and its sources, shape the fate of pushed vs. pulled range expansions

Ecological and evolutionary dynamics of range expansions are shaped by both dispersal and population growth. Accordingly, density-dependence in either dispersal or growth can determine whether expansions are pulled or pushed, i.e. whether expansion velocities and genetic diversity are mainly driven by recent, low-density edge populations, or by older populations closer to the core. Despite this and despite abundant evidence of dispersal evolution during expansions, the impact of density-dependent dispersal and its evolution on expansion dynamics remains understudied. Here, we used simulation models to examine the influence of individual trait variation in both dispersal capacity and dispersal density-dependence on expansions, and how it impacts the position of expansions on the pulled-pushed continuum. First, we found that knowing about the evolution of density-dependent dispersal at the range edge can greatly improve our ability to predict whether an expansion is (more) pushed or (more) pulled. Second, we found that both dispersal costs and the sources of variation in dispersal (genetic or non-genetic, in dispersal capacity versus in density-dependence) greatly influence how expansion dynamics evolve. Among other scenarios, pushed expansions tended to become more pulled with time only when density-dependence was highly heritable, dispersal costs were low and dispersal capacity could not evolve. When, on the other hand, variation in density-dependence had no genetic basis, but dispersal capacity could evolve, then pushed expansions tended to become more pushed with time, and pulled expansions more pulled. More generally, our results show that trying to predict expansion velocities and dynamics using trait information from non-expanding regions only may be problematic, that both dispersal variation and its sources play a key role in determining whether an expansion is and stays pushed, and that environmental context (here dispersal costs) cannot be neglected. Those simulations suggest new avenues of research to explore, both in terms of theoretical studies and regarding ways to empirically study pushed vs. pulled range expansions.

Plant host shift: The viability and persistence of a polyphagous non-native insect

Drosophila suzukii (Diptera: Drosophilidae) has become a pervasive pest in several countries around the world. Many studies have investigated the preference and attractiveness of potential hosts on this invasive, polyphagous drosophilid. Thus far, no studies have investigated whether a shift of fruit host could affect its ecological viability or spatiotemporal persistence. In this study, we analysed the fecundity and oviposition period jointly with the survival time of D. suzukii subject to a shift in host fruit, using a joint modelling method for longitudinal outcomes and time-until-event outcomes. The number of eggs laid by females was higher in raspberry than in strawberry and when setting adults of F1 generation underwent a first host shift. The joint modelling also suggested that insects reared on raspberry survived longer. We then combined experimental results with a two-patch dispersal model to investigate how host shift in a species that exhibits both passive and density-dependent dispersal may affect its asymptotic dynamics. In line with empirical evidence, we found that a shift in host choice can significantly affect the growth potential and fecundity of a species such as D. suzukii, which ultimately could aid such invasive populations in their ability to persist within a changing environment.

Predator avoidance and foraging for food shape synchrony and response to perturbations in trophic metacommunities

The response of species to perturbations strongly depends on spatial aspects in populations connected by dispersal. Asynchronous fluctuations in biomass among populations lower the risk of simultaneous local extinctions and thus reduce the regional extinction risk. However, dispersal is often seen as passive diffusion that balances species abundance between distant patches, whereas ecological constraints, such as predator avoidance or foraging for food, trigger the movement of individuals. Here, we propose a model in which dispersal rates depend on the abundance of the species interacting with the dispersing species (e.g., prey or predators) to determine how density-dependent dispersal shapes spatial synchrony in trophic metacommunities in response to stochastic perturbations. Thus, unlike those with passive dispersal, this model with density-dependent dispersal bypasses the classic vertical transmission of perturbations due to trophic interactions and deeply alters synchrony patterns. We show that the species with the highest coefficient of variation of biomass governs the dispersal rate of the dispersing species and determines the synchrony of its populations. In addition, we show that this mechanism can be modulated by the relative impact of each species on the growth rate of the dispersing species. Species affected by several constraints disperse to mitigate the strongest constraints (e.g., predation), which does not necessarily experience the highest variations due to perturbations. Our approach can disentangle the joint effects of several factors implied in dispersal and provides a more accurate description of dispersal and its consequences on metacommunity dynamics.

Negative density-dependent dispersal in tsetse (Glossina spp): An artefact of inappropriate analysis

Published analysis of genetic material from field-collected tsetse (Glossina spp, primarily from the Palpalis group) has been used to predict that the distance (δ) dispersed per generation increases as effective population densities (De) decrease, displaying negative density-dependent dispersal (NDDD). Using the published data we show this result is an artefact arising primarily from errors in estimates of S, the area occupied by a subpopulation, and thereby in De. The errors arise from the assumption that S can be estimated as the area (S^) regarded as being covered by traps. We use modelling to show that such errors result in anomalously high correlations between δ^ and S^ and the appearance of NDDD, with a slope of -0.5 for the regressions of log(δ^) on log(D^e), even in simulations where we specifically assume density-independent dispersal (DID). A complementary mathematical analysis confirms our findings. Modelling of field results shows, similarly, that the false signal of NDDD can be produced by varying trap deployment patterns. Errors in the estimates of δ in the published analysis were magnified because variation in estimates of S were greater than for all other variables measured, and accounted for the greatest proportion of variation in δ^. Errors in census population estimates result from an erroneous understanding of the relationship between trap placement and expected tsetse catch, exacerbated through failure to adjust for variations in trapping intensity, trap performance, and in capture probabilities between geographical situations and between tsetse species. Claims of support in the literature for NDDD are spurious. There is no suggested explanation for how NDDD might have evolved. We reject the NDDD hypothesis and caution that the idea should not be allowed to influence policy on tsetse and trypanosomiasis control.

Landscape connectivity alters the evolution of density-dependent dispersal during pushed range expansions

As human influence reshapes communities worldwide, many species expand or shift their ranges as a result, with extensive consequences across levels of biological organization. Range expansions can be ranked on a continuum going from pulled dynamics, in which low-density edge populations provide the "fuel" for the advance, to pushed dynamics in which high-density rear populations "push" the expansion forward. While theory suggests that evolution by spatial sorting, a common feature of range expansions, could lead pushed expansions to become pulled with time, empirical comparisons of phenotypic divergence in pushed vs. pulled contexts are lacking. In a previous experiment using Trichogramma brassicae wasps as a model, we showed that expansions were more pushed when connectivity was lower. Here we used descendants from these experimental landscapes to look at how the range expansion process and connectivity interact to shape phenotypic evolution. Interestingly, we found no clear and consistent phenotypic shifts, whether along expansion gradients or between treatments, when we focused on low-density trait expression. However, we found evidence of changes in density-dependence, in particular regarding dispersal: populations went from positive to negative density-dependent dispersal at the expansion edge, but only when connectivity was high. As positive density-dependent dispersal leads to pushed expansions, our results confirm predictions that evolution during range expansions may lead pushed expansions to become pulled, but add nuance by showing environmental context may slow down or cancel this process. This shows we need to jointly consider evolution and ecological context to accurately predict range expansion dynamics and their consequences.

No experimental evidence for vector-free, long-range, upstream dispersal of adult Asian clams [Corbicula fluminea (Müller, 1774)]

The Asian clam (Corbicula fluminea) is one of the rapidly spreading, very successful aquatic invasive species, which has become established widely in many parts of the world. Its spread is assumed to be by both passive and active dispersal. However, the importance of active pedal movement in dispersal is hardly known. Since there was no direct evidence of this phenomenon, field observations were combined with laboratory experiments to find out if the clams move upstream actively, and how this is affected by the quality of the substrate, the density of the clams, and the water velocity. Field observations were conducted at a small watercourse with no waterborne transport. Experiments were done in an indoor artificial stream system, where the distances moved by adult clams were measured via digital image analysis. Substrate grain size, starting density of clams, and water velocity significantly affected clam movement. Fine grain sediment and slow flow velocity both facilitated spread, while there was no clear pattern of density-dependent dispersal. Also, we found no clear preference for either upstream or downstream movement. The maximum distance moved in the lab experiments predicts no more than 0.15 km/y active pedal movement in an upstream direction, while our field observations detected a much faster (0.5–11 km/y) upstream movement, which might be explained by passive dispersal, such as via human transport and ecto- or endozoochory. Overall, it seems that active movement of the species cannot read to long-distance migration.

Bifurcation, uniqueness and multiplicity results for classes of reaction diffusion equations arising in ecology with nonlinear boundary conditions

<p style='text-indent:20px;'>We study the structure of positive solutions to steady state ecological models of the form:</p><p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \begin{array}{l} \left\{ \begin{split} -\Delta u&amp; = \lambda uf(u)\; \; &amp;&amp; {\rm{in}}\; \; \Omega,\\ \alpha(u)&amp;\frac{\partial u}{\partial \eta}+[1-\alpha(u)]u = 0 &amp;&amp;\;\;\;{\rm{on}}\; \; \partial\Omega, \end{split} \right. \end{array} $\end{document} </tex-math></disp-formula></p><p style='text-indent:20px;'>where <inline-formula><tex-math id="M1">\begin{document}$ \Omega $\end{document}</tex-math></inline-formula> is a bounded domain in <inline-formula><tex-math id="M2">\begin{document}$ \mathbb{R}^n; $\end{document}</tex-math></inline-formula> <inline-formula><tex-math id="M3">\begin{document}$ n&gt;1 $\end{document}</tex-math></inline-formula> with smooth boundary <inline-formula><tex-math id="M4">\begin{document}$ \partial\Omega $\end{document}</tex-math></inline-formula> or <inline-formula><tex-math id="M5">\begin{document}$ \Omega = (0,1) $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M6">\begin{document}$ \frac{\partial}{\partial\eta} $\end{document}</tex-math></inline-formula> represents the outward normal derivative on the boundary, <inline-formula><tex-math id="M7">\begin{document}$ \lambda $\end{document}</tex-math></inline-formula> is a positive parameter, <inline-formula><tex-math id="M8">\begin{document}$ f:[0,\infty)\to \mathbb{R} $\end{document}</tex-math></inline-formula> is a <inline-formula><tex-math id="M9">\begin{document}$ C^2 $\end{document}</tex-math></inline-formula> function such that <inline-formula><tex-math id="M10">\begin{document}$ \tfrac{f(s)}{k-s}&gt;0 $\end{document}</tex-math></inline-formula> for some <inline-formula><tex-math id="M11">\begin{document}$ k&gt;0 $\end{document}</tex-math></inline-formula>, and <inline-formula><tex-math id="M12">\begin{document}$ \alpha:[0,k]\to[0,1] $\end{document}</tex-math></inline-formula> is also a <inline-formula><tex-math id="M13">\begin{document}$ C^2 $\end{document}</tex-math></inline-formula> function. Here <inline-formula><tex-math id="M14">\begin{document}$ f(u) $\end{document}</tex-math></inline-formula> represents the per capita growth rate, <inline-formula><tex-math id="M15">\begin{document}$ \alpha(u) $\end{document}</tex-math></inline-formula> represents the fraction of the population that stays on the patch upon reaching the boundary, and <inline-formula><tex-math id="M16">\begin{document}$ \lambda $\end{document}</tex-math></inline-formula> relates to the patch size and the diffusion rate. In particular, we will discuss models in which the per capita growth rate is increasing for small <inline-formula><tex-math id="M17">\begin{document}$ u $\end{document}</tex-math></inline-formula>, and models where grazing is involved. We will focus on the cases when <inline-formula><tex-math id="M18">\begin{document}$ \alpha'(s)\geq 0 $\end{document}</tex-math></inline-formula>; <inline-formula><tex-math id="M19">\begin{document}$ [0,k] $\end{document}</tex-math></inline-formula>, which represents negative density dependent dispersal on the boundary. We employ the method of sub-super solutions, bifurcation theory, and stability analysis to obtain our results. We provide detailed bifurcation diagrams via a quadrature method for the case <inline-formula><tex-math id="M20">\begin{document}$ \Omega = (0,1) $\end{document}</tex-math></inline-formula>.</p>

Apparent negative density-dependent dispersal in tsetse (Glossina spp) is an artefact of inappropriate analysis

Analysis of genetic material from field-collected tsetse (Glossina spp) in ten study areas has been used to predict that the distance (δ) dispersed per generation increases as effective population densities (De) decrease, displaying negative density dependent dispersal (NDDD). This result is an artefact arising primarily from errors in estimates of S, the area occupied by a subpopulation, and thereby in De, the effective subpopulation density. The fundamental, dangerously misleading, error lies in the assumption that S can be estimated as the area (Ŝ) regarded as being covered by traps. Errors in the estimates of δ are magnified because variation in estimates of S is greater than for all other variables measured, and accounts for the greatest proportion of variation in δ. The errors result in anomalously high correlations between δ and S, and the appearance of NDDD, with a slope of −0.5 for the regressions of log(δ) on log(e), even in simulations where dispersal has been set as density independent. A complementary mathematical analysis confirms these findings. Improved error estimates for the crucial parameter b, the rate of increase in genetic distance with increasing geographic separation, suggest that three of the study areas should have been excluded because b is not significantly greater than zero. Errors in census population estimates result from a fundamental misunderstanding of the relationship between trap placement and expected tsetse catch. These errors are exacerbated through failure to adjust for variations in trapping intensity, trap performance, and in capture probabilities between geographical situations and between tsetse species. Claims of support in the literature for NDDD are spurious. There is no suggested explanation for how NDDD might have evolved. We reject the NDDD hypothesis and caution that the idea should not be allowed to influence policy on tsetse and trypanosomiasis control.Author summaryGenetic analysis of field-sampled tsetse (Glossina spp) has been used to suggest that, as tsetse population densities decrease, rates of dispersal increase – displaying negative density dependent dispersal (NDDD). It is further suggested that NDDD might apply to all tsetse species and that, consequently, tsetse control operations might unleash enhanced invasion of areas cleared of tsetse, prejudicing the long-term success of control campaigns. We demonstrate that NDDD in tsetse is an artefact consequent on multiple errors of analysis and interpretation. The most serious of these errors stems from a fundamental misunderstanding of the way in which traps sample tsetse, resulting in huge errors in estimates of the areas sampled by the traps, and occupied by the subpopulations being sampled. Errors in census population estimates are made worse through failure to adjust for variations in trapping intensity, trap performance, and in capture probabilities between geographical situations, and between tsetse species. The errors result in the appearance of NDDD, even in modelling situations where rates of dispersal are expressly assumed independent of population density. We reject the NDDD hypothesis and caution that the idea should not be allowed to influence policy on tsetse and trypanosomiasis control.