Access to analytical code is essential for transparent and reproducible research. We review the state of code availability in ecology using a random sample of 346 nonmolecular articles published between 2015 and 2019 under mandatory or encouraged code-sharing policies. Our results call for urgent action to increase code availability: only 27% of eligible articles were accompanied by code. In contrast, data were available for 79% of eligible articles, highlighting that code availability is an important limiting factor for computational reproducibility in ecology. Although the percentage of ecological journals with mandatory or encouraged code-sharing policies has increased considerably, from 15% in 2015 to 75% in 2020, our results show that code-sharing policies are not adhered to by most authors. We hope these results will encourage journals, institutions, funding agencies, and researchers to address this alarming situation.

Dispersal is an important form of movement influencing population dynamics, species distribution and gene flow between populations. In population models, dispersal is often included in a simplified manner by removing a random proportion of the population. Many ecologists now argue that models should be formulated at the level of individuals instead of the population level. To fully understand the effects of dispersal on natural systems, it is therefore necessary to incorporate individual‐level differences in dispersal behavior in population models. Here, we parameterized an integral projection model, which allows for studying how individual life histories determine population‐level processes, using bulb mites, Rhizoglyphus robini, to assess to what extent dispersal expression (frequency of individuals in the dispersal stage) and dispersal probability affect the proportion of successful dispersers and natal population growth rate. We find that allowing for life‐history differences between resident phenotypes and disperser phenotypes shows that multiple combinations of dispersal probability and dispersal expression can produce the same proportion of leaving individuals. Additionally, a given proportion of successful dispersing individuals result in different natal population growth rates. The results highlight that dispersal life histories, and the frequency with which disperser phenotypes occur in the natal population, significantly affect population‐level processes. Thus, biological realism of dispersal population models can be increased by incorporating the typically observed life‐history differences between resident phenotypes and disperser phenotypes, and we here present a methodology to do so.

23Dispersal, an important form of movement, influences population dynamics, species 24 distribution, and gene flow between populations. In population models, dispersal is often 25 included in a simplified manner by removing a random proportion of the population. Many 26 ecologists now argue that models should be formulated at the individual-level instead of the 27 population-level to accurately capture how dispersal affects population dynamics. This is 28 especially true for populations exhibiting boom-bust dynamics as these often harbour 29 specialised disperser morphs, life histories or behaviours, as dispersal is essential for 30 persistence. Within a management context, such dynamics play a key role in the stability of 31 populations and in turn extinction risk of species. Here we parameterised an integral 32 projection model, which allows studying how individual life histories determine population-33 level processes. Using bulb mites (Rhizoglyphus robini), a species that shows boom-bust 34 dynamics, we assess the extent dispersal expression (frequency of disperser morphs) and 35 dispersal probability (probability to emigrate) affect the proportion of dispersers and natal 36 population growth rate. We find that, if residents and dispersers differ in life history, different 37 combinations of dispersal probability and dispersal expression produce the same proportion 38 of leaving dispersers. Additionally, for a given proportion of dispersing individuals different 39 natal population growth rates occur. Dispersal life histories, and frequency of disperser 40 morphs occurring in the natal population, can thus significantly affect population-level 41 processes. It is therefore important for our understanding of boom-bust dynamics to 42 elucidate how dispersal characteristics of individuals relate to population resilience and 43 potential re-establishment for populations after a bust phase. 44