Population size is a crucial parameter for both ecological research and conservation planning. When individuals are aggregated, estimating the size of a population through sampling raises methodological challenges, as the high variance between sampling units leads to imprecise estimates. Choosing the right sample design depending on the population aggregation level could improve the precision of estimates; however, this is difficult because studies comparing sample designs for aggregated populations have been limited to a few populations and sampling designs, so their results cannot be generalised. To address this gap, we combined simulations of spatial point populations and field counts of three plant species to compare the relative precision of estimates between three sampling methods: simple random sampling (SRS), systematic sampling (SYS) and spatially balanced sampling (SBS). Comparisons were performed on density and aggregation gradients for a range of sample sizes. Our simulations showed that SYS and SBS were always more precise than SRS when individuals were aggregated, reducing sampling variance up to 80% and 60%. The highest precision for estimating population size was always obtained when the average distance between sampling units equalled the diameter of the clusters (i.e. the groups of individuals). The difference in precision was similar for the natural populations, with sampling variance lowered by up to 75% (SYS) and 60% (SBS) compared to SRS. These findings lead us to recommend using SYS or SBS rather than SRS to estimate population size when individuals are spatially aggregated, as these consistently provide more precise estimates. Assessing cluster diameters in the field enables a quick assessment of the potential gain in precision to expect, and thus the best choice of sampling method depending on the trade‐off between precision and field constraints.

Many countries have legislation intended to limit or offset the impact of anthropogenic disturbance and development on threatened plants. Translocations are often integral to those mitigation policies. When translocation is used exclusively to mitigate development impacts, it is often termed a ‘mitigation translocation.’ However, both the terminology and processes vary regarding interpretation and application, resulting in inconsistent standards, often leading to poorly planned and implemented projects. These mitigation projects rarely achieve the intended ‘no net loss’ of protected species due to issues with timelines and procedures that result in the mortality of translocated individuals. Instead, such projects are often process driven, focused on meeting legislative requirements which enable the development to proceed, rather than meaningful attempts to minimise the ecological impact of developments and demonstrate conservation outcomes. Here, we propose to reframe mitigation translocations as conservation driven, ensuring best practice implementation and hence, a quantified no net loss for impacted species. These methods include redefining the term mitigation translocation to include conservation objectives and outlining issues associated with the mitigation translocation processes worldwide. We also nominate global standards of practice to which all proposals should adhere, to ensure each project follows a trajectory towards quantified success, with genuine impact mitigation. These proposed standards focus on building efficient translocation plans and improving governance to facilitate a transition from project centred to ecology-driven translocation. Employment of these standards is relevant to development proponents, government regulators, researchers, and translocation practitioners and will increase the likelihood of conservation gains within the mitigation translocation sector.

The estimation of population size and its variation across space and time largely relies on counts of individuals, generally carried out within spatial units such as quadrats or sites. Missing individuals during counting (i.e. imperfect detection) results in biased estimates of population size and trends. Imperfect detection has been shown to be the rule in animal studies, and most studies now correct for this bias by estimating detection probability. Yet this correction remains exceptional in plant studies, suggesting that most plant ecologists implicitly assume that all individuals are always detected. To assess if this assumption is valid, we conducted a field experiment to estimate individual detection probability in plant counts conducted in 1 × 1 m quadrats. We selected 30 herbaceous plant species along a gradient of conspicuousness at 24 sites along a gradient of habitat closure, and asked groups of observers to count individuals in 10 quadrats using three counting methods requiring progressively increasing times to complete (quick count, unlimited count and cell count). In total, 158 participants took part in the experiment, allowing an analysis of the results of 5024 counts. Over all field sessions, no observer succeeded in detecting all the individuals in the 10 quadrats. The mean detection rate was 0.44 (ranging from 0.11 to 0.82) for the quick count, 0.59 for the unlimited count (range 0.18–0.87) and 0.74 for the cell count (range 0.46–0.94). Detection probability increased with the conspicuousness of the target species and decreased with the density of individuals and habitat closure. The observer's experience in botany had little effect on detection probability, whereas detection was strongly affected by the time observers spent counting. Yet although the more time‐consuming methods increased detection probability, none achieved perfect detection, nor did they reduce the effect on detection probability of the variables we measured. Synthesis. Our results show that detection is imperfect and highly heterogeneous when counting plants. To avoid biased estimates when assessing the size, temporal or spatial trends of plant populations, plant ecologists should use methods that estimate the detection probability of individuals rather than relying on raw counts.

The mapping of plant biodiversity represents a fundamental stage in establishing conservation priorities, particularly in identifying groups of species that share ecological requirements or evolutionary histories. This is often achieved by assessing different spatial diversity patterns in plant population distributions. In this paper, we present two primary data sources crucial for biodiversity monitoring: in situ measurements from botanical observations and remote sensing (RS). In situ methods involve directly collecting data from specific sites, providing detailed insights into ecological patterns but often constrained by resource limitations. Integrating in situ and RS data highlights their complementary strengths, which depend on factors such as study scale, resolution, and logistical feasibility. While in situ approaches are characterized by precision, RS offers efficiency and extensive, repeated coverage. This research integrates in situ and RS data to analyze plant and spectral diversity across France at a spatial resolution of 5 km, encompassing over 23 000 grid cells. We employ four established diversity metrics leveraging the spatial distribution of 6650 plant species and 250 spectral clusters (derived from MODIS data at a 500‐m resolution). Through bioregionalization network analysis combining these data sources, we identified five distinct bioregions that capture the biogeographical structure of plant biodiversity in France. Additionally, we explore the relationship between plant species diversity and spectral cluster diversity within and between these bioregions, offering novel insights into the spatial dynamics of plant biodiversity.