Ziphius cavirostris presence relative to the vertical and temporal variability of oceanographic conditions in the Southern California Bight.

The oceanographic conditions of the Southern California Bight (SCB) dictate the distribution and abundance of prey resources and therefore the presence of mobile predators, such as goose-beaked whales (Ziphius cavirostris). Goose-beaked whales are deep-diving odontocetes that spend a majority of their time foraging at depth. Due to their cryptic behavior, little is known about how they respond to seasonal and interannual changes in their environment. This study utilizes passive acoustic data recorded from two sites within the SCB to explore the oceanographic conditions that goose-beaked whales appear to favor. Utilizing optimum multiparameter analysis, modeled temperature and salinity data are used to identify and quantify these source waters: Pacific Subarctic Upper Water (PSUW), Pacific Equatorial Water (PEW), and Eastern North Pacific Central Water (ENPCW). The interannual and seasonal variability in goose-beaked whale presence was related to the variability in El Niño Southern Oscillation events and the fraction and vertical distribution of the three source waters. Goose-beaked whale acoustic presence was highest during the winter and spring and decreased during the late summer and early fall. These seasonal increases occurred at times of increased fractions of PEW in the California Undercurrent and decreased fractions of ENPCW in surface waters. Interannual increases in goose-beaked whale presence occurred during El Niño events. These results establish a baseline understanding of the oceanographic characteristics that correlate with goose-beaked whale presence in the SCB. Furthering our knowledge of this elusive species is key to understanding how anthropogenic activities impact goose-beaked whales.

Long-distance communication can enable collective migration in a dynamic seascape.

Social information is predicted to enhance the quality of animals' migratory decisions in dynamic ecosystems, but the relative benefits of social information in the long-range movements of marine megafauna are unknown. In particular, whether and how migrants use nonlocal information gained through social communication at the large spatial scale of oceanic ecosystems remains unclear. Here we test hypotheses about the cues underlying timing of blue whales' breeding migration in the Northeast Pacific via individual-based models parameterized by empirical behavioral data. Comparing emergent patterns from individual-based models to individual and population-level empirical metrics of migration timing, we find that individual whales likely rely on both personal and social sources of information about forage availability in deciding when to depart from their vast and dynamic foraging habitat and initiate breeding migration. Empirical patterns of migratory phenology can only be reproduced by models in which individuals use long-distance social information about conspecifics' behavioral state, which is known to be encoded in the patterning of their widely propagating songs. Further, social communication improves pre-migration seasonal foraging performance by over 60% relative to asocial movement mechanisms. Our results suggest that long-range communication enhances the perceptual ranges of migrating whales beyond that of any individual, resulting in increased foraging performance and more collective migration timing. These findings indicate the value of nonlocal social information in an oceanic migrant and suggest the importance of long-distance acoustic communication in the collective migration of wide-ranging marine megafauna.

Ecological forecasts for marine resource management during climate extremes.

Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members. Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally.

Impacts of marine heatwaves on top predator distributions are variable but predictable.

Marine heatwaves cause widespread environmental, biological, and socio-economic impacts, placing them at the forefront of 21st-century management challenges. However, heatwaves vary in intensity and evolution, and a paucity of information on how this variability impacts marine species limits our ability to proactively manage for these extreme events. Here, we model the effects of four recent heatwaves (2014, 2015, 2019, 2020) in the Northeastern Pacific on the distributions of 14 top predator species of ecological, cultural, and commercial importance. Predicted responses were highly variable across species and heatwaves, ranging from near total loss of habitat to a two-fold increase. Heatwaves rapidly altered political bio-geographies, with up to 10% of predicted habitat across all species shifting jurisdictions during individual heatwaves. The variability in predicted responses across species and heatwaves portends the need for novel management solutions that can rapidly respond to extreme climate events. As proof-of-concept, we developed an operational dynamic ocean management tool that predicts predator distributions and responses to extreme conditions in near real-time.

Widespread habitat loss and redistribution of marine top predators in a changing ocean.

The Northwest Atlantic Ocean and Gulf of Mexico are among the fastest warming ocean regions, a trend that is expected to continue through this century with far-reaching implications for marine ecosystems. We examine the distribution of 12 highly migratory top predator species using predictive models and project expected habitat changes using downscaled climate models. Our models predict widespread losses of suitable habitat for most species, concurrent with substantial northward displacement of core habitats >500 km. These changes include up to >70% loss of suitable habitat area for some commercially and ecologically important species. We also identify predicted hot spots of multi-species habitat loss focused offshore of the U.S. Southeast and Mid-Atlantic coasts. For several species, the predicted changes are already underway, which are likely to have substantial impacts on the efficacy of static regulatory frameworks used to manage highly migratory species. The ongoing and projected effects of climate change highlight the urgent need to adaptively and proactively manage dynamic marine ecosystems.

Building use-inspired species distribution models: Using multiple data types to examine and improve model performance.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.

An anchovy ecosystem indicator of marine predator foraging and reproduction.

Forage fishes are key energy conduits that transfer primary and secondary productivity to higher trophic levels. As novel environmental conditions caused by climate change alter ecosystems and predator-prey dynamics, there is a critical need to understand how forage fish control bottom-up forcing of food web dynamics. In the northeast Pacific, northern anchovy (Engraulis mordax) is an important forage species with high interannual variability in population size that subsequently impacts the foraging and reproductive ecology of marine predators. Anchovy habitat suitability from a species distribution model (SDM) was assessed as an indicator of the diet, distribution and reproduction of four predator species. Across 22 years (1998-2019), this anchovy ecosystem indicator (AEI) was significantly positively correlated with diet composition of all species and the distribution of common murres (Uria aalge), Brandt's cormorants (Phalacrocorax penicillatus) and California sea lions (Zalophus californianus), but not rhinoceros auklets (Cerorhinca monocerata). The capacity for the AEI to explain variability in predator reproduction varied by species but was strongest with cormorants and sea lions. The AEI demonstrates the utility of forage SDMs in creating ecosystem indicators to guide ecosystem-based management.

Climate Change Impacts on Eastern Boundary Upwelling Systems.

The world's eastern boundary upwelling systems (EBUSs) contribute disproportionately to global ocean productivity and provide critical ecosystem services to human society. The impact of climate change on EBUSs and the ecosystems they support is thus a subject of considerable interest. Here, we review hypotheses of climate-driven change in the physics, biogeochemistry, and ecology of EBUSs; describe observed changes over recent decades; and present projected changes over the twenty-first century. Similarities in historical and projected change among EBUSs include a trend toward upwelling intensification in poleward regions, mitigatedwarming in near-coastal regions where upwelling intensifies, and enhanced water-column stratification and a shoaling mixed layer. However, there remains significant uncertainty in how EBUSs will evolve with climate change, particularly in how the sometimes competing changes in upwelling intensity, source-water chemistry, and stratification will affect productivity and ecosystem structure. We summarize the commonalities and differences in historical and projected change in EBUSs and conclude with an assessment of key remaining uncertainties and questions. Future studies will need to address these questions to better understand, project, and adapt to climate-driven changes in EBUSs.

Revenue loss due to whale entanglement mitigation and fishery closures.

Whale entanglements with fishing gear, exacerbated by changing environmental conditions, pose significant risk to whale populations. Management tools used to reduce entanglement risk, for example temporary area restrictions on fishing, can have negative economic consequences for fishing communities. Balancing whale protection with sustaining productive fisheries is therefore a challenge experienced worldwide. In the California Current Ecosystem, ecosystem indicators have been used to understand the environmental dynamics that lead to increased whale entanglement risk in a lucrative crab fishery. However, an assessment of socio-economic risk for this fishery, as in many other regions, is missing. We estimate retrospectively the losses from ex-vessel revenue experienced by commercial Dungeness crab fishers in California during two seasons subject to whale entanglement mitigation measures using a Linear-Cragg hurdle modeling approach which incorporated estimates of pre-season crab abundance. In the 2020 fishing season, our results suggest total revenues would have been $14.4 million higher in the Central Management Area of California in the absence of closures and other disturbances. In the 2019 fishing season, our results suggest ex-vessel revenues would have been $9.4 million higher in the Central Management Area and $0.3 million higher in the Northern Management Area. Our evaluation should motivate the development of strategies which maximize whale protection whilst promoting productive, sustainable and economically-viable fisheries.

Recommendations for quantifying and reducing uncertainty in climate projections of species distributions.

Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change-rather than accurately predict specific outcomes-it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling. The simulations encompass marine species with different functional traits and ecological preferences to more broadly address resource manager and fishery stakeholder needs, and provide a simulated true state with which to evaluate projections. We present our results relative to the degree of environmental extrapolation from historical conditions, which helps facilitate interpretation by ecological modelers working in diverse systems. We found uncertainty associated with species distribution models can exceed uncertainty generated from diverging earth system models (up to 70% of total uncertainty by 2100), and that this result was consistent across species traits. Species distribution model uncertainty increased through time and was primarily related to the degree to which models extrapolated into novel environmental conditions but moderated by how well models captured the underlying dynamics driving species distributions. The predictive power of simulated species distribution models remained relatively high in the first 30 years of projections, in alignment with the time period in which stakeholders make strategic decisions based on climate information. By understanding sources of uncertainty, and how they change at different forecast horizons, we provide recommendations for projecting species distribution models under global climate change.

Global decline in ocean memory over the 21st century.

Ocean memory, the persistence of ocean conditions, is a major source of predictability in the climate system beyond weather time scales. We show that ocean memory, as measured by the year-to-year persistence of sea surface temperature anomalies, is projected to steadily decline in the coming decades over much of the globe. This global decline in ocean memory is predominantly driven by shoaling of the upper-ocean mixed layer depth in response to global surface warming, while thermodynamic and dynamic feedbacks can contribute substantially regionally. As the mixed layer depth shoals, stochastic forcing becomes more effective in driving sea surface temperature anomalies, increasing high-frequency noise at the expense of persistent signals. Reduced ocean memory results in shorter lead times of skillful persistence-based predictions of sea surface thermal conditions, which may present previously unknown challenges for predicting climate extremes and managing marine biological resources under climate change.

Global seasonal forecasts of marine heatwaves.

Marine heatwaves (MHWs)-periods of exceptionally warm ocean temperature lasting weeks to years-are now widely recognized for their capacity to disrupt marine ecosystems1-3. The substantial ecological and socioeconomic impacts of these extreme events present significant challenges to marine resource managers4-7, who would benefit from forewarning of MHWs to facilitate proactive decision-making8-11. However, despite extensive research into the physical drivers of MHWs11,12, there has been no comprehensive global assessment of our ability to predict these events. Here we use a large multimodel ensemble of global climate forecasts13,14 to develop and assess MHW forecasts that cover the world's oceans with lead times of up to a year. Using 30 years of retrospective forecasts, we show that the onset, intensity and duration of MHWs are often predictable, with skilful forecasts possible from 1 to 12 months in advance depending on region, season and the state of large-scale climate modes, such as the El Niño/Southern Oscillation. We discuss considerations for setting decision thresholds based on the probability that a MHW will occur, empowering stakeholders to take appropriate actions based on their risk profile. These results highlight the potential for operational MHW forecasts, analogous to forecasts of extreme weather phenomena, to promote climate resilience in global marine ecosystems.

Flexible use of a dynamic energy landscape buffers a marine predator against extreme climate variability.

Animal migrations track predictable seasonal patterns of resource availability and suitable thermal habitat. As climate change alters this 'energy landscape', some migratory species may struggle to adapt. We examined how climate variability influences movements, thermal habitat selection and energy intake by juvenile Pacific bluefin tuna (Thunnus orientalis) during seasonal foraging migrations in the California Current. We tracked 242 tuna across 15 years (2002-2016) with high-resolution archival tags, estimating their daily energy intake via abdominal warming associated with digestion (the 'heat increment of feeding'). The poleward extent of foraging migrations was flexible in response to climate variability, allowing tuna to track poleward displacements of thermal habitat where their standard metabolic rates were minimized. During a marine heatwave that saw temperature anomalies of up to +2.5°C in the California Current, spatially explicit energy intake by tuna was approximately 15% lower than average. However, by shifting their mean seasonal migration approximately 900 km poleward, tuna remained in waters within their optimal temperature range and increased their energy intake. Our findings illustrate how tradeoffs between physiology and prey availability structure migration in a highly mobile vertebrate, and suggest that flexible migration strategies can buffer animals against energetic costs associated with climate variability and change.

A satellite-based mobile warning system to reduce interactions with an endangered species.

Earth-observing satellites are a major research tool for spatially explicit ecosystem nowcasting and forecasting. However, there are practical challenges when integrating satellite data into usable real-time products for stakeholders. The need of forecast immediacy and accuracy means that forecast systems must account for missing data and data latency while delivering a timely, accurate, and actionable product to stakeholders. This is especially true for species that have legal protection. Acipenser oxyrinchus oxyrinchus (Atlantic sturgeon) were listed under the United States Endangered Species Act in 2012, which triggered immediate management action to foster population recovery and increase conservation measures. Building upon an existing research occurrence model, we developed an Atlantic sturgeon forecast system in the Delaware Bay, USA. To overcome missing satellite data due to clouds and produce a 3-d forecast of ocean conditions, we implemented data interpolating empirical orthogonal functions (DINEOF) on daily observed satellite data. We applied the Atlantic sturgeon research model to the DINEOF output and found that it correctly predicted Atlantic sturgeon telemetry occurrences over 90% of the time within a 3-d forecast. A similar framework has been utilized to forecast harmful algal blooms, but to our knowledge, this is the first time a species distribution model has been applied to DINEOF gap-filled data to produce a forecast product for fishes. To implement this product into an applied management setting, we worked with state and federal organizations to develop real-time and forecasted risk maps in the Delaware River Estuary for both state-level managers and commercial fishers. An automated system creates and distributes these risk maps to subscribers' mobile devices, highlighting areas that should be avoided to reduce interactions. Additionally, an interactive web interface allows users to plot historic, current, future, and climatological risk maps as well as the underlying model output of Atlantic sturgeon occurrence. The mobile system and web tool provide both stakeholders and managers real-time access to estimated occurrences of Atlantic sturgeon, enabling conservation planning and informing fisher behavior to reduce interactions with this endangered species while minimizing impacts to fisheries and other projects.

Where did they not go? Considerations for generating pseudo-absences for telemetry-based habitat models.

BACKGROUND: Habitat suitability models give insight into the ecological drivers of species distributions and are increasingly common in management and conservation planning. Telemetry data can be used in habitat models to describe where animals were present, however this requires the use of presence-only modeling approaches or the generation of 'pseudo-absences' to simulate locations where animals did not go. To highlight considerations for generating pseudo-absences for telemetry-based habitat models, we explored how different methods of pseudo-absence generation affect model performance across species' movement strategies, model types, and environments. METHODS: We built habitat models for marine and terrestrial case studies, Northeast Pacific blue whales (Balaenoptera musculus) and African elephants (Loxodonta africana). We tested four pseudo-absence generation methods commonly used in telemetry-based habitat models: (1) background sampling; (2) sampling within a buffer zone around presence locations; (3) correlated random walks beginning at the tag release location; (4) reverse correlated random walks beginning at the last tag location. Habitat models were built using generalised linear mixed models, generalised additive mixed models, and boosted regression trees. RESULTS: We found that the separation in environmental niche space between presences and pseudo-absences was the single most important driver of model explanatory power and predictive skill. This result was consistent across marine and terrestrial habitats, two species with vastly different movement syndromes, and three different model types. The best-performing pseudo-absence method depended on which created the greatest environmental separation: background sampling for blue whales and reverse correlated random walks for elephants. However, despite the fact that models with greater environmental separation performed better according to traditional predictive skill metrics, they did not always produce biologically realistic spatial predictions relative to known distributions. CONCLUSIONS: Habitat model performance may be positively biased in cases where pseudo-absences are sampled from environments that are dissimilar to presences. This emphasizes the need to carefully consider spatial extent of the sampling domain and environmental heterogeneity of pseudo-absence samples when developing habitat models, and highlights the importance of scrutinizing spatial predictions to ensure that habitat models are biologically realistic and fit for modeling objectives.

Thermal displacement by marine heatwaves.

Marine heatwaves (MHWs)-discrete but prolonged periods of anomalously warm ocean temperatures-can drastically alter ocean ecosystems, with profound ecological and socioeconomic impacts1-8. Considerable effort has been directed at understanding the patterns, drivers and trends of MHWs globally9-11. Typically, MHWs are characterized on the basis of their intensity and persistence at a given location-an approach that is particularly relevant for corals and other sessile organisms that must endure increased temperatures. However, many ecologically and commercially important marine species respond to environmental disruptions by relocating to favourable habitats, and dramatic range shifts of mobile marine species are among the conspicuous impacts of MHWs1,4,12,13. Whereas spatial temperature shifts have been studied extensively in the context of long-term warming trends14-18, they are unaccounted for in existing global MHW analyses. Here we introduce thermal displacement as a metric that characterizes MHWs by the spatial shifts of surface temperature contours, instead of by local temperature anomalies, and use an observation-based global sea surface temperature dataset to calculate thermal displacements for all MHWs from 1982 to 2019. We show that thermal displacements during MHWs vary from tens to thousands of kilometres across the world's oceans and do not correlate spatially with MHW intensity. Furthermore, short-term thermal displacements during MHWs are of comparable magnitude to century-scale shifts inferred from warming trends18, although their global spatial patterns are very different. These results expand our understanding of MHWs and their potential impacts on marine species, revealing which regions are most susceptible to thermal displacement, and how such shifts may change under projected ocean warming. The findings also highlight the need for marine resource management to account for MHW-driven spatial shifts, which are of comparable scale to those associated with long-term climate change and are already happening.

Towards a global understanding of the drivers of marine and terrestrial biodiversity.

Understanding the distribution of life's variety has driven naturalists and scientists for centuries, yet this has been constrained both by the available data and the models needed for their analysis. Here we compiled data for over 67,000 marine and terrestrial species and used artificial neural networks to model species richness with the state and variability of climate, productivity, and multiple other environmental variables. We find terrestrial diversity is better predicted by the available environmental drivers than is marine diversity, and that marine diversity can be predicted with a smaller set of variables. Ecological mechanisms such as geographic isolation and structural complexity appear to explain model residuals and also identify regions and processes that deserve further attention at the global scale. Improving estimates of the relationships between the patterns of global biodiversity, and the environmental mechanisms that support them, should help in efforts to mitigate the impacts of climate change and provide guidance for adapting to life in the Anthropocene.

Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements.

Climate change and increased variability and intensity of climate events, in combination with recovering protected species populations and highly capitalized fisheries, are posing new challenges for fisheries management. We examine socio-ecological features of the unprecedented 2014-2016 northeast Pacific marine heatwave to understand the potential causes for record numbers of whale entanglements in the central California Current crab fishery. We observed habitat compression of coastal upwelling, changes in availability of forage species (krill and anchovy), and shoreward distribution shift of foraging whales. We propose that these ecosystem changes, combined with recovering whale populations, contributed to the exacerbation of entanglements throughout the marine heatwave. In 2016, domoic acid contamination prompted an unprecedented delay in the opening of California's Dungeness crab fishery that inadvertently intensified the spatial overlap between whales and crab fishery gear. We present a retroactive assessment of entanglements to demonstrate that cooperation of fishers, resource managers, and scientists could mitigate future entanglement risk by developing climate-ready fisheries approaches, while supporting thriving fishing communities.

Decision-support tools for dynamic management.

Spatial management is a valuable strategy to advance regional goals for nature conservation, economic development, and human health. One challenge of spatial management is navigating the prioritization of multiple features. This challenge becomes more pronounced in dynamic management scenarios, in which boundaries are flexible in space and time in response to changing biological, environmental, or socioeconomic conditions. To implement dynamic management, decision-support tools are needed to guide spatial prioritization as feature distributions shift under changing conditions. Marxan is a widely applied decision-support tool designed for static management scenarios, but its utility in dynamic management has not been evaluated. EcoCast is a new decision-support tool developed explicitly for the dynamic management of multiple features, but it lacks some of Marxan's functionality. We used a hindcast analysis to compare the capacity of these 2 tools to prioritize 4 marine species in a dynamic management scenario for fisheries sustainability. We successfully configured Marxan to operate dynamically on a daily time scale to resemble EcoCast. The relationship between EcoCast solutions and the underlying species distributions was more linear and less noisy, whereas Marxan solutions had more contrast between waters that were good and poor to fish. Neither decision-support tool clearly outperformed the other; the appropriateness of each depends on management purpose, resource-manager preference, and technological capacity of tool developers. Article impact statement: Marxan can function as a decision-support tool for dynamic management scenarios in which boundaries are flexible in space and time.

Herramientas de Apoyo para la Toma de Decisiones en el Manejo Dinámico Resumen El manejo espacial es una estrategia valiosa para llevar hacia adelante los objetivos regionales para la conservación de la naturaleza, el desarrollo económico y la salud humana. Uno de los retos del manejo espacial es la navegación a través de la priorización de múltiples caracteres. Este reto se vuelve más pronunciado dentro de los escenarios de manejo dinámico, en los cuales los límites son flexibles en el tiempo y en el espacio como respuesta a las cambiantes condiciones biológicas, ambientales o socioeconómicas. Para implementar el manejo dinámico, se necesitan herramientas de apoyo para la toma de decisiones para guiar a la priorización espacial conforme la distribución de los caracteres se modifica bajo condiciones cambiantes. Marxan es una herramienta de apoyo para la toma de decisiones utilizada ampliamente y diseñada para escenarios de manejo estático, pero su utilidad para el manejo dinámico no ha sido evaluada. EcoCast es una nueva herramienta de apoyo para la toma de decisiones desarrollada explícitamente para el manejo dinámico de múltiples caracteres, pero carece de algunas funcionalidades que tiene Marxan. Usamos un análisis de información retrospectiva para comparar la capacidad de estas dos herramientas para priorizar a cuatro especies marinas en un escenario de manejo dinámico con respecto a la sustentabilidad de las pesquerías. Configuramos exitosamente la herramienta Marxan para que operara dinámicamente con respecto a una escala diaria de tiempo y así se asemejara a EcoCast. La relación entre las soluciones de EcoCast y las distribuciones subyacentes de las especies fue más lineal y menos ruidosa, mientras que las soluciones de Marxan tuvieron un mayor contraste entre las aguas que eran buenas y aquellas que eran pobres para los peces. Ninguna de las dos herramientas de apoyo para la toma de decisiones tuvo un mejor desempeño que la otra; la pertinencia de cada una depende del propósito del manejo, la preferencia del administrador de los recursos y la capacidad tecnológica de quienes desarrollan la herramienta.