Current estimates of population resilience do not predict resilience to directional environmental shifts

Current estimates of population resilience do not predict resilience to directional environmental shifts | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Current estimates of population resilience do not predict resilience to directional environmental shifts James Cant , Christina M. Hernández , Rachael H. Thornley , Andy Hector , Iain Stott , Roberto Salguero-Gómez doi: https://doi.org/10.1101/2025.04.04.647258 James Cant 1 Department of Biology, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: james.cant{at}biology.ox.ac.uk Christina M. Hernández 1 Department of Biology, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rachael H. Thornley 1 Department of Biology, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andy Hector 1 Department of Biology, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Iain Stott 2 Department of Life Sciences, University of Lincoln , Lincoln, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roberto Salguero-Gómez 1 Department of Biology, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Natural systems worldwide are exposed to gradual changes imposed by directional environmental shifts, known as ramp disturbances. However, contemporary assessments of population resilience focus on the resistance and recovery of systems following one-off ( i.e. , pulse) disturbances. Thus, our current perception of demographic resilience overlooks potential trade-offs associated with countering pulse vs . ramp disturbances. Simulating 50-year ramp disturbance scenarios, we evaluate shifts in both the resistance and capacity to boom ( i.e. , amplification) following disturbance of 511 populations across 344 species. We also test the extent to which these populations can maintain their resilience despite disturbance-driven impacts to their survival and/or fecundity. We illustrate how existing estimates of resilience fall short in predicting population responses to ramp disturbances. Instead, non-linear patterns in how ramp disturbances reshape the resilience of natural populations underscore an urgent need to quantify how the relative partitioning of energetic resources into survival vs. fecundity informs population resilience. Our findings challenge well-established approaches in forecasting population resilience to ongoing global change. Introduction The urgent need to predict the responses of ecological systems to ongoing global change has fueled decades of research assessing how these systems resist and recover from disturbances 1 – 3 . This research has provided extensive insights into the resilience of ecological systems. These efforts have revealed differential recovery rates in primary and secondary forest communities following deforestation 4 , how population responses to disturbances relate to their life histories 5 , 6 , and how these population-level responses mediate the vulnerability of communities to disturbances such as droughts 7 and hurricanes 8 . Common to our understanding of community and population resilience, however, is that this research has largely focused on the implications of pulse disturbances 9 , which impose a single, abrupt, and discrete impact on some population or community parameter(s) 10 . Yet in reality, ecological systems are exposed to more complex disturbance regimes characterised by gradual changes imposed by directional environmental shifts 9 , or ramp disturbances ( sensu 11 ). Ramp disturbances are a prominent feature of ecological systems worldwide 12 . For instance, rising ocean temperatures erode the structure and diversity of global coral reefs 13 ; gradually shifting thermal regimes facilitate the poleward expansion of novel biotic assemblages 14 ; and continued losses of sea-ice cover drive annual declines in the reproductive success of keystone Antarctic species 15 . Note the distinction here between ramp (gradual, directional change) and press (constant and sustained pressure) disturbances ( Fig. 1 ). Beyond human-driven global change, ramp disturbances are also a natural part of ecological systems, with successional changes in resource availability and niche partitioning associated with gradual shifts in individual performance 16 , population dynamics 17 , and community composition 18 . As such, predicting and managing the responses of ecological systems to ongoing global change requires understanding how the accumulation of impacts imposed by ramp disturbances influences their resilience over time 19 , 20 . Download figure Open in new tab Figure 1. Ramp disturbances inflict very different perturbation regimes on the dynamics of natural populations, compared to pulse and press disturbances. The differing responses of population vital rates (pink; e.g. survival and fecundity) during their exposure to the disturbance intensity patterns (orange) associated with pulse-, press- and ramp disturbances. Demographic resilience can be quantified by comparing short-term deviations in observed or forecasted population growth rates against stationary ( i.e. , asymptotic) expectations 21 . Under stationary conditions, natural populations are expected to change in size at a constant rate over time, termed their asymptotic population growth rate ( λ ) 22 , 23 . In reality, however, populations regularly experience impacts to their survival and fecundity rates ( e.g. , due to disease outbreaks 24 and resource competition 25 ), and/or their population structure ( i.e., the distribution of individuals across their life-cycle) ( e.g. , due to trophy hunting 26 and restorative outplanting 27 ). These individual-level impacts result in populations expressing short-term ( i.e. , transient) dynamics differing greatly from their expected stationary trajectories 28 , 29 ; dynamics which maintain populations within a transient state 30 , 31 . Within this transient state, populations can experience either increased ( i.e ., amplification) or decreased growth ( i.e. attenuation) relative to their long-term growth rate. Measures of population amplification quantify a population’s potential for increasing its growth rate relative to λ , following a hypothetical disturbance, enabling the population to benefit from disturbances 32 , 33 . Alternatively, quantifying the difference between a population’s expected attenuated growth rate and its long-term stationary projection offers insight into their resistance potential 6 , 34 – 36 . Combined, metrics of these amplification and attenuation bounds represent a framework for evaluating demographic resilience ( Fig. 2A ) 21 , 37 . Crucially, however, this framework of demographic resilience centres upon the impacts of pulse disturbances 21 . Our perception of population resilience is, therefore, incomplete, as we do not yet know how natural populations respond to the gradual, directional shifts associated with ramp disturbances. Download figure Open in new tab Figure 2. Pipeline used to evaluate how population resilience changes when exposed to ramp disturbances. (A) Under stationary conditions populations are expected to change in size at a constant rate over time (i.e. their long-term population growth rate, λ ). However, following a disturbance (!), populations can experience either amplification ( i.e. increases in growth rate relative to λ ) or attenuation ( i.e . decreases in growth rate relative to λ ). Quantifying these characteristics offers insight into population resilience. Here, we focused on estimates of amplification ( a ) and resistance ( b ). (B) We exposed a sample of matrix population models (MPMs) to a series of 50-year-long ramp disturbance scenarios. These scenarios entailed differing annual shifts in each population’s and fecundity (both independent and simultaneous shifts). (C) We calculated amplification and resistance estimates for each MPM along these time series which we then used to estimate the rate at which the resilience of each population changed, per unit time, under each disturbance scenario ( δ x ). Relating this rate of change back to established estimates of each population’s amplification and resistance ( x 0 ) then allowed us to assess how the change in resilience experienced by populations in response to ramp disturbances compares to our current perception of their demographic resilience following pulse disturbances. Being resilient to one disturbance type does not necessarily translate into resilience to all disturbance types 38 . One might assume that, if a system is highly resilient to pulse disturbances, then it is also resilient to ramp disturbances. Systems with high functional redundancy ( i.e. , greater duplication of ecological functions across species 39 ), might be expected to withstand both types of disturbances 40 . Yet, pulse and ramp disturbances elicit differing mechanistic responses, suggesting a trade-off in the selective pressures they impose. For instance, some populations promote their resilience to pulse disturbances through traits associated with structural integrity ( e.g., thicker bark 41 , and robust morphologies 42 ). Yet, these adaptations can leave individuals physiologically ill-equipped to handle gradual, long-term environmental shifts 43 . Similarly, during early phases of gradual shifts, populations may exhibit disturbance acclimation, whereby reversible changes in phenotypic and/or demographic traits maintain population viability 44 . However, hidden vulnerabilities can overwhelm these populations once pushed beyond their tipping points 45 . Here, we examine how ramp disturbances could be expected to modify the resilience of natural populations ( Fig. 2 ). To do so, we simulate a series of 50-year directional annual changes on the survival and fecundity of 511 natural populations 46 , 47 , representing 344 animal and plant species. We evaluate the rate at which these changes modify the highest and lowest population growth rates, relative to stationary conditions, expected within the first time step after a disturbance for each population (henceforth amplification and resistance , respectively) ( Fig. 2A ). Pulse and ramp disturbances inflict differing selective pressures on the dynamics of populations. Thus, we hypothesise that (H1) ramp disturbances will enforce greater magnitudes of change upon the resilience of populations that, in response to pulse disturbances, possess higher amplification and/or resistance potential. Furthermore, we expect that (H2) this pattern will be stronger in sessile species compared to mobile species because of their differing reliance on mobility vs. modularity 48 . Indeed, sessile organisms ( e.g ., plants, corals) often exhibit traits relating to slow population turnover 49 , dormancy/resprouting 50 , or structural resilience 41 , all of which are beneficial for enduring the impacts of discrete, pulse disturbances. In contrast, mobile species can simply move away to avoid discrete disturbances 48 . Thus, we expect sessile species to exhibit higher amplification and/or resistance, and therefore to experience greater magnitudes of change in their resilience when exposed to ramp disturbances. Finally, (H3) we expect smaller shifts in population resilience in scenarios comprising opposite shifts in survival and fecundity, compared to those to which survival and fecundity respond in the same direction. This expectation corresponds with evidence that contrasting shifts in survival and fecundity can allow populations to maintain their growth rates 51 , 52 , and that higher survival benefits resistance whilst enhanced fecundity promotes amplification 6 , 53 . Results Revealing non-linear responses to ramp disturbances To test (H1) whether ramp disturbances will cause greater magnitudes of change in the resilience of populations that, in response to pulse disturbances, possess higher amplification and/or resistance potential, we used a phylogenetically-corrected Bayesian multilevel modelling approach. Reflecting established estimates of population resilience to pulse dsiturbances, we obtained estimates of amplification and resistance from 511 selected population models ( Amplification 0 and Resistance 0 , respectively) representing the dynamics of unmanipulated populations in natural settings (265 plant and 79 animal species). Next, we simulated a series of 50-year-long ramp disturbance scenarios on the same populations. These scenarios comprised consistent, directional shifts in survival and fecundity, with the magnitude of annual changes imposed on these vital rates ranging from -5% to +5%. Imposing this range of negative and positive impacts on survival and/or fecundity allowed us to investigate the impacts of expected declines, as such information is not available for most individual studies, while also enabling us to evaluate the impact of disturbance-induced increases ( e.g., 24 , 54 ). To describe how the amplification and resistance of each population changes following ramp disturbances of varying intensities, we quantified the rate of change in their amplification and resistance (henceforth δ Amplification and δ Resistance ). We found partial support for our first hypothesis (H1) that the magnitude of change populations experience in their resilience is greater in populations that, in response to pulse disturbances, possess higher amplification and/or resistance potential. However, this relationship is non-linear ( Figs. 3 & 4 ). Focusing on ramp disturbance scenarios affecting only survival or only fecundity, we modelled the relationship between the rate of change in population amplification ( δ Amplification ) and established population amplification estimates ( Amplification 0 ), alongside the corresponding relationship between our measures of population resistance ( δ Resistance and Resistance 0 ). Across our disturbance scenarios, the magnitude of change in amplification increased exponentially with increasing initial amplification estimates ( Fig. 3 ). This relationship becomes more pronounced under more intense disturbance scenarios, irrespective of whether the population was subjected to declines or increases in survival or fecundity. Download figure Open in new tab Figure 3. Populations expected to display greater amplification potential following pulse disturbances will experience a greater change in their amplification potential following exposure to ramp disturbances. The relationship between amplification to pulse disturbance ( Amplification 0 ) and the rate of change in amplification ( δ Amplfication ) when exposed to independant rates of directional change in survival and fecundity, in ( A & C ) mobile and ( B & D ) sessile populations (only patterns for ±5%, ±1%, ±0.5%, ±0.3%, ±0.1% & 0% change scenarios shown to ease visualisation). Solid lines represent the mean conditional effects extracted from phylogenetically weighted Bayesian multilevel regression models. Point colours are shaded based on their corresponding ramp disturbance scenario, ranging from the +5% rate of change scenario (lightest shading) to the -5% rate of change scenario (darkest shading). Error displayed as 95% CI. Download figure Open in new tab Figure 4. Populations expected to display either low or high resistance to pulse disturbances will experience less pronounced changes in their resistance following ramp disturbances. The relationship between resistance to pulse disturbance ( Resistance 0 ) and the rate of change in resistance ( δ Resistance ) when exposed to independant rates of directional change in survival and fecundity, in ( A & C ) mobile and ( B & D ) sessile populations (only patterns for ±5%, ±1%, ±0.5%, ±0.3%, ±0.1% & 0% change scenarios shown to ease visualisation). Solid lines represent the mean conditional effects extracted from phylogenetically weighted Bayesian multilevel regression models. Point colours are shaded based on their corresponding ramp disturbance scenario, ranging from the +5% rate of change scenario (lightest shading) to the -5% rate of change scenario (darkest shading). Error displayed as 95% CI. The relationship between change in resistance and established resistance estimates follows a somewhat shallow bell-curve shape. Populations exhibiting either minimal or high resistance in response to pulse disturbances, experience the lowest rates of change in their resistance under ramp disturbances ( Fig. 4 ). Thus, irrespective of the intensity or direction of the disturbance imposed, populations with comparatively intermediate Resistance 0 values ( Resistance 0 ≈ 0.5) exhibit greater absolute δ Resistance ; a pattern that again becomes increasingly more pronounced under increasing disturbance intensities. However, absolute values of δ Resistance and δ Amplification are not mirrored across contrasting rates of directional change. For instance an annual 5% decline in survival or fecundity does not instigate the same magnitude of change in population resistance as an annual 5% increase ( Fig. 4 ). Across our observed patterns in δ Resistance and δ Amplification , declines in survival correspond with increases in δ Amplification ( Fig. 3A & B ), whilst declines in fecundity result in increases in δ Resistance ( Fig. 4C & D ). To interpret these patterns, it is important to remember that the patterns shown in Figures 3 & 4 correspond with only changes in either survival or fecundity. As such, declines in either one of the two rates result in the displacement of a population’s dynamics towards the other rate. For instance, in a scenario in which a population’s survival rate declines whilst its fecundity rate is held constant, the influence of fecundity on that population’s dynamics will become increasingly prevalent over time. As the influence of fecundity increases on a population’s dynamics, we would subsequently expect an increase in its amplification potential 6 , 53 . Accordingly, the responses of δ Resistance and δ Amplification , to shifts in survival and fecundity highlight how the relative balance between a population’s rates of survival and fecundity helps to determine its resilience, not just the observed changes in any one particular rate. The ability of organisms to escape disturbances is a key predictor of how natural populations resist, amplify, and recover from disturbances 55 . To test (H2) whether changes in resilience following ramp disturbances are more evident in sessile species compared to mobile species, we carried out our assessment of the relationship between change in resilience ( δ Amplification and δ Resistance ) and established estimates of resilience to pulse dsiturbances ( Amplification 0 and Resistance 0 ) separately for populations of sessile (396 populations across 267 species) and mobile species (115 populations across 77 species). We classified all plants and corals as sessile, with established individuals across these taxa unable to relocate. Meanwhile, our mobile populations represent a variety of large bodied and/or typically migratory mammals ( e.g ., Ursus americanus [Pallas, 1780], American Black Bear), birds ( e.g ., Calidris temminckii [Leisler, 1812], Temminck’s Stint), fish ( e.g ., Oncorhynchus tshawytscha [Walbaum, 1792], Chinook Salmon) and reptiles ( e.g ., Crocodylus johnsoni [Krefft, 1873], Freshwater Crocodile), alongside two small bodied invertebrate species ( Umbonium costatum [Kiener, 1838] and Scolytus ventralis [Geoffroy, 1762]). Our findings provide partial support to our hypothesis (H2) that changes in resilience following ramp disturbances are strongest in sessile species. The relationship between change in amplification following ramp disturbances ( δ Amplification ) and established amplification estimates ( Amplification 0 ) is most evident in sessile populations ( Fig. 3 ). An annual 5% decline in survival elicits a rate of increase in amplification in sessile populations one order of magnitude greater than observed in mobile populations exhibiting comparable initial amplification ( Fig 3A & B ). However, the relationship between δ Resistance and Resistance 0 is more consistent across sessile and mobile populations ( Fig. 4 ). Yet, this similarity does not mean that the relationship between δ Resistance and Resistance 0 follows exactly the same shape across the two classifications. For instance, following an annual 5% decrease in survival, while mobile and sessile populations experience similar maximal declines in δ Resistance (-0.0061 and -0.0104, respectively), the relationship tapers off more rapidly in sessile populations ( Fig. 4A & B ). No evidence of compensatory mechanisms We found no evidence supporting our expectation (H3) that contrasting shifts in survival and fecundity would compensate their individual effects on amplification and resistance, allowing populations to maintain constant resilience ( Fig 5 ). To test how changes in population resilience caused by ramp disturbances map onto the associated changes in survival and fecundity, and how this correlates with patterns in stochastic population growth rates ( λ S ), we again used phylogenetically-corrected Bayesian multilevel models. With these models, we evaluated how changes in amplification ( δ Amplification ), resistance ( δ Resistance ), and λ S , manifest across a two dimensional parameter space describing the combinations of changes imposed by said ramp disturbances on survival ( δ Survival ) and fecundity ( δ Fecundity ). Across both mobile and sessile populations, higher estimates of δ Amplification are associated with scenarios of decreasing survival (lower δ Survival ) and increasing fecundity (higher δ Fecundity ) ( Fig. 5A & B ). Meanwhile, higher estimates of δ Resistance are associated with scenarios of increasing survival and decreasing fecundity ( Fig. 5C & D ). Again, these patterns indicate how it is not the actual changes in survival and fecundity that drive our observed changes in either δ Amplification and δ Resistance . Rather, the interaction between the effects of survival and fecundity suggests that populations cannot compensate their amplification and resistance against shifts in their underlying dynamics. Download figure Open in new tab Figure 5. Patterns of change in population amplification and resistance associated with ramp disturbances may mask signals of population collapse. Changes in amplification ( δ Amplification ), resistance ( δ Resistance ), and stochastic population growth rate ( λ S ), corresponding with simultaneous directional shifts in survival and fecundity observed in ( A, C & E ) mobile and ( B, D & F ) sessile populations. Colour gradients show mean predicted estimates of δ Amplification , δ Resistance , and λ S , extracted from 30 resampling iterations of phylogenetically weighted Bayesian multilevel regression models. Demographic compensation occurs when populations offset declines in survival or fecundity with increases in the other to maintain their overall demographic performance 51 , 52 . Evidence of similar compensatory mechanisms underlying population resilience would appear as higher values of δ Amplification and/or δ Resistance located in the upper-right diagonal of the corresponding panels in Figure 5 . Accordingly, declines in either survival or fecundity would have less impact on population amplification or resistance. Instead, here, higher values of δ Amplification and/or δ Resistance appear in the upper-left and lower-right, respectively. Thus, if a ramp disturbance impacts the survival and fecundity of a given population at the same intensity and direction, the population will display no change in its estimated amplification, as indicated by the zero contours positioned diagonally across Fig. 5A & B . This observation is also true of resistance, although we report some evidence that increases in survival, at annual rates of >2%, can offset the negative influence of changing fecundity on population resistance ( δ Survival ≈ 0.02 in Fig. 5C & D ). Demographic compensation is, however, evident in λ S across mobile and sessile species ( Fig 5E & F ). Like the measure of asymptotic population growth rate ( λ ), λ S describes the rate of change in population size over time with λ S > 1 reflecting increases in size and λ S < 1 indicating population declines. However, as the geometric mean calculated for a sequence of λ values generated over time, λ S captures how variation in λ , due to stochasticity in population vital rates, combines to inform a population’s long-term growth trajectory 56 . In mobile and sessile populations increases in survival correspond with λ S ≥ 1 irrespective of any changes in fecundity ( Fig. 5E & F ); thereby allowing for demographic compensation. Yet, these patterns in λ S , do not correlate with the patterns of change observed in amplification and resistance ( Fig. 5 ). Subsequently, reports of little to no change in population amplification or resistance over time can actually mask severe trajectories of population collapse. Discussion Our findings illustrate how gradual, directional shifts associated with ramp disturbances could be expected to reshape the resilience of natural populations. In doing so, we show how, to accommodate ramp disturbances within assessments of demographic resilience, we need to establish how the partitioning of energetic resources across survival and fecundity informs population resilience. The transient characteristics of natural populations 28 , 29 offer insight into their resilience, particularly their resistance, recovery, and amplification, following disturbances 21 , 37 ; a perception that represents the culmination of decades of effort to standardise assessments of population resilience 37 , 57 – 59 . This framework has provided key insights into the evolution of resilience across taxonomic boundaries 53 , 60 – 62 , the drivers of population declines in vertebrates 63 , 64 , the geographic expansion of coral assemblages 33 , and the ecological implications of losing older individuals 65 and social learning 66 . However, the use of this framework must align with the context in which it was developed. This context focuses on assessing the impacts of pulse disturbances 21 . As such, the existing toolbox of demographic resilience in population ecology cannot yet be used to evaluate the implications of gradual directional shifts 21 . The exposure of natural populations to ramp disturbances may compound existing variation in their resilience to disturbances. Across natural systems, rarer and already more vulnerable species are expected to be the most affected by an increasing frequency and intensity of recurrent disturbances 67 . In contrast, highly competitive invasive species are more likely to reap the benefits of global change 68 . Here, using a big-data simulation approach, we present congruent evidence that advantageous ramp disturbances that increase survival and/or fecundity will disproportionately benefit species with a tendency to amplify more. Intricate links between the phenology, survival, and fecundity of natural populations and their climate drivers, has resulted in unintuitive increases in the survival and fecundity of various species following climate shifts. Disturbances to forest canopies have been illustrated to benefit the survival and growth of forest ungulates 54 . Similarly, disease outbreaks and warming induced shifts in emergence patterns, have increased reproductive outputs in Tasmanian devils 24 and British butterfly species 69 . With high amplification potential linked to enhanced invasive potential 32 , our findings present a potential concern from the perspective of mitigating species invasions. Specifically, if conditions are suitable, we can expect invasive species to become more competitively superior. Environmental shifts associated with a reduction in survival and/or fecundity will exert a greater impact on the resilience of populations with a higher tendency for amplification. Investing in more enhanced transient dynamics, particularly higher amplification, is an advantageous strategy within more variable environments 33 , 35 , 70 . Indeed, early successional plant species, and mammals adopting a faster pace of life ( i.e. shorter life spans, earlier maturity, and higher fecundity) all exhibit a greater potential for demographic amplification 34 , 36 , reflecting their reliance on boom-and-bust strategies for rapidly colonising available niche space. We have illustrated how populations with greater amplification potential are likely prone to larger shifts in their amplification potential following a ramp disturbance. Thus, fast-living species and populations, who possess greater amplification potential, may be particularly sensitive to the negative impacts of ramp disturbances. This sensitivity to ramp disturbances carries cascading implications that could undermine the capacity for ecosystem recovery following physical disturbances. For instance, changing the composition of pioneer species assemblages shapes the post-fire recovery of forest communities 71 . Thus, diminishing the viability of early successional species reliant on amplification has the potential to inhibit future ecosystem recovery. We observed a very different relationship between change in population resistance following ramp disturbances and established estimates of resistance to pulse dsiturbances. A cursory glance at our findings suggest that natural populations expected to display intermediate resistance to pulse disturbances are the most sensitive to ramp disturbances. The persistence of this pattern under scenarios of increasing survival and fecundity indicates that it may be difficult for interventions to enhance the resistance of species with already naturally low resistance. There is a limit to the extent to which the dynamics of populations can maintain their viability under changing environmental scenarios 72 . Beyond this threshold, shifts in the demographic profiles of natural populations become increasingly indicative of trajectories towards population collapse 73 . At the other end of the spectrum, our findings suggest that species currently perceived as more resistant (higher Resistance 0 ) may be able to maintain their resistance when exposed to unfavourable shifts. However, unlike population amplification, measures of resistance are bounded. Following a disturbance, if the growth rate of a population attenuates in the short-term ( λ t ) relative to its stationary condition ( λ ), the magnitude of this attenuation can only vary between 0 and 1 ( i.e ., λ t / λ ). Subsequently, there is only so far one can increase the resistance of already highly resistant populations, and likewise, decrease the resistance of populations with already low resistance. Ramp disturbances inflict similar patterns of change on the resistance of mobile and sessile populations. However, ramp disturbances elicit a more pronounced response in the amplification of sessile populations. Fundamentally, resistance relates to how vulnerable the most vulnerable individuals and/or systems are 41 , 74 . Therefore, although the mechanisms of their responses will be different, being resistant likely demands similar energetic investments across mobile and sessile species 75 . Meanwhile, amplification relates to an increase in the number of individuals following a disturbance. With their individuals typically unable to relocate themselves following establishment, sessile species cannot move to escape from disturbance impacts 48 . Thus, the greater response of amplification in sessile populations possibly relates to the fact that these populations will need to remain viable despite their established individuals having to remain in one location and endure any imposed environmental shifts. Alternatively, during gradual environmental shifts, mobile populations are able to move to track their preferred conditions. Subsequently, there is less need for mobile populations to amplify in order to respond to said environmental shifts, and thus amplification ability is under less selective pressure in mobile species. Crucially, we have also illustrated how, provided that the impact of a ramp disturbance on a population’s survival and fecundity are of the same magnitude and direction, estimates of its amplification and resistance will remain largely consistent. These findings do not undermine the value of established understanding of demographic resilience for assessing population responses to discrete disturbances. However, to move beyond assessing the impacts of pulse disturbances to forecast population resilience, we require a greater understanding for how a population’s resilience is underpinned by the relative balance of survival vs . fecundity across its life-cycle. At the very least, these findings highlight the necessity of obtaining a comprehensive insight into a population’s dynamics when implementing demographic monitoring as part of management and restoration efforts. Without this comprehensive insight, temporal records showing small deviations in the amplification and resistance potential of monitored populations would suggest stable resilience. Yet, these patterns may be concealing underlying and disastrous declines in population survival and fecundity. To a small extent, we show slight evidence that populations can compensate for the negative effect of shifts in fecundity on their resistance through increases in survival; making resistance a moderately more robust temporal measure of resilience to ramp disturbances than amplification. Amplification, as a measure of the propensity for populations to boom, is intuitively associated with pulses in reproduction 6 . However, trade-offs between reproductive output and offspring establishment are well documented 76 . Thus, over extended periods, a reliance on amplification, rather than resistance, likely increases the volatility of population size through time, thereby increasing the risk of extinction 77 . There are three key caveats that require attention in the context of our findings. First, the analytical approach used to calculate the transient dynamics of structured populations requires first normalising their survival, development, and fecundity rates by the population’s asymptotic growth rate 59 . This normalisation step is necessary for isolating a population’s transient dynamics from its asymptotic behaviour 28 , 29 . However, during exposure to a ramp disturbance, as the survival and/or fecundity rates of a population change over time, its associated asymptotic growth rate must also change 23 . Thus, the asymptotic growth rate value being used to normalise the dynamics of a population across the ramp disturbance period is not consistent through time. Subsequently, we are not computing each new iteration of a population’s amplification and resistance from a consistent baseline (Appendix S1). This circumstance does not affect the use of transient dynamics as a comparative tool for assessing the resilience of populations to pulse disturbances. However, the challenges associated with differentiating mathematical artefacts from true temporal shifts in resilience means that existing measures of demographic resilience are difficult to compare through time. Second, we have focused on the transient bounds of population amplification and resistance. These bounds represent the most extreme possibilities in each metric, given a population’s rates of survival, growth, and fecundity 28 . Crucially, these bounds require no prior knowledge of the population structure ( i.e., the distribution of individuals across their life-cycle), and so are ideal for implementing comparative assessments into population resilience potential 59 . However, the true transient dynamics expressed by any population depend not only on its survival, development, and fecundity rates, but also the specific, realized effect of a disturbance on its population structure 29 . Comprehensive mechanistic insights into the resilience trajectories of populations have been obtained from the simultaneous assessment of their corresponding dynamics and state structures 78 – 80 . Therefore, the role of population structure should be accommodated into comparative assessments, such as ours here, through efforts to empirically link population dynamics to their state structure. Our focus on metrics of transient bounds is also necessitated by the lack of empirical insight quantifying the impacts of specific disturbances (pulse, press, and ramp disturbances) on the dynamics of natural populations 81 . Hence, our decision here, to implement a series of disturbance scenarios comprising varying combinations of changes to the survival and fecundity rates of different populations. This approach allowed us to comprehensively explore the parameter space of impacts arising from potential disturbance scenarios. However, the value of obtaining detailed species/population specific information about how different disturbances affect population dynamics cannot be overstated 81 . Finally, we acknowledge that our sample of 511 populations is a biased representation of Earth’s taxonomic diversity. The COMPADRE and COMADRE databases, whilst the most comprehensive source for demographic information on the world’s biota, display the same signals of geographic and taxonomic biases implicit across most ecological databases 82 – 84 . Whilst work is ongoing to remedy these biases 84 , it is necessary that we consider how they inform our current perspectives. In the context of assessing the impacts of ramp disturbances on population resilience, we urge for greater focus on quantifying the dynamics of amphibian and tropical coral populations. These two taxonomic groups are extremely vulnerable to the ramp disturbances associated with ongoing global change 85 , 86 , and yet we know little about their demographic attributes 81 , 87 . Here, we illustrate how considering the reality that natural populations and communities are routinely exposed to gradual and directional shifts could be expected to change our perception of demographic resilience. Our intention is to appraise previous efforts examining the resilience of populations to pulse disturbances, whilst cautioning against applying these tools to predict population resilience beyond the snapshot in time in which it was assessed 62 . In light of this admission, efforts are ongoing to accommodate disturbances that maintain their intensity over time ( i.e. , press disturbances 10 ) within our understanding of transient dynamics and demographic resilience 88 . In a field of research that is already rife with controversy and debate 1 , 9 , 37 , it is important to emphasise what existing frameworks can and cannot tell us when we consider the real-world complexities of natural systems 89 . We have demonstrated how gradual, directional shifts in survival and fecundity associated with ramp disturbances impose non-linear changes in the resilience of populations. Subsequently, it is difficult to predict population resilience to ongoing global change using contemporary assessments of their resilience to pulse disturbances. Instead, assessing population resilience over time requires quantifying the link between changes to a population’s relative investment across survival and fecundity and directional shifts in its amplification and resistance. Methods To assess the impacts of ramp disturbances on our current understanding of resilience across natural populations, we developed an analytical pipeline comprising the following steps: (1) Extracting a sample of structured population models, (2) Simulating directional environmental shifts in the survival and/or fecundity rates of these models, (3) Quantifying patterns of change in population resilience, and (4) Implementing comparative analyses. All analyses were carried out in R 90 , with the commented scripts archived open-access on Zenodo (10.5281/zenodo.15148632). Sampling population models To test the impact of ramp disturbances upon the amplification and resistance of natural populations, we extracted matrix population models (MPMs hereafter) for plant and animal species from the COMPADRE 46 (version 6.23.5.0) and COMADRE 47 (version 4.23.3.1) databases. MPMs describe the vital rates of survival, growth (progression & retrogression), and fecundity in discrete time along a life cycle made up by discrete stages. Accordingly, MPMs enable the detailed quantification of species/population life cycles and, subsequently, the calculation of population performance 23 and demographic resilience 21 . COMPADRE and COMADRE contain demographic records for >12,000 populations across >1,200 species, with the immense majority coming from peer-reviewed publications addressing different research questions. Thus, to ensure comparability in our analyses, we imposed a series of strict selection criteria. We only extracted MPMs consisting of three or more life stages, since lower dimension MPMs provide unreliable estimates of population transient dynamics 29 . During this extraction we only retained mean MPMs ( i.e. the element-by-element mean MPM derived from a sequence of MPMs describing the dynamics of a single population through time) to mitigate against unrealistic transition probabilities arising from only observing a given population over only a single time period. In cases (24% of initial MPM sample) where the same population was represented by multiple mean MPMs (identified based on duplicated species identity, latitude and longitude details), we computed, and retained, the element-by-element mean of these multiple entries. This process entailed aligning the dimensions of each replicate MPM to the dimensions of the smallest replicate (d), by collapsing together all life stages ≥ d into a single terminal stage, using the matrix collapsing function ( mpm_collapse ) of the Rcompadre R package 91 , which is an approach that has been demonstrated to best maintain the eigenvalue structure of the original matrix 92 . To ensure consistency across MPMs of differing dimensions, once we had obtained a single mean MPM for each population, we collapsed all MPMs into 3 × 3 matrices; following the approach described above to condense together all life stages ≥ 3 into a single third stage. Following our initial MPM extraction we then filtered our sample based on the metadata provided with each MPM from the original source study. Firstly, to ensure the examination of demographic resilience in the wild, we focused on natural populations, thus excluding MPMs exposed to experimental treatments or parameterised with zoo/greenhouse data. Secondly, to ensure our dataset represented population characteristics documented over consistent units of time, we retained only MPMs representing an annual survey periodicity. Next, we only retained MPMs determined to be ergodic, primitive, and irreducible, via the respective isErgodic , isPrimitive , and isIrreducible functions of the popdemo R package 93 . Briefly, an ergodic, primitive, irreducible MPM possesses a single dominant eigenvalue, and consists of transition pathways facilitating direct or indirect movements between all life cycle stages 94 . This ensured we rejected any MPMs comprising incomplete life cycles (e.g., missing data on survival, progression, retrogression, or fecundity) to ensure we could compute their transient dynamics 28 . Finally, we omitted MPMs for populations exhibiting clonality, due to the challenges associated with differentiating and comparing the dynamics of genets and ramets ( i.e. , colonies vs. individuals). These selection criteria resulted in an initial population sample of 561 populations (120 animals and 441 plants) from 372 species (Appendix S2). We note here that we did not account for pre- ( i.e. , reproduction is product of fecundity and offspring survival) vs post-reproductive ( i.e. , reproduction is product of adult survival and fecundity) MPMs. Post-reproductive MPMs are associated with higher values of recruitment 32 . However, because the magnitudes of change we imposed upon each population’s dynamics during our ramp disturbance scenario were relative to their initial condition the distinction between pre- and post-reproductive MPMs does not affect the compatibility of our measures of change in population resilience. Quantifying changes in population resilience & performance To evaluate how the demographic resilience of natural populations change following ramp disturbances, we imposed differing scenarios of changing survival and fecundity on each of our MPMs. These scenarios each corresponded with a differing pairwise combination of percentage annual changes in the stage-specific profiles of survival and fecundity. The changes we imposed across each vital rate were: ±5%, ±4.5%, ±4%, ±3.5%, ±3%, ±2.5%, ±2%, ±1.5%, ±1%, ±0.7%, ±0.5%, ±0.3%, ±0.1%, & 0%, resulting in 729 differing pairwise combinations. Our rationale behind implementing this parameter-space exploration, rather than a fixed scenario, was to explore for potential evidence of mechanisms that may allow populations to maintain their resilience despite declines in a particular vital rate. This process, known as demographic compensation, has been demonstrated to moderate population growth rates across distributional gradients 51 , 52 . To implement each ramp disturbance scenario, we assigned each MPM obtained from COMPADRE or COMADRE as its populations’ initial condition ( A 0 ). We then exposed each population to the selected ramp disturbance scenario over a 50-year period, quantifying the annual element-level changes across its MPM associated with the imposed pairwise changes to its corresponding state-specific survival probabilities and per-capita fecundity ( A t ; Fig. 2B ). We note here that across our different ramp disturbance scenarios we monitored for instances where survival and/or fecundity rates became biologically impossible ( i.e. , survival probabilities 1 & fecundity rates < 0). On occasions where such events occurred we halted the imposed scenario, and retained outputs from only the iterations prior to the exceeding of biological limits. To describe how measures of demographic resilience obtained for a given population change in response to ramp disturbances, we used the two indices: δ Amplification and δ Resistance . To obtain these measures, at each time-step along our simulations of directional change described above, we used our sequence of A t products to calculate sequential estimates of the bounds of population reactivity ( ; maximum increase in population growth rate within one-time step of a disturbance, relative to stationary conditions) and first-step attenuation ( ; ditto maximum decrease); termed amplification and resistance in the main article for clarity. We note that a proliferation of terms exists for describing the different aspects of demographic resilience. Specifically, in the context of demographic resilience, amplification is also referred to as demographic compensation 21 . However, the term demographic compensation is also used to describe the process whereby opposing shifts in population vital rates allows for the maintenance of demographic performance 51 , 52 ; a process we also discuss here. Thus, to distinguish between differing demographic processes, and to make use of more common vernacular, we opted to use amplification and resistance when referring to increases and decreases in population growth rates, relative to stationary conditions. We used the sequences of and , obtained for each population, to calculate our indices of change in demographic resilience under each scenario ( Fig. 2C ). Both δ Amplification and δ Resistance describe the rate at which the relative reactivity and first-step attenuation values of a population change, per unit time, during their exposure to ramp disturbances. We calculated these measures as the slope coefficients of linear regression models fit to the respective sequences of and , obtained for each population under each scenario. For each population, we also computed estimates of reactivity and first-step attenuation from their unmodified MPM ( A 0 ). We retained these estimates as measures of our current perception of each population’s resilience to pulse disturbances, termed Amplification 0 and Resistance 0 , respectively. To quantify the effect of our simulated ramp disturbance scenarios on population performance we used the measure of stochastic population growth rate ( λ S ). λ S describes how variation across an iterative sequence of population growth rates, due to fluctuations in population vital rates over time, combines to inform a population’s long-term growth trajectory 56 . For each population, we defined a starting population structure comprising 1000 individuals distributed according to the stable state distribution (SSD) of the population’s initial MPM ( A 0 ; SSD is equal to the dominant right eigenvector of A 0 ). At each time-step during our ramp disturbance simulations, we used our sequences of A t products to generate iterative estimates of population size ( N t ). We then calculated λ S as the geometric mean of the sequence of N t / N t-1 estimates produced for each population under each disturbance scenario. Following the computation of our demographic measures, we applied a final refinement to our data sample. First, we omitted all estimates outside the 95% percentiles of each of our δ Amplification , δ Resistance , Amplification 0 , and Resistance 0 , variables. Next we pruned our population sample to remove populations for which we had omitted estimates of Amplification 0 and Resistance 0 (14 populations), and for which we could not obtain phylogenetic details (see Sourcing phylogenetic details ). This pruning resulted in a retained population sample of 511 populations (119 animals and 392 plants) comprising 344 species (Appendix S2). Prior to further analyses, to ensure all variables were normally distributed, we then applied a power transformation to our estimates of Amplification 0 (-1 × Amplification 0 -0.45 ) and applied a cube root transformation to . Establishing phylogenetic relationships To accommodate ancestral relatedness between populations of similar species, we computed the phylogenetic distances represented across our population sample. Keeping mobile and sessile species separate, we identified each species’ unique identifier within the Open Tree of Life 95 (OTL; taxonomy version 3.6), which we used to construct species-level phylogenetic sub-trees using the phytools 96 and ape R packages 97 . Briefly, across our population sample we defined all plant and coral species as sessile. This definition resulted in a collection of populations composed largely of plant species (392 populations from 265 species) plus four populations from two species of coral ( Paramuricea clavata [Risso, 1826] and Pocillopora damicornis [Linnaeus, 1758]). We classified all remaining animal species as mobile (115 populations from 77 species). After computing branch lengths for our phylogenetic sub-trees (following Grafen’s computation method 98 ) and ensuring each sub-tree was rooted (contains a single common ancestor) and clear of polytomies (≥ 3 species emerging from a single origin), we expanded the branch tips of species for which we had mean MPMs for multiple populations (15 animal species, and 51 plant species; Appendix S2). Implementing this expansion required replicate populations of the same species to be rooted onto the species’ corresponding branch tip, each separated by short branch lengths of infinitesimal size (ε = 0.0000001 units). This expansion approach assumes that populations of the same species are very closely related, and only imposes a small distance between populations to prevent introducing polytomies. With recent work evidencing how analyses of demographic resilience are not sensitive to the order in which population replicates are introduced to population-level phylogenetic trees 55 , the order in which we added population replicates to our phylogenetic trees was random (the exact trees used in this study can be sourced from 10.5281/zenodo.15148632). During the construction of our population-level phylogenetic sub-trees, it was necessary for us to drop eight MPMs (already accounted for in sample count above) for which no phylogenetic information could be extracted from the OTL. Comparing change in resilience vs. established measures To evaluate the relationship between established estimates of resilience to pulse dsiturbances ( Amplification 0 & Resistance 0 ) and the change in their resilience when exposed to ramp disturbances ( δ Amplification and δ Resistance ) we implemented a Bayesian multilevel modelling approach. Separately for our mobile and sessile groupings, we isolated estimates of δ Amplification and δ Resistance from scenarios involving changes in either survival or fecundity, during which the other vital rate remained unchanged. Individually for scenarios involving changes in survival and fecundity, we then executed phylogenetically weighted regression models initiated with default priors using the brms R package 99 . In each case, we modelled the relationship between change in resilience following ramp disturbances ( δ Amplification or δ Resistance ) and established estimates of population resilience to pulse disturbances ( Amplification 0 or Resistance 0 ). These models also included the fixed effect of disturbance scenario ( i.e. the inflicted rate of change in either survival or fecundity) and a grouping term that allowed us to impose phylogenetic weighting to account for variance-covariance among populations corresponding with our phylogenetic subtrees. To select the most appropriate model we fitted linear (L), polynomial (P) and exponential (E) model distributions, and used leave one out comparison (LOO) to select the best model fit in each case. Across all cases, the most appropriate model followed a polynomial distribution (LOO weights. Reactivity scenarios . Survival [Mobile] : L = 0.015, E = 0.087 & P = 0.898; Fecundity [Mobile] : L < 0.001, E = 0.117 & P = 0.883; Survival [Sessile] : L = 0.042, E < 0.001 & P = 0.958; Fecundity [Sessile] : L = 0.010, E < 0.001 & P = 0.990. Attenuation scenarios . Survival [Mobile] : L = 0.108 & P = 0.892; Survival [Mobile] : L = 0.062 & P = 0.938; Survival [Sessile] : L = 0.142 & P = 0.858; Fecundity [Sessile] : L = 0.120 & P = 0.880). After selecting the most appropriate model fit, we ran our selected models over four Markov Chains, each consisting of 3,000 iterations. In each case we extracted the conditional effects of our independent variables on the observed patterns in δ Amplification and δ Resistance . Testing for evidence of compensatory mechanisms To assess for evidence of compensatory mechanisms underlying our measures of population resilience, we again used a Bayesian multilevel modelling framework. We also used this modelling framework to explore how changes in resilience ( δ Amplification and δ Resistance ) correlate with patterns in stochastic population performance ( λ S ). On this occasion we retained outputs derived from all our scenarios of inflicted ramp disturbances. Separately for our measures of δ Amplification , δ Resistance , and λ S , we then implemented brms models with default priors quantifying how patterns in each of these metrics align across a two dimensional parameter space defined by relative changes in population survival and fecundity rates. Again, we also included a phylogenetic grouping term to account for ancestral signals across the data. Likewise we once again preliminarily imposed linear (L) and polynomial (P) distributions onto our models, before using LOO comparison to select the most appropriate distribution. Again, for both our mobile and sessile groupings, a polynomial distribution was deemed the most appropriate format in all cases (LOO weights. Reactivity [Mobile] : L = 0.107 & P = 0.893; Attenuation [Mobile] : L = 0.123 & P = 0.877; λ S[Mobile] : L = 0.070 & P = 0.930; Reactivity [Sessile] : L = 0.063 & P = 0.937; Attenuation [Sessile] : L = 0.129 & P = 0.871; λ S[Sessile] : L = 0.067 & P = 0.933). To streamline our use of computational memory, we implemented this analysis using a Monte-Carlo resampling approach, each time selecting a 3% sample of our animal and plant datasets, running our selected model over a single Markov Chain consisting of 3,000 iterations. 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